山区双车道公路货车碰撞预测的双变量冲突极值模型

doi:10.16097/j.cnki.1009-6744.2022.02.023

交通运输系统工程与信息 ›› 2022, Vol. 22 ›› Issue (2): 230-238.DOI: 10.16097/j.cnki.1009-6744.2022.02.023

山区双车道公路货车碰撞预测的双变量冲突极值模型

戢晓峰^*，耿昭师

昆明理工大学，交通工程学院，昆明 650500

收稿日期:2021-10-31 修回日期:2021-11-29 接受日期:2021-12-02 出版日期:2022-04-25 发布日期:2022-04-23
作者简介:戢晓峰(1982- )，男，湖北随州人，教授，博士。
基金资助:
国家自然科学基金

Bivariate Traffic Conflict Extreme Value Model of Truck Collision Prediction on Two-lane Mountain Highway

JI Xiao-feng^* , GENG Zhao-shi

Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China

Received:2021-10-31 Revised:2021-11-29 Accepted:2021-12-02 Online:2022-04-25 Published:2022-04-23
Supported by:
National Natural Science Foundation of China (52062024)。

摘要/Abstract

摘要： 为预测山区双车道公路货车与冲突车辆发生的碰撞，本文基于无人机视频，提取货车与交互车辆的高精度轨迹数据，选取适用于不同运行轨迹的交通冲突指标，结合极值理论，构建双变量冲突极值(BTCEV)模型，将后侵入时间(PET)与碰撞时间(TTC)纳入统一框架，实现山区双车道公路货车与冲突车辆的碰撞预测，并以云南省货车事故高发的山区双车道公路为例，验证 BTCEV模型的预测性能。研究表明：PET为0.382 s、TTC为4.471 s是山区双车道公路货车严重冲突的阈值；BTCEV 模型预测山区双车道公路货车年事故发生率为 5.84%，预测准确性高达 98.92%，较PET模型以及TTC模型分别提高了167.33%和10.80%；且相比于单变量模型，双变量模型所估计的置信区间更窄，预测精度更高。研究结果将山区双车道公路货车碰撞预测方法从单变量扩展到双变量，在山区货车交通安全分析方面有广阔的应用前景。

关键词: 交通工程, 货车碰撞预测, 双变量冲突极值模型, 山区双车道公路, 车辆运行轨迹

Abstract: To predict collisions between trucks and conflicting vehicles on two-lane mountain highway, this paper proposes a bivariate traffic conflict extreme value (BTCEV) model. The high-precision trajectory data of trucks and interactive vehicles were extracted based on drone video. The traffic conflict indicators suitable for different trajectories were selected and the extreme value theory was also considered in the model. The Post Encroachment Time (PET) and Time to Collision (TTC) were incorporated into a unified framework to realize the collision prediction of trucks and conflicting vehicles on two-lane mountain highway. The prediction performance of the BTCEV model was verified by the example of the two-lane mountain highway that has a high truck incidence rate in Yunnan Province. The result shows that PET is 0.382 s and TTC is 4.471 s are the thresholds for serious conflicts of trucks on two-lane mountain highway. The annual accident probability of truck on two-lane mountain highway predicted by the BTCEV model is 5.84%, and the prediction accuracy is as high as 98.92%. The prediction accuracy is respectively 167.33% and 10.80% higher than that of the PET model and the TTC model. Compared with the univariate model, the confidence interval estimated by the bivariate model is narrower and the prediction accuracy is higher. The proposed method extends the truck collision prediction method on two-lane mountain highway from single variable to double variables, and has broad application prospects in the truck safety analysis on two-lane mountain highway.

Key words: traffic engineering, truck collision prediction, bivariate traffic conflict extreme value model, two-lane mountain highway, vehicle trajectory

中图分类号:

U491.31

戢晓峰, 耿昭师. 山区双车道公路货车碰撞预测的双变量冲突极值模型[J]. 交通运输系统工程与信息, 2022, 22(2): 230-238.

JI Xiao-feng , GENG Zhao-shi. Bivariate Traffic Conflict Extreme Value Model of Truck Collision Prediction on Two-lane Mountain Highway[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 230-238.

