Development of Safety Improvement Method in City Zones Based on Road Network Characteristics


1 Department of Road Safety and Intelligent Transportation System, Tarahan Parseh Transportation Research Institute, Tehran, Iran

2 Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran

3 Department of Civil Engineering, Islamic Azad University, Tehran Science and Research Branch, Tehran, Iran

4 Graduate Student of Carleton University, Ottawa, Canada


Background and Objective: Extensive studies have so far been carried out on developing safety models. Despite the extensive efforts made in identifying independent variables and methods for developing models, little research has been carried out in providing amendatory solutions for enhancing the level of safety. Thus, the present study first developed separate accident frequency prediction models by transportation modes, and then in the second phase, a development of safety improvement method (DSIM) was proposed. Materials and Methods: To this end, the data related to 14,903 accidents in 96 traffic analysis zones in Tehran, Iran, were collected. To evaluate the effect of intra‑zone correlation, a multilevel model and a negative binomial (NB) model were developed based on both micro‑ and macro‑level independent variables. Next, the DSIM was provided, aiming a causing the least change in the area under study and with attention to the defined constraints and ideal gas molecular movement algorithm. Results: Based on a comparison of the goodness‑of‑fit measures for the multilevel model with those of the NB model, the multilevel models showed a better performance in explaining the factors affecting accidents. This is due to considering the multilevel structure of the data in such models. The final results were obtained after 200 iterations of the optimization algorithm. Thus, to decrease accidents by 30% and cause the least change in the area under study, the independent variable of “vehicle kilometer traveled per road segment” underwent a considerable change, while little change was observed for the other variables. Conclusions: The final results of the DSIM showed that the ultimate solutions derived from this method can be different from the final solutions derived from the analysis of the results from the safety models. Hence, it is necessary to develop new methods to propose solutions for increasin safety.


