HIGHWAY RESEARCHJOURNAL Volume 5 No. 1 January - June 2012 * PAVEMENT * TRAFFIC ENGINEERING * BRIDGE ENGINEERING * GEOTECHNICAL ENGINEERING HIGHWAY RESEARCH BOARD INDIAN ROADS CONGRESS (All Rights Reserved. No part of this Publication shall be reproduced, translated or transmitted in any form or by any means without the permission of the Indian Roads Congress) Edited and Published by the Secretary, IRC Highway Research Board, New Delhi-110 011 Printed at India Offset Press, A-1, Mayapuri Industrial Area, New Delhi-110 064 13500 copies Members MEMBERS OF IRC HIGHWAY RESEARCH BOARD (2012-2014) 1. The Director General (Road Development) & Special Secretary to the Govt. of India (Shri C. Kandasamy) Ministry of Road Transport & Highways, Transport Bhawan, 1, Parliament Street, NEW DELHI – 110 001 [in the event of DG(RD) not in position, the President, IRC will function] Chairman 2. The Secretary General (Shri Arun Kumar Sharma) Indian Roads Congress, Kama Koti Marg, Sector 6, R.K. Puram, New Delhi – 110 022 Secretary 3. The President, IRC (Shri P.N. Jain) Chief Engineer (NH) and AS, R&B Deptt. Govt. of Gujarat, 14, Bandhusamaj Society, Nr. Panchsheel Bus Stand, Usmanpura, AHMEDABAD – 380 013 4. The Director (Dr. S. Gangopadhyay) CSIR–Central Road Research Institute, P.O. CRRI, Delhi-Mathura Road, NEW DELHI – 110 020 5. The Additional Director General-I Ministry of Road Transport & Highways, Transport Bhawan, 1, Parliament Street, NEW DELHI – 110 001 6. The Additional Director General-II Ministry of Road Transport & Highways, Transport Bhawan, 1, Parliament Street, NEW DELHI – 110 001 7. The Director General Border Roads Seema Sadak Bhawan, Ring Road, Delhi Cantt., NEW DELHI – 110 010 8. The Member (Technical) National Highways Authority of India, Plot No.G-5 & 6, Dwarka, NEW DELHI – 110 075 9. The Director (Technical) (Dr. I.K. Pateriya) National Rural Roads Development Agency, NBCC Tower, 5th Floor, Bhikaji Cama Place, NEW DELHI – 110 066 10. The Director Indian Academy of Highway Engineers, A-5, Institutional Area, Sector 62, NH-24 Bypass, NOIDA – 201 301 (U.P.) 11. The Engineer-in-Chief (Shri Mahesh Kumar) Haryana Public Works (B&R) Department Nirman Sadan, Plot No. 1, Dakshin Marg, Sector-33A, CHANDIGARH (Haryana) 12. The Chief Engineer (NH), (Shri R.P. Singh) Punjab P.W.D. B&R Branch, Nirman Bhawan, Block-C, Mini Secretariat, PATIALA-147 001 (Punjab) 13. The Chief Engineer (NH), (Shri B.P. Chauhan) PWD Rajasthan, J AIPUR-302 006 (Rajasthan) 14. The Chief Engineer (NH) R&B Deptt., Block No.14, 1st Floor, New Sachivalaya, GANDHINAGAR-382 010 (Gujarat) 15. The Chief Engineer (NH) Public Works Region, Konkan Bhawan, NAVI MUMBAI-400 614 (Maharashtra) 16. The Engineer-in-Chief (R&B), Admn. & NH (Shri K. Siva Reddy) R&B Department, Errummanzil, HYDERABAD – 500 082 (Andhra Pradesh) 17. The Chief Engineer (NH), (Shri M. Bhagat) Road Construction Deptt., Engineering Hostel, HEC, Dhurwa, RANCHI-834 004 (J harkhand) 18. The Engineer-in-Chief-cum-Secretary to the Govt. of Odisha (Shri Subhendu Kumar Ray) Works Department, Odisha Secretariat, BHUBANESWAR – 751 001 (Odisha) 19. The Chief Engineer (NH), P.W.D. (Roads), Barik Compound, Opposite State Library, SHILLONG-793 001 (Meghalaya) 20. The Chief Engineer, (N.H. Works) Assam, Chandmari, GUWAHATI-781 003(Assam) 21. Thiru R. Rajaraman Chief Engineer, Quality Assurance and Research, 76, Sardar Patel Road, Opp. Raj Bhavan, Chennai – 600 025 (Tamil Nadu) 22. The J oint Director Kerala Highway Research Institute, PWD Kariyavattom, THIRUVANANTHAPURAM – 695 581 (Kerala) 23. The J oint Director (Roads) (Shri M.K. Sheth) Gujarat Engineering Research Institute (GERI), Race Course, VADODARA – 390 007 (Gujarat) 24. The Director General (Shri D.D. Bhide) Design, Training, Hydrology, Research & Safety (DTHRS), Maharashtra Engineering Research Institute (MERI), Dindori Road, NASHIK – 422 004 (Maharashtra) 25. The Director, UPPWD Research Institute and Quality Promotion Organisation, Nirman Bhawan, 96, M.G. Marg, LUCKNOW-226 001 (UP) 26. Dr. S.S. Jain Professor of Civil Engineering, Deptt. of Civil Engineering, Indian Institute of Technology Roorkee, ROORKEE – 247 667 (Uttarakhand) 27. Prof. A. Veeraragavan Professor, Deptt. of Civil Engineering, Indian Institute of Technology Madras, CHENNAI – 600 036 (Tamil Nadu) 28. Dr. P.K. Sarkar Professor, Deptt. of Transport Planning, School of Planning & Architecture, E-799, C.R. Park, NEW DELHI – 110 019 29. Prof. L.S. Ramchandra Head, Deptt. of Civil Engineering, Indian Institute of Technology Kharagpur, KHARAGPUR-721 302 (West Bengal) 30. Dr. Animesh Das Associate Professor, Deptt. of Civil Engineering, Indian Institute of Technology Kanpur, KANPUR-208 016 (Uttar Pradesh) 31. The Chief Executive Offcer (Shri S. B. Vasava) Gujarat State Rural Road Development Agency (GSRRDA), 2nd foor, Nirman Bhavan, Sector 10-A, GANDHINAGAR – 382 010 (Gujarat) 32. The Chief Operating Offcer (Shri Mahesh M. Hiremath) Karnataka Rural Road Development Agency, Nirman Bhavan, I I Floor, KSCC Building, Rajajinagar Ist Block, Dr. Rajkumar Road, BANAGLORE-560 010 (Karanataka) 33. The Chief Engineer (Shri C. P. Tongden) Rural Management & Development Department, Tashiling, Secretariate, GANGTOK (Sikkim) 34. The Chief Executive Offcer (Shri Ranjit Kumar Majumder) Tripura Rural Road Development Agency (TRRDA) & J S PWD Urban Development, 3rd Floor of Khadya Bhavan, Pandit Nehru Complex, AGARTALA-799 006 Tripura (W) 35. The Chief Engineer (Shri K. K. Srivastava) Uttarakhand Rural Roads Agency, Opp. I. T. Park, Shastradhara Road, DEHRADUN- 248 001 (Uttarakhand) 36. The Director (Shri G. Dinshaw) Central Institute of Road Transport, Post Box No. 1897, Bhosari, Pune-Nasik Road, PUNE – 411 026 (Maharashtra) 37. Prof. P.K. Sikdar (Former Director, CRRI), President, Intercontinental Consultants & Technocrats (ICT) Pvt. Ltd., A-9, Green Park, NEW DELHI – 110 016 38. Shri R.S. Sharma J t. Managing Director, Consulting Engineers Group Limited, C-478, 2nd Floor, Block-C, Vikas Puri, NEW DELHI-110 018 39. Shri A.K. Banerjee Director (Technical), Scott Wilson India Pvt. Ltd., B-210, Second Floor, CR Park, NEW DELHI – 110 019 40. Major V.C. Verma Executive Director - Marketing, Oriental Structural Engineers Pvt. Ltd., 21, Commercial Complex, Malcha Marg, Diplomatic Enclave, NEW DELHI – 110 021 41. Shri M.M. Khan Vice-President, Gammon India Ltd., Library Tech., Gammon House, Veer Savarkar Marg, Prabhadevi, MUMBAI – 400 025 42. Shri U. Jayakodi Director (Technical), BSCPL Infrastructure Ltd., M. No. 8-2-502/1/A, J IVI Towers, Road No. 7, Banjara Hills, HYDERABAD – 500 034 43. Shri S.C. Sharma DG (RD) & AS (Retd.), MORTH, 175, Vigyapan Lok, 15, Mayur Vihar Phase-I Extn., (Near Samachar Aptt.), DELHI – 110 091 44. Shri N.K. Sinha DG(RD) & SS (Retd.), MORTH, G-1365, Ground Floor, Chitranjan Park, NEW DELHI – 110 019 45. Shri P.L. Bongirwar Advisor, L&T, B/1102, Pataliputra CHS, Near Four Bunglow Signal, Andheri (E), MUMBAI – 400 053 46. Shri G. Sharan DG (RD) & Special Secretary (Retd.), MOSRT&H, 17, Nalanda Apartments, Vikaspuri, NEW DELHI – 110 018 47. Shri Subhash Patel (Past President, IRC), Former Secretary, R&B Department, GANDHINAGAR (Gujarat) 48. Shri V. Velayutham DG (RD) & Special Secretary (Retd.), MOSRT&H, Flat No. 4, Nalanda Apartment, D-Block, Vikaspuri, NEW DELHI – 110 018 49. Prof. P.K. Sikdar (Former Director, CRRI), President, Intercontinental Consultants &Technocrats (ICT) Pvt. Ltd., A-8, Green Park, NEW DELHI – 110 016 50. Shri A.D. Narain Former DG (RD) & Addl. Secretary, MOST, B-186, Sector 26, NOIDA – 201 301 (U.P.) 51. Shri R. K. Jain Former Chief Engineer, Haryana PWD, H.No. 452, Sector-14, SONIPAT – 131 001 (Haryana) 52. Dr. B. P. Bagish C-2, 2013, VasantKunj, NEW DELHI – 110 070 Co-opted Members [upto Mid-term Council Meeting to be held at Kohima (Nagaland) in 2012] CONTENTS PAVEMENT Page Lab Study on Chemical and Rheological Changes in Modifed Binders 1 Praveen Kumar, M.R. Maurya, Manoj Gupta & Maninder Singh TRAFFIC ENGINEERING Infuence of Socio-Demographic Attributes in Travel Mode Selection for 15 Single Day Excursion Trips Harikrishna M., Rajat Rastogi & Daya Purushothaman Headway Analysis at Signalised Intersections – With and Without Countdown Timer 33 M.S. Harshitha, Sonu Agarwal & Lelitha Vanajakshi Modifcation of Webster’s Delay Formula Using Modifed Saturation Flow Model 41 for Non-Lane Based Heterogeneous Traffc Conditions N.G. Raval & P.J. Gundaliya BRIDGE ENGINEERING Dynamic Amplifcation Factors for Highway Bridge Design – A Review of International 49 Codal Provisions S. Arun, Devdas Menon & A. Mehar Prasad GEOTECHNICAL ENGINEERING A Laboratory Study of Construction and Demolition Waste for Use in Road Works 57 U.K. Guru Vittal, Farhat Azad, J. Ganesh, Binod Kumar & Sudhir Mathur WRITTEN COMMENTS ON THE PAPERS PUBLISHED IN THIS HIGHWAY RESEARCH JOURNAL ARE INVITED AND MAY BE SENT AT
[email protected] BEFORE 31 ST JULY, 2012 The opinions and conclusions in this Journal are those of the Authors and not of the IRC Highway Research Board HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 1 1 INTRODUCTION India is a very vast country, having widely varying climate, terrain, construction materials and mixed traffc both in terms of loads and volume. The fexible roads constitute about 98 per cent of the total roads network. Most of the roads develop distress conditions, like, raveling, undulations, rutting, cracking, bleeding, shoving and potholing of bituminous surfacing. A factor which causes further concern in India, is very high and very low pavement temperatures in some parts of the country. The road structures have deteriorated rapidly than expected due to increase in traffc density, axle loading and tyre pressure and an insuffcient degree of maintenance. Road performance is determined by properties of bitumen as the bitumen is the continuous phase and only deformable component. Bitumen as a binding material and in protective coatings plays a key role in the performance related properties of bitumen mixes. Bitumen, from crude oil distillation processes, is a complex polymeric mixture of chemical compounds. Traditional bituminous binders have various limitations to cope with the excessive overloading and increasingly severe climatic conditions, which lead to important stress related problems. Binder modification is a major breakthrough and the continuous research and is aiming to produce new binders with better rheological and mechanical characteristics which allow the manufacturing and application of road bituminous mixes with higher performance. Increased traffc factors, such as, heavier loads, higher traffc volume and higher tyre pressure demand higher performance pavements. The purpose of bitumen modifcation using polymers and rubbers is to achieve desired engineering properties, such as, increased shear modulus and reduced plastic fow at high temperatures and increased resistance to thermal fracture at low temperatures. 1.1 Dynamic Rheological Properties of Bitumen Dynamic rheological properties refer to responses of a material to periodically varying strains or stresses. The Dynamic Shear Rheometer (DSR) is used for determination of the rheological properties of bitumen in a wide range of temperature. The parameters for characterization of the bitumen are complex modulus (G*), storage modulus (G’), loss modulus (G”) and phase angle (δ). Phase angle measures the viscoelastic character of the bitumen. A purely viscous liquid and an ideal elastic solid demonstrate δ of 90° and 0°, respectively. The viscoelastic parameters of bitumen are functions of temperature and frequency, which may be modifed by the addition of polymers. 1.2 Chemical Properties Modifers used for bitumen modifcation are normally polymeric materials, which have different structures, such as, atactic, isotactic and syndiotactic. These structures LAB STUDY ON CHEMICAL AND RHEOLOGICAL CHANGES IN MODIFIED BINDERS PRAVEEN KUMAR*, M.R. MAURYA**, MANOJ GUPTA*** & MANINDER SINGH **** ABSTRACT The challenge in physical property characterization is to develop physical tests that can satisfactorily characterize key asphalt binder parameters and how these parameters change throughout the life of an HMA pavement. In India, the methods for rheological characterization of bituminous binders are inadequate to characterize the bitumen. Hence a complete rheological study and characterization of bitumen using dynamic shear rheometer would be helpful. Also there is limited insight about the chemistry of modifed bitumen. Ethylene Vinyl Acetate (EVA), a plastomer; Linear Styrene Butadiene Styrene (SBS), an elastomer and Crumb Rubber (CR) were used in the present study. The changes in rheological and chemical properties of 60/70 and 80/100 grades bitumen modifed with different percentage of CR, EVA and SBS (2 to 8 per cent) were studied. The rheological properties of the bituminous binders in terms of their complex modulus (G*), stiffness and overall resistance to deformation, storage modulus (G’), binder elasticity, loss modulus (G”), viscous behaviour and phase angle(δ), viscoelastic behaviour were measured. Testing was performed at temperatures ranging from 46°C to 82°C in increments of 6°C at a frequency of 10 rad/sec. The chemistry was studied using infrared spectroscopy. * Professor & Coordinator, Transportation Engg. Group, Civil Engg. Deptt. IIT Roorkee. ** Professor, Chemistry Deptt. *** M. Tech. Student, Civil Engg. Deptt. **** Research Scholar, Civil Engg. Deptt. } 2 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 KUMAR, MAURYA, GUPTA & SINGH ON give characteristic features in spectroscopy analysis. Thus, spectroscopy techniques are highly useful in identifying the structure and functional groups present in polymeric materials. 2 LITERATURE REVIEW Wahhab and Amri (1991) evaluated the effects of reclaimed rubber particles from scrap tyres in the preparation of local asphaltic concrete mixes. The results show that the softening point of the binder increases with increasing rubber content. Panda and Mazumdar (1997) developed and evaluated a bituminous paving binder containing reclaimed PE and used LDPE for modifcation of bitumen. It was observed that the penetration, ductility and the specifc gravity of the modifed binder decreases, while the softening point and viscosity are increased. The temperature susceptibility of the modifed binder is also improved. Mehndiratta and Chandra (2000) studied bitumen modifed with CR and EVA and reported that properties, like, low temperature ductility, elastic recovery, water and temperature susceptibility, viscosity and marshall stability improved by adding modifers to bitumen. Yousefi (2002) incorporated non-vulcanized rubbers into bitumen. The resulting range of blends showed higher and intermediate performance compared to the base bitumen. Lepe et al. (2003) found that mixing of polymers into bitumen has important consequences on engineering properties of bituminous binders. Singh K.L. (2006) studied the changes of rheological properties of 60/70 and 80/100 grades bitumen modified with different percentage of CR, EVA and SBS (3 to 9 per cent) using dynamic shear rheometer. Lu and Issacson (1997, 1999) investigated the effects of polymer content and bitumen type on viscosity characteristics of SBS modifed bitumen. Airey (2003) concluded that increase in SBS content leads to decrease in penetration and increase in softening point. Gonzalez et.al. (2004) concluded that viscous properties of bitumen at high temperature are improved by adding recycled EVA copolymer. Lougheed and Papagiannakis (1996) explained that addition of 3-18 per cent CR results in increase in viscosity from 1.3 to 12 times compare to unmodifed asphalt. Ageing or hardening of bituminous binder occurs during mixing and laydown process and during service. The Thin Film Oven Test (TFOT), ASTM D 1754 and the Rolling Film Oven Test (RTFOT), ASTM D 2872 are the existing ageing methods. 3 EXPERIMENTAL PROGRAMME The earlier physical tests to evaluate viscous properties of bitumen were empirically derived tests. Now, the rheological properties of the binders are measured in terms of complex modulus, G* (stiffness and overall resistance to deformation), storage modulus, G’ (elastic behaviour of binder), loss modulus, G” (viscous behaviour), and phase angle, δ (viscoelastic properties). 3.1 Materials 3.1.1 Bitumen : Two grades of bitumen, 60/70 and 80/100 were used for the present study. Their physical properties described as per IS:73-2006 are listed in Table 1. Table 1 Physical Properties of 60/70 and 80/100 Bitumen Test 60/70 80/100 Penetration at 25ºC (0.1mm, 100g, 5s) 65 89 Softening Point, ºC 48 42 Ductility at 27ºC, cm 100+ 100+ Specifc Gravity 1.01 0.998 Flash Point,ºC 285 310 3.1.2 Modifiers : Crumb Rubber (CR) and two types of polymers, Ethylene Vinyl Acetate (EVA) and Styrene Butadiene Styrene (SBS) were used in this study. CR is material locally available and material passing through 1.18 mm IS sieve and retained on 200 micron IS sieve was used. The natural rubber, carbon black and ash contents in CR were 35 per cent, 32 per cent and 7 per cent, respectively Ethylene Vinyl Acetate (EVA) copolymer, available as pellets 4 to 5 mm in diameter supplied by KLJ Polymers, New Delhi was used. Styrene Butadiene Styrene (SBS) polymer used was powdered Finaprene 503 supplied by ATOFINA. Finaprene 503, a linear SBS polymer contains 31 per cent styrene. The rubberized bitumen binder was prepared in the laboratory at blending temperature of 180°C (Navarro et al., 2004) and blending time of 60 min (J eong et al., 2010). High shear type mixture was used for the blending purpose (Mohamed, 2009). Three different concentrations of crumb rubber were prepared by frst heating the bitumen to 180°C. Upon reaching 180°C, a weighted amount of rubber (3,5 and 8 per cent by weight of bitumen binder) were slowly added to the original bitumen while mixing at 180°C using the propeller blade mixer at a blending speed of 200 rpm for blending times of 1h. In preparing the modifed binders, about 500 g of the bitumen was heated to fuid condition in a 1.5 litre capacity HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 3 LAB STUDY ON CHEMICAL AND RHEOLOGICAL CHANGES IN MODIFIED BINDERS metal container. The mixing was performed in the laboratory using an oven ftted with a mechanical stirrer and rotated at 1550 rpm for mixing the bitumen and modifiers. For preparation of EVA blends, bitumen was heated to a temperature of 170°C. As the bitumen attained a temperature of 170°C, the different EVA polymer contents by mass (2 to 8 per cent) were added to the bitumen and vigorously agitated. The temperature was maintained between 175°C to 180°C and mixing was then continued for 80-90 min. For SBS blends, bitumen was heated to a temperature of 170°C and the appropriate quantity of SBS copolymer was added. The temperature was maintained between 175°C to 180°C. The contents were gradually stirred for about 55 min. The modifed bitumen was cooled to room temperature and suitably stored for testing. 3.1.3 Testing : Brookfeld Viscometer (ASTM D4402): It was used for determining the kinematic viscosities of the samples at 135 o C for 5 min. at 20 rpm. The rotational viscosity was determined by measuring the torque required to maintain a constant rotational speed of 20 rpm of a cylindrical spindle submerged in bitumen maintained at the test temperature through thermosel. The torque is directly related to binder viscosity, the test parameters were given and results were obtained using the Rheocalc32 software. Dynamic Shear Rheometer: SR 5 Asphalt Rheometer was used for measuring the dynamic rheological properties, as per guidelines prescribed in AASHTO TP5-1994. The basic principle of DSR is shown in Fig.1. The binder is sandwiched between two parallel plates, one is fxed and other oscillates. When torque is applied to oscillating plate, it starts from A and moves to point B and then back to A, then to C and then again back to A. This comprises one cycle of oscillation. DSR measures Complex modulus and phase angles at the desired temperature and frequency of loading. The 25 mm parallel plate geometry was used for testing of neat and TFOT samples and measurements were taken in temperature range from 46°C to 70°C in an increment of 6°C. The 8 mm plates were used for testing the PAV aged samples in temperature range from 13°C to 40°C in an increment of 9°C. All the binders were tested at a frequency of 10 rad/s. Ageing: Ageing of the binders was performed by two methods, Thin Film Oven Test (TFOT, ASTM D1754) and Pressure Ageing Vessel (PAV, ASTM D6521), respectively. For TFOT the samples are placed on a rotating disc for 5 hr at 163ºC. For long term ageing, PAV was used. The samples of TFOT are subjected to a pressure of 2.1±0.1 MPa and 100ºC temperature for 20 h±10 min. PAV samples were then vaccum degassed using Vaccum Degassing Oven. A A B C A A Applied Stress or Strain Fixed Plate Oscillating Plate Bitumen Time 1 cycle Figure 4.1: Dynamic Shear Rheometer Operation B C Water Bath Fig. 1 Dynamic Shear Rheometer Operation 4 RESULTS AND DISCUSSION Due to limitation of space at some places only the data for either one (60/70 or 80/100) has been provided. 4.1 Physical Properties PMB 40 grade of modifed binders are obtained by adding 2 to 8 per cent SBS and EVA to 60/70 bitumen. But in the case of 80/100 bitumen modifed with EVA, it was found to be PMB 70 grade at 2 per cent while 5 and 8 per cent was in the group of PMB 40 grade. For CR modifed binder, it is of CRMB 50 grade when 2 to 5 per cent CR is added to 80/100 bitumen grade, however, it is of CRMB 55 grade with increase in CR percentage to 8. 4.1.1 Penetration Test Results : Table 2 represents the results of penetration test. The penetration values are decreasing signifcantly for 60/70 and 80/100 bitumen mixed with CR, EVA and SBS and this variation is more in 80/100 modifed bitumen. It is observed that the penetration value decreases as the concentration of modifer increases. Further, the bitumen modifed with EVA seems to be more 4 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 KUMAR, MAURYA, GUPTA & SINGH ON effective in the penetration values as compared to CR and SBS modifers for 60/70 and 80/100 grades of bitumen. The modifed bitumen shows the characteristic similar to VG 30 and VG 40 grade bitumen in penetration. 4.1.2 Softening Point Test Results : The softening point increases with increase in percentage of modifers as the bitumen becomes increasingly viscous. The effect of SBS on softening point is much more than that of EVA and CR as shown in Table 2. The softening point for 60/70 and 80/100 bitumen increases to more than 70°C by addition of 8 per cent EVA and 5 per cent and 8 per cent SBS and should not be used in road construction, but may be used as a roofng material. 4.1.3 Elastic Recovery Results : The elastic recovery increases with increasing in percentage of modifiers. However, in case of bitumen modifed with EVA, it slightly increases after 5 per cent. Elastic recovery values for neat binders are very low as compared to modifed bitumen. The elastic recovery for 60/70 grade bitumen modifed with CR is more than that of 80/100 grade bitumen modifed with CR. However, elastic recovery of 80/100 grade binder is more than 60/70 binder in case of EVA and SBS modifcation. It is clearly shown in Fig. 2 that the bitumen modifed with SBS gives the maximum elastic recovery than that of bitumen modifed with CR and EVA. Fig. 2 Effect on Elastic Recovery with Different Percentage of Modifers 4.1.4 Viscosity Results The results achieved in centipoise “cP” (g/cm.s) were divided by density (specifc gravity) to arrive at kinematic viscosity in centistrokes “cSt” (cm 2 /sec) as per requirement of IS:1206 (Part III)-1978. The variation in viscosity with varying percentage of modifers at 135ºC is shown in Fig 3. Fig. 3 Effect on Viscosity with Different Percentage of Modifers 4.2 Ageing Effect on Modifed Binders The physical properties undergo a change after subjecting samples to ageing. 4.2.1 Changes in Penetration and Softening Point : The penetration and softening point values before and after short term ageing carried out in TFOT at different percentage of CR, EVA and SBS are shown in Table 2. As expected, following the ageing process, higher softening points as well as lower penetration values are found. Table 2 Changes in Penetration and Softening Point Values Before and After Ageing Binder Penetration Value Before Ageing, dmm Penetration Value After Ageing, dmm Softening Point Value Before Ageing, °C Softening Point Value After Ageing, °C 60/70 65 58 48.5 51 60/70+CR2% 59 39 52.5 56 60/70+CR5 56 38 57 60 60/70+CR8 50 36.5 59 62.5 60/70+EVA2 45 33.5 63 67.5 60/70+EVA5 41 32 70 74 60/70+EVA8 34 26 74 77.5 60/70+SBS2 50 42 65 68 60/70+SBS5 43 37.5 75 79 60/70+SBS8 36 30 83 85.5 80/100 91 82 44 48.5 80/100+CR2 66 47 46 50 .... Contd. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 5 LAB STUDY ON CHEMICAL AND RHEOLOGICAL CHANGES IN MODIFIED BINDERS 80/100+CR5 59 42.5 53 56.5 80/100+CR8 53 40 55 59.5 80/100+EVA2 61 48 57 60.5 80/100+EVA5 50 38.5 65 69.5 80/100+EVA8 44 33 70 72.5 80/100+SBS2 68 48.5 59 63.5 80/100+SBS5 55 45.5 67 70 80/100+SBS8 47 41 79 82.5 4.2.2 Loss in Weight : The loss of volatile fractions contributes to the difference in weights between original and aged sample. The maximum loss in weight should be 1 per cent as per IRC: SP: 53:2002. From Fig. 4, it is observed that there are greater variations in the values after the modifcation is 5 per cent or more. Fig. 4 Variations in Percentage Loss in Weight After Short Term Ageing 4.3 Rheological Properties of Modifed Binders Bitumen rheology can broadly be represented by two main viscoelastic parameters: complex modulus and phase angle which changes with temperature and loading time. Complex modulus represents stiffness, whilst phase angle is normally used to demonstrate the viscoelastic response of bituminous materials. Purely viscous and perfectly elastic materials will have phase angles of 90° and 0°, respectively. Therefore, higher values of phase angle indicate a tendency towards more viscous behaviour, whilst lower values indicate more elastic response. The complex modulus (G*) and phase angle (δ) defne the resistance to deformation of the binder in the viscoelastic region. In Indian specifcations (IS: 15462:2004), the complex modulus is determined at 10 rad/s and at temperatures varying from 35°C to 85°C. For unmodifed bitumen, elastic behaviour (lower phase angle) is generally associated with high stiffness and increased brittleness; while the viscous response (higher phase angle) refects high ductility and low stiffness. The shear modulus (G*/sin δ) is an indicator of stiffness or resistance of binder to deformation under load at specifed temperature. 