Application of Traffic Conflict Techniques as Surrogate Safety Measures: A Sustainable Solution for Developing Countries

  • S. M. Sohel MahmudEmail author
  • Luis Ferreira
  • Shamsul Hoque
  • Ahmad Tavassoli
Conference paper
Part of the Sustainable Civil Infrastructures book series (SUCI)


Social, economic and infrastructure losses due to road traffic accidents and their consequences are very significant all over the world, particularly in developing countries. The evaluation of causative factors of accidents and the selection of remedial measures continues to be based mainly on traditional approaches. Whereas, accident statistics are frequently questioned due to large underreporting of accidents, injuries and property damages, coupled with incomplete and inconsistent recording of information on reported accidents. Poor timelines, ethical issues, biasness and human error are also critical issues. This paper present a comprehensive assessment of the data quality of reported accident databases, in terms of the degree and diversity of the reporting and recording inconsistency, using a case study from Bangladesh.

For a more rigorous and sustainable form of safety analysis there is a need for robust methods that may yield targeted safety measures without the need to use accident data. Application of traffic conflict techniques for the diagnosis of accidents has gained research interest as a proactive surrogate approach. However, this has been developed and tested primarily based on lane based homogeneous traffic conditions prevailing in developed countries. Development of advanced image processing systems, as well as video analysis techniques for automatic discrimination of conflicts, has open new prospects. Traffic safety micro-simulation modeling using surrogate indicators is also a promising advancement in this context. This paper provides a framework for safety evaluation beyond the traditional approaches with the integration of recent advancement in surrogate safety evaluation for non-lane based traffic environments. Finally, future research directions, designed to achieve sustainable road safety objectives in developing counties, are outlined.


  1. Appleton, I.: Progress with the introduction of road safety audit in Australia and New Zealand. Paper presented at the 18th ARRB Transport Research Conference & Transit New Zealand Land Transport Symposium (1996)Google Scholar
  2. Archer, J.: Indicators for traffic safety assessment and prediction and their application in micro-simulation modelling: A study of urban and suburban intersections. Ph.D. thesis, Royal Institute of Technology, Stockholm, Sweden (2005)Google Scholar
  3. ARI. Road Accident in Bangladesh: Problem of Data Analysis. Retrieved from Accident Research Institute (ARI) BUET, Bangladesh (Internation report, unpublished) (2010)Google Scholar
  4. ARI. Road Safety Facts in Bangladesh (Facts). Retrieved from Accident Research Institute (ARI), BUET, Bangladesh (2015).
  5. ATC. National Road Safety Strategy 2011 – 2020. Retrieved from Australian Transport Council (ATC) (2011)Google Scholar
  6. Austin, R.D., Carson, J.L.: An alternative accident prediction model for highway-rail interfaces. Accid. Anal. Prev. 34(1), 31–42 (2002)Google Scholar
  7. Austroads. Road safety audits. Publication AP-30/94. Austroads, Sydney (1994)Google Scholar
  8. Brehmer, B.: Psychological aspects of traffic safety. Eur. J. Oper. Res. 75(3), 540–552 (1994)Google Scholar
  9. Chen, H., Meuleners, L.: A literature review of road safety strategies and the safe system approach (2011)Google Scholar
  10. Chin, H.C., Quddus, M.A.: Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. Accid. Anal. Prev. 35(2), 253–259 (2003)Google Scholar
  11. Council, T.S.: Before-and-After Study Technical Brief. Institute of Transportation Engineers (2009)Google Scholar
  12. Datta, T., et al.: Using GIS to analyze statewide traffic crash data in Michigan. Paper presented at the Proceedings of The Aet European Transport Conference, Held 10–12 September, 2001, Homerton College, Cambridge, Uk-CD-Rom (2001)Google Scholar