1. Soltani N, Mamdoohi AR. A model of driver behavior in response to road roughness: A case study of Yazd arterials. J Geotechnical Transp Eng 2016;2:45-50. 
2. Naderan A, Shahi J. Aggregate crash prediction models: Introducing crash generation concept. Accid Anal Prev 2010;42:339‑46. 
3. Elmitiny N, Yan X, Radwan E, Russo C, Nashar D. Classification analysis of driver’s stop/go decision and red‑light running violation. Accid Anal Prev 2010;42:101‑11. 
4. Köll H, Bader M, Axhausen KW. Driver behaviour during flashing green before amber: A comparative study. Accid Anal Prev 2004;36:273‑80. 
5. Papaioannou P. Driver behaviour, dilemma zone and safety effects at urban signalized intersections in Greece. Accid Anal Prev 2007;39:147‑58. 
6. Cai Q, Lee J, Eluru N, Abdel‑Aty M. Macro‑level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models. Accid Anal Prev 2016;93:14‑22. 
7. Miaou SP. The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accid Anal Prev 1994;26:471‑82.
8. El‑Basyouny K, Sayed T. Accident prediction models with random corridor parameters. Accid Anal Prev 2009;41:1118‑23. 
9. Qin X, Ivan JN, Ravishanker N. Selecting exposure measures in crash rate prediction for two‑lane highway segments. Accid Anal Prev 2004;36:183‑91. 10. Zeng Q, Wen H, Huang H, Pei X, Wong SC. A multivariate random‑parameters Tobit model for analyzing highway crash rates by injury severity. Accid Anal Prev 2017;99:184‑91. 
11. Park BJ, Lord D, Hart JD. Bias properties of Bayesian statistics in finite mixture of negative binomial regression models in crash data analysis. Accid Anal Prev 2010;42:741‑9. 
12. Xie K, Wang X, Huang H, Chen X. Corridor‑level signalized intersection safety analysis in Shanghai, China using Bayesian hierarchical models. Accid Anal Prev 2013;50:25‑33. 
13. Lee J, Abdel‑Aty M, Choi K, Huang H. Multi‑level hot zone identification for pedestrian safety. Accid Anal Prev 2015;76:64‑73. 
14. Lord D, Mannering F. The statistical analysis of crash‑frequency data: A review and assessment of methodological alternatives. Transp Res Part A Policy Pract 2010;44:291‑305. 
15. Mannering FL, Bhat CR. Analytic methods in accident research: Methodological frontier and future directions. Anal Methods Accident Res 2014;1:1‑22. 
16. Anastasopoulos PC, Mannering FL. A note on modeling vehicle accident frequencies with random‑parameters count models. Accid Anal Prev 2009;41:153‑9. 
17. Lord D, Guikema SD, Geedipally SR. Application of the Conway‑Maxwell‑Poisson generalized linear model for analyzing motor vehicle crashes. Accid Anal Prev 2008;40:1123‑34. 
18. Couto A, Ferreira S. A note on modeling road accident frequency: A flexible elasticity model. Accid Anal Prev 2011;43:2104‑11. 
19. Quddus MA. Modelling area‑wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data. Accid Anal Prev 2008;40:1486‑97. 
20. Hadayeghi A, Shalaby AS, Persaud BN. Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. Accid Anal Prev 2010;42:676‑88. 
21. Abdel‑Aty M, Siddiqui C, Huang H, Wang X. Integrating trip and roadway characteristics to manage safety in traffic analysis zones. Transp Res Record J Transp Res Board 2011;22:20‑8. 
22. Pulugurtha SS, Duddu VR, Kotagiri Y. Traffic analysis zone level crash estimation models based on land use characteristics. Accid Anal Prev 2013;50:678‑87. 
23. Xu P, Huang H. Modeling crash spatial heterogeneity: Random parameter versus geographically weighting. Accid Anal Prev 2015;75:16‑25. 
24. Gelman A, Hill J. Data Analysis using Regression and Multilevel Hierarchical Models. New York, USA: Cambridge University Press; 2007. 
25. Dupont E, Papadimitriou E, Martensen H, Yannis G. Multilevel analysis in road safety research. Accid Anal Prev 2013;60:402‑11. 
26. Shi Q, Abdel‑Aty M, Yu R. Multi‑level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway. Accid Anal Prev 2016;88:68‑76. 
27. Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report‑tr06, Erciyes University, Engineering Faculty, Computer Engineering Department; 2005. 
28. Yang XS, Deb S. Cuckoo search via lévy flights. In: Proceedings of the Nature and Biologically Inspired Computing, 2009. NaBIC. World Congress on; 2009. p. 210‑4. 
29. Mirjalili S. The ant lion optimizer. Adv Eng Software 2015;83:80‑98. 
30. Wang GG, Guo L, Gandomi AH, Hao GS, Wang H. Chaotic krill herd algorithm Inf Sci 2014;274:17‑34. 
31. Guo L, Wang GG, Gandomi AH, Alavi AH, Duan H. A new improved krill herd algorithm for global numerical optimization Neurocomputing 2014;138:392‑402. 
32. Wang GG, Deb S, Coelho L. Earthworm optimization algorithm: A bioinspired metaheuristic algorithm for global optimization problems. International Journal of Bio-Inspired Computation 2018;12:1-22. 
33. Rashedi E, Nezamabadi‑Pour H, Saryazdi S. Gsa: A gravitational search algorithm. Inf Sci 2009;179:2232‑48. 
34. Feng Y, Wang GG, Deb S, Lu M, Zhao XJ. Solving 0‑1 knapsack problem by a novel binary monarch butterfly optimization. Neural Comput Applicat 2017;28:1619‑34. 
35. Wang GG, Deb S, Coelho LD. Elephant herding optimization. 3rd International Symposium on Computational and Business Intelligence (ISCBI), Bali, Indonesia, 2015:1-5. 
36. Wang GG, Deb S, Gao XZ, Coelho LD. Anew metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio Inspired Comput 2016;8:394‑409. 
37. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Software 2014;69:46‑61. 
38. Shobeiri V. The optimal design of structures using ACO and EFG. Eng Comput 2016;32:645‑53. 
39. Fabritius B, Tabor G. Improving the quality of finite volume meshes through genetic optimisation. Eng Comput 2016;32:425‑40. 
40. PuthaR, QuadrifoglioL, ZechmanE. Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Comput Aided Civil Infrastructure Eng 2012;27:14‑28. 
41. Abbas M, Bullock D, Head L. Real‑time offset transitioning algorithm for coordinating traffic signals. Transp Res Record J Transp Res Board 2001;1748:26‑39. 
42. He J, Hou Z. Ant colony algorithm for traffic signal timing optimization. Adv Eng Software 2012;43:14‑8. 
43. Lee J, Kim J, Park BB. A genetic algorithm‑based procedure for determining optimal time‑of‑day break points for coordinated actuated traffic signal systems. KSCE J Civil Eng 2011;15:197‑203. 
44. Lin DY, Ku YH. Using genetic algorithms to optimize stopping patterns for passenger rail transportation. Comput Aided Civil Infrastructure Eng 2014;29:264‑78. 
45. PeñabaenaNiebles R, Cantillo V, Moura JL, Ibeas A. Design and evaluation of a mathematical optimization model for traffc signal plan transition based on social cost function. Journal of Advanced Transportation 2017:1-12. 
46. Abbas KA. Traffic safety assessment and development of predictive models for accidents on rural roads in Egypt. Accid Anal Prev 2004;36:149‑63. 47. Greibe P. Accident prediction models for urban roads. Accid Anal Prev 2003;35:273‑85. 
48. Gujarati DN. Basic Econometrics. New York, Tata McGrawHill Education; 2009 
49. Jones K. Using multilevel models for survey analysis. J Market Res Soc 1993;35:249. 
50. Huang H, Zhou H, Wang J, Chang F, Ma M. Amultivariate spatial model of crash frequency by transportation modes for urban intersections. Anal Methods Accident Res 2017;14:10‑21. 
51. Varaee H, Ghasemi MR. Engineering optimization based on ideal gas molecular movement algorithm. Eng Comput 2017;33:71‑93.