吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (06): 1482-1487.doi: 10.7964/jdxbgxb201306008

• 论文 • 上一篇    下一篇

高速公路常发拥堵路段追尾事故风险实时预测

李志斌1, 刘攀1, 金茂菁2, 徐铖铖1   

  1. 1. 东南大学 交通学院, 南京 210096;
    2. 科技部 高技术研究发展中心, 北京 100044
  • 收稿日期:2012-05-28 出版日期:2013-11-01 发布日期:2013-11-01
  • 作者简介:李志斌(1983-),男,博士研究生.研究方向:交通运输安全.E-mail:lizhibin@seu.edu.cn
  • 基金资助:

    "863"国家高技术研究发展计划项目(2011AA110303;2012AA112304);"973"国家重点基础研究发展计划项目(2012CB725400);东南大学优秀博士学位论文基金项目(YBPY1211).

Real-time prediction of rear-end crashes near freeway recurrent bottlenecks

LI Zhi-bin1, LIU Pan1, JIN Mao-jing2, XU Cheng-cheng1   

  1. 1. School of Transportation, Southeast University, Nanjing 210096, China;
    2. High Technology Research and Development Center of Ministry of Science and Technology, Beijing 100044, China
  • Received:2012-05-28 Online:2013-11-01 Published:2013-11-01

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摘要：

研究了个体车辆遇到运动波(Kinematic waves)前后行驶轨迹的特征,分析了追尾事故发生条件,将多个车辆轨迹进行集计分析,提出了基于集计交通流数据的追尾事故风险预测模型。结果表明:运动波传播过程中追尾事故概率与事故风险指数及上游占有率标准差显著相关;采用模型对实际高速公路常发拥堵路段追尾事故的预测结果符合真实情况;交通流由自由流转向拥堵过程中追尾事故风险最大,拥堵中运动波的传播会增加追尾事故风险。

关键词: 交通运输安全工程, 追尾事故, 实时预测, 常发拥堵, 运动波

Abstract:

To predict the real-time collision risk for rear-end crashes at freeway recurrent bottlenecks, in this study, the features of trajectories of individual vehicles when encountering kinematic waves were investigated, the collision conditions were analyzed, aggregate analysis of the trajectories of multiple vehicles was conducted. Then a model to predict the collision risks was developed using aggregated traffic flow data. Results show that the rear-end collision probability during propagation of kinematic waves was significantly related to the collision risk index and standard deviation of upstream occupancy. The model was used to predict the collisions in the bottleneck section of a real-world freeway;the predictions were consistent with observations. The highest collision risk occurs when traffic changes from free-flow to congested states. The propagation of kinematic waves in congested traffic also increases the rear-end collision risk.

Key words: traffic safety engineering, rear-end collision, real-time prediction, recurrent congestion, kinematic wave

中图分类号: 

  • U491

[1] Daganzo C F. Fundamentals of Transportation and Traffic Operations[M]. Oxford: Pergamon Press, 1997.

[2] Abdel-Aty M, Uddin N, Pande A. Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways[J]. Journal of the Transportation Research Board, 2005,1908: 51-58.

[3] Abdel-Aty M, Uddin N, Pande A, et al. Predicting freeway crashes from loop detector data by matched case-control logistic regression[J]. Journal of the Transportation Research Board, 2004, 1897: 88-95.

[4] Lee C, Hellinga B, Saccomanno F. Real-time-crash prediction model for application to crash prevention in freeway traffic[J]. Journal of the Transportation Research Board, 2003,1840: 67-77.

[5] Lee C, Saccomanno F, Hellinga B. Analysis of crash precursors on instrumented freeways[J]. Journal of the Transportation Research Board, 2002,1784: 1-8.

[6] Zheng Z D, Ahn S, Monsere C M. Impact of traffic oscillations on freeway crash occurrences[J]. Accident Analysis and Prevention, 2010, 42(2): 626-636.

[7] Oh C, Kim T. Estimation of rear-end crash potential using vehicle trajectory data[J]. Accident Analysis and Prevention, 2010, 42(6): 1888-1893.

[8] Saccomanno F, Cunto F, Guido G, et al. Comparing safety at signalized intersections and roundabouts using simulated rear-end conflicts[J]. Journal of the Transportation Research Board, 2008, 2078: 90-95.

[9] Oh C, Park S, Ritchie S G. A method for identifying rear-end collision risks using inductive loop detectors[J]. Accident Analysis and Prevention, 2006, 38(2):295-301.

[10] Oh C, Oh J, Min J. Real-time detection of hazardous traffic events on freeways methodology and prototypical implementation[J]. Journal of the Transportation Research Board, 2009, 2129: 35-44.

[11] Hourdo J, Garg V, Michalopoulos P G, et al. Real-time detection of crash-prone conditions at freeway high-crash locations[J]. Journal of the Transportation Research Board, 2006,1968: 83-91.

[12] Hwasoo Y,Kitae Jang,Alexander S. Impact of traffic states on freeway collision frequency[C]//The 89th Annual Meeting of the Transportation Research Board, Washington D C,USA, 2010.

[13] Xu C, Liu P, Wang W, et al. Exploration and identification of hazardous traffic flow states before crash occurrences on freeways[C]//The 89th Annual Meeting of the Transportation Research Board, Washington D C,USA, 2010.

[14] Chung K, Jang K, Oum S, et al. Investigation of attributes of kinematic waves preceding traffic collisions[C]//The 90th Annual Meeting of the Transportation Research Board, Washington D C,USA,2011.

[15] 徐铖铖, 刘攀, 王炜,等. 基于判别分析的高速公路交通安全实时评价指标[J]. 东南大学学报, 2012, 42(3): 555-559. Xu Cheng-cheng, Liu Pan, Wang Wei,et al. Discriminant analysis based method to develop real-time crash indicator for evaluating freeway safety[J].Journal of Southeast University, 2012, 42(3): 555-559.

[16] 徐铖铖, 刘攀, 王炜, 等. 恶劣天气下高速公路实时事故风险预测模型[J]. 吉林大学学报:工学版, 2013, 43(1): 68-73. Xu Cheng-cheng, Liu Pan, Wang Wei, et al. Using adverse weather conditions data to establish a real time crash risk prediction model[J]. Journal of Jilin University(Engineering and Technology Edition), 2013, 43(1): 68-73.

[17] 吕宜生. 基于实时数据的道路交通事故预测方法与仿真试验研究[D]. 哈尔滨:哈尔滨工业大学交通科学与工程学院, 2007. LüYi-sheng. Highway traffic accident forecast based on real-time data and its matching simulation test[D]. Harbin: School of Transportation Science and Engineering,Harbin Institute of Technology, 2007.

[18] Gross F, Jovanis P P. Estimation of the safety effectiveness of lane and shoulder width: Case-control approach[J]. Journal of Transportation Engineering, 2007, 133(6): 362-369.
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