  13. Debnath, A.K., et al.: Proactive safety assessment in roadwork zones: a synthesis of surrogate measures of safety. Paper presented at the Proceedings of the 2014 Occupational Safety in Transport Conference (2014)Google Scholar
  14. Eenink, R., et al.: Accident prediction models and road safety impact assessment: recommendations for using these tools. Institute for Road Safety Research, Leidschendam (2008)Google Scholar
  15. Elvik, R.: The predictive validity of empirical Bayes estimates of road safety. Accid. Anal. Prev. 40(6), 1964–1969 (2008)Google Scholar
  16. Elvik, R., et al.: The handbook of road safety measures: Emerald Group Publishing (2009)Google Scholar
  17. Griffin, L.I., Flowers, R.J.: A discussion of six procedures for evaluating highway safety projects: The Division (1997)Google Scholar
  18. Hall, D.B.: Zero‐inflated Poisson and binomial regression with random effects: a case study. Biometrics 56(4), 1030–1039 (2000)Google Scholar
  19. Hankey, J., et al.: Identification and evaluation of driver errors: Task C report, driver error taxonomy development. Project no. Dtfh-61-97-c-00051. Virginia Tech Transportation Institute, Blacksburg, VA (1999)Google Scholar
  20. Harwood, D., et al.: Safety effectiveness of intersection left-and right-turn lanes. Transp. Res. Rec. J. Transp. Res. Board 1840, 131–139 (2003)Google Scholar
  21. Hauer, E.: Observational Before/After Studies in Road Safety. Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety (1997)Google Scholar
  22. Hoque, M.M.: Road Safety Audit in Developing Countries. Retrieved from transportation Research Group, Department of Civil and Environment Engineering, University of Southampton, UK (1997)Google Scholar
  23. Hoque, M.M., Mahmud, S.S.: Road Safety Engineering Challenges in Bangladesh. Accident Research Institute. Bangladesh university of Engineering and Technology (2009)Google Scholar
  24. Hoque, M.M., et al.: Road Safety Hazards At Jamuna Multipurpose Bridge (JMB) Site: Implications for Bridge Management. Paper presented at the 23rd ARRB Conference–Research Partnering with Practitioners, Adelaide, Australia (2008)Google Scholar
  25. Hoque, M.M., et al.: Observational studies of hazardous road locations on national highways in Bangladesh. Paper presented at the ARRB Conference, 22nd, 2006, Canberra, ACT, Australia (2006)Google Scholar
  26. Hsiao, C.: Analysis of Panel Data, Econometric Society Monograph No. 11. Cambridge University Press, Cambridge (1986)Google Scholar
  27. Hutchinson, T.P.: Statistical modelling of injury severity, with special reference to driver and front seat passenger in single-vehicle crashes. Accid. Anal. Prev. 18(2), 157–167 (1986)Google Scholar
  28. iRAP. Vaccines for Roads, 2nd edn. Retrieved from International Road Assessment Program (iRAP), Hampshire, UK (2012)Google Scholar
  29. Ismail, K., et al.: Automated analysis of pedestrian-vehicle conflicts using video data. Transp. Res. Rec. J. Transp. Res. Board 2140, 44–54 (2009)Google Scholar
  30. Job, R.S.: Advantages and disadvantages of reactive (black spot) and proactive (road rating) approaches to road safety engineering treatments: When should each be used? Paper presented at the Australasian Road Safety Research Policing Education Conference, 2012, Wellington, New Zealand (2012)Google Scholar
  31. Jovanis, P.P., Chang, H.-L.: Modeling the relationship of accidents to miles traveled. Transp. Res. Rec. 1068, 42–51 (1986)Google Scholar
  32. Khan, S., Maini, P.: Modeling heterogeneous traffic flow. Transp. Res. Rec. J. Transp. Res. Board 1678, 234–241 (1999)Google Scholar
  33. Kumara, S., Chin, H.C.: Modeling accident occurrence at signalized tee intersections with special emphasis on excess zeros. Traffic Inj. Prev. 4(1), 53–57 (2003)Google Scholar
  34. LGED. Rural Road Safety Manual for LGED. Retrieved from Local Government Engineering Department (LGED), Bangladesh, June 2015Google Scholar
  35. Lord, D., et al.: Further notes on the application of zero-inflated models in highway safety. Accid. Anal. Prev. 39(1), 53–57 (2007)Google Scholar
  36. Lord, D., et al.: Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accid. Anal. Prev. 37(1), 35–46 (2005)Google Scholar
  37. Lund-University. The Swedish Traffic Conflict Technique. Retrieved from Sweden (2014)Google Scholar
