Abstract

This study aims at modeling the influence of highway geometric and traffic characteristics on road crash frequency under heterogenous traffic conditions using the Negative Binomial regression technique. Over a span of three years, data from 618 road accidents on the Dhaka–Aricha highway (N5) in Bangladesh were analyzed to develop the model.. The model variables include Annual Average Daily Traffic (AADT), % of heavy vehicle and non-motorised vehicles (NMV), pavement carriageway width (m), shoulder width (m) and presence of median. The model outcome showed that heavy traffic volume, NMV, reduced pavement carriageway, narrow shoulder width and absence of median increase the likelihood for overall crash involvement. But for pedestrian crash analysis increased traffic volume ,% of heavy vehicle, narrow pavement, narrow shoulder width and absence of median increase the likelihood ; and % of NMV was not found to be significant in this case. Again, in case of  NMT crash, more crashes were resulted with higher percent of heavy vehicles and reduced shoulder and narrow pavement width having no median. The developed model was further analyzed using an elasticity approach to determine the key factors influencing accident occurrence and to assess their relative importance. The research results may provide significant guidance to highway improvement and related investment policy in developing countries conditions similar to Bangladesh.

Keywords- Negative binomial, Accident frequency, Elasticity, Developing countries and Bangladesh

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Mahmud, S. M. S., Sarker, A., Hoque, M. M., (2011). Identifications and investigations of hazardous road locations on Dhaka-Aricha highway, 4th Annual paper meet and 1st Civil Engineering Congress, Dhaka, Bangladesh, December 22-24
2. Singh, R. K. & Suman, S.K. (2012). Accident analysis and prediction of model on national International Journal of Advanced Technology in Civil Engineering,Vol. 1.
3. Milton, J., Mannering, F., 1996. The Relationship Between Highway Geometrics, Traffic Related Elements and Motor Vehicle. Washington State Dept. of Transportation.
4. Abdel-Aty, M.A., Radwan, A.E. (2000) Modelling traffic accident occurrence and involvement. Accident Analysis and Prevention, Vol. 32, pp. 633-642.
5. Abdel-Aty, M., Abdalla, M.F. (2004) Linking roadway geometrics and real-time traffic characteristics to model daytime freeway crashes: generalized estimating equations for correlated data. Transportation Research Record, Vol. 1897, pp. 106-115.
6. EI-Basyouny, K., Sayed, T. (2006) Comparison of Two Negative Binomial Regression Techniques in Developing Accident Prediction Models. Transportation Research Record, 1950, pp. 9-16.
7. Ivan, J., O’Mara, P., 1997. Prediction of Traffic Accident Rates Using Poisson Regression. Presented at the 76th Annual Meeting of the Transportation Research Board.
8. Karlaftis, M. G., Golias, I. 2002. Effects of road geometry and traffic volumes on rural roadway  accident rates. Accident Analysis and Prevention, Vol. 34, 357–365.
9. Lord, D., Washington, S.P., Ivan, J.N. (2005) Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accident Analysis and Prevention, Vol. 37, pp. 35-46.
10. Lord, D. (2006). Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accident Analysis and Prevention, 38(4), 751–766.
11. Lord, D., Washington, S. P., & Ivan, J. N. (2007). Further notes on the application of Zero- inflated models in highway safety. Accident Analysis and Prevention, 39, 53–
12. Montella, A. (2008) Crash prediction models for rural motorways. Transportation Research Record, Vol. 2083, pp. 180-189.
13. Wong, S. C., Sze , N.N., Li, Y.C. (2007). Contributory factors to traffic crashes at signalized intersections in Hong Kong. Accident Analysis and Prevention, 39, 1107- 1113.
14. Jovanis, P., Chang, H., 1986. Modeling the relationship of accidents to miles traveled. Transportation Research Record 1068.
15. Saccomanno, F. F.; Buyco, C. (1988) Generalized loglinear models of truck accident rates, paper presented at Transportation Research Board 67th Annual Meeting, Washington, DC.
16. Joshua, S., Garber, N., 1990. Estimating truck accident rate and involvement using linear and Poisson regression models. Transportation Planning and Technology 15, 41–58.
17. Mohamedshah, Y.M., Paniati, J.F., Hobeika, A.G., 1993. Truck accident models for interstate and two-lane rural roads. Transportation Research Record 1407, 35–41.
18. Miaou, S., Lum, H., 1993. Modeling vehicle, accidents and highway geometric design relationships. Accident Analysis and Prevention 25 (6), 689–709.
19. Miaou, S., 1994. The relationship between truck accidents and geometric design of road section: Poisson versus Negative Binomial regression. Accident Analysis and Prevention 26(4).
20. Poch, M., Mannering, F. (1996). Negative Binomial Analysis of Intersection-Accident Journal of Transportation Engineering, Vol. 122.
21. Hadi, M.A., Aruldhas, J., Chow, L., Wattleworth, J.A. (1995) Estimating safety effects of cross-section design for various highway types using negative binomial regression. Transportation Research Record,1500, pp. 169-177.
22. Shankar, V., Mannering, F., Barfield, W. 1995. Effect of roadway geometric and environment factors on rural freeway accident frequencies. Accident Analysis and Prevention 27(30).
23. McCarthy, P.S., 1999. Public policy and highway safety: a city-wide perspective. Regional Science and Urban Economics 29 (3), 231–244.
24. Carson, J., Mannering, F., 2001. The effect of ice warning signs on ice-accident frequency and Accident Analysis and Prevention 33 (1), 99–109.
25. Shankar, V.N., Milton, J., Mannering, F., 1997. Modeling accident frequencies as zero-altered probabilty processes: an empirical inquiry. Accident Analysis and Prevention 29 (6).
26. Lee, J., Mannering, F., 2002. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis and Prevention 34 (2).
27. Lee, A.H., Stevenson, M.R., Wang, K., Yau, K., 2002. Modeling young driver motor vehicle crashes: data with extra zero. Accident Analysis and Prevention 34 (4), 515–521.
28. Chang L. (2005) Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network. Safety Science, Vol. 43, Issue 8, pp. 541-557.
29. Chengye, P., Ranjitkar, P. (2013)Modelling motorway accidents using negative binomial regression, presented at Proceedings of the Eastern Asia Society for Transportation Studies, Vol.9, 2013.
30. Ayati, E., Abbasi, E. (2011) Investigation on the role of traffic volume in accidents on urban Journal of Safety Research, Vol. 42, pp. 209-214.
31. Lord, D., & Park, Y. J. (2008). Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates. Accident Analysis and Prevention, 40(4), 1441–1457.
32. Pemmanaboina, R. (2005) Assessing occurrence on urban freeways using static and dynamic factors by applying a system of interrelated equations. M.E. thesis, University of Central Florida.
33. Savolainen, P. T., & Tarko, A. P. (2005). Safety impacts at intersections on curved segments. Transportation Research Record, 1908, 130–
34. Cameron, A. C.; Trivedi, P. K. Econometric models based on count data: Comparison and applications of some estimators and tests. Journal of Applied Econometrics 1:29-53; 1986.
35. Greene, W. Econometric analysis. New York, NY: Macmillan Publishing; 1993.
36. Fridstrom, L., Ifver, J., Ingebrigtsen, S., Kulmala, R., Thomsen, L., 1995. Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts. Accident Analysis and Prevention 27 (1), 1–20.
37. Agresti, A., 1990. Categorical Data Analysis. Wiley, New York.