4.3.1 Relationship between Complex Modulus, Shear Modulus and Phase Angle with Temperature : The complex modulus (G*) and phase angle (δ) versus temperature at 10 rad/s, and variation in shear modulus with temperature for 60/70 grade of bitumen modifed with CR, EVA and SBS are shown in Table 3. As may be seen, complex modulus of the modifed binders is higher as compared to neat bitumen. Complex modulus decreases with increase in temperature and increases with increase in percentage of modifer. The improved elasticity between the hard grade bitumen (60/70) and soft grade bitumen (80/100) is also different for the same percentage of modifer. The phase angle increases with increase in temperature and decreases with increase in percentage of modifer contents. The phase angle of modifed binder is lower than that of neat bitumen illustrating improved elastic response. Whereas the phase angles of the two base bitumen approach 90° and, therefore, predominantly viscous behaviour; with increasing temperatures, the modifer signifcantly improves the elastic response of the modifed binders. This increase in elastic response at high temperatures can be attributed to the viscosity of the base bitumen being low enough to allow the elastic network of the polymer to infuence the mechanical properties of the modifed binders. Rutting of bituminous pavements is the most prevalent problem in India. It is useful to determine the stiffness of the bitumen at 60°C so that it can specify its minimum stiffness to ensure adequate resistance to rutting during summer. The parameter, shear modulus (G*/sin δ) is a measure of stiffness of the binder which is also used as an indicator for rutting resistance in the current superpave specifcations. The rutting resistance of the binder increases with increase in the percentage of modifer. The rutting resistance increases by nearly three times with addition of 2 per cent SBS in 60/70 and 80/100 bitumen at 58°C. By addition of 2 per cent EVA with 60/70 and 80/100 bitumen, the rutting resistance is increased by more than four times. However, the increment in rutting resistance is less than two times in case of 80/100 bitumen modifed even with 5 per cent CR. EVA modifed binder has shown higher rutting resistance value than SBS and CR modifed binders at the same percentage of modifer. Therefore, EVA binder is suggested to use in the area of heavy traffc and at high temperatures. 6 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 KUMAR, MAURYA, GUPTA & SINGH ON Table 3 Variation of Rheological Parameters with Temperature Bituminous Binder Temperature (°C) Complex Modulus (G*) (kPa) Shear Modulus (G*/sinδ) (kPa) Loss Modulus (G* sinδ) (kPa) Storage Modulus (G*cosδ) (kPa) Phase Angle (δ) (°) 60/70 46 4.933 4.951 4.909 0.446 84.8 58 2.698 2.702 2.694 0.146 88.8 70 0.805 0.806 0.804 0.000 92.1 60/70 +2%CR 46 7.452 7.578 7.324 1.354 79.5 58 4.952 5.015 4.889 0.786 80.8 70 1.432 1.446 1.417 0.202 81.9 60/70 +5%CR 46 9.755 10.149 9.376 2.692 73.9 58 5.963 6.112 5.817 1.311 77.3 70 1.582 1.605 1.558 0.269 80.2 60/70 +8%CR 46 10.589 11.261 9.956 3.604 70.1 58 7.834 8.162 7.518 2.201 73.6 70 2.423 2.449 2.397 0.353 81.6 60/70 +2%EVA 46 24.325 25.614 23.103 7.621 71.7 58 13.648 14.091 13.219 3.394 75.6 70 2.742 2.766 2.723 0.376 82.1 60/70 +5%EVA 46 38.715 42.952 34.896 16.764 64.3 58 21.136 22.26 20.068 6.633 71.7 70 5.123 5.282 4.968 1.248 75.4 60/70 +8%EVA 46 87.982 125.637 61.612 62.8 44.4 58 48.217 62.29 37.322 30.526 58.7 70 8.859 10.639 7.376 4.906 56.4 70 3.153 3.278 3.032 0.863 74.1 60/70 +2%SBS 46 14.815 15.515 14.146 4.40 72.7 58 8.62 8.789 8.453 1.686 78.7 70 2.06 2.065 2.054 0.151 85.8 60/70 +5%SBS 46 19.369 21.328 17.589 8.109 65.2 58 11.338 12.254 10.49 4.302 67.7 70 3.694 3.826 3.566 0.962 74.9 60/70 +8%SBS 46 18.906 21.705 16.467 9.286 60.5 58 14.443 16.231 12.851 6.59 62.8 70 7.35 7.944 6.8 2.789 67.7 4.3.2 Relationship between Loss Modulus and Storage Modulus with Temperature : Loss modulus (G”) is associated with viscous effects and storage modulus (G’) provides information on the elastic responses of binder. The binder should have a large value of storage modulus (G’) at high temperatures for deformation resistance, because G’ measures the binder elasticity. Decrease in elasticity of the binder at low temperatures facilitates in avoiding cracking by dissipating the absorbed energy, but high elasticity is convenient at high temperatures to avoid viscous fow of the binder. The loss modulus (G”) and storage modulus (G’) of modifed binders at different temperatures are shown in Figs. 5-7. As the temperature increases both loss and storage moduli decrease. The variation in loss modulus and storage modulus is more at lower temperature than HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 7 LAB STUDY ON CHEMICAL AND RHEOLOGICAL CHANGES IN MODIFIED BINDERS at higher temperature. The CR modifed binder shows a predominantly viscous behaviour (G”>G’) in the whole temperature range from 46 to 70°C. Bitumen modifed with EVA, indicates that 8 per cent percentage of EVA shows fully elastic behaviour of binder at temperature lower than 52°C, but with 2 per cent and 5 per cent of EVA it indicates viscous behaviour of binder as dominated by loss modulus as shown in Fig. 6. It is also observed that loss modulus is dominated at all temperatures for SBS modifed binders as shown in Fig. 7. The EVA modifed binder has higher loss and storage moduli as compared to CR and SBS modifed binders. Therefore, the EVA modified binder will cause cracking at low temperatures. Thus, from these results it can be seen that the viscoelastic properties of binder depends on temperature, bitumen grade and polymer content. Fig 5. Relationship of G’ and G” with Temperature for CR Modifed Bitumen Fig 6. Relationship of G’ and G” with Temperature for EVA Modifed Bitumen Fig 7. Relationship of G’ and G” with Temperature for SBS Modifed Bitumen 4.3.3 Rheology of Modifed Binders after Short Term Ageing : Ageing of bitumen occurs by chemical and/ or physical changes during the construction stage and throughout its service life. The process is usually accompanied by loss of volatiles and hardening of the binder, which in turn infuences the deterioration of the asphalt pavement. Ageing of the binders was performed using TFOT and PAV in the present study. The shear modulus after short term ageing of bitumen at different temperatures is shown in Table 4. As can be seen from Table 5, there is a constant increase in complex modulus, G*, with increase in percentage modification after ageing the unmodifed and modifed bitumen. There is also a regular decrease of the phase angle, δ, over the temperature domain after ageing. The variations in complex modulus after TFOT ageing is shown in Table 5. The result of ageing is an increase in complex modulus G*, which indicates the hardening of the bitumen. The decrease in phase angle indicates an increase in the elastic behaviour of the bitumen. The rutting resistance as expected, is found to increase with increase in the percentage of modifer after TFOT Ageing. Also, G*/sin δ varies almost linearly with temperature. The 60/70 modified binders have shown higher rutting resistance value than 80/100 modifed binders at the same percentage of modifer. Therefore, 60/70 binder can benefcially be used in the areas of heavy traffc and at high temperatures. Table 4 Shear Modulus After Short Term Ageing % Modifer G*/sin δ for 60/70 Bitumen (kPa) G*/sin δ for 80/100 Bitumen (kPa) at 46°C at 52°C at 58°C at 46°C at 52°C at 58°C 0.0 10.45 9.34 4.3 8.77 8.88 3.48 CR 2 19.19 13.55 6.56 16.82 12.38 5.76 CR 5 24.56 14.32 9.43 21.22 13.62 6.81 CR 8 26.62 19.06 14.21 23.2 17.77 11.89 EVA 2 55.35 37.99 21.31 55.67 32.45 18.52 EVA 5 85.96 68.76 32.44 82.78 65.25 29.25 EVA 8 181.9 118.34 75.67 128.9 113.91 55.64 SBS 2 34.78 24.5 13.89 34.21 22.56 9.4 SBS 5 44.78 29.89 17.87 39.54 27.15 15.99 SBS 8 56.99 43.65 25.35 53.32 37.92 24.87 8 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 KUMAR, MAURYA, GUPTA & SINGH ON Table 5 Complex Modulus After Short Term Ageing % Modifer G* for 60/70 Bitumen (kPa) G* for 80/100 Bitumen (kPa) at 46°C at 52°C at 58°C at 46°C at 52°C at 58°C 0.0 9.93 6.42 3.24 6.99 4.58 2.46 CR 2 15.32 10.9 5.21 12.11 7.34 4.33 CR 5 20.13 9.34 7.76 14.54 8.62 4.45 CR 8 21.81 15.31 10.95 17.86 12.03 7.22 EVA 2 44.46 32.67 17.7 46.45 27.85 12.34 EVA 5 79.35 51.46 26.62 63.78 40.33 19.76 EVA 8 167.3 97.31 61.9 96.45 72.28 36.43 SBS 2 29.18 19.81 10.11 27.41 17.76 7.54 SBS 5 36.81 25.87 13.83 31.87 19.34 9.96 SBS 8 45.3 35.47 18.77 38.78 21.43 11.75 4.3.4 Rheology of Modified Binders after Short Term Ageing : The rheological properties of PAV aged samples modifed with varying percentages of modifer were tested at temperatures ranging from 13°C to 40°C. The values of complex modulus corresponding to temperature are shown in Table 6. An increase in the value of complex modulus with an increase in the amount of modifer is seen. The increase in G* after long term ageing is understandably greater than that after TFOT ageing due to the prolonged ageing process in the PAV. Table 6 Complex Modulus after PAV Ageing Bituminous Binder + Modifer Complex Modulus G*(kPa) at 13ºC 22ºC 31ºC 40ºC 60/70 1894 1244 717 395 60/70+2% CR 2216 1689 1043 591 60/70+5% CR 2981 2018 1566 711 60/70+8% CR 3831 2397 1884 858 60/70+2% EVA 3798 1804 1299 788 60/70+5% EVA 6176 3823 1947 1094 60/70+8% EVA 9712 5410 2843 1694 60/70+2% SBS 3620 1784 1220 669 60/70+5% SBS 5483 3662 1889 837 60/70+8% SBS 7841 5251 2466 1134 80/100 1670 1098 540 220 80/100+2% CR 2040 1448 678 311 80/100+5% CR 2572 1688 1143 665 80/100+8% CR 2767 1972 1368 701 80/100+2% EVA 3504 1568 946 599 80/100+5% EVA 4989 2832 1431 880 80/100+8% EVA 7932 4378 2135 1213 80/100+2% SBS 3114 1590 833 476 80/100+5% SBS 4244 1931 1181 772 80/100+8% SBS 5771 3883 2186 971 4.4 Relationship between G*/sin δ and Test Temperature The performance based SHRP specifcations have suggested different grades of bituminous binders on the basis of G*/sin δ. As per the specifcations, to assure adequate performance at the hot-mix plant and during laying, the value for G*/sin δ, of the original unaged binder must be greater than 1.0 kPa. From results, it can be clearly seen that the values of shear modulus are as per the specifcation values for all temperatures of 60/70 and 80/100 bitumen except higher temperatures (i.e.>64°C) meet the criterion. To evaluate asphalt’s ability to resist rutting, the aged residue must have a value of G*/sin δ, greater than 2.2 kPa at the service environment’s highest temperature. From the values of G*/sin δ stated in Table 4, it is observed that both 60/70 and 80/100 bitumen just meet the specifcations at lower percentage of modifcation and higher temperatures. The 60/70 and 80/100 grade bitumen attain the value of 1.0 kPa at a temperature of 67°C and 62.5°C before ageing and a value of 2.2 kPa at a temperature of 63.5°C and 60.5°C after ageing, respectively. Therefore, performance grades of 60/70 and 80/100 bitumen are PG 64 and PG 58, respectively. Similarly, performance grades of modifed binders were determined and these are given in Table 7. Table 7 Performance Grade of Different Polymer Modifed Bitumen Binder Type Polymer Content (%) PMB Grade as per IRC SP : 53-2002 Temperature corresponding to (°C) Performance Grade as per SHRP Specifcations G*/sin δ = 1kPa (Original Binders) G*/sin δ = 2.2 kPa (Aged Binders) 60/70 - - 67 63.5 PG 64 CR 2 50 72 65.5 PG 64 5 55 74 69.5 PG 70 8 55 80 72 PG 70 EVA 2 40 74.5 76 PG 70 5 40 76.5 80 PG 76 8 40 84 88.5 PG 82 .... Contd. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 9 LAB STUDY ON CHEMICAL AND RHEOLOGICAL CHANGES IN MODIFIED BINDERS SBS 2 40 70 72 PG 70 5 40 73 79 PG 70 8 40 77 83.5 PG 76 80/100 - - 62.5 60.5 PG 58 CR 2 50 66 64.5 PG 64 5 50 68 66 PG 64 8 55 72 68.5 PG 64 EVA 2 70 71.5 74 PG 70 5 40 76 79 PG 76 8 40 81.5 85.5 PG 76 SBS 2 70 68 68.5 PG 64 5 70 72.5 74 PG 70 8 40 74.5 78.5 PG 70 EVA modified with 60/70 and 80/100 bitumen with 5 per cent polymer content were meeting the specifcation requirements of PG 76. However, SBS modifed binder with the same percentage above are satisfying the PG 70 requirements. For CR modifed with 60/70 and 80/100 bitumen with 5 per cent crumb rubber are meeting the PG 70 and PG 64 requirements, respectively. There is no change in PG requirements if the percentage of crumb rubber is increased also. Therefore, 5 per cent seems to be optimum modifer for CR modifed binders. 4.5 Optimum Percentage of Polymers and Crumb Rubber The percentage of polymer and crumb rubber is optimized generally on the basis of empirical tests as per Indian specifcations. The requirement of penetration, softening point and ductility is satisfed at 2 per cent of EVA and SBS as per IRC: SP: 53-2002 and IS : 15462-2004. In the case of CRMB, it is satisfed at 5 per cent. There was an increase in the G*/sin δ parameter for unmodifed binder when the polymer was mixed. The optimum dose of the modifer was found to be 5 per cent of CR, 2 per cent EVA and 2 per cent SBS for 60/70 grade bitumen on the basis of performance grade 70. The performance grade PG 70 was selected for optimization of polymer content as the properties obtained at this percentage are very close to PMB 70 grade and CRMB 55 defned in IRC : SP : 53-2002 specifcations for Indian conditions. 4.6 Chemical Analysis In general while observing the modifed samples, it was seen that SBS has mixed homogenously with bitumen to form the true solution. However, EVA was found to form a colloidal type solution with some focculates on top. CR formed suspension with some sediment at the bottom in the container and required agitation for complete mixing. 4.6.1 Infra Red Spectroscopy (IR) : The IR spectra were recorded on a Nicolet NEXUS Aligent 1100 FT-IR spectrometer. The plot of IR tests on X-axis gives wave number i.e frequency of light radiations and Y-axis gives information about radiation transmitted. 4.6.1.1 Neat Bitumen (60/70 & 80/100) : As bitumen contains a mix of asphaltenes and maltenes, all characteristics bands have been observed in the IR spectrum of bitumen as shown in Fig. 8. For example, the band at 1633 cm -1 is the characteristics of ν (C=O)/δ (N―H), the band at 1550 cm -1 is the characteristics of ν (C=C) of benzene, the band at ca. 700 cm -1 is the characteristics of the (C―H) (rocking) and the band at ca. 3400 cm -1 is the characteristics of ν (O―H). 4000 3000 2000 1000 2933 609 1464 1453 2924 1633 728 A r b i t r a r y u n i t Wave number (cm -1 ) Bitumen 60/70 Bitumen 80/100 Fig. 8 IR Spectrum of Neat Bitumen 4.6.1.2 CR Modified Bitumen : IR spectrum of CR modified bitumen as shown in Fig. 9 exhibits major characteristic bands due to CH 2 at 1460 cm -1 and between the ranges 2850 to 2910 cm -1 . In addition, three medium intensity bands at ca. 3400, 1617 and 750 cm -1 were also observed which are characteristic bands observed in case of bitumen also. The shift of 1633 cm -1 band due to ν (C=O)/ δ (N―H) or 1600 cm -1 due to ν (C―C) to 1617 cm -1 suggests the bonding between CR and bitumen. The mixing of these two materials were done at 170 o C during preparation stage and at 14 ton pressure during IR pellet formation stage, forced the two materials to mix homogeneously to give covalent bonding between these two. The IR spectrum of aged CR modifed samples shows that aged sample retains all the characteristics IR bands suggesting the retaining of covalent 10 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 KUMAR, MAURYA, GUPTA & SINGH ON bonding between CR and bitumen and hence shows the stability and durability of the modifed binder. 4000 3500 3000 2500 2000 1500 1000 500 1356 2924 3437 1593 2849 1454 715 A r b i t r a t r y u n i t Wave number (cm -1 ) P 80 CR 2 P 80 CR 5 P 80 CR 8 4000 3500 3000 2500 2000 1500 1000 500 1371 3437 2920 2849 1454 1463 1593 805 1454 A r b i t r a r y u n i t Wave number (cm -1 ) 80 CR 2 80 CR 5 80 CR 8 Fig. 9 IR Spectrum of CR Modifed Bitumen After Ageing (left) and Before Aged 4.6.1.3 EVA Modifed Bitumen : IR spectrum of EVA modifed bitumen as shown in Fig. 10 exhibits major characteristic bands due to CH 2 at ca. 1450 cm -1 and between the ranges 2850 to 2910 cm -1 . In addition, three medium intensity bands were also observed. 4000 3500 3000 2500 2000 1500 1000 500 715 1443 2920 1602 2840 A r b i t r a r y u n i t Wave number(cm -1 ) 80 EVA 2 80 EVA 5 80 EVA 8 4000 3500 3000 2500 2000 1500 1000 500 1377 1602 696 2840 2929 1454 A r b i t r a r y u n i t Wave number(cm -1 ) P 80 EVA 2 P 80 EVA 5 P 80 EVA 8 Fig. 10 IR Spectrum of EVA Modifed Bitumen (left) Before and (right) After Aged The band at ca. 3430 cm -1 indicates presence of ν (O―H), at 1610 cm -1 ν (C=O)/δ (N―H) and at 750 cm -1 is due to (C―H) (rocking).These bands are characteristic bands observed in case of bitumen also. The bands observed in case of aged modifed samples indicate slight shifting of ca. 750 cm -1 band due to (C―H) (rocking) to ca. 700 cm -1 . The IR spectrum of aged EVA modifed samples shows that aged sample retains all the characteristics IR bands. 4.6.1.4 SBS Modifed Bitumen : IR spectrum of SBS modified bitumen as shown in Fig. 11 exhibits major characteristic bands due to CH 2 at ca. 1450 cm -1 and between the ranges 2840 to 2920 cm -1 . In addition, four medium intensity bands were also observed. The band at ca. 3420 cm -1 indicates the presence of ν (O―H), at 1650 cm -1 due to ν (C=C) of CH 2 = CH 2 , at ca. 1490 cm -1 is due to the ring breathing mode of benzene and at ca. 850 cm -1 due to (C―H) (rocking).These observed bands are characteristic observed in case of bitumen and SBS also. 4000 3500 3000 2500 2000 1500 1000 500 1338 1462 3420 2844 1659 2916 A r b i t r a r y U n i t Wave number(cm -1 ) 80 SBS 2 80 SBS 5 80 SBS 8 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 11 LAB STUDY ON CHEMICAL AND RHEOLOGICAL CHANGES IN MODIFIED BINDERS 4000 3500 3000 2500 2000 1500 1000 500 741 1377 1462 2846 2924 3460 A r b i t r a r y u n i t Wave number(cm -1 ) P 80 SBS 2 P 80 SBS 5 P 80 SBS 8 Fig. 11 IR Spectrum of SBS Modifed Bitumen (left) Before and (right) After Aged The bands observed in case of aged modifed samples indicate slight shifting of ca. 850 cm -1 band due to (C―H) (rocking) to ca. 741 cm -1 and of ca. 1490 cm -1 due to ring breathing mode to ca. 1390 cm -1 . However, the IR spectrum of aged SBS modifed samples retains all the characteristics IR bands, suggesting the retaining of proper covalent bonding between SBS and bitumen and hence shows the stability and durability of the modifed binder. 5 CONCLUSIONS The study has a limited scope because only two grades of bitumen were used. Different bitumen reacts chemically in different manner with polymers. The following conclusions are drawn based on the results obtained in the study: ● The physical properties of bitumen, such as penetration, softening point and elastic recovery are improved with addition of the polymers and crumb rubber. EVA modifed binder gives lower penetration value. SBS modifed binder gives higher softening point and better elastic recovery than EVA and CR modifed binders. ● The Indian specifications (IRC: SP: 53-2002 and IS 15462: 2004) specify different grades of modifed binders based on empirical tests and are generally unable to quantify the unique rheological characteristics of polymer modified bitumen. Therefore, it is suggested that rheological property such as G*/sin δ should be used for development of the performance grades as per SHRP specifcations. Maximum phase angle such as 75ºC can also be used to ensure adequate elasticity in the modifed binder. ● Complex modulus (G*), shear modulus (G*/sin δ) and loss modulus (G*sin δ) are found to be in important roles to characterize the rheological properties and predict performance of modifed bitumen at high temperature. ● Following the ageing process, higher softening points as well as lower penetration values are found. The loss of volatile fractions contributes to the difference in weights between original and aged sample. Greater loss in weight is seen for PAV aged samples as compared to the TFOT aged samples. ● The complex modulus of the modifed binders is higher as compared to neat bitumen. The phase angle values clearly illustrate the improved elastic response (reduced δ) of the modified binders compared to their respective base bitumen. ● There is a constant increase in complex modulus, G*, with increase in percentage modifcation after ageing the 60/70 and 80/100 bitumen. There is also a regular decrease of the phase angle, δ, over the temperature domain after ageing. The complex modulus increases with increase in percentage of modifer and decreases with increase in temperature. However, phase angle decreases with increase in percentage of modifer and increases with increase in temperature. ● Neat bitumens lost their elasticity at 70°C as their phase angle is more than 90°. However, all the modifed binders still display considerable elasticity at this temperature. The value of complex modulus at 46°C increased 1.5 and 1.6 times by adding 2 per cent CR to 60/70 and 80/100 bitumen respectively. By addition of 2 per cent SBS with 60/70 and 80/100 bitumens the value of complex modulus are increased 2.9 and 3.5 times respectively at 46°C while with the same percentage of EVA it increases 5 and 6.3 times. ● The parameter shear modulus (G*/sin δ) as expected is found to increase with increase in the percentage of modifer after TFOT ageing. G*/sin δ varies almost linearly at higher temperature of 58°C. Also 60/70 modifed binders have shown higher rutting resistance value than 80/100 modifed binders at 12 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 KUMAR, MAURYA, GUPTA & SINGH ON the same percentage of modifer. Therefore, 60/70 binder can beneficially be used in the areas of heavy traffc and at high temperatures. The rutting resistance of the binder increases with increase in the percentage of modifer. EVA modifed binders were found to have higher rutting resistance to deformation under loading as compared to CR and SBS modifed binders at high temperature. ● The EVA modifed binder has higher loss and storage moduli as compared to CR and SBS modified binders. Therefore, the EVA modifed binder will cause cracking at low temperatures. ● CRMB is the cheapest binder available in India. However, its rheological behaviour is not very consistent. But, its elastic recovery is more as compared to EVA. So, it can be used in low cost roads with more percentages as compared to SBS and EVA. However, due precaution is to be taken for continuously mixing using mechanical stirrer during transportation and maintaining the temperature otherwise it may prove to be inferior than even neat unmodifed binder. . ● In the present study, the value of G* (complex modulus), G*/sin δ (shear modulus) and G*sin δ (loss modulus) for EVA binders are found to be higher than SBS and CR modified binders. So EVA modifed binders can be used in very high temperature and heavy traffc areas but it cannot be used at lower temperature as its ductility value is less. SBS modifed binders can be used in all climatic conditions of low as well as high temperature (all places of India). CR modified binders will be suitable for moderately high temperature zone with continuously mixing using mechanical stirrer during transportation and maintaining the temperature. ● After long term ageing, an increase in the value of complex modulus with an increase in the amount of modifer is seen. The increase in G* after long term ageing is understandably greater than that after TFOT ageing due to the prolonged ageing process in the PAV. ● From the relationships of G*/sin δ with test temperature before and after ageing, the 60/70 and 80/100 penetration bitumens meet the PG 58 specifcation requirements of SHRP (G*/sin δ = 1.0 kPa for unaged and 2.2 kPa for short aged binder). ● The optimum dose of the SBS and EVA was found to be 2 per cent and of CR as 5 per cent on the basis of performance grade 70. ● The binders modifed with polymers will certainly nominal increase in the initial cost of construction but will prove to be economical if the life cycle cost is taken into consideration by providing better and long lasting roads. 5.1 Based on Chemical Analysis The following conclusions are drawn based on the results obtained in the study: ● SBS mixed homogenously with bitumen to form the true solution. EVA formed a colloidal solution with some focculates on top while CR formed suspension with some sediment at the bottom in the container. ● Infrared analysis of modifed bitumen indicates retaining of covalent bonding between modifer and bitumen. This suggests the stability and durability of modifed binders to sustain climatic changes during its course of life. ● The spectral analysis results suggest that SBS is the best material amongst three for bitumen modifcation to enhance the chemical properties during service life. REFERENCES 1. Airey, G.D. and Brown S.F. (1998), “Rheological Performance of Aged Polymer Modifed Bitumens”, Journal of the Association of Asphalt Paving Technologists, Vol 67, pp 66-94. 2. Airey, G.D. (2003), “Rheological Properties of Styrene Butadiene Styrene Polymer Modifed Road Bitumens”, Fuel, Vol 82, pp 1709-1719. 3. Ewing G. W. (1982), “Instrumental Methods of Chemical Analysis”, Fifth Edition, pp 429-438. 4. Gonzalez, O., Munoz, M.E., Santamaria, A., Garcia, M.G, Navarro, F.J . and Partal, P. (2004), “Rheology and Stability of Bitumen/EVA Blends” European Polymer Journal, Vol 40, pp 2365-2372. 5. Jeong KD, Lee SJ, Amirkhanian SN, KimKW (2010), Interaction of Crumb Rubber Modifed Asphalt Binder, Constr. Build. Mater., 24: 824-831. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 13 LAB STUDY ON CHEMICAL AND RHEOLOGICAL CHANGES IN MODIFIED BINDERS 6. Lepe A.P., Boza F.J .M., Gallegos C., Gonzalez O., Muzoz M.E., Santamari A. (2003), “Infuence of the Processing Conditions on the Rheological Behaviour of Polymer Modifed Bitumen”, Fuel 82, pp1339-1348. 7. Lougheed, T. J ., and Papagiannakis, A.T., (1996), “Viscosity Characteristics of Rubber-Modified Asphalts”, Journal of Materials in Civil Engineering, ASCE, Vol. 8, No. 3, pp 153- 203-156. 8. Lu, X. and Isacsson, U. (1997), “Infuence of Styrene-Butadiene- Styrene Polymer Modifcation on Bitumen Viscosity”, Fuel, Vol 77, No.14-15, pp 1353-1359. 9. Lu, X., Isacsson, U. and Ekblad, J . (1999), “Phase Separation of SBS Polymer Modifed Bitumens”, Journal of Materials in Civil Engineering, ASCE, Vol 11, No. 1, pp 51-57. 10. Mehndiratta H.C. and Chandra S. (2000), “Investigation on Bituminous Mixes with Blended Modifers”, Journal of Institution of Engineers, India, Vol 81, pp 115-119. 11. Navarro FJ , Paratal P, Martinez BF, Gallegos C (2004), “Thermo –Rheological Behaviour and Storage Stability of Ground Tire Rubber Modifed Bitumen”, Fuel, 83: 2041-2049. 12. Mohamed and Husaini Omar, (2009), “Rheological Properties of Crumb Rubber Modifed Bitumen Containing Antioxidant”, The Arabian J ournal for Science and Engineering, Vol 34, Number 1B. 13. Panda M. and Mazumdar M. (1997), “Development and Evaluation of a Bituminous Paving Binder Containing Reclaimed Polyethylene”, Indian Highways, IRC, Vol 25, pp 11-21. 14. Read J. and Whiteoak D., “The Shell Bitumen Handbook”, Fifth Edition, Thomas Telford, pp 29-45. 15. Singh K. L., (2006), “Rheological Behaviour of Bituminous Binders for Indian Conditions”, Ph.D. Thesis, Transportation Engineering Section, Department of Civil Engineering, IIT Roorkee, pp 21-230. 16. Solomon E.I., Lever A.B.P., (1999), “Inorganic Electronic Structure and Spectroscopy”, Vol II- Applications and Case Studies, pp 533-574. 17. Wahhab, H.A.A. and Amri, G.A. (1991), “Laboratory Evaluation of Reclaimed Rubber Asphaltic Concrete Mixes”, Journal of Materials in Civil Engineering, ASCE, Vol 3, No. 3, pp 189- 203. 18. Yousef A.A., (2002), “Rubber Modifed Bitumen”, Iranian Polymer J ournal, Vol 11, No.5, pp 303-309. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 15 1 LEISURE TRAVEL IN INDIA India has been a country of tourism interest and destination since old times for the tourists around the world. The size of the country is vast and its unique socio-cultural and eco-diversityprovides ample leisure opportunities to domestic tourists also. There has been an increase in the number of domestic tourists from approximately 160 million domestic visits in 1997 to 740 million in 2010. The Ministry of Tourism’s vision is to achieve a level of 760 million domestic tourist visits by the year 2011, the end of the 11 th Plan at an annual average growth of 12 per cent. There has also been an increase in foreign tourist arrivals to India from 2.48 million in 1999 to an estimated 14.72 million in 2010, with a consequent increase in foreign exchange earnings from ` 12,951 crores in 1999 to ` 64,889 crores in 2010 (Tourism Statistics 1 ). Moreover, the National Policy on Tourism, 2002 (National Tourism Policy 2 ), aims to position tourism as a major engine of economic growth and to focus on domestic tourism as a major driver to tourism growth, with increased thrust on conservation of resources and ecotourism initiatives. The trips made by tourists are termed as leisure trips. Broadly, these can be defined as trips undertaken for purposes other than for work, education, shopping, etc, and are non-mandatory in nature. The classifcation of leisure activities, are given in Table 1. A single day excursion trip can be defned as a leisure trip undertaken for the purpose of outdoor informal recreation or a ‘day out’, which does not involve an over-night stay at the location visited. Majority of the studies have studied behaviour of daily commuters in urban areas, especially in India, with very little emphasis given to leisure trips and travellers. As per Guiver et al. 3 , leisure travel differs signifcantly from utility travel in terms of degree of discretion involved in decisions, like, whether to travel or not, when to travel, where to travel and how to travel. They observed that most of the travellers chose the mode of travel before deciding, where to go, whereby projecting modes as a separate market with little or no interchangeability. The choice of a particular mode was found to be infuenced by factors, like, attributes of the available alternatives, characteristics of the decision maker, and the interaction between the two (Gitelson and Kerstetter 4 ). The infuence of trip makers’ and travel mode related variables on mode choices, as well as, on the choice of destination for leisure trips have also been studied by Reece 5 and Sung et al. 6 . The problem of unobservable or latent variables in mode choice models have been discussed INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS HARIKRISHNA M.*, RAJAT RASTOGI** & DAYA PURUSHOTHAMAN*** ABSTRACT This Paper explores the infuence of socio-demographic characteristics of leisure travellers on their mode choice decisions for single-day excursion trips. The mode chosen for a trip is infuenced by both quantitative and qualitative attributes of the traveller as well as of the mode. Data pertaining to the personal and trip characteristics of the travellers was collected at two leisure trip locations in Kozhikode district of Kerala state, India. The infuence of socio-demographic variables, like, age, occupation, household income, group size and household size on choice of travel mode was studied using cross classifcation analysis. All these variables were found causing a trend in the selection of travel modes. Discrete probability distributions were ftted to the infuencing variables. In general, negative binomial and Poisson distributions were found ftting well, and varied as per mode chosen. A multinomial logistic regression model was developed incorporating the above variables to study their effect on mode choices. It indicated that annual household income and group size signifcantly infuenced the choice of a mode. Quantifcation of attitudinal data indicated that personal vehicle users attached higher importance to comfort and convenience, taxi users attached importance to travel time and reliability, while bus users considered travel cost and, noise and dust as signifcant attitudinal variables. * Research Scholar, Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee– 247 667, and Assistant Professor in Civil Engineering, NIT Calicut, Kozhikode, Kerala– 673 601. ** Assistant Professor, Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee– 247 667. *** Former Post Graduate Student, Department of Civil Engineering, National Institute of Technology Calicut, Kozhikode, Kerala – 673 601. } 16 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARIKRISHNA, RASTOGI & PURUSHOTHAMAN ON Table 1 Classifcation of Leisure Activities Activity Facility Outdoor sport and recreation Sports pitches/ courts, golf course, bowling greens, stadia, marinas, ski slopes, race tracks, etc Indoor sport and recreation Swimming pools, sports halls, gymnasia, ice rinks, leisure centres, etc Outdoor informal recreation Play spaces, parks, beaches, lakes and rivers Countryside recreation Country parks, national parks, picnic sites, trails/ cycle paths, etc Cultural recreation Theatres, concert halls, art centres/ galleries, museums Entertainment Public halls, pavilions, cinemas, bowling, night-clubs, etc ‘Domestic’ activity Play centres, allotments, day centres, community halls, etc Gambling Bingo halls, casinos Social recreation Pubs, restaurants, dance clubs Spectating Football, cricket, rugby, tennis, greyhound/horse racing Days out Various destinations including formal attractions such as theme parks, heritage attractions, etc Tourism and holidays Hotels, holiday camps, camp sites, caravan parks, conference centres, etc Source: http://www.communities.gov.uk/documents/planningandbuilding/pdf/148901.pdf, accessed on 11 th January 2012 by J ohansson et al. 7 . It was found that the causes for individual’s latent preferences were the individual’s age, income and gender,children in a household, and education and house tenure; in-vehicle travel time and travel costs were signifcant in modal choices; fexibility and comfort were important factors; and safety was insignifcant. The issue of how an individual’s socio-economic characteristics, values and attitudes could explain the holiday impact has been explored by Bohler et al. 8 . The study recommended further research that concerns environmental consequences of holiday mobility and the need for changes in individual behaviour. Anable et al. 9 examined the relative importance that people attach to various instrumental and affective journey attributes, when travelling either for work or for leisure trips. The instrumental factors considered were cost, fexibility, etc and non-instrumental or affective factors were stress, excitement, pleasure, boredom etc. It was observed that, in the case of work trips, instrumental factors such as fexibility, convenience, cost and predictability were perceived to be important. Higher importance was accorded to instrumental factors, especially convenience, than to affective factors. In case of leisure travel, almost equal importance was accorded to both types of factors. The factors were fexibility, convenience, cost, relaxation, freedom and no stress. The relationships between vacation factors and socio-demographic and travelling characteristics have been studied by Heung et al. 10 . The vacation factors were classifed as push and pull factors (Dann 11 ). Push factors included cognitive processes and travel motivations like socialisation, adventure seeking etc, whereas, pull factors were tangible and intangible cues of a specifc destination that drove travellers to realise their needs from a particular travel experience. The survey of J apanese leisure travellers indicated that they perceived ‘enjoying holidays’ as the most important travel motive,with the lowest importance given to motive ‘visiting friends and relatives’. Signifcant relationship between vacation factors, socio-demographic and trip characteristics of the traveller was established. From the above mentioned studies, it could be concluded that: (a) Mode choice analysis, the third step in the conventional transportation planning process, is an important component in a traveller’s decision making process; (b) Socio-demographic attributes of travellers have a signifcant bearing on the choice of a mode for the trip. (c) Attitudinal and behavioural indicator variables can be used to explain the latent variables infuencing travel behaviour; HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 17 INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS (d) Instrumental and affective factors infuence leisure trips to a considerable extent; (e) There is a greater need for research on decision making sequence and choice sets pertaining to leisure travel. In this study, a cross- classifcation analysis is undertaken to understand the infuence of socio-demographic factors on mode choices pertaining to single day excursion (leisure) trips in India.This is specifc to frst two points as mentioned above. The study area is defned and a brief outline of data collection questionnaire is presented. This is followed by a discussion on infuence of socio-demographic variables, in selection of travel modes. Statistical examination of the same is also carried out. In addition, an attitudinal analysis of qualitative attributes infuencing mode choice decision of travellers is also presented. 2 STUDY AREA AND SURVEY QUESTIONNAIRE The study was conducted at two leisure locations in the Kozhikode district of Kerala state, situated on the southwest coast of India. The district is 38.25 per cent urbanized, as per the 2001 census. The Kozhikode district has a total geographical area of 23,444 sq.kms and the population density is 1,228 per sq.kms (http://www.kkd.kerala.gov.in/ home.htm) 12 . The study locations are chosen so as to fulfll the following criteria: (a) It should be a place, where all people with different socio-demographic profles make a visit, and (b) It should be accessible to the people by available travel modes. The study locations selected were Thusharagiri Waterfalls and Kappad beach, both well known recreational areas in Kozhikode. Thusharagiri waterfall lies 50 kms east of Kozhikode city. The Chalippuzha river at this place diverges into three water falls creating a snowy spray, which gives the name, “Thusharagiri”. It is well connected to Kozhikode and Wayanad districts of Kerala by road transport. The State owned transport buses provide bus services to this location. The sandy beach at Kappad is one of the famous beaches of Kerala, due its historical background and charming beauty. It is located at a distance of 16 km from the Kozhikode town and it is at Kappad, where the famous Portuguese sailor, Vasco da Gama arrived in India in the year 1498. Fig. 1 shows the location of Kozhikode district and the two leisure places. Fig. 2 (a) and (b) gives a view of the two leisure places selected for the study. A questionnaire was designed to collect information about behaviour of single day excursion traveller to the two locations (shown in Appendix A). The questionnaire included questions related to transportation service attributes, such as, travel time and travel cost, and qualitative attributes, such as, comfort, convenience, reliability, and dust and noise; trip makers’ attributes, like, age, gender, marital status, occupation and education; and household attributes, like, family, size, vehicle ownership and income. Comfort in a mode is defned in terms of privacy of traveller, availability of seat, journey with minimum jerks and without tension, and protection from rain. Convenience is defned as a state of quality of being easy to use. A mode is said to be convenient, if it is available when the passenger requires, avoids long walk both at origin and destination and when better facilities are provided at the shelter. Reliability is defned in terms of service quality. If a mode has less likelihood of breakdown, reaches the destination in time, is regular in schedules and causes minimum delays during the journey, then that mode is considered as reliable. Unlike the above attributes, combined noise and dust is a negative attribute. In order to make it positive one, it is defned as minimum disturbance that is caused due to vehicle horn, vehicle vibrations, friction between wheels and pavement and dust. A pilot study was conducted at both the study locations to standardise the questionnaire. Ninety samples were collected. It was observed that the respondents showed reluctance in revealing the exact age and income. Hence, these questions were modifed by making classifcation groups for age and household income. The level of importance of selected attributes i.e., travel time, travel cost, comfort, convenience, reliability, and dust and noise was obtained on a rating scale comprising of fve levels (1 to 5), namely ‘not extremely important’, ‘not important’, ‘immaterial’, ‘important’ and ‘extremely important’ respectively. Fig.1 Location of Two Study Places 18 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARIKRISHNA, RASTOGI & PURUSHOTHAMAN ON 2(a) Thusharagiri Waterfalls 2 (b) Kappad Beach Fig. 2 Study Places 3 SAMPLE SIZE The sample size required for the analysis was estimated using the following formula: ss = Z 2 - p - (1 -p) C 2 ... Eqn.(1) Where, SS = Sample size required Z = Z value i.e. normal variate (e.g. 1.96 for 95 per cent confdence level), p = Percentage picking a choice, expressed as decimal, C = Confidence interval, expressed as decimal (e.g., 0.05 =±5). From the annual tourism statistics published by the Department of Tourism, Government of Kerala 13 , the number of domestic tourist visits to Kerala was 66,42,941, out of which 5,70,832 visits were made to Kozhikode district. Since, the exact data regarding the number of domestic tourist visits to Kappad and Thusharagiri were not available, it was assumed that 5,70,832 visits were made to the two leisure locations. This accounted for 8.6 per cent of the total tourist visits in Kerala. Therefore, on taking, p =0.086, C =0.05 and Z = 1.96 for 95 per cent confdence interval, the Sample Size (SS) came out to be 120. On the whole, 500 samples were collected for analysis, the size was increased to adequately capture the travellers, who were opting for various modes in different proportions. 4 INFLUENCE OF SOCIO-DEMOGRAPHIC VARIABLES The identifcation of the infuence of socio-demographic variables ontravel mode choices was carried out using cross-classification analysis. The influence of five socio-demographic variables namely, trip maker’s age, occupation, household size, annual household income and size of the group were studied. Cross classifcation of the data was done for such examination. These are now discussed in successive sections. 4.1 Age of Tourists Table 2 gives the distribution of mode choices made by the tourists who are classifed by age. In case of a group, the age of decision maker was considered. Five age groups namely children and young (<18 years), young adults (18- 25 years), adults (25-45 years), older adults (45-60 years) and the senior citizens or elderly (above 60 years) were considered. Table 2 Mode Chosen in Different Age Groups Age Group Bus Taxi Own Vehicle Total Children/Young 27 11 4 42 Young Adult 89 47 33 169 Adult 107 48 60 215 Older Adult 21 16 14 51 Elderly 11 8 4 23 Total 255 130 115 500 It was observed that almost 50 per cent of the respondents travelled by bus. It was the main mode of travel across all age groups, varying between 41and 64 per cent, lowest in older adults and highest in children. A good connectivity by state transport bus and cost economics might have induced such travel pattern. The next major travel mode chosen was taxi, except by adults who showed liking towards own vehicle. Older adults showed almost equal liking for taxi and own vehicle. Taxis and own vehicles are used for around one-quarter of the overall trips studied. This is contrary to the normal belief that leisure trip makers mostly use their own vehicle for a single day excursion trip. Personal comfort and personal enjoyment during leisure travel might have offset the higher cost of travel by taxi as compared to own vehicle, thus causing relatively more travel by taxi. Highest leisure trips were made by adults (43 per cent), followed by young adults (34 per cent). Elderly travellers made only 5 per cent of such trips. Taxi was preferred more by elderly and older adults (31-35 per cent) and least by adults (22 per cent). Own vehicle was preferred by adults and older adults (27-28 per cent) and least by children (10 per cent) and elderly (17 per cent). HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 19 INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS 4.2 Occupation of Tourists Table 3 presents travel mode distribution by occupation of the trip makers. The occupation categories considered were unemployed, government employed, private employed, business, student and retired. 76 data sets were discarded because of incomplete information. Higher preference to bus was given by students (64 per cent), followed by business class respondents and retired persons (51-50 per cent). Least preference to bus was given by respondents in government occupation. Taxi was highly preferred by retired persons (40 per cent), followed by respondents in private organisation and unemployed (35-32 per cent). Nearly half of the respondents in government organisations preferred own vehicle for single day excursion trips. Retired persons (10 per cent) and students (16 per cent) preferred it the least.Students made more trips (36 per cent) as compared to other categories of the trip makers. They were followed by private employed respondents (28 per cent). Retired persons made only 2 per cent of the trips and unemployed made 9 per cent trips. Respondents in government organisations and involved in business were found making 12-13 per cent trips each. Out of those who used bus as a most preferred travel mode, the students share was 46 per cent and share of persons in private organisations was 23 per cent. Similarly, out of total taxi trips, 39 per cent were made by private employed respondents and 29 per cent by students. Similarly, out of total own vehicle trips, 28 per cent were made by private employed respondents and 23 per cent each were made by government employed respondents and students. Table 3 Mode Chosen by Respondents with Different Occupation Occupation Bus Taxi Own Vehicle Total Unemployed 17 12 8 37 Government 15 10 24 49 Private 48 42 30 120 Business 29 9 19 57 Student 96 31 24 151 Retired 5 4 1 10 Total 210 108 106 424 The categorisation of trip makers by occupation brings out their behaviour quite clearly. More trips by students probably indicate that this is their most preferred way of relaxing. Lower share of elderly and unemployed in such travel indicates that these are not a priority for these groups. This may be due to fnancial constraints. Out of the earning categories, respondents employed in private organisations made more leisure trips, almost 2 to 2.5 times of trips made by respondents involved in business or employed in government organisations. This may be due to their relatively better economic standing. 56 per cent bus trips were shared by non-earning respondents and their sharein taxi trips was 44 per cent and 31 per cent in own vehicle trips. 4.3 Annual Household Income The trip makers were also categorised based on annual household income into fve different categories namely; ` 50,000 and below, `50,000-`1,50,000, `1,50,000– `3,00,000, `3,00,000– `5,00,000 and greater than `5,00,000 (based on Fifth Pay Commission Scales). Table 4 gives the distribution of the trip makers based on their annual household income and the mode chosen for the trip. Distinct trend could be observed in the chosen modes by trip makers belonging to different income categories. Bus was used more, 75 per cent by trip makers having annual household income upto `50,000, and 48 per cent in income group `50,001– `1,50,000. On the whole, these two categories constitute 81 per cent of the bus trips. Trip makers in household income range of `1,50,000 to `3,00,000 preferred bus and own vehicle almost equally (39-34 per cent), whereas, those in the income group `3,00,000 to `5,00,000 preferred taxi and own vehicle (37-39 per cent). The obvious choice in household income range above `5,00,000 was for own vehicle (74 per cent). Almost 50 per cent bus trips were made by respondents in income group less than and equal to `50,000. No bus trips were made in income group above `5,00,000. Out of the overall taxi trips, 38 per cent were made by respondents in income group `50,001– `1,50,000. Taxi trips by respondents in income group less than and equal to ` 50,000 and income group `1,50,001– `3,00,000 were 28 per cent and 22 per cent respectively. In case of own vehicle trips, 31 per cent to 34 per cent trips were made by respondents in lower middle income group, 13 per cent in middle income group and 15 per cent in upper income group. The distribution of trip makers with reference to their annual household income indicates predominant usage of bus in lower and lower middle income groups. As the household income level rises, there is a prevalent shift towards own vehicle. In the 20 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARIKRISHNA, RASTOGI & PURUSHOTHAMAN ON upper middle income group,preference is shifted towards using own vehicle for leisure trips. However, the middle income groups gave almost equal importance to bus and own vehicle. The categorisation of trip makers by their household income clearly demarcates the change in trend of choosing a travel mode if the division of households is done based on their household income level. It indicates that respondents can be categorised in three income groups, namely lower income, middle income (again having 3 categories) and high income group. Table 4 Mode Chosen in Different Household Income Categories Annual Household Income (`1000) Bus Taxi Own Vehicle Total <=50 126 33 8 167 50.1 – 150 80 49 39 168 150.1 – 300 40 28 36 104 300.1 – 500 9 14 15 38 >500 0 6 17 23 Total 255 130 115 500 4.4 Group Size The number of people in a group, which undertakes the trip, could have an infuence on the type of mode chosen for the trip. As the average size of a family in India is 4.8 (www. nhfsindia.org 14 ), a set of 6 or more persons is considered as a group. It is observed that certain groups behave more or less similarly and hence they are clubbed together from analysis point of view. Table 5 gives the distribution of single day excursion trip makers and the travel modes used by these groups. Table 5 Mode Chosen by Different Group Sizes Group size Bus Taxi Own Vehicle Total 6 or 7 81 38 31 150 8 14 27 5 46 9 or 10 22 13 1 36 11 6 7 3 16 12 or 13 1 5 0 6 14 or 15 2 1 0 3 Above 15 0 6 0 6 Total 126 97 40 263 Out of the total trips made using own vehicle, almost 78 per cent trips were made by respondent group size of 6 or 7. It was not preferred, if the group size increased above 8. In case of total bus trips, around 64 per cent trips were made by group size 6 or 7, 11 per cent by group size 8 and 17 per cent by group size 9 or 10. Thereafter, the use of bus reduced sharply. Group size 9 or 10 mainly relied on bus and taxi mode. Taxi was used in 39 per cent of the taxi based trips by group size of 6 or 7, 28 per cent trips by group size of 8 and in 13 per cent trips by group size 9 or 10. Group sizes above 12, in general, rely mainly on taxi travel to such locations. The above examination indicated that group size also play an important role in the selection of mode for short leisure trips. Trip makers in the group size of up to 7 preferred bus as their travel mode in little more than half of the trips made by them, while travellers in groups of size 11 onwards showed preference towards taxi as their travel mode. Group sizes of 8, 9,10 and 11 represent the transition condition between choosing bus or taxi as a travel mode for single day excursion trip. The use of own vehicle for single day excursion trip was not found suitable, if the size of group increased beyond 7. It is inferred that the travel mode selection is based on the trade-off between economy of travel and convenience. 4.5 Household Size Apart from group size, respondents were also categorised into household size 1 to 5.The distribution of the travellers’ choices with respect to the household size is given in Table 6. The data set does not include students and unemployed respondents, who have come in a group of 2 or above, as this would not result in unique household size. Table 6 Mode Chosen by Respondents of Different Household sizes Household Size Bus Taxi Own Vehicle Total 1 7 2 2 11 2 19 9 8 36 3 44 22 30 96 4 41 25 23 89 5 16 21 10 47 Total 127 79 73 279 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 21 INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS It was observed that among the families of different household sizes, 46 per cent preferred bus, while 26-28 per cent preferred taxi or own vehicle. It was also observed that as the size of the household increased from 1 to 5, the share of bus reduced from 64 per cent to 34 per cent. In case of own vehicle, the share initially increased from 18 per cent to 31 per cent with increase in household size upto 3 and then reduced back to 21 per cent. Above household size 3, the share of taxi increased from 23 per cent to 45 per cent. Among bus users, major share was found to be of household size 3 and 4 (35 per cent-32 per cent). The same was true for own vehicle usage, the share being 41 per cent-32 per cent for household size 3 and 4, respectively. In case of taxi, the major share came from household size 3 to 5 (each ranging between 27 per cent-32 per cent). Staunch preference to bus was observed by leisure trip travellers with household size upto 4. Smaller size households showed a marked preference to bus over taxi and own vehicle. In case of travellers having household sizes 3 and 4, although bus continued to be the most preferred mode, its mode shares decreased. In case of household size 5, taxi users dominate the mode share. 