  38. Mahmud, et al.: Road Safety Problems in Bangladesh: Achievable Target and Tangible Sustainable Actions. Jurnal Teknologi, 70(4) (2014)Google Scholar
  39. Mahmud, et al.: Traditional Approaches to Traffic Safety Evaluation (TSE): Application Challenges and Future Directions. Bridging the East and West, 242 (2016)Google Scholar
  40. Mahmud, et al.: Road Safety Problems in Bangladesh: Some Major Initiatives, Constraints and Requirements. Transport and Communications Bulletin for Asia and the Pacific, 61 (2009)Google Scholar
  41. McCullagh, P., Nelder, J.A.: Generalized Linear Models, vol. 37. CRC Press (1989)Google Scholar
  42. Meuleners, L., Fraser, M.: Review of the Wa state black spot program: a literature review of Australian and international black spot programs. Centre For Population Health Research, School of Public Health, Curtin University of Technology, Perth. [Links] (2008)Google Scholar
  43. Miaou, S.-P.: The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accid. Anal. Prev. 26(4), 471–482 (1994)Google Scholar
  44. Miaou, S.-P., Lord, D.: Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transp. Res. Rec. J. Transp. Res. Board 1840, 31–40 (2003)Google Scholar
  45. Michon, J.A.: Explanatory pitfalls and rule-based driver models. Accid. Anal. Prev. 21(4), 341–353 (1989)Google Scholar
  46. Mikulik, J., Hollo, P.: Road Accident Investigation Guidelines for Road Engineers. World Road Association PIRAC Technical Committee (2007)Google Scholar
  47. Mooren, L., et al.: Safe System–International Comparisons of this Approach. Paper presented at the A Safe System-making it happen: Proceedings of the Australasian College of road Safety Conference, Melbourne (2011a)Google Scholar
  48. Mooren, L., et al.: Safe system–comparisons of this approach in Australia. Paper presented at the Australasian College of Road Safety National Conference, Melbourne (2011b)Google Scholar
  49. MoT. Safe Journeys: New Zealand’s Road Safety Strategy 2010-2020. Retrieved from Wellington: Minsitry of Transport (MoT), New Zealand (2010)Google Scholar
  50. Muhlrad, N. Traffic conflict techniques and other forms of behavioural analysis: Application to safety diagnoses: na (1993)Google Scholar
  51. Näätänen, R., Summala, H.: Road-user behaviour and traffic accidents. Publication of: North-Holland Publishing Company (1976)Google Scholar
  52. Namjune, P., Ihn, L.Y.: Comparison of Empirical Bayes Method and Before-After Study Method. Paper presented at the Proceedings of the Eastern Asia Society for Transportation Studies (2013)Google Scholar
  53. OECD. Road safety principles and models: review of descriptive, predictive, risk and accident consequence models (1997)Google Scholar
  54. Ogden, K.W.: Safer roads: a guide to road safety engineering. Institute of Transport Studies, Department of Civil Engineering, Monash University, Melbourne, Australia (1996)Google Scholar
  55. Oh, J., et al.: Accident prediction model for railway-highway interfaces. Accid. Anal. Prev. 38(2), 346–356 (2006)Google Scholar
  56. Persaud, B., Lyon, C.: Empirical Bayes before–after safety studies: lessons learned from two decades of experience and future directions. Accid. Anal. Prev. 39(3), 546–555 (2007)Google Scholar
  57. Pham, M.H., Mouzon, O., Chung, E., El Faouzi, N.E.: Sensitivity of road safety indicators in normal and crash cases. Paper presented at the 10th International Conference on Application of Advanced Technologies in Transportation, Athens, Greece (2008)Google Scholar
  58. Rahman, A.: Bangladesh health and injury survey: report on children: Directorate General of Health Services Ministry of Health an (2005)Google Scholar
  59. Roper, P., Turner, B.: Why do we need to take a risk assessment based approach in road safety? Paper presented at the ARRB Conference, 23rd, 2008, Adelaide, South Australia, Australia (2008)Google Scholar
  60. Rumar, K.: The role of perceptual and cognitive filters in observed behavior. In: Human Behavior and Traffic Safety, pp. 151–170. Springer (1985)Google Scholar
  61. Sabey, B.: Safety audit procedures and practice. Paper presented at the Ptrc Traffex 93 Conference Proceedings. Seminar On Traffic Management And Road Safety, Tuesday 20 April 1993, National Exhibition Centre, Birmingham (1993)Google Scholar
