Identifying Expressway Accident Black Spots Based on the Secondary Division of Road Units

  • Guohua Liang Chang'an University, College of Transportation Engineering
  • Xujiao Sun Chang'an University, College of Transportation Engineering
  • Yidan Zhang Chang'an University, College of Transportation Engineering
  • Mingli Chen Chang'an University, College of Transportation Engineering
  • Wanting Zhang Chang'an University, College of Transportation Engineering
Keywords: traffic safety;, accident black spots identification;, expressway;, division of road units;, road safety index;, empirical Bayes method;

Abstract

For the purpose of reducing the harm of expressway traffic accidents and improving the accuracy of traffic accident black spots identification, this paper proposes a method for black spots identification of expressway accidents based on road unit secondary division and empirical Bayes method. Based on the modelling ideas of expressway accident prediction models in HSM (Highway Safety Manual), an expressway accident prediction model is established as a prior distribution and combined with empirical Bayes method safety estimation to obtain a Bayes posterior estimate. The posterior estimated value is substituted into the quality control method to obtain the black spots identification threshold. Finally, combining the Xi'an-Baoji expressway related data and using the method proposed in this paper, a case study of Xibao Expressway is carried out, and sections 9, 19, and 25 of Xibao Expressway are identified as black spots. The results show that the method of secondary segmentation based on dynamic clustering can objectively describe the concentration and dispersion of accident spots on the expressway, and the proposed black point recognition method based on empirical Bayes method can accurately identify accident black spots. The research results of this paper can provide a basis for decision-making of expressway management departments, take targeted safety improvement measures.

References

Statistics Bureau of the People's Republic of China. China Statistical Yearbook. Beijing: China Statistics Press; 2018.

Elvik R. A survey of operational definitions of hazardous road locations in some European countries. Accident Analysis and Prevention. 2008;40(6): 1830-1835. DOI: 10.1016/j.aap.2008.08.001

Zhang D. Analysis of road traffic accidents and black spots. Beijing: People's Communications Press; 2005.

Geng C, Peng Y. [A black spot identification method for traffic accidents based on dynamic segmentation and DBSCAN algorithm]. 长安大学学报(自然科学版). 2018;38(5): 131-138. Chinese.

Yakar F. Identification of accident-prone road sections by using relative frequency method. Promet – Traffic&Transportation. 2015;27(6): 539-547. DOI: 10.7307/ptt.v27i6.1609

Borsos A, Cafiso S, D’Agostino C, Miletics D. Comparison of Italian and Hungarian black spot ranking. Proceedings of 6th Transportation Research Arena; 2016.

Jordan P. ITE and Road Safety Audit – A partnership for traffic safety. ITE Journal. 1999;69(3): 24-27. DOI: 10.1109/25.752590

Li Q, Cheng C, Chen L. [Black point model of traffic accident based on GA-BP neural network algorithm and rough set theory]. 武汉理工大学学报(交通科学与工程版). 2011;35(4): 756-760. Chinese.

Guan M. [Comparative study on identification methods of traffic accident frequent point]. 公路. 2009;2009(4): 191-195. Chinese.

Park B, Lord D, Lee C. Finite mixture modeling for vehicle crash data with application to hotspot identification. Accident Analysis and Prevention. 2014;71: 319-326. DOI: 10.1016/j.aap.2014.05.030

Elyasi MR, Saffarzade M, Boroujerdian AM. A novel dynamic segmentation model for identification and prioritization of black spots based on the pattern of potential for safety improvement. Transportation Research Part A: Policy and Practice. 2016;91: 346-357. DOI: 10.1016/j.tra.2016.06.020

Richard KR, Kim S, Ulfarsson GF. A hierarchical Bayesian logistic regression with a finite mixture for identifying higher-than-expected crash proportions at intersections. Journal of Transportation Safety & Security. 2019;11(1): 1-20. DOI: 10.1080/19439962.2017.1337054

Meng X, Li M, Mai Q, Guan Z. Research on identification of black spot and accident inducing factor for freeway. Journal of Transportation Systems Engineering & Information Technology. 2011;11(1): 114-120. DOI: 10.1186/s12889-016-2722-9

Niu Z, et al. [Identification of frequent occurrences of highway traffic accidents considering system clustering]. 中国安全科学学报. 2018;28(11): 104-109. Chinese.

