货车移动遮断影响下的跟驰风险异质性建模
CSTR:
作者:
作者单位:

1.同济大学 道路与交通工程教育部重点实验室,上海 201804;2.昆明理工大学 交通工程学院,云南 昆明 650500

作者简介:

谢世坤(1996—),女,博士生,主要研究方向为道路安全与环境工程。E-mail: 2111521@tongji.edu.cn

通讯作者:

戢晓峰(1982—),男,教授,博士生导师,工学博士,主要研究方向为交通安全。E-mail: yiluxinshi@sina.com

中图分类号:

U491

基金项目:

国家自然科学基金(52062024);上海市科委科研计划(19DZ1209102)


Modeling Heterogeneity for Car-following Risk Evaluation Under Truck Movement Block
Author:
Affiliation:

1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;2.Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China

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    摘要:

    基于移动瓶颈理论和交通流理论构建“货车移动遮断”效应模型,解析货车移动遮断形成机理,选用无人机采集货车移动遮断场景中车辆行驶视频数据并提取高精度车辆跟驰轨迹样本,基于此提出考虑冲突可能性和冲突严重度的小客车跟驰风险评价方法和分级标准,利用RP-ORP模型构建了考虑异质性跟驰风险概率预测模型。结果表明:货车移动遮断动态影响交通流稳定性,其形成过程包括减速跟驰和加速超车两个阶段;考虑异质性的RP-ORP模型能实现特定条件下小客车跟驰行为处于不同风险等级的概率预测,且拟合优度高于FP-ORP模型高;货车纵向加速度、跟驰车头间距、跟驰持续时间、小客车与货车速度差、激进型驾驶员5个变量显著影响小客车跟驰风险水平,且跟驰持续时间和激进型驾驶员2个变量具有随机参数特性。

    Abstract:

    Based on the movement bottleneck and traffic flow theory, a "truck movement block" effect model was constructed to analyze occurrence mechanism and the car-following behavior. Then, the UAV was selected to collect the vehicle driving video under the truck movement block, and the high-precision car-following trajectory was extracted by trajectory software. Last, the car-following risk assessment methods and grading standards that considers both the possibility and the severity of the risk was proposed. And the random parameter ordered probit model (RP-ORP) was established to assess the car-following risk, and the elasticity coefficient was introduced to analyze the magnitude and direction of the impact on car-following risk. This finding demonstrates that truck movement block includes two processes: car-following and overtaking, which dynamically affects the traffic stability. the RP-ORP model has a better fit than the FP-ORP model, and can realize the probability prediction that different car-following levels under certain conditions. Five variables significantly affect the car-following risk level, such as truck longitudinal acceleration, car-following distance, car-following time, average car-following speed difference, radical driver, among the variables of car-following time and radical driver have random parameter characteristics.