  62. Saccomanno, F.F., Buyco, C.: Generalized loglinear models of truck accident rates (1988)Google Scholar
  63. Saunier, N., et al.: Large-scale automated analysis of vehicle interactions and collisions. Transp. Res. Rec. J. Transp. Res. Board 2147, 42–50 (2010)Google Scholar
  64. Sayed, T., et al.: Safety evaluation of stop sign in-fill program. Transp. Res. Rec. J. Transp. Res. Board 1953, 201–210 (2006)Google Scholar
  65. Sayed, T., et al.: Automated safety diagnosis of vehicle–bicycle interactions using computer vision analysis. Saf. Sci. 59, 163–172 (2013)Google Scholar
  66. Shinar, D.: Psychology on the road. The human factor in traffic safety (1978)Google Scholar
  67. Silcock, R.: Guidelines for estimating the cost of road crashes in developing countries. London, Department for International Development Project, 7780 (2003)Google Scholar
  68. St-Aubin, P.G.: Traffic Safety Analysis for Urban Highway Ramps and Lane-Change Bans Using Accident Data and Video-Based Surrogate Safety Measures. Paper presented at the Masters Abstracts International (2012)Google Scholar
  69. Stanton, N.A., Salmon, P.M.: Human error taxonomies applied to driving: A generic driver error taxonomy and its implications for intelligent transport systems. Saf. Sci. 47(2), 227–237 (2009)Google Scholar
  70. Tingvall, C.: The Swedish’Vision Zero’and how Parliamentary approval was obtained. Paper presented at the Road Safety Research, Policing, Education Conference, 1998, Wellington, New Zealand, vol. 1 (1998)Google Scholar
  71. TRL. Bangladesh Road Crash Costing Discussion Document Retrieved from Roads and Highways Department (RHD), Bangladesh (2003)Google Scholar
  72. Van Der Molen, H., Boetticher, A.: Risk models for traffic participants: a concerted effort for theoretical operationalizations. road users and traffic safety (1987)Google Scholar
  73. Vlahogianni, E.I.: Modeling duration of overtaking in two lane highways. Transp. Res. Part F Traffic Psychol. Behav. 20, 135–146 (2013)Google Scholar
  74. Walz, F., et al.: The car-pedestrian collision, injury reduction, accident reconstruction, mathematical and experimental simulation, head injuries in two wheeler collisions. Interdisciplinary Working Group for Accident Mechanics, University of Zurich and Swiss Federal Institute of Technology (1986)Google Scholar
  75. WHO. Global status report on road safety: time for action: World Health Organization (2009)Google Scholar
  76. WHO. Data systems: a road safety manual for decision-makers and practitioners (2010a)Google Scholar
  77. WHO. Global Plan for the Decade of Action for Road Safety 2011-2020 Retrieved from World Health Organization (WHO), Geneva (2010b)Google Scholar
  78. WHO. WHO global status report on road safety 2013: supporting a decade of action: World Health Organization (2013)Google Scholar
  79. WHO. Global status report on road safety 2015: World Health Organization (2015)Google Scholar
  80. Williams, A.F., et al.: Factors that drivers say motivate safe driving practices. J. Saf. Res. 26(2), 119–124 (1995)Google Scholar
  81. Yang, H.: Simulation-based evaluation of traffic safety performance using surrogate safety measures. Rutgers University-Graduate School-New Brunswick (2012)Google Scholar
  82. Young, W., et al.: Simulation of safety: A review of the state of the art in road safety simulation modelling. Accid. Anal. Prev. 66, 89–103 (2014)Google Scholar
  83. Zaki, M., et al.: Application of computer vision to diagnosis of pedestrian safety issues. Transp. Res. Rec. J. Transp. Res. Board 2393, 75–84 (2013)Google Scholar
  84. Zheng, L., et al.: Traffic conflict techniques for road safety analysis: open questions and some insights. Can. J. Civil Eng. 41(7), 633–641 (2014). doi: 10.1139/cjce-2013-0558

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • S. M. Sohel Mahmud
    • 1
    Email author
  • Luis Ferreira
    • 1
  • Shamsul Hoque
    • 2
  • Ahmad Tavassoli
    • 1
  1. 1.School of Civil EngineeringThe University of Queensland (UQ)BrisbaneAustralia
  2. 2.Department Civil EngineeringBangladesh University of Engineering and Technology (BUET)DhakaBangladesh

Personalised recommendations