A highway crash risk assessment method based on traffic safety state division

Autoři: Dongye Sun aff001;  Yunfei Ai aff001;  Yunhua Sun aff001;  Liping Zhao aff003
Působiště autorů: China Transport Telecommunications & Information Center, Beijing China aff001;  National Engineering Laboratory for Transportation Safety and Emergency informatics, Beijing, China aff002;  Beijing Institute of New Technology Applications, Beijing, China aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227609


In order to quantitatively analyze the influence of different traffic conditions on highway crash risk, a method of crash risk assessment based on traffic safety state division is proposed in this paper. Firstly, the highway crash data and corresponding traffic data of upstream and downstream are extracted and processed by using the matched case-control method to exclude the influence of other factors on the model. Secondly, considering the weight of traffic volume, speed and occupancy, a multi-parameter fusion cluster method is applied to divide traffic safety state. In addition, the quantitative relationship between different traffic states and highway crash risk is analyzed by using Bayesian conditional logistic regression model. Finally, the results of case study show that different traffic safety conditions are in different crash risk levels. The highway traffic management department can improve the safety risk management level by focusing on the prevention and control of high-risk traffic safety conditions.

Klíčová slova:

Clustering algorithms – Data processing – Forecasting – Markov models – Optimization – Probability distribution – Statistical models – Traffic safety


1. Wang L, Abdel-Aty M, Lee J. Safety Analytics for Integrating Crash Frequency and Real-Time Risk Modeling for Expressways[J]. Accident Analysis & Prevention, 2017, 104:58–64.

2. Mannering F L, Bhat C R. Analytic methods in accident research: Methodological frontier and future directions[J]. Analytic Methods in Accident Research, 2014, 1:1–22.

3. Feng C, Xiaoxiang M, Suren C, et al. Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data[J]. International Journal of Environmental Research and Public Health, 2016, 13(11): 1043.

4. Chen F, Chen S, Ma X. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data[J]. Journal of safety research, 2018, 65: 153–159. doi: 10.1016/j.jsr.2018.02.010 29776524

5. Feng C, Suren C, Xiaoxiang M. Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models[J]. International Journal of Environmental Research and Public Health, 2016, 13(6): 609.

6. Zeng Q, Gu W, Zhang X, et al. Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors[J]. Accident Analysis & Prevention, 2019, 127: 87–95.

7. Zeng Q, Wen H, Huang H, et al. A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments[J]. Accident Analysis & Prevention, 2017, 100: 37–43.

8. Zeng Q, Guo Q, Wong S C, et al. Jointly modeling area-level crash rates by severity: A Bayesian multivariate random-parameters spatio-temporal Tobit regression[J]. Transportmetrica A: Transport Science, 2019, 15 (2): 1867–1884.

9. Zeng Q, Wen H, Huang H, et al. Incorporating temporal correlation into a multivariate random parameters Tobit model for modeling crash rate by injury severity[J]. Transportmetrica A: Transport Science, 2018, 14 (3): 177–191.

10. Li Y, Wang H, Wang W, et al. Evaluation of the impacts of cooperative adaptive cruise control on reducing rear-end collision risks on freeways[J]. Accident Analysis & Prevention, 2017, 98:87–95.

11. Wang C, Xu C, Xia J, et al. A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation[J]. Transportation Research Part C: Emerging Technologies, 2018, 90:281–291.

12. Rongjie Y, Mohammed Q, Xuesong W, et al. Impact of data aggregation approaches on the relationships between operating speed and traffic safety[J]. Accident Analysis & Prevention, 2018, 120, 304–310.

13. Weng J, Zhu J Z, Yan X, et al. Investigation of Work Zone Crash Casualty Patterns Using Association Rules[J]. Accident Analysis and Prevention, 2016, 92, 43–52. doi: 10.1016/j.aap.2016.03.017 27038500

14. Golob T F, Recker W W. Relationships Among Urban Freeway Accidents, Traffic Flow, Weather, and Lighting Conditions[J]. Journal of Transportation Engineering, 2003, 129(4):342–353.

15. Golob T F, Recker W W, Alvarez V M. Freeway safety as a function of traffic flow[J]. Accid Anal Prev, 2004, 36(6):933–946. doi: 10.1016/j.aap.2003.09.006 15350870

16. Golob T F, Recker W W. A method for relating type of crash to traffic flow characteristics on urban freeways[J]. Transportation Research, Part A (Policy and Practice), 2004, 38(1):0–80.

17. Xu C, Tarko A P, Wang W, et al. Predicting crash likelihood and severity on freeways with real-time loop detector data[J]. Accident Analysis & Prevention, 2013, 57:30–39.

18. Xu C, Liu P, Wang W, et al. Development of a crash risk index to identify real time crash risks on freeways[J]. KSCE Journal of Civil Engineering, 2013, 17(7):1788–1797.

19. Xu Chengcheng. Relationship between traffic flow state and traffic safety on expressway [D], Southeast university. 2014.

20. Sun D Y, Jia Y H, Qin L Q, et al. A Variance Maximization Based Weight Optimization Method for Railway Transportation Safety Performance Measurement [J]. Sustainability, 2018, 10(8) 2903.

21. Lee Chris, Hellinga Bruce, and Saccomanno Frank. Proactive freeway crash prevention using real-time traffic control[J]. Canadian Journal of Civil Engineering, 2003, 30(6):1034–1041.

22. Pande A, Abdel-Aty M. A novel approach for analyzing severe crash patterns on multilane highways[J]. Accident Analysis & Prevention, 2009, 41(5):985–994.

23. Wang Jun, Ma Linmao. Logistic regression diagnosis and SAS implementation [J]. Journal of mathematical medicine, 2005, 18(1):35–37.

24. Abdel-Aty M, Uddin N, Pande A. Split Models for Predicting Multivehicle Crashes During High-Speed and Low-Speed Operating Conditions on Freeways[J]. Transportation Research Record: Journal of the Transportation Research Board, 2005, 1908(1):51–58.

25. Zhang Liangliang, Jia Yuanhua, Niu Zhonghai, et al. S Traffic State Classification Based on Parameter Weighting and Clustering Method [J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6):147–151.

26. Weng J, Du G, Li D, et al. Time-varying Mixed Logit Model for Vehicle Merging Behavior in Work Zone Merging Areas[J]. Accident Analysis and Prevention, 2018, 117, 328–339. doi: 10.1016/j.aap.2018.05.005 29754006

27. Chen F, Chen S. Injury severities of truck drivers in single- and multi-vehicle accidents on rural highway[J]. Accident Analysis and Prevention, 2011, 43(5): 1677–1688. doi: 10.1016/j.aap.2011.03.026 21658494

28. Feng C, Ming T, Xiaoxiang M. Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model[J]. International Journal of Environmental Research and Public Health, 2019, 16(14): 2632.

29. Bowen D, Xiaoxiang M, Feng C, et al. Investigating the Differences of Single- and Multi-vehicle Accident Probability Using Mixed Logit Model[J]. Journal of Advanced Transportation, 2018, UNSP 2702360. doi: 10.1007/s11116-016-9747-x

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2020 Číslo 1
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