Abstract

This study aims at modeling the influence of highway geometric and traffic characteristics on road crash frequency under heterogenous traffic conditions using the Negative Binomial regression technique. Over a span of three years, data from 618 road accidents on the Dhaka–Aricha highway (N5) in Bangladesh were analyzed to develop the model.. The model variables include Annual Average Daily Traffic (AADT), % of heavy vehicle and non-motorised vehicles (NMV), pavement carriageway width (m), shoulder width (m) and presence of median. The model outcome showed that heavy traffic volume, NMV, reduced pavement carriageway, narrow shoulder width and absence of median increase the likelihood for overall crash involvement. But for pedestrian crash analysis increased traffic volume ,% of heavy vehicle, narrow pavement, narrow shoulder width and absence of median increase the likelihood ; and % of NMV was not found to be significant in this case. Again, in case of  NMT crash, more crashes were resulted with higher percent of heavy vehicles and reduced shoulder and narrow pavement width having no median. The developed model was further analyzed using an elasticity approach to determine the key factors influencing accident occurrence and to assess their relative importance. The research results may provide significant guidance to highway improvement and related investment policy in developing countries conditions similar to Bangladesh.

Keywords- Negative binomial, Accident frequency, Elasticity, Developing countries and Bangladesh

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Mahmud, S. M. S., Sarker, A., Hoque, M. M., (2011). Identifications and investigations of hazardous road locations on Dhaka-Aricha highway, 4th Annual paper meet and 1st Civil Engineering Congress, Dhaka, Bangladesh, December 22-24
2. Singh, R. K. & Suman, S.K. (2012). Accident analysis and prediction of model on national International Journal of Advanced Technology in Civil Engineering,Vol. 1.
3. Milton, J., Mannering, F., 1996. The Relationship Between Highway Geometrics, Traffic Related Elements and Motor Vehicle. Washington State Dept. of Transportation.
4. Abdel-Aty, M.A., Radwan, A.E. (2000) Modelling traffic accident occurrence and involvement. Accident Analysis and Prevention, Vol. 32, pp. 633-642.
5. Abdel-Aty, M., Abdalla, M.F. (2004) Linking roadway geometrics and real-time traffic characteristics to model daytime freeway crashes: generalized estimating equations for correlated data. Transportation Research Record, Vol. 1897, pp. 106-115.
6. EI-Basyouny, K., Sayed, T. (2006) Comparison of Two Negative Binomial Regression Techniques in Developing Accident Prediction Models. Transportation Research Record, 1950, pp. 9-16.
7. Ivan, J., O’Mara, P., 1997. Prediction of Traffic Accident Rates Using Poisson Regression. Presented at the 76th Annual Meeting of the Transportation Research Board.
8. Karlaftis, M. G., Golias, I. 2002. Effects of road geometry and traffic volumes on rural roadway  accident rates. Accident Analysis and Prevention, Vol. 34, 357–365.
9. Lord, D., Washington, S.P., Ivan, J.N. (2005) Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory. Accident Analysis and Prevention, Vol. 37, pp. 35-46.
10. Lord, D. (2006). Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accident Analysis and Prevention, 38(4), 751–766.
11. Lord, D., Washington, S. P., & Ivan, J. N. (2007). Further notes on the application of Zero- inflated models in highway safety. Accident Analysis and Prevention, 39, 53–
12. Montella, A. (2008) Crash prediction models for rural motorways. Transportation Research Record, Vol. 2083, pp. 180-189.
13. Wong, S. C., Sze , N.N., Li, Y.C. (2007). Contributory factors to traffic crashes at signalized intersections in Hong Kong. Accident Analysis and Prevention, 39, 1107- 1113.