5 STATISTICAL ANALYSIS OF SOCIO- DEMOGRAPHIC VARIABLES 5.1 Fitting Distributions The variables, such as, age of the trip decision maker, annual household income, group size and household size were identifed as those infuencing the choice of a travel mode for a single day excursion trip. These variables were considered as random variables. Set of ranges for the variables were identifed along with data points falling in each range. For the present study, various probability density functions were examined for ranges of random variables. Discrete probability distributions, like, binomial, geometric, uniform, negative binomial, Bernoulli etc., were considered. It was observed that all the density function plots were skewed at the tail ends. The best ftting probability distributions, their parameters and test statistic are given in Table 7. The parameter ‘n’ and ‘p’ refers to the number of failures until the experiment is stopped and the success probability in each experiment, respectively. The parameter ‘λ’ in Poisson distribution is a positive real number, which is equal to the expected number of occurrences during the given interval. The best ftting probability distribution is identifed based on Anderson-Darling (A-D) test statistic. The A-D test is based on the squared difference between the observed distribution function and empirical distribution function considered (Peng et al., 15 ). Further, the A-D test statistic gives more weight to the tails of the distribution and is a more sensitive test (Law 16 ). The A-D test statistic assesses whether a sample comes from a specifed distribution. The A-D test statistic, A n 2 is the weighted average of the squared differences [ ] 2 , where Fn(x) is the probability distribution function of the data and is the probability distribution function of the hypothesised distribution. The test statistic was compared against the critical values of the theoretical distribution. It can be observed from the statistics presented in the Table 7 that negative binomial function got ft in for ‘age’ across all the travel modes. This indicates that for the various mode users, as the age increases, the probability of choosing that particular mode decreases. Similarly, geometric distribution function got ft for variable ‘income’ for bus and taxi modes, which indicates that as the income level rises, the patronage of that particular mode decreases. Negative binomial distribution had a good ft in case of income for ‘own-vehicle’. Poisson probability distribution function was found ftting for variable ‘group size’ with travel mode bus and own-vehicle, whereas, binomial function was found ftting well for taxi. In case of household size, Poisson distribution was found ftting well across all travel modes, which indicates that there is more possibility of people to choose a particular mode. Table 7 Best Fitting Probability Distributions Mode Random Variable Probability Distribution Function Parameter/s A-D Test Statistics Bus Age Negative Binomial n=7 p=0.15937 17.417 Income Geometric p=7.1910E-6 21.166 Group Size Poisson λ=7.127 19.05 Household Size Poisson λ=3.5986 14.971 22 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARIKRISHNA, RASTOGI & PURUSHOTHAMAN ON The results of the likelihood ratio tests of the model related to earning and non-earning members are given in Table 8 and Table 9, respectively. The results indicate that income and group size were signifcant factors infuencing choice of mode for single day excursion trips. The Pseudo R-square values of the model for earning group were obtained as Cox and Snell-0.373, Nagelkerke – 0.423 and McFadden-0.219. The Pseudo R-square values of the model for non-earning group were obtained as Cox and Snell-0.452, Nagelkerke – 0.533 and McFadden-0.319. These indicate that probably more factors play a role in the mode choice selection (as noted by Gitelson and Kerstetter 4 , Reece 5 , and Sung et al., 17 ).The model would improve with the inclusion of system variables such as travel time, travel cost, as well as attitudinal variables such as comfort, convenience, safety and reliability. Table 8 Likelihood Ratio Test Results for Earning Group of Travellers Effect Model Fitting Criteria Likelihood Ratio Tests -2 Log Likelihood of Reduced Model Chi- Square df Sig. Intercept 2.645E2 0.000 0 . Age 269.683 5.173 6 0.522 Group size 333.679 69.169 10 0.000 Annual Household Income 322.285 57.775 8 0.000 5.2 Development of a Multinomial Logistic Regression Model Logistic regression model was developed to evaluate the relative infuence of the variables, namely; age, household income and group size on the choice of a travel mode. Household size was not considered in the development of logistic model as it is a part of the group size that visits a leisure location. Logistic regression applies the principle of maximum likelihood in the estimation of odds of choosing a mode with respect to a reference mode. Here, taxi is considered as the reference mode. Two models were developed, one for the earning group of travellers and another for non-earning group of travellers comprising of students and unemployed. This allows the comparison of groups with reference to income. For earning group of travellers, age was coded in increasing order of age groups as ‘18-25 years’, ‘25-45 years’, ‘45- 60 years’ and ‘greater than 60 years’. The variable, annual household income was coded in fve categories as 1 to 5, representing increase in income as already mentioned in section 4.3. Group size was considered in six categories, with group sizes 1 and 2 together, 3, 4, 5, 6and 7 together, and above 7 being coded from ‘1’ to ‘6’, respectively. For non-earning group of travellers, age was coded in four categories namely ‘18 years and less’, ’18-25 years’, ‘25- 45 years’ and ‘greater than 45 years’. The variable annual household income was coded in four categories as ‘less than `50000’, ‘`50000 - `1.50 lakhs’, ‘`1.5 - `3.00 lakhs’ and ‘above `3 lakhs’. The coding for group size for the non- earning group was same as that for earning group. Taxi Age Negative Binomial n=6 p=0.14154 9.9393 Income Geometric p=4.4520E-6 6.6149 Group Size Binomial n=35 p=0.23492 6.2058 Household Size Poisson λ=3.8966 10.252 Own Vehicle Age Negative Binomial n=10 p=0.21005 15.817 Income Negative Binomial n=2 p=6.2422E-6 4.6158 Group Size Poisson λ=7.1628 7.3658 Household Size Poisson λ=3.7349 9.9008 * Critical value of A-D test statistic at 99 per cent confdence level is 3.9074. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 23 INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS Table 9 Likelihood Ratio Test Results for Non-earning Group of Travellers Effect Model Fitting Criteria Likelihood Ratio Tests -2 Log Likelihood of Reduced Model Chi- Square df Sig. Intercept 1.434E2 0.000 0 . Age 153.162 9.773 6 0.135 Group size 179.717 36.329 10 0.000 Annual Household Income 230.864 87.476 6 0.000 The parameter estimates of the logistic regression model for earning and non-earning group of travellers are given in Table 10 and Table 11, respectively. It was observed that age had a positive infuence on the choice of bus as a travel mode as compared to taxi by the non-earning group. But in case of earning groups, bus had a positive infuence only in the adult category, implying that rest of the earning age groups are more likely to use taxi. In case of own vehicle users, the trend was observed to be more or less opposite to that compared to bus use. In the case of earning group, own vehicle was more preferred than taxi by all age groups. Non-earning groups of age up to 25 years were found to be inclined towards taxi use and the adults were found to prefer own vehicle. Group size was found to have a positive infuence on the use of bus as travel mode upto group size of fve members for earning group, and across all group sizes in the non-earning group. Similarly, for use of own vehicle, positive infuence was observed across all group sizes indicating preference above taxi. Comparison of bus and own-vehicle usage indicated higher propensity of use of own vehicle compared to bus. But in case of non-earning group, the use of own vehicle was preferred upto group size 3, after which taxi was given higher preference. The effect of income was found to be positive on usage of bus as compared to taxi across all the income categories of earning and non-earning groups. However, it was found that taxi was more preferred compared to own vehicle by both earning and non-earning groups. This again substantiate that own vehicle may not be the most preferred mode of travel for single-day excursion trips. Table 10 Parameter Estimates of Logistic Regression Model for Earning Group Mode- Bus Coeffcient Value Standard Error Mode- Own Vehicle Coeffcient Value Standard Error Intercept –18.891 0.817 Intercept – 2.672 1.416 [Age=1] – 0.529 0.632 [Age =1] 0.489 0.776 [Age =2] 0.107 0.582 [Age =2] 0.982 0.719 [Age =3] – 0.071 0.663 [Age =3] 0.488 0.800 [Age =4] 0b . [Age =4] 0b . [Group size=1] 3.227 0.811 [Group size =1] 5.325 1.272 [Group size =2] 1.513 0.573 [Group size =2] 3.806 1.153 [Group size =3] 0.369 0.523 [Group size =3] 3.525 1.084 [Group size =4] 1.135 0.493 [Group size =4] 3.125 1.116 [Group size =5] – 0.209 0.454 [Group size =5] 2.046 1.098 [Group size =6] 0b . [Group size =6] 0b . [Income=1] 19.373 0.595 [Income=1] – 2.469 0.899 [Income =2] 18.292 0.587 [Income=2] – 1.073 0.781 [Income =3] 18.472 0.653 [Income =3] – 0.670 0.814 [Income =4] 18.454 0.000 [Income =4] – 0.886 0.907 [Income =5] 0b . [Income =5] 0b . Note: b – This parameter is set to zero as it is redundant 24 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARIKRISHNA, RASTOGI & PURUSHOTHAMAN ON Table 11 Parameter Estimates of Logistic Regression Model for Non-Earning Group Mode- Bus Coeffcient value Standard Error Mode- Own vehicle Coeffcient value Standard Error Intercept – 5.164 1.634 Intercept 1.268 1.945 [Age=1] 0.584 1.065 [Age =1] – 1.327 1.779 [Age =2] 0.228 .962 [Age =2] – 0.094 1.553 [Age =3] 1.598 1.100 [Age =3] 1.026 1.618 [Age =4] 0b . [Age =4] 0b . [Group size=1] 1.897 0.928 [Group size =1] 2.883 1.247 [Group size =2] 1.509 0.714 [Group size =2] 0.852 1.030 [Group size =3] 3.195 0.867 [Group size =3] – 0.466 1.269 [Group size =4] 1.364 0.694 [Group size =4] – 0.349 1.084 [Group size =5] 1.912 0.709 [Group size =5] – 1.687 1.295 [Group size =6] 0b . [Group size =6] 0b . [Income=1] 5.220 1.270 [Income =1] – 2.937 1.213 [Income =2] 4.192 1.198 [Income =2] – 4.126 1.296 [Income =3] 3.208 1.186 [Income =3] – 1.372 0.869 [Income =4] 0b . [Income =4] 0b . Note: b – This parameter is set to zero as it is redundant 5.3 Quantifcation of Importance Scores To improve and augment the existing infrastructure as well as transport facilities to the leisure locations, the attitudinal variables were quantifed. The attitude of the traveller towards travel attributes such as travel time, travel cost, comfort, convenience, reliability and dust and noise were recorded in the questionnaire. The respondents were asked to indicate the importance they give to these factors while deciding about the leisure trip. The levels of importance were considered on a 5-point rating scale in an increasing numerical scaleranging from ‘extremely unimportant’, ‘not important’, ‘immaterial’, ‘important’to‘extremely important’. In order to quantify the attitudinal data, the Scaling Theory of Successive Categories, proposed by Thurstone in 1928 (Maurin 18 ) was used. The assumption made in the analysis was that the distribution of responses to astimulus was normal on the psychological continuum (Purushothaman 19 ). The computation of the relative weights was done using the procedure described by the following steps: Step 1: The categories of the data were arranged as columns (say ‘m’ columns) and the questions as rows (say ‘n’ rows). In this case, the columns refer to the five point scale of ‘extremely unimportant’, ‘not important’, ‘immaterial, ‘important’ and ‘extremely important’ and the rows are the travel attributes, such as, travel time, travel cost, comfort, convenience, reliability and dust and noise. The total number of travellers who gave a particular score to a particular attribute was flled in the appropriate cell of format to produce a matrix like structure. Step 2: Each of the values were then expressed as a fraction of the row total. Then cumulative of the values was calculated, as one moves from point scale of 1 to 5. Step 3: Considering the values in the table as leftward area of the standard normal distribution curve, the z-values for the corresponding cell values were obtained and recorded as a new ‘n’× ‘(m-1)’ table, which was called as z ij array. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 25 INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS Step 4: For each row ‘i’ of the z ij array, the row average was computed and for each column of z ij array, the column average was also computed. Let the column averages be called as ‘b j ’s. The grand average ‘X’ of all the values in z ij array was also computed. Step 5: Compute (b j – X) for each column. Step 6: Compute X - = (h j - X) 2 j=(m-1) j=1 . Step 7: For each row, compute Y | = (z |j - z | ) 2 j=(m-1) j=1 Step 8: For each row, compute square root of X*/Y i . This value corresponding to each attribute was then expressed as a fraction of the total for all the attributes. One set of sample calculation showing the step by step procedure for computation of importance scores is shown in Appendix B. Table 12 gives the relative weights attributed to various qualitative attributes based on the above analysis. It was observed from the results that bus travellers attach more importance to travel cost, followed by dust and noise. People who used taxi as their mode attached more importance to travel time, as well as, to reliability of the mode. It is a known fact that comfort and convenience will be lower in bus and travel time will be higher than taxi. The main difference is the reliability which is relatively low in case of bus when compared to taxi. People who chose their own vehicle rated comfort and convenience as more important. Rest of the variables were found to have negligible weights indicating strong affnity to own-vehicle as a travel mode to a leisure location. It also indicates that in comparison to comfort and convenience offered by own vehicle, the other attributes are immaterial. Table 12 Quantifcation of Attitudinal Variables Attributes Bus Taxi Own vehicle Travel Time 0.1498 0.1840 0.0460 Travel Cost 0.1970 0.1321 0.0253 Comfort 0.1544 0.1785 0.4247 Convenience 0.1623 0.1640 0.4427 Reliability 0.1598 0.1838 0.0314 Dust & Noise 0.1768 0.1575 0.0298 6 FINDINGS AND CONCLUSIONS The major fndings of the study are listed below: (i) Majority of the single day excursionists are found to use bus as their travel mode, contrary to the popular belief that personal vehicle is preferred for excursion trips to nearby areas of the city. Its share decreases with an increase in household income, and for household size 5 and above. (ii) In consonance with the social norms in India, children and elderly are usually accompanied with other adult members of the household for single day excursion trips. (iii) The share of non-earning sections of the society in such leisure travel is less, indicating that excursion travel is not a priority for such groups. Their major choice of mode is bus. (iv) The share of private employed respondents is found to be more when compared with business or government employed sections, which is indicative of the relative economic status. (v) Higher income groups gave more importance to privacy, which is indicated by higher use of own vehicle for leisure trips. (vi) Group size is found to infuence mode selection, with trip makers of group size up to 7 preferring bus as their travel mode,while, those with group size above 11 showing higher preference to taxi as their travel mode. The analysis indicated that there is a need to make bus more attractive to users in different age groups. A shift to bus can be stimulated by keeping some seats reserved for children and elderly. Better bus facilities and frequency may attract leisure trip makers in the adult category. Increase in cost of parking at leisure location may deter the adults from using their own vehicle. There is also a need to evolve policies for inducing shift from taxi to bus in case of respondents working in private organisations; and from own vehicle to bus in case of respondents in government organisations and those involved in business. The choices of groups can also be looked in the same respect. Groups, when travelling to a leisure location want to travel together so that they can enjoy their time as per their will. This is not possible while travelling by bus because of possible disturbance to comfort of co-passengers. One possibility of improving public transport patronage is to operate small size buses 26 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARIKRISHNA, RASTOGI & PURUSHOTHAMAN ON to such leisure locations, especially during weekends or during vacation, so that big groups can avail dedicated bus service. The travel mode preferences were observed to be infuenced by the household income. Travellers were found categorised in their travel mode choices. It indicates that patronage to public transport like state transport bus can be improved if bus services and facilities are improved to meet their desires and satisfaction. The quantifcation of attitudinal variables clearly indicated the characteristics of different travel modes. Own vehicle is mostly used due to the level of comfort it provides and its convenience of use. When moving as a group, or especially as a family, these two variables are more important than other travel attributes. These two factors are found substantial in case of taxi use and better than for bus. Travel attributes, like, travel time and reliability are to be given due consideration for busservices. Better services and facilities in a bus may improve the comfort level. Convenience can be improved by providing better access to leisure location from various spatially dispersed locations in a city. Cleanliness of bus interiors and proper design of bus can reduce dust and noise. The main aspect to be considered is reliability wherein punctual bus services needs to be assured so that people feel assured of having a reliable bus service. It can be fnally concluded that leisure trips account for a substantial number of domestic trips and it is pertinent that the variables infuencing the choice of mode are identifed to provide proper understanding of the mode choice behaviour of single-day leisure travellers. The attitudinal variables, which are major infuencing factors for leisure travellers should be incorporated into the modelling process to give a better insight into the decision making process of such leisure travellers. REFERENCES 1. “Tourism Statistics at a Glance-2010”(2011), Ministry of Tourism, Government of India, New Delhi, India,1-17. 2. “National Tourism Policy, 2002”(2002), Ministry of Tourism, Government of India, New Delhi, India 1-30. 3. Guiver, J ., Lumsdon, L. and Weston, R., (2008), “Traffic Reduction at Visitor Attractions: The Case of Hadrian’s Wall”, J ournal of Transport Geography, Vol. 16, Elsevier Ltd, 142- 150. 4. Gitelson, R.J . and Kerstetter, D.L. (1990) “The Relationship Between Socio demographic Variables, Benefts Sought and Subsequent Vacation Behaviour”, Journal of Travel Research, Volume 24, Sage Publications, 24-29. 5. Reece, W.S. (2003) “Demographics of Hawaii Leisure Travel”, J ournal of Hospitality and Tourism Research, Volume 27, Sage Publications, 185-199. 6. Sung, H.H., Morrison, A.M., Hong, G-S, and O’Leary, J.T. (2001), “The Effects of Household and Trip Characteristics on Trip- Types: A Consumer Behavioral Approach for Segmenting the U.S. Domestic Travel Market”, Journal of Hospitality and Travel Research, Vol. 25, Sage Publications, 46-67. 7. J ohansson, M.V., Heldt, T. and J ohansson, P. (2006), “The Effects of Attitudes and Personality Traits on Mode Choice”, Transportation Research - Part A (40), Elsevier Ltd, 507- 525. 8. Bohler, S., Grischkat, S. and Haustein, S. (2006), “Encouraging Environmentally Sustainable Holiday Travel”, Transportation Research -Part A, Volume 40, Elsevier Ltd, 652- 670. 9. Anable, J . and Gatersleben, B.(2005), “All Work and No Play? The Role of Instrumental and Affective Factors in Work and Leisure Journeys by Different Travel Modes”, Transportation Research – Part A, Volume 39, Elsevier Ltd, 163-181. 10. Heung, V.C.S., Qu, H. and Chu, R., (2001), “The Relationship Between Vacation Factors and Socio- Demographic and Traveling Characteristics: The Case of Japanese Leisure Travellers”, Tourism Management, Vol. 22, Elsevier Science Ltd., 259- 269. 11. Dann, G.M.S., (1981), ‘Tourism Motivation: An Appraisal’, Annals of Tourism Research, Volume 8, Part 2, Elsevier Ltd., 187-219. 12. http://www.kkd.kerala.gov.in/home.htm, accessed on 11 th January 2012. 13. Kerala Tourism Development Corporation (2007) “Tourism Statistics”, Website: http://www.keralatourism.org/touriststatistics. php, Accessed on 9 th J anuary 2012. 14. www.nhfsindia.org (Accessed J anuary 15, 2010). 15. Peng, G., Lilly, E. and Company, (2004), “Testing Normality of Data Using SAS”, Proceedings of PHARMASUG 2004 – Pharmaceutical Industry SAS Users Group, San Diego, May 23-26. 16. Law, A. (2008), “Simulation Modeling and Analysis”, Tata McGraw Hill Publishing Company, New York, 4 th Edition, 275-381. 17. Sung, H.H., Morrison, A.M., Hong, G-S, and O’Leary, J.T. (2001), “The Effects of Household and Trip Characteristics on Trip- Types: A Consumer Behavioral Approach for Segmenting the U.S. Domestic Travel Market”, Journal of Hospitality and Travel Research, Vol. 25, Sage Publications, 46-67. 18. Maurin, M., (1998), A Measurement Method for Ordered Category Scales, Proceedings of Sensoral 98, Montpellier, National Institute for Research on Transport and Safety, France, 1-10. 19. Purushothaman, D.,(2009), “Mode Choice Analysis for Leisure Trips”, Unpublished M.Tech Thesis Submitted to Department of Civil Engineering, National Institute of Technology Calicut, Kozhikode, India. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 27 INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS APPENDIX A SAMPLE QUESTIONNAIRE Department of Civil Engineering National Institute of Technology Calicut MODE CHOICE ANALYSIS FOR LEISURE TRIPS This study is taken up to understand the preferences of trip makers, who undertake the trips for engaging in leisure activities, which infuences the choice of a mode for travel. This study is taken up by the Post Graduate students of the \Traffc and Transportation Planning Programme. It is hereby assured that the data collected would be kept confdential and would be utilized for academic purposes only. In case you need to contact us, kindly contact at 0495-2286208. Sample No…………………Date & Day of Survey………………….. Interviewer……………............... It is requested that as much information may please be provided for the questions that follow. We thank you for the support and regret the inconvenience caused to you, if any, in this regard. HOUSEHOLD AND PERSONAL INFORMATION Kindly fll the information as desired or tick the appropriate box, wherever required. 1. Place of Residence ________________________________________________________________ 2. No of Persons in the Family _________________________________________________________ 3. No. of Persons Employed in the Family _________________________________________________________ 4. Gender: Male/Female 5. Marital Status: Married/Single 6. Age: (Tick the appropriate group) <18 years; 18-25 years; 25-45 years; 45-60 years; >60 years 7. Education: SSLC (Std X) Plus Two Diploma Degree Higher Education (specify) Other (Specify) 8. Occupation: Unemployed Government- Employed: Executive Middle level Lower level Pvt. Employed: Executive Middle level Lower level Own Business House wife Student Retired Others 9. Vehicle Ownership: Car Two wheeler Others( Specify) 28 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARIKRISHNA, RASTOGI & PURUSHOTHAMAN ON 10. Annual Family Income (Rs.): 50,000 and below / 50,000- Rs 1, 50,000 / 1, 50,000 –3, 00,000 / 3, 00,000 –5, 00,000 / >5 Lakhs. TRAVEL INFORMATION Kindly provide information related to the leisure travel undertaken by you and your family in the past three months (preferably). 11. Place of Leisure Activity: _________________________________________________________ 12. Location From Nearest City Centre _________________________________________________________ 13. Starting Place of the Journey 14. No. of Persons Accompanying You 15. In case Bus is Used as a Travel Mode, the Distance of the Bus Station from the Starting Place a) Access mode used to reach the bus station b) Time taken to reach bus station (min) c) Money spent to reach the bus station (Rs) 16. Mode Used (Own vehicle / Bus / Taxi) Alternate Mode Travel Time (min) Travel Cost (Rs) Own Vehicle/Bus/Taxi Own Vehicle/Bus/Taxi Reasons for Choosing the Current Travel Mode: Travel Time Travel Cost Comfort of Travel Speed Frequency of Service Convenience in Use Others (If any Specify) Reasons for not choosing the alternate travel mode Reasons for not choosing the alternate travel mode 17. If Traveled by Bus, Tick the Class of Travel Used for Leisure Travel Between Origin and Final Place. Ordinary Or di nar y Limited Stop Or di nar y Town -Town Fast Passenger Express Super Fast Deluxe Super Deluxe Video Coach A/C 18. If Traveled by Own Vehicle or Car Taxi, then the Type of Travel Comfort Preferred During Your Visit: AC / Non-AC 19. Preferred Travel Alternative, if not Using the Current Travel Mode: Bus / Private car / Car taxi QUALITATIVE RESPONSES 20. How much importance do you give to the factors listed below while deciding about the leisure trip to be made with your family members or going on a leisure trip? The responses are listed in ascending order of their importance opposite each factor and you are requested to tick in an appropriate box against each of the factor. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 29 INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS Attributes Extremely not Important Not Important Immaterial Important Extremely Important Cost of Travel Travel Time Reliability Comfort Convenience Dust & Noise Beautiful Scenery 21. How much importance do you give to the factors listed below while deciding about the leisure trip to be made with your family members or going on a leisure trip? The responses are listed in ascending order of their importance opposite each factor and you are requested to tick in an appropriate box against each of the factor. 