Poisson Regression Model of Frequency and Severity of Road Accidents in Rural Roads.

Document Type : پژوهشی

Authors

• morteza asdamraji ¹
• seyedehsan seyedabrishami ¹
• Mahmoud Saffarzadeh ¹
• moin askari ²

¹ Tarbiat Modares

² K. N. Toosi University of Technology

10.22067/civil.v32i1.58128

Abstract

One of the important ways of identifying road accident potentialities is road safety audits. After identifying these points, appropriate strategies to reduce the number and severity of accidents can be considered for them. In this study, using road safety audits, access, pavements, signs, roadside, and geometric problems and accidents leading to the deaths of outlying urban road sections were identified and using these data, the model of crashes in them Was presented. In this paper, the Hamadan-Kermanshah axis was chosen as a case study. In this axis, Poisson's regression model was more reliable and the problems of drainage ditches and vertical alignment were the most important reasons for the accident and resulted in death.

Keywords

• "Safety Audit
• Suburban road
• Accident model

---

References

1. Singh, S., "Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey", No. DOT HS 812 115, (2015).

2. سیمای ایمنی راه‌ها (2)، گزارش ملی وضعیت ایمنی رانندگی در ایران، وزارت راه و شهرسازی، (1394).

2. Highway Safety Manual (HSM), American Association of State Highway and Transportation Officials, (2010).

3. Lee, J.Y., Chung, J.-H. and Son, B., "Analysis of Traffic Accident Size for Korean Highway Using Structural Equation Models", Accident Analysis and Prevention, Vol. 40, No. 6, pp. 1955–1963, (2008).

4. Sun, X., Li, Y., Magri, D., & Shirazi, H. H., "Application of Highway Safety Manual Draft Chapter: Louisiana Experience", Transportation Research Record: Journal of the Transportation Research Board, Vol.1950, No. 1, pp. 55-64, (2006).

5. Phimister, J. R., Bier, V. M., & Kunreuther, H. C. (Eds.), "Accident Precursor Analysis and Management: Reducing Technological Risk Through Diligence", National Academies Press, (2014).

6. Milton, J.C., Shankar, V.N. and Mannering, F.L. "Highway Accident Severities and the Mixed Logit Model: An Exploratory Empirical Analysis", Accident Analysis and Prevention, Vol.40, No. 1, pp. 260–266, (2008).

7. Ma, J., Kockelman, K.M. and Damien, P., "A Multivariate Poisson-lognormal Regression Model for Prediction of Crash Counts by Severity, Using Bayesian Methods", Accident Analysis and Prevention, Vol.40, No. 3, pp. 964-975, (2008).

8. Cafiso, S., La Cava, G., Leonardi, S., Montella, A., and Pappalardo, G, "Operative Procedures for Road Safety Inspections," Varsaw, Poland, (2012).

9. Anastasopoulos, C. and Mannering, F.L., "A Note on Modeling Vehicle Accident Frequencies with Random-parameters Count Models", Accident Analysis and Prevention, Vol. 41, No. 1, pp. 153–159, (2009).

10. Cafiso, S., Cava, G. L., & Montella, A., "Safety Index for Evaluation of Two-lane Rural Highways". Transportation Research Record: Journal of the Transportation Research Board, Vol. 2019, No.1, pp. 136-145, (2007).

11. Chang, L., "Analysis of Freeway Accident Frequencies: Negative Binomial Regression Versus Artificial Neural Network", Safety Science, Vol.43, No. 8, pp. 541–557, (2005).

12. Hermans, E., Van den Bossche, F., & Wets, G., "Combining Road Safety Information in A Performance Index", Accident Analysis & Prevention, Vol. 40, No. 4, pp. 1337-1344, (2008).

13. Zheng, L., and Meng, X., "An Approach to Predict Road Accident Frequencies: Application of Fuzzy Neural Network", 3rd International Conference on Road Safety and Simulation, September 14-16, Indianapolis, USA, (2011).

14. Mc Elhinney, C. P., Kumar, P., Cahalane, C., & McCarthy, T., "Initial Results from European Road Safety Inspection (EURSI) Mobile Mapping Project", In ISPRS Commission V Technical Symposium, pp. 440-445, (2010).

15. Zeng, Q., & Huang, H., "Bayesian Spatial Joint Modeling of Traffic Crashes on an Urban Road N-etwork", Accident Analysis & Prevention, Vol. 67, pp. 105-112, (2014).