Jia H, Sang J, Yang L, Feng T. [Identification method of potential accident risk points (sections) for newly built expressways]. 北京工业大学学报. 2016;42(8): 1233-1238. Chinese.

Ghadi M, Torok A. A comparative analysis of black spot identification methods and road accident segmentation methods. Accident Analysis and Prevention. 2019;128: 1-7. DOI: 10.1016/j.aap.2019.03.002

Ghadi M, Torok A, Tanczos K. Integration of probability and clustering based approaches in the field of black spot identification. Periodica Polytechnica Civil Engineering. 2019;63(1): 46-52. DOI: 10.3311/PPci.11753

Ahmed M, Huang H, Abdel-Aty M, Guevara B. Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway. Accident Analysis and Prevention. 2011;43(4): 1581-1589. DOI: 10.1016/j.aap.2011.03.021

Ana F, Jose N. An approach to accidents modeling based on compounds road environments. Accident Analysis and Prevention. 2013;53: 39-45. DOI: 10.1016/j.aap.2012.12.041

Uzondu C, Jamson S, Lai F. Investigating unsafe behaviours in traffic conflict situations: An observational study in Nigeria. Journal of Traffic and Transportation Engineering (English Edition). 2019;6(5): 482-492. DOI: 10.1016/j.jtte.2018.06.002

Gregoriades A, Mouskos KC. Black spots identification through a Bayesian networks quantification of accident risk index. Transportation Research Part C: Emerging Technologies. 2013;28(3): 28-43. DOI: 10.1016/j.trc.2012.12.008

Sandhu HAS, Singh G, Sisodia MS, Chauhan R. Identification of black spots on highway with kernel density estimation method. Journal of the Indian Society of Remote Sensing. 2016;44(3): 457-464. DOI: 10.1007/s12524-015-0500-2

Colak HE, Memisoglu T, Erbas YS, Bediroglu S. Hot spot analysis based on network spatial weights to determine spatial statistics of traffic accidents in Rize, Turkey. Arabian Journal of Geosciences. 2018;11(151). DOI: 10.1007/s12517-018-3492-8

Washington S, Haque MM, Oh J, Lee D. Applying quantile regression for modeling equivalent property damage only crashes to identify accident black spots. Accident Analysis & Prevention. 2014;66(1): 136-146. DOI: 10.1016/j.aap.2014.01.007

Zhang C, Ivan JN. Effects of geometric characteristics on head-on crash incidence on two-lane roads in Connecticut. Transportation Research Record. 2005;1908(1): 159-164. DOI: 10.3141/1908-19

Ma Z, Zhang H, Zhang H, Wang J. [A model for predicting the number of roadside accidents on expressways]. 长安大学学报(自然科学版). 2017;37(4): 119-126. Chinese.

Ministry of Transport of the People’s Republic of China. Guidelines for Safety Evaluation of Highway Projects: JTG/TB05-2015. Beijing: Ministry of Transport of the People’s Republic of China; 2015.

AASHTO. Highway Safety Manual (1st Edition). Washington D. C.: AASHTO; 2010.

Xie L, Wu C, Lyu N, Duan Z. Studying the effects of freeway alignment, traffic flow, and sign information on subjective driving workload and performance. Advances in Mechanical Engineering. 2019;11(5). DOI: 10.1177/1687814019853925

Gelman A, et al. Bayesian Data Analysis. 3rd ed. New York: Chapman and Hall/CRC; 2013.

Published
2021-10-08
How to Cite
1.
Liang G, Sun X, Zhang Y, Chen M, Zhang W. Identifying Expressway Accident Black Spots Based on the Secondary Division of Road Units. Promet - Traffic&Transportation. 2021;33(5):731-43. DOI: 10.7307/ptt.v33i5.3680
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Articles