1. Highly Unsatisfactory 2 .Unsatisfactory 3. Undecided 4.Satisfactory 5. Highly Satisfactory Attributes Satisfaction Scale Bus Taxi Own vehicle 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Cost of Travel Travel Time Reliability Comfort Convenience Dust & Noise Beautiful Scenery 30 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARIKRISHNA, RASTOGI & PURUSHOTHAMAN ON APPENDIX B SAMPLE CALCULATION FOR QUANTIFICATION OF IMPORTANCE SCORES Step 1 Extremely Not Important Not Important Immaterial Important Extremely Important Total Time 58 68 22 48 59 255 Cost 8 10 16 96 125 255 Comfort 54 46 50 60 45 255 Convenience 59 46 74 42 34 255 Reliability 45 48 65 56 41 255 Dust & Noise 63 67 74 35 16 255 Step 2 Time 0.2275 0.2667 0.0863 0.1882 0.2314 1.0000 Cost 0.0314 0.0392 0.0627 0.3765 0.4902 1.0000 Comfort 0.2118 0.1804 0.1961 0.2353 0.1765 1.0000 Convenience 0.2314 0.1804 0.2902 0.1647 0.1333 1.0000 Reliability 0.1765 0.1882 0.2549 0.2196 0.1608 1.0000 Dust & Noise 0.2471 0.2627 0.2902 0.1373 0.0627 1.0000 Time 0.2275 0.4941 0.5804 0.7686 1.0000 Cost 0.0314 0.0706 0.1333 0.5098 1.0000 Comfort 0.2118 0.3922 0.5882 0.8235 1.0000 Convenience 0.2314 0.4118 0.7020 0.8667 1.0000 Reliability 0.1765 0.3647 0.6196 0.8392 1.0000 Dust & Noise 0.2471 0.5098 0.8000 0.9373 1.0000 Steps 3 & 4 j1 j2 j3 j4 Row average i1 Time 0.6000 2.5400 4.2000 4.7200 3.0150 i2 Cost 0.0700 0.1800 0.3400 4.0200 1.1525 i3 Comfort 0.5600 1.2400 4.2000 4.9200 2.7300 i4 Convenience 0.6200 1.3500 4.5200 5.1100 2.9000 i5 Reliability 0.4500 1.1000 4.2900 4.9800 2.7050 i6 Dust & Noise 0.6700 4.0100 4.8400 5.5200 3.7600 bj Column average 0.4950 1.7367 3.7317 4.8783 X =2.7104 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 31 INFLUENCE OF SOCIO-DEMOGRAPHIC ATTRIBUTES IN TRAVEL MODE SELECTION FOR SINGLE DAY EXCURSION TRIPS Steps 5 & 6 j1 j2 j3 j4 Row average i1 Time 0.6000 2.5400 4.2000 4.7200 3.0150 i2 Cost 0.0700 0.1800 0.3400 4.0200 1.1525 i3 Comfort 0.5600 1.2400 4.2000 4.9200 2.7300 i4 Convenience 0.6200 1.3500 4.5200 5.1100 2.9000 i5 Reliability 0.4500 1.1000 4.2900 4.9800 2.7050 i6 Dust & Noise 0.6700 4.0100 4.8400 5.5200 3.7600 bj Column average 0.4950 1.7367 3.7317 4.8783 X =2.7104 bj-x -2.2154 -0.9738 1.0213 2.1679 -2.2154 (bj-x)2 4.9081 0.9482 1.0430 4.6999 4.9081 X - = (b ] -X) 2 ]=(m-1) ]=1 11.5991 Step 7 (z ij - z j ) j1 j2 j3 j4 i1 Time -2.4150 -0.4750 1.1850 1.7050 i2 Cost -1.0825 -0.9725 -0.8125 2.8675 i3 Comfort -2.1700 -1.4900 1.4700 2.1900 i4 Convenience -2.2800 -1.5500 1.6200 2.2100 i5 Reliability -2.2550 -1.6050 1.5850 2.2750 i6 Dust & Noise -3.0900 0.2500 1.0800 1.7600 (zij - zj) 2 j1 j2 j3 j4 Yi i1 Time -2.4150 -0.4750 1.1850 1.7050 16.3691 i2 Cost -1.0825 -0.9725 -0.8125 2.8675 12.4499 i3 Comfort -2.1700 -1.4900 1.4700 2.1900 15.8860 i4 Convenience -2.2800 -1.5500 1.6200 2.2100 15.1094 i5 Reliability -2.2550 -1.6050 1.5850 2.2750 15.3489 i6 Dust & Noise -3.0900 0.2500 1.0800 1.7600 13.8746 Step 8 Attribute (X*/ Yi ) Importance score Time 0.7086 0.1498 Cost 0.9317 0.1970 Comfort 0.7301 0.1544 Convenience 0.7677 0.1623 Reliability 0.7557 0.1598 Dust & Noise 0.8360 0.1768 Total 4.7298 1.0000 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 33 1 INTRODUCTION In traffc terminology, headway can be represented in two different ways, as space headway, and time headway. Specifcally, time interval between passage of successive vehicles moving in the same lane as the same points of successive vehicles pass a reference point on road, is known as time headway. In traffc engineering, it is a decisive factor controlling the longitudinal distribution of vehicles. With respect to the queue discharge at signals, frst headway is defned as the time elapsed between start of green and the time the frst vehicle crosses the reference line, second headway is the time between frst and second vehicles crossing the reference line etc. In the earlier studies, it was reported that the frst waiting driver will usually take more time to react to the red-to-green change before releasing the brake and start accelerating. Following drivers will also incur some reaction time, which will be shorter with every subsequent driver in the line since the reaction times overlap. Finally, headways tend to level out to the minimum headway value. This generally occurs when vehicles have fully accelerated by the time they reach the curb line. It is reported that this “leveling off” begins with the fourth or ffth headway. Fig. 1 represents this ideal change in headway and the suggested value of constant headway after it levels out is 1.9 sec (Highway Capacity Manual, 2000). However, these observations were based on studies mainly conducted under homogeneous and lane disciplined traffc conditions. Obviously, these results may not hold true under heterogeneous and less lane disciplined traffic conditions, such as, the one existing in India. However, reported studies in this area of discharge headway under heterogeneous traffc conditions are minimal. Fig. 1 Headway Variation at Start of Green Traffc conditions in India, and many of the developing countries, differ from their western counter parts mainly in the composition and lack of lane discipline. Under such conditions, different genres of vehicles widely varying HEADWAY ANALYSIS AT SIGNALISED INTERSECTIONS – WITH AND WITHOUT COUNTDOWN TIMER M. S. HARSHITHA*, SONU AGARWAL** & LELITHA VANAJAKSHI*** ABSTRACT Discharge time headway, defned as time elapsed between consecutive vehicles as they get discharged from a queue, is an important parameter in the analysis of signalized intersections. Reported studies in this area so far observed the discharge headway to be high at the start of green for frst few vehicles, and stabilize to a minimum value by 4 th or 5 th vehicle in queue, which continue till end of the queue. However, these studies were analyzing data from homogeneous traffc conditions, which may not hold good for heterogeneous traffc conditions, such as, the one existing in India. Also, most of the intersections in India have signals facilitated with countdown timers, indicating the time remaining for change of signal phase, which is also expected to affect queue discharge characteristics. This Paper quantitatively analyses the changes in discharge headway characteristics, in the presence and absence of countdown timers under heterogeneous traffc conditions and compare the results against the standard discharge headway distribution. The analysis was carried out using data collected from selected intersection in Chennai, India, by videographic technique. The results indicate that the conventional headway distribution is followed in the case of no timer, with the exception of having increased headway values towards the end of queue. However, with the presence of timer the discharge headway remains constant throughout, which is around the stabilized minimum discharge headway of the no timer case. Thus, the results indicate that the presence of timers considerably reduces the initial losses and delays, leading to a more effcient intersection. These observations will be useful for practicing engineers to design the signals and to decide on the requirement of countdown timers. * Undergraduate Student, National Institute of Technology Calicut. ** Undergraduate Student, National Institute of Technology Rourkela. *** Assistant Professor, Deptt. of Civil Engineering, IIT Madras, Chennai – 600 036, India 34 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARSHITHA, AGARWAL & LELITHA ON in their size, accelerating abilities, and performance capabilities share same lanes of the road. This disparity in static and dynamic characteristics of vehicles will affect the queuing and discharge characteristics. In addition, variations in dimensions of vehicles end up in wide bodied vehicles occupying full lane width, while small vehicles traveling side by side as well as in between the bigger vehicles. These situations create complexity in measurement and non- compatibility in analysis of headway values. In addition, signal countdown timer, which continuously display the time remaining for each phase of cycle, including changes from green to amber, amber to red and red to green, is becoming popular in traffc congested Asian Cities, including India. The presence of timers also can affect the queue discharge at intersections, as drivers have prior knowledge about the time of start of green and red. Thus, countdown timers could infuence start-up lost time, discharge headways and the saturation fow rate. The present study concentrates on the discharge headway characteristics, since that will give an insight into the parameters, like, initial time losses and saturation fow. Obviously, the traditionally accepted distribution of discharge headway with initial losses for the frst few vehicles resulting in an increased headway, which gradually levels off to an optimum or minimum value by 4 th or 5 th vehicle may not hold good for the above discussed traffc conditions, particularly with timers in place. Owing to the diffculty in measuring headway from mixed traffc, not many studies are reported from heterogeneous traffc conditions analyzing discharge headway at signalized intersections and its variation with presence of timers. Limited reported studies in this area compared the characteristics from two different intersections, one with and one without timer, which could not lead to exhaustive conclusions (Sharma et al. 2007). The present study addresses these issues by collecting and analyzing discharge headways from the same signalized intersection, with and without timer, under heterogeneous traffc conditions, taking Chennai as a representative city. The aims of the study are to analyse the distribution of discharge headway at signals to verify whether they follow the standard trend and also to study the effect of countdown timers on discharge headway distribution. A quantitative analysis of headway variation in the presence and absence of signal timers is carried out, and the results are compared against the classic headway distribution. Also, it presents numerical analysis of headway of classifed traffc fow, pertaining to two- wheelers and cars. The data is compatible for comparative studies, in all respects, as both sets of data, one with timer and the other without timer, are collected from the same intersection under similar traffc conditions. 2 LITERATURE REVIEW Over the past several years, many researchers have reported their studies on headway distribution at signalized intersections. Some of the relevant and recent studies in this area are enlisted below. Many of the earlier studies agreed with the observation of the initial headway values to be high at start of green and leveling off after 4 th or 5 th vehicle, and are listed in HCM 2000. Recent studies in this area showed that distribution of departure headway at each position in a queue, approximately follow a certain log-normal distribution and corresponding mean value levels out gradually (J in et al., 2009). Variation of discharge headway based on vehicle type, traffc conditions, intersectional properties, etc. were reported in several studies, some of which are discussed below. In one such study, it was reported that discharge headway change with the type of vehicle being followed and little variation was found with changes in overall traffc fow and road type (Brackstone et al., 2009). Lu (1984) analysed the protected and unprotected left-turn vehicles at signalized intersections and showed that smaller vehicles require only smaller discharge headways. The study also reported that left-turn vehicles had lower discharge headway values than the other vehicles. Studies conducted in Bangkok by Limanond, et al. (2009) reported that countdown timers had a signifcant impact on the start-up lost time, reducing it by 1-1.92 sec per cycle. However, effect of timer on saturation headway was found to be trivial. The study used two days data – one day with timer and the other without timer. Chiou and Chang (2010) investigated the effects of Green Signal Countdown Display (GSCD) and Red Signal Countdown Display (RSCD) on driver behaviour and in turn on intersection safety and effciency. Results showed that RSCD enhanced intersection effciency and was clearly less controversial and more benefcial than GSCD. Tong and Hung (2002) proposed a neural network (NN) approach to simulate the queued vehicle discharge headway. Studies conducted by Ayres, et al. (2001), showed that even during rush hour traffc, the time headway between vehicles varies between 1 and 2 sec for a range of traffc speeds. Sharma et al. (2009) reported similar analysis on the effect of timers on headway values. However, the data used were from two different intersections, one with and one without timer, making it diffcult to conclude that the changes in headway HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 35 HEADWAY ANALYSIS AT SIGNALISED INTERSECTIONS – WITH AND WITHOUT COUNTDOWN TIMER distribution is purely the effect of timers. Ibrahim et al. (2008) analysed the impact of timer on queue discharge patterns using data from three intersections with signal timers and three intersections without signal timers. The results showed the mean headways for the frst six vehicles in the queue at the intersections with countdown timers were less than the mean headways of the corresponding six positions at the intersections without countdown timers. The study concluded that the countdown timer had a signifcant effect on the discharge headway of the frst six vehicles in the queue, though there was no corresponding statistical analysis used to prove the fndings. Also, it can be seen that over the years, studies showed a gradual reduction in start-up lost time with more aggressive driving habits and better acceleration performance of vehicles. However, very few studies concentrated on heterogeneous traffc conditions. In the area of impact of countdown timers on queue discharge patterns, studies are scarce, focus on one geographic area, had only limited data, or analysed data from different intersections. This highlights a need for additional studies to investigate the signal countdown impact in detail, and perhaps the need to perform similar studies in different geographical areas with more sample size and data being collected from the same intersection, with and without timer. In the present study these limitations are addressed by studying the headway variation at signalized intersections under heterogeneous traffc conditions. Also, the study evaluates the effect of countdown timers on headway and presents a quantitative analysis for the same in a systematic way. A representative intersection is selected in Chennai, India and data were collected with and without timer, from the same location for a total of 10 days. 3 DATA COLLECTION AND EXTRACTION With respect to data collection, selection of site having a signalized intersection with added facility of countdown timer, was of prime importance. Collecting data for headway measurement is challenging as requirement of collecting data on signal indication and corresponding traffic movement simultaneously becomes mandatory. Hence, videographic technique was adopted and the recording was carried out from a vantage point with two cameras, one for capturing the signal head and the other facing the corresponding traffc. Since, the time intervals of interest are of few seconds, it was needed to synchronise the two cameras. This was achieved by using special adaptors which connect both the cameras to the same laptop and synchronise their timing. Thus, live recording from both video cameras needed to be synchronized and brought into the laptop, and was achieved with the assistance of specialised hardware and software. The laptop used screen capture software to time stamp and record the live feed. Sample screen shots are shown in Fig. 2. Fig. 2 Screen Shot of Data Collected Using Synchronized Video Setting Data were collected from the same intersection with and without timer, each for 5 days, during the same time periods. After the reconnaissance survey, appropriate location for the study was selected based on availability and permission to use the vantage point. The study intersection selected for the present study is a signalized intersection at Kotturpuram on Gandhi Mandapam Road, Chennai, India. This intersection has four legs with a total of four lanes for through traffc with a constant cycle time of 105 sec. The North bound through movement was selected for the present study. It had 60 sec red, 39 sec green and 6 sec amber during the data collection period. A suitable vantage point was selected, at the intersection from where the signal indication and the corresponding traffc fow could be simultaneously captured using video. The classifed as well as total fow observed for all the days are summarised in Table 1. Extraction of headway data, in general, stipulates the need for fner details, as small time intervals are involved in the high speed discharge of vehicles at the start of green. Additional challenges are posed by the heterogeneity and lack of lane discipline existing under Indian conditions. Moreover, to avoid the error posed due to parallel movement of vehicles in different lanes, only vehicles following one behind the other were included in the data extraction. Also, to incorporate the effect of heterogeneity, classifed discharge headways were noted down. In this study, the following vehicle was assumed to be decisive in the headway estimation and hence the classifcation was dependent on the following vehicle type. Thus, the headway was measured as the time elapsed between consecutive 36 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARSHITHA, AGARWAL & LELITHA ON vehicles, as their rear ends cross the reference line, which includes the characteristics of the following vehicle. The usual reference point selected in such studies will be the curb line of the leg under consideration. However, under Indian scenario, many smaller vehicles cross the curb line and stop during the red interval. Hence, the reference line in this study was selected much ahead of the curb line, more towards the center of the intersection. The headways were extracted till the end of queue clearance for each cycle. For effcient extraction of these data, a program in Matlab was developed to acquire a database of time headways of each vehicle type separately, for every green cycle. Table 1 Data Summary Day Time Timer Number of Vehicles Total no. of Vehicles Two Wheeler Auto Car Bus Day1/Day6 Mon 11AM- 12PM Timer 1529 307 1085 30 2951 11AM- 12PM No Timer 1703 315 1021 37 3076 Day2/Day7 Tues 11AM- 12PM Timer 1325 278 953 32 2588 11AM- 12PM No Timer 1226 247 810 33 2316 Day3/Day8 Wed 11AM- 12PM Timer 1513 277 1005 32 2827 11AM- 12PM No Timer 1013 221 750 40 2024 Day4/Day9 Thur 11AM- 12PM Timer 1395 306 876 42 2619 11AM- 12PM No Timer 1474 327 876 45 2722 Day5/ Day10 Fri 11AM- 12PM Timer 1514 270 995 38 2817 11AM- 12PM No Timer 1384 321 854 37 2596 4 DATA ANALYSIS AND METHODOLOGY The data extracted from the collected videos were analysed from two perceptions: (a) to check whether the data variation agree with the standard headway variation and (b) to study the effect of timer on headway distribution. Each of these, were further studied by concentrating separately on unclassifed and classifed traffc. Unclassifed study involved the time headways of all motorized vehicles while the classified study concentrated on two major classes, namely cars and two-wheelers. This approach was justifable as the proportion of other two types, namely; autorickshas and buses, represented a meager percentage of net traffc volume, providing inadequate data points and hence were not included in the detailed analysis. Fig. 3 shows a pie chart of percentage distribution of vehicle types in the collected data. Fig. 3 Pie Chart Showing Traffc Composition To start with, the average discharge headway per vehicle was plotted against green signal time for unclassifed and classifed conditions separately to compare with the traditional headway distribution as discussed below. Unclassifed headway analysis: The average discharge headway per vehicle against queue clearance time for the unclassifed data, both with and without timer cases, were plotted separately intending to (a) compare with the traditional headway distribution and (b) study the effect of timer on the discharge headway and are shown in Fig. 4. Fig. 4 Comparison of Discharge Time Headway – With and Without Timer HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 37 HEADWAY ANALYSIS AT SIGNALISED INTERSECTIONS – WITH AND WITHOUT COUNTDOWN TIMER It can be seen from Fig. 4 that the headway distribution under both with and without timer case differs from the classic distribution that is followed based on homogeneous conditions. In the case of without timer, the curves clearly show three distinctive zones: (i) starting zone (ii) middle zone and (iii) ending zone. Starting zone representing frontage of queue recorded high values for discharge headways, which may be mainly due to response and accelerating time losses. Middle zone showed the values stabilizing to an optimum value followed by an end zone showing an increasing trend. This third regime, which is not part of the standard headway distribution, could be due to ambiguous knowledge that drivers acquire about left-over time in green signal phase, resulting in apprehensions to stop or to proceed. This unconventional trend is reported in HCM for longer green time signals, with greens more than 40 sec. The intersection under study has a green time of 40 sec, which may also be the reason for this trend in discharge headway. However, the magnitude of the headways in the ending zones are lower than that of corresponding starting zones, which may be because end zones deals with post accelerated vehicles and do not include accelerating losses. The observed values of the time headways in starting zone lie in range of 2 to 3 sec and optimizes in the middle zone to 1.2 to 2 sec, followed by value of 1.5 to 2.5 sec in the third zone. In the case of with timer conditions, the trend was observed to be completely different from the classical distribution. It was seen that the start up loss was completely saved and the discharge headway started at the minimal value from the start of green and was in the range of 1.5 to 2.2 sec. This may be explained because the timers provide drivers prior knowledge about time of onset of green. Hence, vehicles/ drivers accumulated in queue will be alert and ready before the actual start of green, eliminating subsequent time losses. Time headways of middle zones are observed to fall in same range as that of no timer case. This demonstrates that the saturation headway value at a signalized intersection is not changed due to the presence of timers. The end zone also was not very evident showing all vehicles traveling with minimum headway throughout the green, when timer information is available. This may be because exact knowledge about the time left for green to end makes the drivers more aware about the risk and reduces rash behavior towards the end of green. Overall, it was observed that the headway distribution under both with and without timer cases differ from the classic distribution and the timers affect the headway distribution. Fig. 5 Average Discharge Headway – with and without Timer Fig. 5 shows the average headway combining all days (average line in Fig. 4) with the corresponding data points. Functional forms for both with and without timer cases were explored. It can be seen that in the case of without timer, a polynomial form was the best ft and for the without timer case the trend was linear indicating the minimum headway maintained from beginning to end of queued vehicles. In order to estimate the magnitude of variation in the headway values due to the presence of timers, statistical analysis of the data was also performed. Comparison of means and variances were carried out using Z test and F test to check whether there is any statistically signifcant change in the values. The results are shown in Table 2. The results confrmed the change in headway values due to the presence of countdown timers in the starting zone. It can be seen that the mean headway values of starting zones of without timer condition, is signifcantly different from its counterpart values of with timer conditions, when checked at the 95 per cent confdence interval. On the other hand, middle zone and end zone have reported no statistically signifcant difference between headways of both cases. One plausible explanation for this observation could be as follows: In the case of with timer, prior knowledge about green signal starting results in drastic reduction in headway 38 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 HARSHITHA, AGARWAL & LELITHA ON values of starting zones, due to removal of reaction time and start up losses, which is considerably large in magnitude. Since middle zones and end zones have post accelerated headway values, they lack such radical reduction. Table 2 Results of Statistical Analysis of Headway Values Start Zone Middle End Mean Without timer 2.11 1.42 1.51 With timer 1.72 1.49 1.48 Std Dev. Without timer 1.122 0.608 0.635 With timer 0.841 0.656 0.665 Sample Size Without timer 304 367 114 With timer 280 277 114 Test for Mean Z-calc 4.776360745 -1.38327538 0.34836115 signifcant not signifcant not signifcant Test for Variance F-calc 1.779892292 1.164127424 1.096720193 signifcant not signifcant not signifcant Classifed headway analysis: Similar analysis was carried out for classifed cases of two-wheelers and cars. Fig. 6 show sample graphs of with and without timer cases of two-wheelers for all days. Similar trend were observed for cars too. H e a d w a y / v e h i c l e 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 0 5 10 1 Gre Without 15 20 een time (sec timer TW y = 0.002 25 cs) 2x 2 0.104x + 1 R² = 0.475 30 day1 day2 day3 day4 day5 average 1.657 35 H e a d w a y / v e h i c l e 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 0 5 110 15 Gre With ti 20 een time (sec imer TW y = 0.0 25 30 cs) 000x 2 0.035x + R² = 0.304 0 35 day1 day2 day3 day4 day5 averag + 1.100 40 ge Fig. 6 Comparison of Discharge Time Headway of Two-Wheelers – With and Without Timer From the Fig. 6, it can be seen that the trends observed in the case of unclassifed vehicles continue here as well. In the case of without timer, there are three distinct zones. Time headway values in starting zone, lie in range of 1.5 to 2.3 sec for two-wheelers (corresponding values for cars were in the range of 2.3 to 3.4 sec). This is justifable as two-wheelers have greater accelerating capacities, in comparison to cars. Also, two-wheelers tend to occupy the front of the queue much ahead of the stop line and hence may need lesser time to reach the observation point. The wider range of headway values is observed for cars may be due to the presence of numerous classes of cars, differing in their dimensions, braking efficiencies, accelerating capacities, etc. It can be observed that the headway values of unclassifed data in starting zone, are mostly comparable to that of two-wheelers. This emphasizes the presence of large number of two-wheelers at the starting of queue, which is a common scenario under heterogeneous and less lane disciplined traffc fow. Additionally, starting zones in all cases of analysis are seen to occupy quarter length of green signal time. In the middle zones, saturation headway values of two-wheelers lie in the range of 0.5 to 0.8 sec. Corresponding values for cars were observed in the range of 1.2 to 1.8 sec. As expected, cars have higher headway values compared to two wheelers. Here, it can be observed that the mixed traffc headway values in middle zone, are inclined to that of cars, indicating higher percentage of cars in the middle of the queue. Ending zones of classifed traffc fow, also bear similar headway values as unclassifed traffc fow, with values ranging between 0.8 to 1.4 sec for two-wheelers and 1.2 to 2 sec for cars. For with timer conditions, as in the case of unclassifed data, fatter curves in all zones are observed for both the types. Discussions mentioned in unclassifed traffc fow for no timer cases hold true for classifed analysis as well. Time headways, in starting zones, experienced a drastic fall to 0.9 to 1.2 sec for two-wheelers, while it reduced to 2.1 to 2.5 sec for cars. Reduction in the start up lost time due to countdown timers explains this trend. As before, middle zones have saturation headway values, comparable to the without timer conditions, ranging in 0.8 to 1.1 sec for two- wheelers and 1.5 to 1.8 sec for cars. Similar to unclassifed traffc fow, headways of end zones of both vehicle types have faced a fall with respect to the corresponding end zones of no timer conditions. