Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (10): 2817-2825.doi: 10.13229/j.cnki.jdxbgxb.20211356

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Traffic accident risk assessment method for road network considering risk heterogeneity

Qian CAO(),Zhi-hui LI(),Peng-fei TAO,Yong-jian MA,Chen-xi YANG   

  1. College of Transportation，Jilin University，Changchun 130022，China
  • Received:2021-12-05 Online:2023-10-01 Published:2023-12-13
  • Contact: Zhi-hui LI E-mail:18844548258@163.com;lizhih@jlu.edu.cn

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Abstract:

At present， the risk assessment for road network is mainly based on accident data-driven methods. These methods ignore the risk factors differences on different types of roads， such as the ordinary road， tunnel， bridge， and so on. And this will induce the risk assessment for road network is inaccuracy. Aiming at this problem， a traffic accident risk assessment method for road network was presented considering risk heterogeneity. In this method， a road cascading segmentation strategy from coarse to fine was proposed according to the risk factors prior knowledge of different road types and the distribution characteristics of accidents density. This would achieve the accurate division of road segments with heterogeneous risk. Then an adaptive accident risk spatial distribution function was introduced. And a traffic risk assessment model for road segments with heterogeneous risk was established to realize the overall risk assessment of road network. Based on the international open accident database， the proposed method was compared with kernel density estimation with fixed bandwidth. The results show that the presented method has a better ability to discover traffic risk. And the assessment of road network risk is more consistent with actual situation by using the presented method.

Key words: traffic engineering, road network risk assessment, density peaks clustering, heterogeneous risk, traffic safety

CLC Number: 

  • U491.3

Fig.1

Coarse division diagram of road segments withheterogeneous risk"

Fig.2

Fine division diagram of road segments withheterogeneous risk"

Fig.3

Spatial distribution function of accident risk"

Fig.4

Risk normalization function（α=4）"

Fig.5

Study area and roads"

Fig.6

Traffic accidents on study roads in 2020"

Fig.7

Traffic accidents clustering results"

Fig.8

Traffic risk assessment results of road network （basic unit is 1 m）"

Fig.9

Traffic risk assessment results of road network （basic unit is 5 m）"

Fig.10

Traffic risk assessment results of road network （basic unit is 10 m）"

Fig.11

Traffic risk assessment results of road network by using F-KDE （basic unit is 10 m）"

Fig.12

Risk distribution curve of road section"

Table1

Statistical results of road traffic accident"