    参考文献
    [1] DANIEL B. The relative contribution of truck drivers and passenger vehicle drivers to truck- passenger vehicle traffic crashes[D]. Washington D C: The University of Michigan Transportation Research Institute, 1998.
    [2] GAZIS D C, HERMAN R. The moving and phantom bottlenecks[J]. Transportation Science. 1992, 26(3): 223.
    [3] AL-KAISY A, BHATT J, RAKHA H. Modeling the effect of heavy vehicles on sign occlusion at multilane highways[J]. Journal of Transportation Engineering. 2005, 131(3): 219.
    [4] 冯树民, 聂涔, 胡宝雨. 基于元胞自动机的高速公路货车结伴行为研究[J]. 交通运输系统工程与信息, 2016, 16(5): 97.FENG Shumin, NIE Cen, HU Baoyu. Partnering behavior of truck platoon on freeway based on cellular Automaton[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(5): 97.
    [5] 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.JI Xiaofeng, LU Mengyuan, QIN Wenwen. Passenger cars driving behaviors recognition under truck movement interruption[J]. Journal of Transportation Systems Engineering and Information Technology, 2021,21(5): 174.
    [6] 田钧方, 朱陈强, 贾宁, 等. 基于轨迹数据的车辆跟驰行为分析与建模综述[J]. 交通运输系统工程与信息,2021,21(5): 148.TIAN Junfang, ZHU Chenqiang, JIA Ning, et al. Review of car-following behavior analysis and modeling based on trajectory data[J]. Journal of Transportation Systems Engineering and Information Technology, 2021,21(5): 148.
    [7] 汪敏, 涂辉招, 李浩. 基于跟驰行为谱的跟驰风险状态预测[J]. 同济大学学报(自然科学版). 2021, 49(6): 843.WANG Min, TU Huizhao, LI Hao. Prediction of car-following risk status based on car-following behavior spectrum[J]. Journal of Tongji University (Natural Science), 2021, 49(6): 843.
    [8] 王雪松, 孙平, 张晓春,等. 基于自然驾驶数据的高速公路跟驰模型参数标定[J]. 中国公路学报. 2020, 33(5): 132.WANG Xuesong, SUN Ping, ZHANG Xiaochun, et al. Calibrating car-following models on freeway based on naturalistic driving data[J]. China Journal of Highway and Transportation, 2020, 33(5): 132.
    [9] 王健, 温常磊, 张香, 等. 高速公路合流区车辆跟驰行为分车道差异性[J]. 交通运输研究. 2020, 6(5): 59.WANG Jian, WEN Changlei, ZHANG Xiang, et al. Difference of vehicle following behavior in different lanes of freeway merging area[J]. Transportation Research, 2020, 6(5): 59.
    [10] WU B, YAN Y, NI D, et al. A longitudinal car-following risk assessment model based on risk field theory for autonomous vehicles[J]. International Journal of Transportation Science and Technology. 2021, 10(1): 60.
    [11] AHMED A, SARAH K. Car-following interaction and the definition of free-moving vehicles on two-lane rural highways[J]. Journal of Transportation Engineering, 2010,136(10): 925.
    [13] 刘畅,唐阳山.低速货车对高速公路驾驶员愤怒情绪影响分析[J].汽车实用技术,2021,46(19):203.LIU Chang, TANG Yangshan. Analyze the impact of low-speed trucks on the anger of drivers on highways [J]. Automoble Applied Technology,2021,46(19):203.
    [14] Transportation Research Board.Highway capacity manual[M]. Washington D C: Transportation Research Board, 2000.
    [15] 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.ZHU Shunying, JIANG Ruoxi, WANG Hong, et al. Review of research on traffic conflict techniques[J]. China Journal of Highway and Transportation,2020, 33(2): 15.
    [16] 郝志国. 高速公路换道冲突预测与安全评价研究[D]. 长春:吉林大学, 2019.HAO Guozhu. Research on prediction and safety assessment of expressway lane change conflict[D]. Changchun:Jilin University,2019.
    [17] 李英帅. 信号交叉口驾驶行为交通安全风险分析[D].南京: 东南大学, 2017.LI Yingshuai. Risk analysis of traffic safety based on driving b ehavior at signalized intersection[D]. Nangjing:Southeast University, 2017.
    [18] LI Y, LU J, XU K. Crash risk prediction model of lane-change behavior on approaching intersections[J]. Discrete Dynamics in Nature and Society. 2017(8): 1.
    [19] FOUNTAS G, ANASTASOPOULOS P C, Abdel-Aty M. Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates[J]. Analytic Methods in Accident Research. 2018, 18: 57.
    [20] CHANG F, XU P, ZHOU H. Investigating injury severities of motorcycle riders: a two-step method integrating latent class cluster analysis and random parameters logit model[J]. Accident Analysis & Prevention, 2019, 131:316.
    [21] 李俊辉,汤左淦.基于混合有序Probit模型的货车翻车驾驶员伤害程度研究[J].重庆交通大学学报(自然科学版), 2021, 40(2):21.LI Junhui, TANG Zuogan. Driver injury severity in truck rollover accidents based on mixed ordered probit model[J]. Journal of Chong Qing Jiao Tong University(Natural science), 2021, 40(2):21.
    [22] KIM J, KIM S, ULFARSSON G F, et al. Bicyclist injury severities in bicycle-motor vehicle accidents[J]. Accident Analysis and Prevention. 2007, 39(2): 238.
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谢世坤,杨轸,戢晓峰.货车移动遮断影响下的跟驰风险异质性建模[J].同济大学学报(自然科学版),2022,50(12):1788~1797

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  • 收稿日期:2021-11-11
  • 在线发布日期: 2023-01-03
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