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 39 HEADWAY ANALYSIS AT SIGNALISED INTERSECTIONS – WITH AND WITHOUT COUNTDOWN TIMER 5 SUMMARY AND CONCLUSIONS Discharge headway is the time elapsed between consecutive vehicles as they get discharged from a queue at signals. The studies reported from homogeneous traffc conditions, observed the discharge headway to be high at start of green for frst few vehicles, and stabilize to minimum value by 4 th or 5 th vehicle in queue, which continue till the end of queue. However, this may not be true under the heterogeneous and less lane disciplined traffc conditions that exist in developing countries, like, India. Moreover, signal count down timers displaying the remaining time for signal phase changes, are gaining popularity in many countries, including India. The presence of timers is expected to affect the discharge headway characteristics. Only limited studies discussed these issues with systematically collected data from same location by considering with and without timer scenarios for multiple days, to draw reliable conclusions in this regard. In this Paper, a systematic study at a selected signalized intersection in Chennai, India, with and without timer, was executed to analyse two aspects : (a)whether the discharge headway distribution of heterogeneous traffc follow the classic model of headway distribution of homogeneous fow and (b) to study the effect of timer on discharge headway. Initial analysis on the distribution of discharge headway against the classic representation of initial high value followed by a stabilized zone with minimum headway was carried out by plotting the headway values against green time. It was observed that under heterogeneous traffc conditions, this trend was followed with an additional ending zone of increased headway values. This is attributed to the dilemma the drivers feel towards the end of green expecting a change in signal phase, which leads to the uncertainty on whether to stop or proceed. Also, it may be due to the longer green time of 40 sec. Also the minimum headway observed is in the range of 1.2 to 2 sec, which is slightly lower than the existing HCM value of 1.9 sec. Separate analysis was carried out for classifed headway, concentrating on two types, namely, cars and two-wheelers, which composed the major proportion of traffc volume, which also agreed to the three regime trend observed with the unclassifed data. Effect of timer on headway distribution was analyzed, separately for unclassifed and classifed cases. It was found that the timer removes the starting and ending zones and showed a leveled and reduced headway from the start to the end of the queue. About 1 to 2 sec reduction in headway values in the starting zone was observed due to the presence of timers, which will result in the benefcial impact of increased discharge rate of the queue. Middle zones share comparable values in both cases, emphasizing no change in discharge rate with respect to countdown timers. The increased end zone also is removed here making the headway in the range of 1 to 2 sec throughout. Statistical analysis was carried out to check whether the differences in headway due to the timer are statistically signifcant and showed a signifcant reduction in the starting zone. These observations were true for the classifed analysis of two- wheelers and cars, as well. Thus, timers at intersections can lead to benefcial impact on increased discharge rate of the queue. However, this study could not probe into a quantitative analysis of time headways of autos and HMVs, owing to inadequate data. Overall, the results of the study emphasize the differing discharge headway distribution under heterogeneous and less lane disciplined traffic in comparison to the homogeneous one. Also, it was observed that the presence of timers reduces the initial losses and delays leading to a positive effect of higher effciency due to the presence of timers. Data from more intersections under different time of the day need to be analysed to confrm these fndings. ACKNOWLEDGEMENT The authors acknowledge the support provided by Ministry of Urban Development through project No. K-14011/28/2007-UT. REFERENCES 1. Ayres, T. J ., Li, L., Schleuning, D. and Young, D., “Preferred Time-Headway of Highway Drivers”, IEEE Intelligent Transportation Systems Conference Proceedings, Oakland, USA, 2001. 2. Brackstone, M., Waterson, B. and McDonald, M., “Determinants of Following Headway in Congested Traffc”, Transportation Research Part F, 12, 2009, pp. 131–142. 3. Chiou, Y. and Chang, C., “Driver Responses to Green and Red Vehicular Signal Countdown Displays: Safety and Effciency Aspects”, Accident Analysis and Prevention, 42, 2010, pp.1057- 1065. 4. Highway Capacity Manual (HCM) - Special Report 209, Transportation Research Board, Washington D.C., 2000. 5. Ibrahim, M. R., Karim M. R. and Kidwai, F. A., 2008. “The Effect of Digital Count-Down Display on Signalized J unction Performance”, American Journal of Applied Sciences, 5, 479- 482. 40 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 6. J in, X., Zhang, Y., Wang, F., Li, L., Yao, D., Su, Y. and Wei, Z., “Departure Headways at Signalized Intersections: A Log-Normal Distribution Model Approach”, Transportation Research Part C, 17, 2009, pp.318–327. 7. Limanond, T., Suebpong, C. and Roubtonglang, N., “Effects of Countdown Timers on Queue Discharge Characteristics of through Movement at A Signalized Intersection”, Transportation Research Part C 17, 2009, pp.662-671. 8. Lu, Y. J . “A Study of Left-Turning Maneuver Time for Signalized Intersections”, Institute of Transportation Engineers, Vol. 5410, 1984, pp.117-126. 9. Sharma, A., Vanajakshi, L. and Rao, N., “Effect of Phase Countdown Timers on Queue Discharge Characteristics Under Heterogeneous Traffc Conditions”, Transportation Research Record J ournal of the Transportation Research Board, No. 2130, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 93–100. 10. Tong H.Y. and Hung W.T., “Neural Network Modeling of Vehicle Discharge Headway at Signalized Intersection: Model Descriptions and Results”, Transportation Research Part- A, 36, 2002, pp.17-40. HARSHITHA, AGARWAL & LELITHA ON HEADWAY ANALYSIS AT SIGNALISED INTERSECTIONS – WITH AND WITHOUT COUNTDOWN TIMER HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 41 1 INTRODUCTION The growth of traffc in the road network of large cities in developing countries, like, India is a serious concern from the traffc engineer’s point of view. The rapid growth of vehicular traffc in the past has imposed heavy loads on the urban street system. The authorities of cities are considering the introduction of modern traffc control techniques to reduce the problems of congestion, air pollution, noise pollution and road accidents. Parking control, improving intersection geometrics, optimizing signal cycle time on intersection, design of coordinated signalized intersection etc. are a few techniques of traffc controlling. For isolated signalized intersections, proper signal phasing and cycle time are very important to allow safe crossing of vehicular as well as pedestrian traffc fow and to over all delay. Arrival rate of vehicles, saturation fow rate, delay and pedestrian fow are the major factors infuencing the design of optimum cycle length. In the developed countries, various methods and models have been developed to measure the saturation fow rate, delays and optimum cycle length, where more or less homogeneous traffc is fowing with lane discipline. While in developing country, like, India, heterogeneous traffc, these methods can not be effectively used. Hence, it is necessary to develop the saturation fow model as well as delay model, which works well in case of heterogonous traffc condition. 2 OBJECTIVE OF THE STUDY The main objectives of present study are: (i) To develop saturation fow model considering width & vehicle composition criteria for non-lane based heterogeneous traffc condition. (ii) To modify the Webster’s delay formula under non- lane based heterogeneous traffc condition. 3 SIGNALIZED INTERSECTION FLOW CHARACTERISTICS Fig. 1 presents some fundamental attributes of fow at signalized intersection. The diagram represents a simple situation of one-way approach to signalized intersection having two phases in the cycle. MODIFICATION OF WEBSTER’S DELAY FORMULA USING MODIFIED SATURATION FLOW MODEL FOR NON-LANE BASED HETEROGENEOUS TRAFFIC CONDITION N.G. RAVAL* & P. J. GUNDALIYA** ABSTRACT Traffc in India consists of both motorized and non-motorized vehicles, as in many other developing countries. The static and dynamic characteristics of the different vehicle vary widely even within the same class. Also, the lack of lane discipline and unrestricted mixing of the various types of vehicle in the same right of way makes the traffc stream heterogeneous in nature. The equation of saturation fow, developed in developed countries, do not take into account the non-lane based traffc conditions prevalent in India. Hence, it is necessary to develop the saturation fow model for non lane based heterogonous traffc condition. However, many researchers worked out different methods and till it is required to come out an acceptable methodology to fnd out saturation fow. Delay is one of the most important performance measures of signalized intersection. Various models including Webster’s classical delay formula have been developed in countries with car dominated traffc stream to estimate average delay per vehicle at signalized intersections. Webster’s classical delay formula under developed countries where the road traffc condition is homogeneous based and consequently the formula may not estimate delays accurately under heterogeneous traffc condition. It is necessary to modify Webster’s delay formula to make it usable under non-lane based heterogeneous traffc. In the present study, feld delay for each approach is worked out for developing delay model. The modifcation is carried out in the Webster’s classical delay formula to suit the feld condition. Traffc data were collected manually at three signalized intersection of Ahmedabad city. Model for saturation fow is also developed based on width and traffc composition criteria. However, the models are required to test & validate considering large number of data for road condition. * PG Student, L.D. College of Engineering, Ahmedabad. ** Asstt. Professor, L.E. College, Morbi. 42 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 RAVAL & GUNDALIYA ON Fig. 1 Fundamental Attributes of Flow at Signalized Intersection The diagram is divided into three parts. The frst part shows a time-space plot of vehicles on the northbound approach to the intersection. Intervals for the signal cycle are indicated in the diagram. In Fig. 1, it is defned that veh#0 is passing the intersection in the amber time period. While veh#1, veh#2, veh#3 , veh#4 and veh#5 have arrived at intersection within red time of approach. These vehicles will have to stop for the certain time period. The time duration for which these vehicles are in the queue is the delay period for the respective vehicles. Veh#3, Veh#4 and Veh#5 are two- wheelers which cross the intersection without following any queue due to maneuvering and its size. Veh#3 overtake Veh#2 and move ahead towards intersection. Veh#6 has to stop for the time of clearing a queue. Veh#7 has a zero delay at the intersection. These vehicles have a less delay compared to veh#1, veh#2, veh#3, veh#4 and veh#5. The second part repeats the timing interval, and labels various time interval of interest with the symbols. The third part is an idealized plot of fow rate passed the stop line, indicating the saturation fow. When the green period commences a certain time elapses, while vehicles are accelerating to normal running speed, but after few seconds the queue discharges at a more or less constant rate, called the saturation fow. If there is a queue at the end of the green period, some vehicles will make use of the amber period to cross the intersection. In these circumstances, traffc moves on both green and amber period signals but the discharge rate is less than the saturation fow both at the beginning and at the end of the right of way period. 4 DATA COLLECTION Data are collected manually during the period of August 2009. All signals are pre-timed signals. The traffc data are recorded for about 120 min for each approach. All intersections comprise both motorized and non-motorized vehicle. In this study, for analysis purpose vehicles have been grouped into fve classes: (i) Car (ii) Bus / Truck (iii) Two-wheelers (iv) Autorickshaw (v) Bicycle As the traffc survey is conducted during day time, trucks are not available as they are not allowed to enter within the city area at this time period. 5 PASSENGER CAR UNIT (PCU) The unrestricted mixing of various classes of vehicles along a road creates many problems to the traffc engineers and planners. One type of vehicles in the traffc stream cannot be considered equivalent to any other type, as there is large differences in their vehicular and fow characteristics (J usto and Tuladhar, 1984). The space of the carriage way is shared by vehicles depending upon their size, speed, headway and lateral gap maintained by them. The non- uniformity in the static and dynamic characteristics of the vehicles is normally taken into account by converting all vehicles in terms of common unit. The most accepted one such unit is passenger car unit. PCU values suggested by Indian Roads Congress for signalized intersections are used in the analysis. 6 SATURATION FLOW Several models are developed over the years for fnding saturation fow. Amongst the most notable of these is the Webster model. For initial design of signal timings, various models are available. Some of them are shown below: (i) Webster Model S =525 x W where, S =Saturation flow (PCU/ hr); W =Width of approach road in meter (ii) Sarna and Malhotra (1967) S =431.7 x W +103.5 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 43 MODIFICATION OF WEBSTER’S DELAY FORMULA USING MODIFIED SATURATION FLOW MODEL FOR NON-LANE BASED HETEROGENEOUS TRAFFIC CONDITION (iii) Bhattachrya & Bhattacharya (1982) S =490 x W - 360 (iv) Chandra (1994) S =293 x W +1241 (v) IRC : 93 Method: S=525 * W (for width greater than 5.5 m) 6.1 Saturation Flow Measurement It is calculated either in PCU/hour or Vehicles/hour. In this study saturation period, which is defned as the period, when a stable moving queue has been crossing the stop line and movement wise classifed traffc volume has been conducted for the whole approach as vehicle does not move in a disciplined way. The observation point is normally stop line (desired position to stop). Start of the green is noted down. Conventional stop watch is used to measure time in seconds. Saturation fow ends when the rear axle of the last vehicle from a queue crosses the stop line. During this time period, different types of vehicles count is done for each movement (Left turn, through and right turn separately). Table 1 shows the traffc data collected for fnding saturation fow on the study approach. Traffc data are collected manually on the intersections under study. Data are also collected for approach width 4 m and 7m from the other intersection. Table 1 Saturation Flow of Traffic on the Study Approach to the Signalized Intersection C y c l e N u m b e r R o a d w i d t h i n m G r e e n T i m e i n S e c Number of Vehicles Crossing the Stop Signal Line During Saturated Green Time B u s A u t o C a r T w o B i c y c l e F l o w , v e h / s e c P C U / S e c 1 9 26 1 13 11 60 10 3.7 1.9 2 9 46 2 12 23 98 15 3.3 1.6 3 9 41 2 10 18 78 10 2.9 1.5 4 9 38 2 6 20 117 10 4.1 1.9 5 9 35 2 9 17 99 15 4.1 1.9 6 9 34 0 5 16 78 18 3.4 1.5 7 9 38 1 15 14 90 10 3.4 1.7 8 9 34 1 9 14 72 10 3.1 1.5 9 9 35 1 12 15 65 14 3.1 1.5 10 9 38 0 12 20 75 10 3.1 1.6 11 9 55 0 25 21 124 11 3.3 1.6 12 7 24 0 6 10 40 10 2.8 1.3 13 7 24 0 9 10 36 7 2.6 1.4 14 7 23 0 8 10 34 10 2.6 1.4 15 7 25 1 10 5 38 12 2.6 1.3 16 7 24 1 5 6 48 10 2.9 1.3 17 7 23 1 7 6 40 5 2.6 1.3 18 7 24 2 5 6 44 7 2.7 1.3 19 6 20 1 2 8 28 2 2.1 1.1 20 6 20 1 1 9 32 1 2.2 1.2 21 6 22 1 6 7 27 4 2.0 1.1 22 4 20 1 3 3 20 2 1.5 0.8 23 4 22 0 4 5 24 2 1.6 1.8 24 4 20 0 4 2 28 4 1.9 0.8 6.2 Composition of Vehicular Traffc: Following vehicles composition is observed during morning peak period for the various intersections. Table 2 shows the vehicle composition at various intersection of the study area. Table 2 Composition of Vehicular Traffc at Various Intersection S l . N o A p p r o a c h T W B u s A u t o C a r c y c l e Pallav Intersection 1 North 73% 2% 7% 9% 9% 2 South 59% 2% 13% 22% 4% 3 East 71% 1% 9% 17% 2% 4 West 65% 2% 21% 8% 4% Akhbarnagar Intersection 1 North 65% 2% 13% 19% 1% 2 South 58% 3% 13% 24% 2% 3 East 67% 2% 24% 5% 2% 4 West 65% 2% 20% 10% 3% Pragatinagar Intersection 1 North 63% 3% 16% 15% 3% 2 South 67% 2% 13% 15% 3% 3 East 82% 3% 7% 5% 3% 4 West 78% 3% 7% 7% 5% 44 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 RAVAL & GUNDALIYA ON It is observed from Table 4 that percentage of motorized two-wheelers is more than the other category of vehicles. Percentage of Two-wheelers varies from 58 per cent to 82 per cent of total vehicles available on the approach. Other types of Non-motorized vehicles are less than 1 per cent, so it is not included in the analysis. Table 3 shows the observed traffc signal data collected manually for various intersections under study. Table 3 Observed Traffc Signal Data of Various Intersection Direction Width in Meter Cycle Time in Seconds Green Time in Seconds Pallav Intersection North 9 190 45 South 9 190 54 East 8 190 21 West 8 190 48 Akhbarnagar Intersection North 9 230 60 South 9 230 72 East 12 230 35 West 12 230 50 Pragatinagar Intersection North 9 100 18 South 9 100 32 East 6 100 18 West 6 100 14 It is observed that Cycle time of the Akhbarnagar intersection is more due to high traffc density. Width of the east and west approach is more than north and south approach. 7 DEVELOPMENT OF SATURATION FLOW MODEL Following Models are developed for estimation of saturation fow for heterogeneous traffc fow for Indian condition using regression analysis. Model 1: (SFMW) Saturation Flow Model Width Approach S =626W +268 ... ... Eqn.1 Model 2: (SFMC) Saturation Flow Model Traffc Composition Approach S =647W+709tw+270b+702au- 1568car- 1552bic ...Eqn. 2 where, W = Width of road in m; tw =Proportion of two-wheeler in percentage; b =Proportion of buses in percentage; au =Proportion of auto rickshaw in percentage; car = Proportion of car in percentage; bic =Proportion of bicycle in percentage; S = Saturation fow in PCU / hour. Fig. 2 shows the trend of Saturation fow with proportion of cars. Saturation fow decreases with increases of proportion of cars in the study area. Saturation fow decreases because of delay due to Maneuvering in cars is more compared to two-wheelers. 0 1000 2000 3000 4000 5000 6000 7000 8000 1 3 5 7 9 11 13 15 17 19 21 23 25 Percentage of Cars S a t u r a t i o n F l o w ( P C U / H o u r ) Saturation Flow Fig. 2 Trend of Saturation Flow with Proportion of Cars Table 4 shows the comparison of observed saturation fow and model output. Saturation Flow calculated using SFMW and SFMC are tabulated as under. Table 4 Comparison of Observed Saturation Flow & Model Output Sl No. W i d t h i n m Observed Saturation Flow Saturation Flow using SFMW Saturation Flow using SFMC 1 9 6036 5902 6114 2 9 5964 5902 5930 3 8 5360 5276 5478 4 8 5580 5276 5602 5 9 5857 5902 6067 6 9 5833 5902 5926 7 12 8010 7780 8303 8 12 7875 7780 8167 9 9 6036 5902 6102 10 9 6353 5902 6112 11 6 4142 4024 4395 12 6 4084 4024 4300 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 45 MODIFICATION OF WEBSTER’S DELAY FORMULA USING MODIFIED SATURATION FLOW MODEL FOR NON-LANE BASED HETEROGENEOUS TRAFFIC CONDITION It is found that observed saturation fow is very much close to model output. The statistical analysis is carried out for validation. F test is carried out for the observed results at a confdence level of 95 per cent. Summary of Statistics for Model: R =0.995 R square =0.990 Adjusted R square =0.990 The value of observed F is found higher than the critical value. Hence, the model is weak for statistical support. More excessive data is to be collected for strengthening the model for statistical validation. 8 FIELD MEASUREMENT OF DELAY Field measurement of delay is done at three intersection approaches. HCM procedure is followed to calculate feld delay. In this method, the numbers of vehicles in queue are recorded at regular interval of 15 sec. It is to be continued for the red period. Number of vehicles stopped in the 15 sec interval is counted and recorded for the red period. Delay of the vehicles for each 15 sec interval is calculated. The summation of the all vehicles stopped during red period is done. A total vehicle stopped during red period is counted. Average delay per vehicle is worked out. 9 MODELS FOR DELAY ANALYSIS Numbers of Models are developed for finding delay analysis. Followings are the important models available for fnding out delay analysis. (i) Webster Model : Average delay per vehicle, ) 5 2 ( 3 1 2 2 2 65 . 0 ) 1 ( 2 ) 1 ( 2 ) 1 ( ì ì ì + | | . | \ | ÷ ÷ + ÷ ÷ = x q c x q x x c d ... Eqn. 3 Where, d =average delay per vehicle on the particular arm of the intersection; C =cycle time; λ =proportion of the cycle which is effectively green for the phase under consideration (i.e. g/c); q = fow; s = saturation fow; x =the degree of saturation. This is the ratio of the actual fow to the maximum fow which can be passed through the intersection from this arm, and is given by x = q/λs (ii) HCM 2000 Method: Uniform Delay, C g X Min C g C d / ) , 1 ( 1 ) / 1 ( 50 . 0 2 1 ... Eqn. 4 Where, d 1 =uniform control delay assuming uniform arrivals, s/ veh; C =cycle length, cycle length used in pretimed signal control, or average cycle length for actuated control; g = effective green time for lane group, green’time used in pretimed signal control, or average lane group effective green time for actuated control; X =v/c ratio or degree of saturation for lane group. Incremental Delay, ( ¸ ( ¸ + ÷ + ÷ = cT kIX X X T d 8 ) 1 ( ) 1 ( 900 2 2 ... Eqn. 5 Where, d 2 =incremental delay to account for the effect of random and over saturation queues, adjusted for the duration of the analysis period and the type of signal control. This delay component assumes that there is no residual demand for the lane group at the start of the analysis period, s/ veh; T =duration of analysis period, h; K =incremental delay factor that is dependent on controller settings; I = upstream fltering/metering adjustment factor; c =lane group capacity in veh/h, and; X =lane group v/c ratio, or degree of saturation. d= d 1 PF + d 2 + d 3 ... Eqn. 6 Where, d =control delay per vehicle, s/veh; d 1 =uniform control delay assuming uniform arrivals, s/veh; PF =uniform delay progression adjustment factor which accounts for the effects of signal progression; d 2 =incremental delay to account for the effect of random and over saturation queues, adjusted for the duration of the analysis period and the type of signal control. This delay component assumes that there is no residual demand for the lane group at the 46 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 RAVAL & GUNDALIYA ON start of the analysis period, s/veh, and; d 3 =supplemental delay to account for over saturation queues that may have existed prior to the analysis period, s/veh. (iii) Md. Shamsul Hoque & Md. Asif Imran: They have provided the following equation for fnding out the delay at intersection. adj x q x x c d ) 1 ( 2 ) 1 ( 2 ) 1 ( 2 2 ... Eqn. 7 pnmv x q x q x x c d 3608 . 0 32 . 37 04 . 46 93 . 46 ) 1 ( 2 ) 1 ( 2 ) 1 ( 2 2 Eqn.8 where, q =Vehicle arrival rate (PCU/Sec); x =Degree of Saturation; pnmv = Percentage NMV in traffc These models do not give correct value of delay for heterogonous traffc condition. Hence, these models can be modifed to suit the feld traffc condition. (iv) Akcelik’s Model : Akcelik and Rouphail have proposed delay model for signalized intersections that is suitable for variable demand condition. They explained application of general delay model by using different delay defnitions, i.e. the average delays for vehicles arriving in the peak, non peak and post peak over –saturation and total fow periods. Average delay experienced by vehicles during the peak fow period is given by: CpTp Xo Xp k Xp Xp Tp g c d ) ( 8 2 ) 1 ( ) 1 [( 900 ) ( 5 . 0 (for Xp >1.0) ... Eqn. 9 CpTp Xo Xp k Xp Xp Tp uXp u C d ) ( 8 ) 1 ( ) 1 [( 900 1 ) 1 ( 5 . 0 2 2 (for Xo <Xp <1.0) ... Eqn. 10 uXp u C d 1 ) 1 ( 5 . 0 2 (forXp<=Xo) ... Eqn. 11 where, Tp = duration of the peak fow period in hour; C =cycle time in sec; g =effective green time in sec; u =g /c; q p =average arrival flow rate in peak flow period (veh/hr); Cp =peak period capacity = C Sg 3600 ; S =saturation fow rate (veh/sec of green); Xo =degree of saturation = 600 67 . 0 Sg ; K =delay parameter = 1.22(Sg) – 0.22; Xp = Cp q p These models do not give the correct value of delay in case of over saturation period for the present study area. 10 MODIFICATION OF WEBSTER’S DELAY MODEL Webster’s classical delay formula has been developed in countries with car dominated traffc stream to estimate average delay per vehicle at signalized intersections. Webster’s classical delay formula has been used in developed countries situation where the road traffic condition is homogeneous and formula does not estimate delays accurately under heterogeneous traffc condition. As a result, it is necessary to modify Webster’s delay formula to make it usable under non-lane based heterogeneous traffc condition. The modifcation of Webster’s delay formula under non lane based heterogeneous traffc condition can be accomplished by adding an empirical adjustment term with the sum of frst and second terms, which has been calibrated based on the feld observations of delays. ) 1 ( 2 ) 1 ( 2 ) 1 ( 2 2 x q x x c d adj ... Eqn. 12 In the Eqn. 12, d is the actual delay observed in the feld. If the left hand side of the Eqn. 12 is taken as the dependent variable, it needs to be regressed against a set of independent variables. 10.1 Development of Model In the present study, Webster’s classical delay formula is modifed, so that it can be used in the non-lane based heterogeneous traffc condition of Indian cities. To achieve this, the frst and second terms of the formula will be kept as it is, because they represent the uniform and random component of delay and derived solely from queuing HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 47 MODIFICATION OF WEBSTER’S DELAY FORMULA USING MODIFIED SATURATION FLOW MODEL FOR NON-LANE BASED HETEROGENEOUS TRAFFIC CONDITION theory. The third term, which is only for adjustment as per heterogeneous condition has to be replaced with a quantity calibrated from feld observations. In the modifed formula, with the frst and second terms, additive adjustment term is introduced to best ft the local traffc condition. In the model, this adjustment taken as dependent variable and regressed against independent variables, which were originally present in the adjustment term for Webster’s classical delay formula. In addition, the percentage of two-wheelers, which is more in non lane based heterogeneous traffc condition, also be taken as an independent variable. The model along with its mathematical form is as follows: 55 . 26 35 . 32 98 . 3 6 . 7 057 . 0 82 . 7 tw x c Q adj Eqn.13 where, adj =Adjustment term for the model; Q =Vehicle arrival rate (PCU/sec); c =Cycle time is seconds; x =Degree of Saturation; λ =Effective green ratio; tw =Percentage two-wheelers. The Modified Webster’s Delay Formula is as shown below : 55 . 26 35 . 32 98 . 3 6 . 7 057 . 0 82 . 7 ) 1 ( 2 ) 1 ( 2 ) 1 ( 2 2 tw x c Q x q x x c d Eqn.14 Table 5 Comparison of Observed Control Delay & Model Output Sl No. Observed Control Delay, Sec Output in Sec using Developed Model Output in Sec using Webster Model 1 75 76 58 2 65 65 52 3 90 88 76 4 90 88 77 5 40 39 26 6 42 41 33 7 44 43 35 8 48 45 38 9 90 88 70 10 102 100 86 11 75 75 62 12 97 98 81 It is observed from Table 5 that Developed model gives the value very closed to the observed value of control delay. It is observed that the feld calculated delays are signifcantly different from those calculated by Webster’s classical delay formula. The modifcation of Webster’s classical delay formula is done to cope with the heterogeneous traffc condition of the India only. The statistical analysis is carried out for validation. F test is carried out for the observed results at a level of confdence of 95 per cent. Summary of Model : R =0.986 R square =0.972 Adjusted R square =0.945 The value of observed F is found higher than the critical value. The model is weak for statistical support. Hence, more excessive data is to be collected for strengthening the model for statistical validation. 11 CONCLUSIONS Followings are the important conclusions that are drawn from the present study. (i) HCM 2000 suggests measurement of saturation fow should start after 10 sec of green initiation, which is considered as start up lost time. From the present study, it was found that auto rickshaws and motor cycle fnd way in between heavy vehicles and try to come near stop line. Most of the times these vehicles cross stop line before green starts. During red period large number of vehicles accumulates near stop line. This causes to discharge large amount of traffc during initial 10 sec. Hence, it is suggested that count for measurement of saturation fow should start after 3 sec of green initiation for non lane based traffc condition. (ii) Regression model developed to estimate saturation fow shows good correlation with feld values. The SFMW can be used to estimate saturation fow at any intersections knowing approach width. (iii) The developed model SFMC of saturation fow gives the value in PCU/hr considering width and vehicle composition of intersection. It gives the satisfactory results nearer to the feld observations. 