路 段方 法路段长度/km事故数/起增加率/%加权事故数/起增加率/%
风险值前10%本文1.352004.72289.1
F-KDE191209
风险值前20%本文2.72823.73095.1
F-KDE272294
风险值前30%本文4.053301.53552.3
F-KDE325347
1 田准, 张生瑞. 优化经验贝叶斯事故黑点识别与排序方法[J]. 长安大学学报:自然科学版, 2019, 39(5): 115-126.
Tian Zhun, Zhang Sheng-rui. Identification and ranking of accident black spots using advanced empirical Bayes method[J]. Journal of Chang'an University(Natural Science Edition), 2019, 39(5): 115-126.
2 Qu X B, Meng Q. A note on hotspot identification for urban expressways[J]. Safety Science, 2014, 66: 87-91.
3 Ghadi M, Torok A, Tanczos K. Integration of probability and clustering based approaches in the field of black spot identification[J]. Periodica Polytechnica-civil Engineering, 2019, 63(1): 46-52.
4 戢晓峰, 谢世坤, 覃文文, 等. 基于轨迹数据的山区危险性弯道路段交通事故风险动态预测[J]. 中国公路学报, 2022, 35(4): 277-285.
Ji Xiao-feng, Xie Shi-kun, Qin Wen-wen, et al. Dynamic prediction of traffic accident risk in risky curve sections based on vehicle trajectory data[J]. China Journal of Highway and Transport, 2022, 35(4): 277-285.
5 范存威. 山区公路长下坡路段运营安全分析与评价[D]. 重庆: 重庆交通大学交通运输学院, 2012.
Fan Cun-wei. Analysis and evaluation of operation safety in mountainous and long downhill sections[D]. Chongqing: College of Traffic and Transportation, Chongqing Jiaotong University, 2012.
6 李林超. 高速公路隧道出入口段交通安全分析与改善措施[D]. 西安: 长安大学公路学院, 2015.
Li Lin-chao. Safety assessment and improvement at the entrance and exit of expressway tunnel[D]. Xi'an: School of Highway,Chang'an University, 2015.
7 过秀成, 盛玉刚, 潘昭宇, 等. 公路交通事故黑点总体特征分析[J]. 东南大学学报:自然科学版, 2007, 37(5): 930-933.
Guo Xiu-cheng, Sheng Yu-gang, Pan Zhao-yu, et al. Analysis of general characteristics for highway traffic accident black-spots[J]. Journal of Southeast University (Natural Science Edition), 2007, 37(5): 930-933.
8 胡新民, 刘涛, 张天华, 等. 道路黑点识别与改善[J]. 交通运输工程学报, 2004, 4(1): 106-109.
Hu Xin-min, Liu Tao, Zhang Tian-hua, et al. Highway black spot recognition and improvement[J]. Journal of Traffic and Transportation Engineering, 2004, 4(1): 106-109.
9 Borsos A, Cafiso S, D'agostino C, et al. Comparison of Italian and Hungarian black spot ranking[C]∥The 6th Transport Research Arena, Warsaw, Poland, 2016: 2148-2157.
10 耿超, 彭余华. 基于动态分段和DBSCAN算法的交通事故黑点路段鉴别方法[J]. 长安大学学报:自然科学版, 2018, 38(5): 131-138.
Geng Chao, Peng Yu-hua. Identification method of traffic accident black spots based on dynamic segmentation and DBSCAN algorithm[J]. Journal of Chang'an University (Natural Science Edition), 2018, 38(5): 131-138.
11 Dereli M A, Erdogan S. A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods[J]. Transportation Research Part A, 2017, 103: 106-117.
12 杨轸, 唐莹, 方守恩. 双曲正切函数在事故黑点鉴别中的应用[J]. 哈尔滨工业大学学报, 2011, 43(10): 143-148.
Yang Zhen, Tang Ying, Fang Shou-en. Application of hyperbolic tangent function for accident prone location identification[J]. Journal of Harbin Institute of Technology, 2011, 43(10): 143-148.
13 崔洪军, 申晓静, 赵述捷, 等. 基于交通事故间距分布特征的事故黑点鉴定新方法[J]. 武汉理工大学学报, 2012, 34(2): 54-58.
Cui Hong-jun, Shen Xiao-jing, Zhao Shu-jie, et al. New traffic accident black spots identification method based on abnormal accidents space[J]. Journal of Wuhan University of Technology, 2012, 34(2): 54-58.
14 Xia Z X, Yan J. Kernel density estimation of traffic accidents in a network space[J]. Computers, Environment and Urban Systems, 2008, 32(5): 396-406.
15 陈金林. 基于网络核密度估计城市路网事故黑点鉴别研究[D]. 南京: 东南大学交通学院, 2015.
Chen Jin-lin. Research on identification hotspots in the urban road networks based on the network kernel density estimation method[D]. Nanjing: School of Transportation, Southeast University, 2015.
16 Anderson T K. Kernel density estimation and k-means clustering to profile road accident hotspots[J]. Accident Analysis and Prevention, 2009, 41(3): 359-364.
17 Bil M, Andrasik R, Janoska Z. Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation[J]. Accident Analysis and Prevention, 2013, 55: 265-273.
18 Xie Z X, Yan J. Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach[J]. Journal of Transport Geography, 2013, 31: 64-71.
19 Rodriguez A, Laio A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191): 1492-1496.
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