14. Jovanis, P., Chang, H., 1986. Modeling the relationship of accidents to miles traveled. Transportation Research Record 1068.
15. Saccomanno, F. F.; Buyco, C. (1988) Generalized loglinear models of truck accident rates, paper presented at Transportation Research Board 67th Annual Meeting, Washington, DC.
16. Joshua, S., Garber, N., 1990. Estimating truck accident rate and involvement using linear and Poisson regression models. Transportation Planning and Technology 15, 41–58.
17. Mohamedshah, Y.M., Paniati, J.F., Hobeika, A.G., 1993. Truck accident models for interstate and two-lane rural roads. Transportation Research Record 1407, 35–41.
18. Miaou, S., Lum, H., 1993. Modeling vehicle, accidents and highway geometric design relationships. Accident Analysis and Prevention 25 (6), 689–709.
19. Miaou, S., 1994. The relationship between truck accidents and geometric design of road section: Poisson versus Negative Binomial regression. Accident Analysis and Prevention 26(4).
20. Poch, M., Mannering, F. (1996). Negative Binomial Analysis of Intersection-Accident Journal of Transportation Engineering, Vol. 122.
21. Hadi, M.A., Aruldhas, J., Chow, L., Wattleworth, J.A. (1995) Estimating safety effects of cross-section design for various highway types using negative binomial regression. Transportation Research Record,1500, pp. 169-177.
22. Shankar, V., Mannering, F., Barfield, W. 1995. Effect of roadway geometric and environment factors on rural freeway accident frequencies. Accident Analysis and Prevention 27(30).
23. McCarthy, P.S., 1999. Public policy and highway safety: a city-wide perspective. Regional Science and Urban Economics 29 (3), 231–244.
24. Carson, J., Mannering, F., 2001. The effect of ice warning signs on ice-accident frequency and Accident Analysis and Prevention 33 (1), 99–109.
25. Shankar, V.N., Milton, J., Mannering, F., 1997. Modeling accident frequencies as zero-altered probabilty processes: an empirical inquiry. Accident Analysis and Prevention 29 (6).
26. Lee, J., Mannering, F., 2002. Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis. Accident Analysis and Prevention 34 (2).
27. Lee, A.H., Stevenson, M.R., Wang, K., Yau, K., 2002. Modeling young driver motor vehicle crashes: data with extra zero. Accident Analysis and Prevention 34 (4), 515–521.
28. Chang L. (2005) Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network. Safety Science, Vol. 43, Issue 8, pp. 541-557.
29. Chengye, P., Ranjitkar, P. (2013)Modelling motorway accidents using negative binomial regression, presented at Proceedings of the Eastern Asia Society for Transportation Studies, Vol.9, 2013.
30. Ayati, E., Abbasi, E. (2011) Investigation on the role of traffic volume in accidents on urban Journal of Safety Research, Vol. 42, pp. 209-214.
31. Lord, D., & Park, Y. J. (2008). Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates. Accident Analysis and Prevention, 40(4), 1441–1457.
32. Pemmanaboina, R. (2005) Assessing occurrence on urban freeways using static and dynamic factors by applying a system of interrelated equations. M.E. thesis, University of Central Florida.
33. Savolainen, P. T., & Tarko, A. P. (2005). Safety impacts at intersections on curved segments. Transportation Research Record, 1908, 130–
34. Cameron, A. C.; Trivedi, P. K. Econometric models based on count data: Comparison and applications of some estimators and tests. Journal of Applied Econometrics 1:29-53; 1986.
35. Greene, W. Econometric analysis. New York, NY: Macmillan Publishing; 1993.
36. Fridstrom, L., Ifver, J., Ingebrigtsen, S., Kulmala, R., Thomsen, L., 1995. Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts. Accident Analysis and Prevention 27 (1), 1–20.
37. Agresti, A., 1990. Categorical Data Analysis. Wiley, New York.