48 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 RAVAL & GUNDALIYA ON MODIFICATION OF WEBSTER’S DELAY FORMULA USING MODIFIED SATURATION FLOW MODEL FOR NON-LANE BASED HETEROGENEOUS TRAFFIC CONDITION (iv) Modifcation is made in the Webster’s delay equation to suit the local heterogeneous traffc condition. (v) It is observed that proportion of two-wheelers is more in the traffc, so it is included in the adjustment factor in the delay’s formula. (vi) The regression model developed for saturation fow is based on traffc condition of Ahmedabad city, which is assumed to be similar to other parts of India. This developed model may be applied in other cities of India and checked for its usefulness. (vii) Saturation flow depends on various factors. In present study all intersections are selected having almost fat surface. Saturation fow is also affected by parking facility near intersection. All these factors need to be studied and to develop new model taking into account maximum possible variables. (viii) Recommended model of Saturation flow must be verifed by applying it in the other cities. In present study analysis has been carried out for three intersection approaches only. Similar analysis should be carried out for large number of intersection approaches. (ix) It is recommended to use greater number of observational cycles including greater number of intersections for model calibration. (x) Besides Webster’s delay model, other delay models should also be modifed under the different traffc condition of Indian cities to estimate delays for oversaturated conditions. REFERENCES 1. Chakroborty Partha & Das Animesh, “Principles of Transportation Engineering”, Prentice Hall of India, New Delhi, 2005. 2. Cheng DingXing, Messer Carroll J., Tian Zong Z. & Liu Juanyu, - Modifcation of Webster’s Minimum Delay Cycle Length Equation Based on HCM 2000, A Paper submitted to the Transportation Research Board, Annual meeting in Washington, D.C. 2003. 3. Guidelines on Design and Installation of Road Traffc Signals, IRC : 93-1985 Indian Roads Congress, New Delhi, 1985. 4. Hoque Md. Shamsul & A, Md. Imran Asif, - Modifcation of Webster’s Delay Formula Under Non-Lane Based Heterogeneous Road Traffc Condition, Journal of Civil Engineering, IEB, 2007. 5. J usto, C.E.G. & Tuldhar, S.B.S. (1984). “Passenger Car Unit Values for Urban Roads.” Journal of Indian Roads Congress, Vol. 45(1), New Delhi, India. 6. Kadiyali, L.R. (2009), “Principles and Practice of Highway Engineering”, Khanna Publishers, Nath Market, Nai Sarak, Delhi, India. 7. Saxena, S.C. (1989), “Traffc Planning and Design”, Dhanpat Rai Publication Pvt. Ltd., New Delhi. 8. Webster, F.V. (1958), “Traffc Signal Settings”, Road Research Technical Paper No. 39, Road Research Laboratory, Crowthorne, England. 9. Dave, H.K. (2005), “I ntersection Improvement through Signal Coordination – A Case Study of Anjalee Elisbridge”, A Dissertation at L. D. College of Engineering, Ahmedabad. 10. Bhattacharya, P.G. & Bhattacharya, A.K. (1982), “Observation and Analysis of Saturation Flow Through Signalized Intersection in Calcutta” – Indian Highways, Vol. 10(4), Indian Roads Congress, New Delhi, PP 11-33. 11. Chandra, S. (1994), “Development of Capacity Analysis Procedure for Urban Intersection”, Ph. D. Thesis, University Of Roorkee, Roorkee, India. 12. Sarna, A.C. & Malhotra, S.K. (1969), “Traffic Delays at Signalized Intersections”, Road Reaserch Paper No. 107, CRRI, New Delhi. 13. Varia, H.R. (1995), “Optimization of Signal Cycle Time and its Implication on Delay and other Operational Parameters” – A Dissertation of Indian Institute of Technology, Bombay. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 49 1 INTRODUCTION These Dynamic effects are a complex phenomenon involving a variety of bridge and vehicle parameters. The most critical ones are the bridge and vehicle dynamic properties, speed of travel, road surface conditions on bridge deck and approach portion. The frequency content and magnitude of dynamic wheel loads of vehicles are closely related to the vehicle speed, pavement roughness and vehicle suspension properties. This renders a proper quantifcation of the dynamic effects diffcult. Traditionally, these dynamic effects are accounted for in bridge design by simply scaling the required bridge response quantities (bending moments and shear forces) by a quantity called Dynamic Amplifcation Factor (DAF). This enhancement, expressed as a fraction of the static response to account for additional dynamic effects is sometimes referred to as Impact Factor (IF) or Dynamic Load Allowance (DLA). The DAF is defned as the ratio of maximum total response (including dynamic effects) at a critical bridge section to the maximum static response at the same section, while IF or DLA =DAF – 1. A proper quantifcation of DAF involves consideration of various inter-related and uncertain parameters mentioned earlier. This Paper reviews the DAF provisions in various important bridge design codes and also the basis for some of those provisions. The topic assumes signifcance especially when there is growing evidence from various reported feld tests suggesting that the current DAF provisions in various international standards are highly conservative so long as good road surface conditions prevail 1 . At the same time, it needs to be mentioned that occasional very high values of DAF exceeding the current design provisions have also been reported 2 . 2 DAF PROVISIONS IN BRIDGE DESIGN CODES The traditional approach has been to relate DAF to bridge span, with an inverse relationship between the two. This is refected in IRC Specifcations 3 , AASHTO Standard Specifcations 4 , J apanese Bridge Design Code J RA 5 , etc. At the same time, provisions refecting the dependence of DAF on the fundamental frequency of bridge deck were followed by early Ontario Highway Bridge Design Code (OHBDC 1979, 1983), Swiss SIA 160 (1988) and Australian Bridge Design Code (Austroads 1992) 6 . Recognizing the infuence of other signifcant parameters and the drive to keep the procedures simple, the more recent OHBDC (1991) 5 made the DAF provisions dependent on the axle number. The limit state format AASHTO LRFD 7 specifes a constant value for dynamic allowance. A compilation of these provisions is summarized in Table 1. It may be noted that while IRC, J RA and French Codes specify separate DAF for steel and concrete bridges, the more recent Codes like, AASHTO, Euro Code, Austroads, etc., avoid distinction between bridge materials for DAF provisions. The fundamental frequency dependent DAF provisions seem to have a physical backing since it is observed that large amplitude vibrations occur mostly when the vehicle critical frequencies (body bounce or axle hop) DYNAMIC AMPLIFICATION FACTORS FOR HIGHWAY BRIDGE DESIGN – A REVIEW OF INTERNATIONAL CODAL PROVISIONS S. ARUN*, DEVDAS MENON** & A. MEHAR PRASAD** ABSTRACT Dynamic effects induced by moving vehicles are accounted for in the design of highway bridges by scaling the static effects by a ‘Dynamic Amplifcation Factor (DAF) or increasing by a fraction called ‘Impact Factor’ (IF). A proper quantifcation of this factor is very diffcult and most of the early bridge design codes had specifed DAF (or IF) as a function of either the span or the fundamental frequency of the bridge deck. A more recent trend is to specify this factor as a function of vehicle axle numbers or simply as a constant. This Paper reviews the impact factor provisions in various international bridge design Codes. The basis for some of these provisions, as well as critical comparison of these provisions with those of the current IRC provisions, is also included. The results show that there is a wide variability in the provisions in current bridge design codes and points a need to arrive at more appropriate expressions suitable for Indian highway bridges. * Research Scholar ** Professor Deptt. of Civil Engineering, IIT Madras, Chennai, India } 50 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 ARUN, MENON & PRASAD ON Table 1 DAF Provisions for Straight Bridge Decks in Various International Bridge Codes Sl No. Specifcations Amplifcation Factors (DAF or IF/DLA) I. Span Dependent Amplifcation Factor Provisions 1 AASHTO - Standard Specifcations 1996 4 (GVW of Design Truck =320 kN) 15.24 IF= L+38.1 ; IF ≤ 0.3 L=Span in m 2. AASHTO LRFD 2006 7 GVW of Design Truck =320 kN IF = 0.33 for main fexural members IF =0.15 for fatigue and fracture IF =0.75 for deck joints 3 J RA 1996 (J apan Road Association ) 5 Steel Bridge 20 ; ( 50) I L = + L in m For Truck and Lane loading R.C Bridge 7 ; ( 20) I L = + for Lane loading R.C Bridge 20 ; ( 50) I L = + for Truck loading PSC Bridge 10 ; ( 25) I L = + for Lane loading PSC Bridge 20 ; ( 50) I L = + for Truck loading 4. French Cahier des Prescriptions Communes (1973) 8 0.64 ; (1 0.2 ) I L = + for Concrete Structures 0.80 ; (1 0.2 ) I L = + for Steel and Composite Structures 5. West German DIN 1072 (1967) 8 0.4 0.008 ( ) I L L in m = − 6. Italy- Code 384 (1962) 8 2 (100 ) ; 100(250 ) L I L − = − 0; I = for span (L) in excess of 100 m 7 Euro Code EN1991-2 (2003) 9 Moment DAF =1.7; for L ≤ 5 m Moment DAF =1.4; for L ≥ 15 m A linear transition of DAF for span range 5 to 15 m DAF for Shear force =1.4; for L ≤ 5 m =1.2; for L ≥ 25 m A linear transition of DAF for span range 5 to 25 m HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 51 DYNAMIC AMPLIFICATION FACTORS FOR HIGHWAY BRIDGE DESIGN – A REVIEW OF INTERNATIONAL CODAL PROVISIONS 8. IRC : 6 (2000) 3 Class A or Class B loading Bridge type IF Reinforced Concrete Bridges 0.5; I = for L ≤ 3 m 4.5 ; (6 ) I L = + 3≤ L ≤45 0.088; I = for L>45 m Steel Bridges 0.545; I = for L ≤ 3 m 9 ; (13.5 ) I L = + 3≤ L ≤45 0.154; I = for L>45 m Class AA and Class 70 R For Spans less than 9 m – for both Steel and RC Bridges Vehicle type IF Tracked vehicles 0.25; I = for L ≤ 5 m. and linearly reducing to 0.1 for spans of 9 m Wheeled vehicles 0.25; I = For Spans greater than 9 m – RC Bridges Tracked vehicles 0.10; I = L ≤ 40 m 4.5 ; (6 ) I L = + 40≤ L ≤45 m 0.088; I = for L>45 m Wheeled vehicle 0.25; I = L ≤ 12 m 4.5 ; (6 ) I L = + 12 ≤ L ≤ 45 0.088; I = L ≥ 45 m For Spans greater than 9 m – Steel Bridges Tracked vehicle 0.10; I = for all spans Wheeled vehicle 0.25; I = L ≤ 23 m 9 ; (13.5 ) I L = + 23≤ L≤ 45 m 0.154; I = L >45 m 52 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 ARUN, MENON & PRASAD ON match the bridge fundamental frequency and the critical vehicle frequencies are excited by the combined effect of road surface unevenness and vehicle speed. The span dependent DAF provisions are attributable to the result of simplifcation of procedure by which the fundamental frequency determination of bridges can be avoided 5 .At the same time, from the comprehensive bridge testing conducted by EMPA 11 , it can be seen that there is a strong correlation between the bridge span and fundamental frequency. Based on this, various simple expressions have been proposed 11 relating span and frequency for bridges with various support conditions. Literature shows that a direct relationship between the bridge span and DAF could not be established 12,13 . Despite this, various bridge codes II. Frequency Dependent Amplifcation Factor Provisions 1. Ontario Highway Bridge Design Code OHBDC (1979) 6 GVW of Design Truck =740 kN Bridge fundamental frequency, f 1 (Hz) DLA f 1 ≤ 1 0.3 2.5 ≤ f 1 ≤ 4.5 0.45 f 1 ≥ 6 0.3 With a linear transition of DLA in the frequency range (1, 2.5) and (4.5,6) 2. Ontario Highway Bridge Design Code OHBDC (1983) 6 GVW of Design Truck =740 kN Bridge fundamental frequency, f 1 (Hz) DLA f 1 ≤ 1 0.2 2.5 ≤ f 1 ≤ 4.5 0.4 f 1 ≥ 6 0.25 With a linear transition of DLA in the frequency range (1, 2.5) and (4.5,6) 3. Australian Standards Austroads (1992) 6 Load Class A 160- Single Axle (for local effects) Load Class M 1600 – 4 axle group with each group of 360 kN (for Simulating Moving Traffc) DLA =0.3 for load class A 160 Bridge frequency, f 1 (Hz) DLA f 1 ≤ 1 0.2 2.5 ≤ f 1 ≤ 4.5 0.4 f 1 ≥ 6 0.25 With a linear transition of DAF in the frequency range (1, 2.5) and (4.5,6) III. Axle Number Dependent and Constant Amplifcation Factor Provisions 1. Ontario Highway Bridge Design Code OHBDC (1991) 5,6 GVW of Design Truck =740 kN No of Axles DLA 1 0.4 2 0.3 3 or more 0.25 2. Swiss SIA 6 160 (1989) DLA =0.8; constant for all Bridge frequencies 3. Trilateral Design and Analysis Group (2005) 10 - A Design Code jointly developed by the U.S, U.K, and Germany Bending moment and shear force DAF =1.2 for all Spans. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 53 DYNAMIC AMPLIFICATION FACTORS FOR HIGHWAY BRIDGE DESIGN – A REVIEW OF INTERNATIONAL CODAL PROVISIONS prescribe a decreasing DAF with increasing span. This may be attributed to two reasons: (i) An increase in span shifts the bridge from the critical frequency range, thus reducing the vulnerability to increased dynamic response. (ii) For bridges with medium to long spans, multiple axles and multiple vehicles govern the design and it is highly unlikely that all the wheel loads exert the maximum load effect simultaneously. Also, it is clear from recent numerical and feld investigations 14,15 that DAF reduces with increasing static effects. The same logic applies to the OHBDC (1991) provisions where a decreasing DAF with increasing axle numbers was followed. This also indicates that short span bridges are more critical from a vehicle induced vibration point of view. Moreover, in the case of short span bridges, it is also observed that the local effect caused by a single axle or axle group is likely to be more critical rather than that of the entire vehicle (s) on the bridge deck. AASHTO Guide Specifcations for horizontally curved highway bridges 16 give DAF provisions for horizontally curved I girder bridges as well as box girder bridges. This is shown in Table 2. Table 2 Impact Factors for Horizontally Curved I Girder Bridges 16 Quantity IF* Reactions and Shear forces 0.3 Moments in longitudinal girders 0.25 Torsional moments in longitudinal girders 0.4 Moments in slab 0.2 Bimoments in longitudinal girders 0.25 Forces and moments in diaphragms 0.25 Defections 0.25 * Subject to the following conditions: o 15.24 m ≤ L ≤ 60.96 m o 60.96 m ≤ R c ≤ 304.8 m o Vehicle speed ≤ 112 km/h o Number of continuous spans ≤ 2 o Vehicle to bridge weight ratio ≤ 0.6 The conditions stated at the end of Table 2 may be attributed to the parameter range selected in the numerical study by Schelling et.al 17 , which in turn formed the basis of AASHTO Guide provisions. The code recommends dynamic analysis if the above parameter range is exceeded. 3 DAF FOR BRIDGE ASSESSMENT The uncertainties associated with various parameters contributing to dynamic increments are less in the case of assessment of an existing bridge compared to that of a bridge at design stage. This is refected in the DAF provisions for assessment in AASHTO Guide Specifcations, shown in Table 3. Table 3 Impact Factors for Bridge Assessment 16 Quality of Wearing Surface IF Good No repair required 0.1 Fair Minor defciency, item still functioning as designed 0.1 Poor Major defciency, item in need of repair to continue functioning as designed 0.2 Critical Item no longer functioning as designed 0.3 4 BASIS OF CODE PROVISIONS AASHTO Standard Specifcations of 1931 - an outcome of a joint committee of AASHTO and American Railway Engineering Association (AREA), fundamentally based on the works done by AREA on railway bridges - formed the basis of span dependent DAF provisions of various international bridge codes 5 . AASHTO LRFD provisions were based on feld test data as well as numerical simulations done by Hwang and Nowak 14 , while the AASHTO Guide Specifcations for horizontally curved bridges were also based on the numerical simulation studies done by Schelling et al. 17 . The bridge fundamental frequency dependent provisions of OHBDC 1979 and 1983 were the result of a series of feld tests performed on 52 highway bridges in Ontario by Wright and Green from 1956 to 1957, 11 highway bridges by Csagoly, Campbell and Agarwal from 1969 to 1971 and on 27 bridges of various confgurations by Billing and Green in 1980 5 . The major observation from these tests was that the maximum DAF obtained (ranged from 30 to 85 per cent) were observed for bridges with fundamental frequencies in the range 2 to 5 Hz. This was again confrmed 54 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 ARUN, MENON & PRASAD ON by the feld tests conducted on 226 highway bridges in Switzerland by Cantieni in 1983 and 1984 11,12 which indeed formed the basis of Swiss SIA 160 1988 6 . Hence, it was concluded that very high dynamic amplifcations are as a result of frequency matching between the bridge fundamental frequency and the vehicle bounce frequency. This fnding was later reinforced by the DIVINE project (Dynamic Interaction between Vehicle and Infrastructure Experiments) 18 , which undertook a comprehensive testing of trucks, pavements and bridges. It is reported that the modifcations to the 1991 edition of OHBDC were made acknowledging the fact that amplifcation factor reduces as vehicle weight increases, the weight of OHBDC design truck being 740 kN. Based on the extensive database of feld studies conducted by Ontario Ministry of Transportation and Communication (OMTC), it was concluded that a value of 0.2 for DAF would be supportable even for the frequency sensitive range of 2 to 5 Hz. However, there was a reluctance to reduce the values below 0.25 and hence was fxed as the DAF for the case of three or more axles 5 . However, it is to be acknowledged that the above provisions are for the case of generally smooth surface conditions. Recognizing the infuence of adverse surface roughness, the OHBDC commentary recommneds an increase in DAF from 0.4 to 0.5 in locations (one-tenth of span) of badly maintained expansion joints 6 . The code also insists on a 6 m long approach slab for reducing the infuence of initial vehicle vibrations before entering the bridge. Early loading standard of UK, BS 153: 1954 had a bulit in allowance for impact of 0.25 in the normal loading of Type HA, which was to be applied to the load of any one axle of one vehicle or any single pair of adjacent wheels of two vehicles travelling abreast, which induces the maximum static effects. At the same time, no allowance for dynamic effects was given to the abnormal load Type HB based on the assumption that the speed of travel of heavy vehicles it represents will be low. The DAF provision remained unchanged when the limit state code BS 5400 was frst introduced in 1978. Acknowledging the signifcance of road surface conditions, there was a drastic increase in DAF provision from 0.25 to 0.8 when the HA type loading was revised for the case of bridges with spans less than 50 m in 1988. The factor of 0.8 was adopted as the extreme value of impact obtained from the measurement of dynamic loads under the rear wheels of two axle rigid truck traversing 30 motorway bridges, the tests being conducted by the Transportation Road Research Laboratory 19 (TRRL) UK. DAF provisions of Euro code 9 , included in the ‘live load’ models, were based on numerical simulations with assumed roughness values for carriage way surface. For spans greater than 15 m, an average roughness was taken, whereas for spans less than 15 m the roughness was represented by a 30 mm thick wooden plank 19 . Regarding the basis of IF provisions in IRC 6:2000, it seems little systematic research has been performed and the current impact factor provisions were adopted from the then existed French code which considered the infuence of vehicle to bridge weight ratio 20 . 5 VARI OUS CODE PROVI SI ONS – A COMPARISON A comparison of various span dependent DAF provisions for RC and steel bridges in various design codes is made in Figs. 1 and 2. Fig. 3 compares the span dependent IRC and more recent Euro code provisions with the frequency dependent provisions of OHBDC:1983, Swiss SIA 160:1988 and Austroads: 1992. The span to frequency transformation was made making use of the following empirical relationship proposed by RILEM committee based on feld tests performed on more than 200 European bridges 13 . 0.9 82 f L − = (Eqn... 1) with L and f representing the bridge span and fundamental frequency, respectively. 5.1 Salient Observations From Figs. 1 and 2, it is seen that though the philosophy for accounting the dynamic effects remain the same, there are considerable variations in DAF provisions in various bridge design codes. For single lane effects, the recent Euro code provisions for moment amplifcation factors refect the highest values for both steel as well as concrete bridges. At the same time the Euro code provisions refects the general trend of decreasing DAF with increasing static effects as can be observed for the case with provisions for two lane bridges. The Euro code DAF values for moment amplifcation are found to be almost twice the corresponding values prescribed for shear except for the span range of 5 to 15 m. For the case of short span bridges, the current IRC DAF provisions for both concrete and steel bridges seem to be conservative compared to other code provisions, except for the latest Euro code provisions for single lane traffc. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 55 DYNAMIC AMPLIFICATION FACTORS FOR HIGHWAY BRIDGE DESIGN – A REVIEW OF INTERNATIONAL CODAL PROVISIONS This seems to be critical as the fundamental frequency characteristics of such bridges falls in the axle hop frequency range of heavy trucks and are shown to exhibit large dynamic responses during various reported feld investigations 18 . Fig. 1 Comparison of Impact Factor Provisions for RC Bridges Fig. 2 Comparison of Impact Factor Provisions for Steel Bridges. Fig. 3 Comparison of Impact Factor Provisions for Military Vehicles Fig. 4 Comparison of Various Frequency Dependent DAF Provisions in Bridge Codes The low values of impact factor for IRC class AA and 70 R loading (military vehicles) seem to be appropriate, acknowledging the low dynamic effects that may be imparted by the slow movement as well as heavy GVW of these vehicles. Comparison of IRC and Euro code provisions with the various frequency dependent provisions (Fig. 4) reveal some interesting features. Though, it is widely acknowledged that bridges with fundamental frequencies in the critical frequency range of 2 to 5 Hz are more vulnerable to vehicle induced vibrations, the current IRC and Euro code provisions are the lowest for this frequency range. At the same time as per the span frequency correlation from Eqn. 1, the approximate span range corresponding to this frequency happens to be 22 to 85 m. For this span range, the critical static loading scenario involves the presence of multiple vehicles, which may in turn reduce the magnitude of DAF due to increase in static effects as well as the dynamic effects from different vehicles getting partly compensated. Further study is needed to confirm the influence of multiple vehicle presence in single as well as two lanes to confrm this. 6 CONCLUSIONS Though the philosophy of accounting for dynamic effects due to moving vehicles remains the same, the various International Codes seem to disagree on the magnitude of dynamic amplifcation. This is especially signifcant for bridges with fundamental frequencies in the range 2 to 5 Hz, where a large discrepancy between the span dependent and frequency dependent provisions is observed. Again, this frequency range happens to be applicable for bridges in the medium to large span range for which occurrence of multiple vehicles over the span governs the design. Further systematic analysis involving multiple vehicles is needed to get a more realistic picture of vehicle induced dynamic effects for such bridges. The current DAF provisions for short span bridges too needs verifcation as many recent feld investigations 18 have reported large DAF values especially for the case of bridges with average to low quality road profles on bridge deck as well as approaches. Also, the current IRC DAF provisions for bridges in such span range are less compared to various recent design codes, especially for the case of single lane traffc. Further investigations are required to obtain a more realistic picture of dynamics of such bridges 56 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 under the infuence of simultaneous excitation produced by critical vehicles on two lanes. Regarding Indian scenario, it seems no systematic study has been carried out to establish the adequacy of current IRC provisions. Also, there is a need to develop separate impact factor provisions for fatigue, especially for the analysis of steel and composite bridges. This may be done taking into consideration the prevailing traffc characteristics. Specifc account of the infuence of road surface roughness and multiple vehicle presence effects need to be made. Factors, like gross vehicle weight, vehicle suspension characteristics, road surface conditions, etc are found to have a signifcant infuence on bridge dynamic response. It is very diffcult to quantify such factors at the bridge design stage. Hence, a suffcient level of conservatism in impact factor in design provisions is desirable. But for the assessment of existing bridges, this level of conservatism is unwarranted as most of the parameters, such as, prevailing traffc and road profle conditions may be known and the use of design DAF for assessment purposes may lead to over estimation of true demand. Hence, a separate DAF specifcally for assessment purposes may also be developed. REFERENCES 1. OBrien, E.J., Rattigan, P., Gonzalez, A., Dowling, J., and Znidaric, A. (2009), Characteristic Dynamic Traffic Load Effects in Bridges, Engineering Structures, 31, pp. 1607 – 1612. 2. Heywood, R., Roberts, W. and Boully, G. (2001), ‘Dynamic Response of Bridges’, Paper No. 2731, Transportation Research Record. 3. IRC:6 – 2000 Standard Specifcations and Code of Practice for Road Bridges. Section II. Loads and Stresses. The Indian Roads Congress. New Delhi. 2000. 4. AASHTO, Standard Specifcations for Highway Bridges, The American Association of State Highway and Transportation Offcials, Washington, D.C. (1996). 5. NCHRP Synthesis, Dynamic Impact Factors for Bridges, Transportation Research Board, NRC, National Academy Press, Washington D.C. 1998. 6. O’Connor C. and Shaw P.A., Bridge Loads: An International Perspective, Taylor & Francis Group London 2000. 7. AASHTO, LRFD Bridge Design Specifcations, The American Association of State Highway and Transportation Offcials, Washington, D.C. (2007). 8. Bridge Loadings Round the World, Transport Communications Monthly Review, Dec 1965, pp 95 – 135. 9. EN 1991-2 Eurocode 1: Actions on Structures- Traffc Loads on Bridges. European Committee for Standardization. Brussels. 2003. 10. Trilateral Design and Test Code for Military Bridging and Gap- Crossing Equipment, The Defence Technical Information Centre Publication. url: http://www.dtic.mil/dtic/. 11. Cantieni, R., Dynamic Load Tests on Highway Bridges in Switzerland- 60 years experience of EMPA, Report No.211, Swiss Federal Laboratories for Material Testing and Research, EMPA, Switzerland 1983. 12. Cantieni, R., Dynamic Behavior of Highway Bridges Under Passage of Heavy Bridges, Report No.220, Swiss Federal Laboratories for Material Testing and Research, EMPA, Switzerland 1992. 13. Chaallal, O. and Shahawy, M., Experimental Evaluation of Dynamic Amplifcation for Evaluation of Bridge Performance, Technical Report No. ETS. DRSR.98.11, University of Quebec, Canada. 