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参考文献

[1] 戢晓峰, 卢梦媛, 覃文文. 货车移动遮断影响下的小客车驾驶行为识别[J]. 交通运输系统工程与信息, 2021, 21(5): 174-182. [JI X F, LU M Y, QIN W W, et al. Passenger cars driving behaviors recognition under truck movement interruption[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 174-182.]

[2] 戢晓峰, 吴亚欣, 郝京京, 等. 平纵组合路段事故严重程度致因辨识模型[J]. 交通运输系统工程与信息, 2020, 20(6): 197-204. [JI X F, W Y X, HAO J J, et al. Severity factors analysis model of traffic accident on combined horizontal and vertical alignments[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(6): 197-204.]

[3] XIE S K, JI X F, YANG W C, et al. Exploring risk factors with crash severity on China two-lane rural roads using a random-parameter ordered probit model[J]. Journal of Advanced Transportation, 2020, 2020: 1-14.

[4] HYUN K, JEONG K, TOK A, et al. Assessing crash risk considering vehicle interactions with trucks using point detector data[J]. Accident Analysis & Prevention, 2019, 130: 75-83.

[5] SHAH D, LEE C. Analysis of effects of driver's evasive action time on rear-end collision risk using a driving simulator[J]. Journal of Safety Research, 2021, 78: 242- 250.

[6] KHORASHADI A, NIEMEIER D, SHANKARC V, et al. Differences in rural and urban driver-injury severities in accidents involving large-trucks: An exploratory analysis [J]. Accident Analysis & Prevention, 2005, 37(5): 910- 921.

[7] CAMPBELL K, JOKSCH H, GREEN P. A bridging analysis for estimating the benefits of active safety technologies[R]. UMTRI-96-18 Final Report, University of Michigan, Transportation Research Institute, 1996.

[8] SONGCHITRUKSA P, TARKO A P. The extreme value theory approach to safety estimation[J]. Accident Analysis & Prevention, 2006, 38(4): 811-822.

[9] ZHENG L, SAYED T, TAGELDIN A. Before-after safety analysis using extreme value theory: A case of left-turn bay extension[J]. Accident Analysis & Prevention, 2018, 121: 258-267.

[10] JONASSON J K, ROOTZÉN H. Internal validation of near-crashes in naturalistic driving studies: A continuous and multivariate approach[J]. Accident Analysis & Prevention, 2014, 62(9): 102-109.

[11] ZHENG L, SAYED T. From univariate to bivariate extreme value models: approaches to integrate traffic conflict indicators for crash estimation[J]. Transportation Research Part C: Emerging Technologies, 2019, 103(4): 211-225.

[12] CAVADAS J, AZEVEDO C L, FSRAH H, et al. Road safety of passing maneuvers: A bivariate extreme value theory approach under non-stationary conditions[J]. Accident Analysis & Prevention, 2020, 134: 105315.

[13] 朱顺应, 蒋若曦, 王红, 等. 机动车交通冲突技术研究综述[J]. 中国公路学报, 2020, 33(2): 15-33. [ZHUN S Y, JIANG R X, WANG H, et al. Review of research on traffic conflict techniques[J]. China Journal of Highway Transportation, 2020, 33(2): 15-33.]

[14] ZHENG L, SAYED T, ESSA M. Validating the bivariate extreme value modeling approach for road safety estimation with different traffic conflict indicators[J]. Accident Analysis & Prevention, 2019, 123: 314-323.

[15] ZHENG L, ISMAIL K, MENG X H. Freeway safety estimation using extreme value theory approaches: A comparative study[J]. Accident Analysis & Prevention, 2014, 62: 32-41.

[16] 陆建, 张文珺, 杨海飞, 等. 基于碰撞时间的追尾风险分析[J]. 交通信息与安全, 2014, 32(5): 58-64, 76. [LU J, ZAHNG W J, YANG H F, et al. Analysis of rear-end risk based on the indicator of time to collison[J]. Traffic Information and Safety, 2014, 32(5): 58-64, 76.]

山区双车道公路货车碰撞预测的双变量冲突极值模型

Bivariate Traffic Conflict Extreme Value Model of Truck Collision Prediction on Two-lane Mountain Highway

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