1998 14. Hwang, E.S. and Nowak, A.S. (1991), ‘Simulation of Dynamic Load for Bridges’, ASCE Journal of Structural Engineering, 117 (5), pp. 1413 – 1433. 15. Kim, S. and Nowak, A.S. (1997) Load Distribution and Impact Factors for I girder Bridges, ASCE J ournal of Bridge Engineering, 2 (3), pp. 97 – 104. 16. AASHTO, Guide Specifications for Horizontally Curved Highway Bridges, The American Association of State Highway and Transportation Offcials, Washington, D.C. (1993) 17. Schelling, D.R., N.H. Galdos, and M.A. Sahin, Evaluation of Impact Factors for Horizontally Curved Steel Box Bridges, J ournal of Structural Engineering, Vol.118, No.11(1992) pp 3203 – 3221. 18. DIVINE. (1998), Dynamic Interaction Between Vehicles and Infrastructure Experiment, Technical Report DSTI/DOT/RTR/ IR6 (98)1, OECD, France. 19. Dawe, P. Traffic Loading on Highway Bridges – Research Perspectives, (1st ed.), Thomas Telford, London 2003. 20. IRC Papers 109 & 112 Standard Specifcations and Code of Practice for Road Bridges, Section I & II (Explanatory Notes & Discussions) 1946. 21. ASCE Committee on Loads and Forces on Bridges, “Bridge Loading: Research Needed” Journal of Structural Division, Proc., ASCE, Vol. 107, No. ST 7 (1981) pp.1161 – 1213 ARUN, MENON & PRASAD ON DYNAMIC AMPLIFICATION FACTORS FOR HIGHWAY BRIDGE DESIGN – A REVIEW OF INTERNATIONAL CODAL PROVISIONS HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 57 1 INTRODUCTION Construction & Demolition wastes consist of the materials generated during the construction, renovation and demolition of buildings and other structures. C&D waste constitutes one of the largest waste streams in the world. Management of C&D waste is a major concern due to increasing quantum of demolition rubble, continuing shortage of dumping sites, increase in transportation and disposal cost and above all growing concern about pollution and environmental degradation. Demolition of Pucca and Semi-Pucca buildings, on an average generates about 500 and 300 kg of waste per sq.m, respectively. C&D wastes thus consist of materials, which had been originally used for construction. Large quantum of bricks and masonry arise as waste during demolition. These are generally mixed with concrete, tiles and other construction materials. Concrete appears in two forms in the waste. Structural elements of building have reinforced concrete, while foundations have mass non-reinforced concrete. Metal waste is generated during demolition in the form of pipes, conduits, and light sheet material used in ventilation system, wires, and sanitary fttings and as reinforcement in the concrete. Metals are recovered and recycled by re-melting. Timber recovered in good condition from beams, window frames, doors, partitions and other fttings can be reused. Even then a large quantity of remaining C&D waste is generally dumped in landfll sites of our country. Management of such huge quantity of waste puts enormous pressure on solid waste management system. The growing population of our cities and requirement of land for other uses has reduced the availability of land for waste disposal. It is mainly due to lack of awareness of the recycling techniques in our country that C&D wastes have not been effectively utilised. To effectively use C&D waste in road works, frst requirement would be to characterise the material in terms of its physical and engineering properties. 2 PREPARATION OF C&D WASTE SAMPLES FOR LABORATORY INVESTIGATIONS CRRI team visited two C&D waste dumping yards in Delhi and examined the type of C&D waste available at these locations. C&D waste dumped at these locations mainly consisted of demolished building rubble having particles of different sizes – Big sized chunks as well as fnely crushed material were found to have been mixed (Fig. 1). So it was decided to crush and sieve the sample to make it suitable for laboratory investigations. One truckload of C&D waste (about 6 tons) was collected from C&D waste dumping yard and the material was got crushed from an aggregate crushing plant in Delhi-Haryana border. The crushed C&D waste was sieved using mechanical sieving screens available at the aggregate crushing plant and separated into three fractions as given below: (a) C&D waste coarse aggregate particles – passing 20 mm sieve and retained on 6.3 mm sieve (Comprising about 42 per cent of the material crushed) A LABORATORY STUDY OF CONSTRUCTION AND DEMOLITION WASTE FOR USE IN ROAD WORKS U.K. GURU VITTAL*, FARHAT AZAD*, J. GANESH*, BINOD KUMAR* & SUDHIR MATHUR** ABSTRACT Use of Construction and Demolition (C&D) waste aggregates in road works is a widely accepted practice in many countries, like, Denmark, USA, UK, France, J apan, etc. But unfortunately its usage for road construction is negligible in India. Delhi city produces about 3000 tonnes of construction and demolition waste every day. Re-utilisation or recycling is an important strategy for management of such waste. Recycling of aggregate material from construction and demolition waste can help to reduce the demand-supply gap for aggregates, conserve depleting sources of good quality stone aggregates and decrease environmental degradation due to quarrying activities. To evolve more avenues for utilisation of C&D waste in the area of road construction, CSIR – Central Road Research Institute (CRRI), New Delhi carried out a ‘Feasibility Study on Use of Construction & Demolition (C&D) Waste in Road Works’. In this study, a detailed laboratory investigation was done on C&D samples collected from Delhi and it was found that C&D waste can be used in different forms in road works. Salient details of this study are presented in this Paper. * Scientist CSIR - Central Road Research Institute, New Delhi – 110 025. ** Head, Geotechnical Engg. Division} 58 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 VITTAL, AZAD, GANESH, KUMAR & MATHUR ON (b) C&D waste aggregate – J eera size particles (6.3 mm nominal size – About 30 per cent of crushed material) (c) Powdered C&D Waste (About 28 per cent of crushed material) These samples are shown in Figs. 2 to 4. 3 CHARACTERISATION OF C&D WASTES Examination of C&D aggregate sample after crushing (passing 20 mm and retained on 6.3 mm) showed that typically, it consisted of about 22 per cent (by weight) of cement mortar/tile pieces, 14 per cent (by weight) were brick pieces and the rest, i.e., 64 per cent consisted of stone aggregates (Delhi quartzite). The chemical composition of C&D wastes showed that silica (about 82 per cent) and alumina (about 6 per cent) are the main components and pH value was about 9.78. C&D aggregate samples were then subjected to various tests as per relevant IS codes of practice. 3.1 Physical Properties of C&D Aggregates The specific gravity of C&D waste aggregates was found to be 2.30, which is lesser than specifc gravity of conventional hard stone aggregates used in road construction. Generally specifc gravity of aggregates used in road works varies from 2.60 to 2.85. Lower value of specifc gravity of C&D waste aggregates indicates lower strength of C&D aggregates and it may probably be attributed to presence of brickbats, which are porous. When C&D waste aggregates are further crushed to either J eera size (less than 6.3 mm) or powder form, the specifc gravity of such a material increases to about 2.67. Fig. 1 C&D Waste at the Dumping Yard Fig. 2 C&D Waste Aggregates (20 mm to 6.3 mm size) Fig. 3 C&D Waste J eera Size Aggregates (6.3 mm nominal size) Fig. 4 Powdered C&D Waste Water absorption of C&D aggregates was found to be about 4.50 per cent, higher than 2 per cent limiting value as specifed by MORTH for many of the road works. Stone aggregates having water absorption upto 4 per cent have been used in base course construction. However such porous aggregates require higher quantity of bitumen when HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 59 A LABORATORY STUDY OF CONSTRUCTION AND DEMOLITION WASTE FOR USE IN ROAD WORKS used in bituminous layers. Further it was noted that water absorption of J eera size C&D particles as well as powdered C&D waste was quite high, at about 13 to 14.8 per cent. This may be because while crushing, all most all brick particles tend to get powdered when compared to stone aggregates. This higher quantity of brick particles in J eera size aggregates and C&D waste powder would contribute to increase in water absorption. Particle size distribution curves of different C&D waste fractions are shown in Fig. 5.The sieve analysis test showed that powdered C&D waste comprises of sand size particles with very low fnes content (low percentage of particles fner than 75 micron sieve). C u and C c value of powdered C&D waste were found to be 6.4 and 1.0, respectively. Thus this material can be classifed as ‘SW’ as per Unifed Soil Classifcation (IS Classifcation) and A-2-4 type as per HRB classifcation. 0 20 40 60 80 100 0.01 0.1 1 10 100 Paricle size in mm P e r c e n t f i n e r Fig. 5 Particle Size Distribution of C&D Waste 3.2 Engineering Properties of C&D Waste Aggregates The test results relating to engineering properties of C&D waste aggregates are presented in Table 1. From Table 1, it may be seen that unit weight of aggregates in loose and compacted state were found to be 1280 and 1650 kg/ m 3 . This value is considerably lower than unit weight of conventional hard stone aggregates. The aggregate crushing value of C&D aggregates was found to be 37 per cent. Ten per cent fnes value of C&D aggregates was determined separately for stone chips, brickbats, mortar pieces as well as representative C&D sample (comprising of all these fractions). MORTH has specifed a limit of 50 kN on ten per cent fnes value for aggregates to be used in GSB. C&D waste sample (combined sample) marginally fails to meet this criterion, with its ten per cent fnes value being 45 kN. As expected, ten per cent fnes value of brickbats and mortar pieces are very much on the lower side (Table 1). Table 1 Engineering Properties of C&D Waste Aggregates (20 mm to 6.3 mm) Property Test Result Permissible Limits as per MORTH Unit weight (C&D aggregates) - Loose state (kg/m3) Compacted state (kg/m3) 1280 1650 - Aggregate crushing value (%) 37 - Aggregate impact value (%) 33 30% (Max) Ten per cent fnes value (C&D Waste aggregate Representative sample) 45 kN 50kN(Max) Ten per cent fnes value (C&D Waste comprising of stone chips only) 98 kN - Ten per cent fnes value (C&D Waste comprising of mortar pieces only) 24 kN - Ten per cent fnes value (C&D Waste aggregate comprising of brick bats only) 25 kN - Soundness (%) 1.6 12% (Max) Aggregate Impact Value (AIV) of C&D aggregate sample was found to be 33 per cent. Generally for aggregates to be used in road construction, AIV should be less than 30 per cent. Hence, C&D waste aggregates can be considered as a marginal material. The higher value of AIV may be attributed to presence of brickbats and mortar pieces in C&D waste aggregates. To further study the AIV characteristics of these materials, C&D waste was segregated into stone chips, brickbats and mortar pieces and AIV test was carried out separately on these individual samples. AIV tests on soft aggregates can be carried out as per IS : 5640. The test procedure as per this code, stipulates that sample passing 12.5 mm sieve and retained on 10 mm sieve should be immersed in water for 3 days before subjecting it to impact test. Accordingly all the three samples were immersed in water for three days and tested. To further study the effect of soaking on AIV results, C&D aggregates soaked in water for 24 hours as well as oven dry aggregates were tested. The results of these AIV tests are given in Table 2. From these results it can be seen that brickbats and mortar pieces have 60 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 VITTAL, AZAD, GANESH, KUMAR & MATHUR ON a high aggregate impact value in dry state itself. Soaking in water further degrades such aggregates and marginally increases the AIV. The stone chips in C&D waste mainly comprise of Delhi Quartzite and hence they have aggregate impact value of about 26 per cent in dry state, which increases to 29.3 per cent after 3 days of soaking. Table 2 Aggregate Impact Value (AIV) of C&D Waste Constituents Constituents Testing Condition AIV (%) Permissible limits as per MORTH Stone Chips in C&D Aggregate 3 days of soaking 29.3 1 day soaking 28.6 Dry 26.0 30% (Max) Brick bats in C&D Aggregates 3 days of soaking 46.4 1 day soaking 45.0 Dry 42.2 Mortar pieces in C&D Aggregates 3 days of soaking 46.5 1 day soaking 45.2 Dry 51.3 Soundness test was conducted to determine the resistance to disintegration of aggregates by using saturated solution of sodium sulphate. This test furnishes information helpful for judging the soundness of aggregates subjected to weathering action. The test indicated a weight loss of 1.6 per cent after 5 cycles of alternate immersion in the Na2SO4 solution and drying. As per the IS: 383, weight loss after 5 cycles should not exceed 12 per cent. Thus, it can be inferred that the C&D waste aggregates satisfy the soundness test requirement. 3.3 Engineering Properties of Powdered C&D Waste The density of compacted layer is one of the important factors, which controls strength properties. From the results tabulated in Table 3, it may be noted that standard proctor compaction test conducted on C&D waste powder yielded MDD value as 1.75 gm/cc and OMC as 12.5 per cent. Modifed proctor compaction test conducted on the same material showed MDD to be 1.93 gm/cc and OMC to be 10.5 per cent. The density and water content relationship curves obtained were found to be relatively fat. The MDD values obtained are comparable to MDD values of soil particles of similar gradation. Table 3 Engineering Properties of Powdered C&D Waste Property Value Modifed Proctor compaction Test Maximum Dry Density (MDD) (gm/cc) Optimum Moisture Content (OMC) (%) 1.93 10.5 Standard Proctor compaction Test Maximum Dry Density (MDD) (gm/cc) Optimum Moisture Content (OMC) (%) 1.75 12.5 California Bearing Ratio (Soaked), (%) 74 Direct Shear Test - Angle of internal friction (Ø) Cohesion (c) 50 0 6 kN/m 2 Liquid limit (%) 31.0 Plasticity index Non Plastic Permeability (cm/sec) 1.86 X 10 -4 Powdered C&D waste was found to be non-plastic in nature and hence could not be rolled into threads to determine its plastic limit. The coeffcient of permeability of powdered C&D waste was found to be 1.86x10 -4 cm/sec (Table 3). This value of permeability indicates that it is a free draining material and has potential for its utilisation in sub-base layer. Powdered C&D waste was found to be having a high angle of internal friction equal to 500. California Bearing Ratio (CBR) of powdered C&D waste compacted to MDD and tested after four days of soaking, was found to be as high as 74 per cent. 4 FEASIBILITY OF USING C&D WASTES IN ROAD WORKS For road works in our country, machinery requirements and methodology for road construction using conventional materials has been given in IRC/MORTH Specifcations. Adoption of C&D waste in road works would be easier in case a similar methodology is adopted. 4.1 C&D Waste for Embankment Construction C&D waste has potential for use as embankment fill material as it meets MORTH criteria for density and plasticity of fll material. However, the maximum size of the material in the fll shall ordinarily not exceed 75 mm when placed in the embankment. C&D waste in unprocessed form normally contain particles bigger than this size. So it would be necessary to crush it so that maximum size of HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 61 A LABORATORY STUDY OF CONSTRUCTION AND DEMOLITION WASTE FOR USE IN ROAD WORKS the particle is lesser than the specifed limit. Since C&D waste is a non-plastic material, embankments constructed using C&D waste would be prone for erosion. Hence, it is suggested that side slopes of embankments constructed using C&D waste should be protected against erosion by providing a good earth cover, in a manner similar to fy ash embankments. 4.2 C&D Waste for Sub-base Construction Powdered C&D waste is a free draining material and possesses good CBR value. However, the gradation of powdered C&D waste does not satisfy the gradation for GSB material specifed by MORTH. By adopting mechanical stabilisation techniques and mixing C&D waste aggregates and powdered waste, it would be possible to obtain desired gradation. In case of material under investigation, it was found that, by mixing in a ratio of 50:31:19 (C&D Powder: J eera size aggregates: 20 mm to 6.3 mm C&D Aggregate) the resulting material would have a gradation conforming to GSB Grading III as per MORTH Specifcations. However, since ten per cent fnes value of the C&D waste is not meeting the specifcation requirements, its usage may be restricted to lower half of the sub-base or for low traffc volume roads on trial basis. 4.3 C&D Waste for Stabilised Base Course Construction The base course of fexible pavements normally consists of either WBM or WMM. C&D waste being a marginal material, stabilisation technique using cement can be adopted for its usage in base course. To study the feasibility of C&D Waste usage in base course, mechanically stabilised C&D waste mix (in the ratio of 50:31:19 – C&D Powder:J eera size aggregates: 20 mm to 6.3 mm C&D Aggregate) was stabilised using cement and Unconfned Compressive Strength (UCS) tests were conducted on the stabilised mix. Results of the tests conducted on cement stabilised C&D waste are given in Table 4. Table 4 UCS of Cement Stabilised C&D Waste Mix Unconfned Compressive Strength (UCS) Test Curing Period UCS of C&D Waste Mix + 3% cement (kg/cm 2 ) UCS of C&D Waste Mix + 5% cement (kg/cm 2 ) UCS of C&D Waste Mix + 7% cement (kg/cm 2 ) 3 days curing 11.35 11.53 13.19 14 days curing 14.00 26.62 31.80 28 days curing 22.21 32.06 44.34 In case of rural roads as per IRC specifcations, unconfned compressive strength of cement stabilised material at 14 days should not be less than 17.5 kg/cm 2 . C&D waste mix with 5 per cent cement (modifed Proctor test) meets this criterion. 4.4 Feasibility of Using C&D Wastes in Bituminous Layers Aggregates, like, crushed stone and stone dust constitute about 94 per cent by weight of bituminous mix. The performance of the bituminous mix is governed by aggregate quality, which in turn is a function of mineral composition, surface texture and chemistry, amount and type of deleterious matter, size and shape of particles, durability characteristics, etc. Characterisation of C&D aggregates showed that water absorption and AIV of these aggregates are higher than the specifed limits for its use in bituminous layers. C&D aggregates contain brickbats, mortar pieces as well as tile particles. Such particles bring down aggregate impact value and also increase the water absorption. In case C&D wastes are used for bituminous wearing course, failure may occur due to stripping because of high water absorption. Hence, use of C&D aggregates in bituminous wearing/binder courses is not advisable. So it was decided to determine the feasibility of using C&D waste aggregates in bituminous macadam (BM). However, laboratory mixes of BM prepared using C&D waste showed that bitumen requirement was on much higher side and also coating was not proper, implying that C&D waste aggregate usage in bituminous layers is not feasible. 4.5 Feasibility of Using C&D Wastes in Cement Concrete Pavement Concrete basically is a mix of two components – aggregates and binder paste. The paste comprises of mainly cement and water. Tests were conducted to investigate the potential of using C&D waste in concrete as partial replacement of coarse aggregate. C&D waste was used in pavement quality concrete (PQC) as well as in Dry Lean Concrete (DLC) mixes in the form of coarse aggregates and its infuence on strength properties was investigated. To investigate the effect of C&D waste on the properties of hardened pavement quality concrete, M 40 grade of concrete designed using Portland Pozzolana Cement (IS 1489 – Part 1) and locally available aggregates (Delhi Quartzite) was adopted. In case of PQC, cement content was kept as 420 kg/m 3 and water/cement ratio was kept as 0.44 in all the mixes tested. For evaluating compressive and tensile strength, concrete 62 HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 VITTAL, AZAD, GANESH, KUMAR & MATHUR ON mixtures containing 20, 40 and 60 per cent of crushed stone coarse aggregate (Delhi Quartzite) replaced by C&D waste aggregates were prepared. Compressive strength results of PQC mixtures at 7 and 28 days are given in Table 5. It can be seen that, admixing C&D waste aggregates in PQC leads to decrease in 28 days compressive strength by about 12 per cent, when 60 per cent of conventional hard stone aggregates has been replaced by C&D waste aggregates. Table 5 Compressive Strength of PQC Prepared With C&D Aggregates Mix Designation C&D Aggregate Percentage Compressive Strength (MPa) 7 Days 28 Days Conventional concrete Nil 28.2 39.5 C&D 20 20 26.3 36.2 C&D 40 40 25.9 35.6 C&D 60 60 24.6 34.7 Flexural strength of PQC mixes was obtained by testing concrete beam specimens of size 100 mm x 100 mm x 500 mm. Beam specimens were tested under third point loading system in Universal Testing Machine. Results of these tests are given in Table 6. These results also show marginal decrease in fexural strength. Table 6 Flexural Strength of PQC Prepared With C&D Aggregates Mix Designation C&D Aggregate Percentage Compressive strength (MPa) 7 Days 28 Days Conventional concrete Nil 3.5 4.4 C&D 20 20 3.3 4.3 C&D 40 40 3.0 4.0 C&D 60 60 2.9 3.8 Table 7 Compressive Strength of DLC Prepared With C&D Waste Aggregates Mix Designation C&D Aggregate Percentage Compressive Strength (MPa) 7 Days 28 Days Conventional concrete Nil 13.5 18.2 C&D 10 10 13.0 17.3 C&D 30 30 11.1 16.3 C&D 50 50 9.9 13.1 In case of dry lean concrete, DLC mix of 10 MPa strength at 7 days was adopted. Cement content in DLC was kept at 150 kg/m 3 of concrete. Compressive strength results of DLC mixes with different C&D content are given in Table 7. It is observed that, decrease in compressive strength of DLC mix is comparatively more than pavement quality concrete. 5. CONCLUSIONS C&D waste is a marginal material having some of its strength properties slightly lesser than the specifed limits as per IRC/MORTH. However, at the same time, it is non-plastic, permeable and its strength can be improved by stabilisation. Hence, C&D waste can be adopted for road construction in different forms. The major conclusions drawn from the laboratory investigations are given below: (a) Crushed C&D waste can be utilised as a fll material for construction of embankment. The side slopes of such embankments should be protected against surface erosion. C&D Waste after crushing can be used for subgrade construction. (b) Mechanically stabilised C&D waste mixture can be used for sub-base layer. However, C&D waste has a marginally lower ten per cent fnes value and hence it may be used in lower half of sub-base course or for low traffc volume roads on a trial basis. (c) Mechanically stabilised C&D waste mix (mixture of C&D waste aggregates and C&D waste powder) admixed with about 5 per cent of cement can be used for base course construction. HIGHWAY RESEARCH JOURNAL, JANUARY – JUNE 2012 63 A LABORATORY STUDY OF CONSTRUCTION AND DEMOLITION WASTE FOR USE IN ROAD WORKS (d) Usage of C&D waste for bituminous wearing courses is not advocated. The tests conducted for using C&D waste aggregates in bituminous macadam showed higher requirement of bitumen and coating of the C&D aggregates with bitumen was not satisfactory. (e) C&D aggregates can be used for partial replacement of conventional hard stone aggregates used for rigid pavement construction. Laboratory tests showed a decrease of about 12 to 28 per cent of compressive strength of concrete mix. Hence, while designing concrete pavement using C&D waste aggregates, proper mix design using the available C&D waste aggregates and conventional hard stone aggregates is to be carried out and replacement of conventional aggregates by C&D waste aggregates can be restricted to about 35 per cent of conventional aggregates. (f) C&D Waste is heterogeneous in nature. It comprises of materials, like, stone aggregates, tile pieces, brick bats, cement concrete, cement mortar, etc. While preparing the samples for laboratory feasibility study, the collected C&D waste was crushed and sieved in aggregate crushing plants. This process ensures that instead of chunks of varying size, C&D waste comprised of material of uniform gradation and such processed material was readily usable. However still, the properties of C&D waste material depends upon its relative percentage composition of stone aggregates, brick bats, mortar pieces, etc and generalisations for the entire C&D waste material based on test results reported in this Paper cannot be made. Each deposit of the C&D waste before usage needs to be characterised. 6 SCOPE FOR FURTHER STUDIES From the visual observations carried out at C&D waste dumping yards, it was noted that ensuring uniformity in the properties of C&D waste aggregates is very diffcult. However, even in natural aggregate deposits/ stone quarries also; there would not be uniformity in the properties of the material available in the entire quarry. The properties of the material in the same quarry vary and material from two different quarries will be having entirely different properties. Such variations are to be taken care by proper sampling and testing of the material at frequencies mentioned in MORTH/ MORD Specifcations. In case of C&D wastes also regular testing of the material is to be carried out when it is used for construction. Since C&D waste is a new material, to begin with, testing frequency on C&D waste material can be kept same as conventional material, but additional studies are required to properly determine variability in properties of such materials. To further substantiate the laboratory feasibility study, there is also need to take up construction of test road sections using C&D waste and observing its performance under actual feld conditions. ACKNOWLEDGEMENT The authors would like to acknowledge Shri P. K. Khandelwal, Superintending Engineer, Municipal Corporation of Delhi and his team for sponsoring this study. Our thanks are also due to IL & FS Ecosmart Ltd for providing technical support and valuable inputs during this work. Our colleague Dr. N. K. S. Pundhir carried out experiments related to bituminous mixes. Our grateful thanks are due to him. This Paper has been published with the kind permission of Dr. S. Gangopadhyay, Director, CSIR - CRRI. REFERENCES 1. ‘Utilisation of Waste from Construction Industry’, Technical Report Published by TI FAC, Department of Science & Technology, Government of India, New Delhi. 2. ‘Landfll Concern – Overfowing with Problems’ – Article in Hindustan Times News Paper, New Delhi (5.5.2007). 3. Dr.A.Ramakrishna, ‘Indian Construction Industry – Challenges and Opportunities’, Fourteenth ICI Lecture at Nagpur, Indian Concrete J ournal, Bulletin 62, J an-Mar 1998. 4. Chandra Dinesh, Gupta R.L, J ain.S.K and Bhise.N.N, ‘Solid Waste Utilisation – An Eco-Friendly Solution’, Indian Journal of Environmental Protection, Vol – 17, No 3, Mar 1997. 5. Sherwood.P.T, ’Alternative Materials in Road Construction’, Thomas Telford Publications, London, U.K, 1995. 6. ‘Recycling of Demolished Concrete and Masonry’, Report of Technical Committee 37-DRC, RILEM, Edited by Hansen.T.C., 1992. 7. BIS Codes 1498, 2720, 5640 and 2386. 8. MORTH, ‘Specifcations for Road and Bridge Works’, Published by Indian Roads Congress, New Delhi. 9. http://www.epa.gov/epaoswer/hazwaste/sqg/c&d-rpt.pdf.