基于交叉口相位切换期间的车辆轨迹数据，分别根据单车和跟车行驶状态，识别和分析了相位切换期间可能发生的危险驾驶行为。通过视频拍摄和图像处理的方式，提取了曹安公路沿线3个交叉口共312条单车状态和四平路-大连路交叉口共449条跟车状态的高精度车辆轨迹数据。针对交叉口相位切换期间的危险驾驶行为特征，利用速度、加减速度、减速度变化率、潜在碰撞时间（TTC）等指标，研究在此期间车辆发生危险驾驶行为的特点和类型。对于单车状态下行驶的车辆，按停止、通过分类，依据减速度、减速度变化率、减速度变化率的峰值差等指标将停止车辆的危险驾驶行为分为紧急减速型、增强减速型和持续急减型，依据过停车线时间、速度、加速度等指标将通过车辆分为闯红灯型、超速过线型、激进加速型和持续高速型。对于在跟车状态下行驶的车辆，按前、后车不同的停止、通过决策组合分类，依据连续5个时间间隔（0.12 s）的TTC分析前、后车的危险驾驶行为及发生追尾事故的危险程度。针对识别出的危险驾驶行为类型，讨论车辆的关键行为参数与危险驾驶行为之间的内在关联。研究结果表明：单车状态下有17%的车辆存在危险驾驶行为，其中53%为紧急减速行为；跟车状态下有19%的跟车行为是危险的，其中停止车辆的比例是通过车辆的2倍以上。研究成果可进一步应用于驾驶行为模型的参数标定、基于车辆轨迹的交叉口安全评价以及预防危险驾驶行为的主动安全控制策略等。
Abstract
Based on the vehicle trajectory data during the phase transition intervals at an intersection, the risky driving behaviors that may occur within these intervals were identified and analyzed individually based on the driving state respectively. Through video capturing and image processing, high-precision vehicle trajectory data, for a total of 312 single vehicle trajectories from three intersections along the Cao'an road and 449 following vehicle trajectories from the Siping-Dalian intersection, were obtained. With the empirical analysis of the risky driving behavior during the phase transition intervals at the intersection, the speed, acceleration, deceleration, jerk peak-to-peak jerk, time to collision (TTC), and other parameters were used to study the characteristic and type of risky driving behaviors within these intervals. For the vehicles traveling in a single state, after a stop-go classification, the risky driving behavior of the stopping vehicle is classified as one of three categories:emergency deceleration, enhanced deceleration, or continuous urgent deceleration, according to the deceleration, jerk, and peak-to-peak jerk. The risky driving behavior of the passing vehicle is classified into one of four categories:red violation, overspeeding, radical acceleration, and continuous high speed, according to the time through the stop line, speed, and acceleration. For the vehicles traveling in the following state, considering the different combinations of leading and following the vehicle's stop-go decision, the risky driving behavior was analyzed with the occurrence of rear-end crash risk according to the TTC in five time intervals (0.12 s). In accordance with the identification of risky driving behavior types, the internal relation of the key parameters and risky driving behaviors were discussed. The identification result shows that 17 percent of the single vehicles have risky driving behaviors, in which 53 percent are emergency deceleration, and 19 percent of the following vehicles are in risk, where the proportion of stopping vehicles is more than two times higher than passing vehicles. The results of the study can be further applied to the parameter calibration reference of a driving behavior model, an intersection safety evaluation method based on the vehicle trajectory, proactive safety control strategy, etc.
关键词
交通工程 /
危险驾驶行为 /
车辆轨迹 /
相位切换 /
信号控制交叉口
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Key words
traffic engineering /
risky driving behavior /
vehicle trajectory /
phase transition /
signalized intersection
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中图分类号：
U491
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参考文献
[1] TANG K S, DONG S, WANG F, et al. Behavior of Riders of Electric Bicycles at Onset of Green and Yellow at Signalized Intersections in China[J]. Transportation Research Record, 2012(2317):85-96.
[2] 李克平,杨佩昆,倪颖.城市道路交叉口信号控制中的黄灯问题[J].城市交通,2010,8(4):67-72. LI Ke-ping, YANG Pei-kun, NI Ying. Amber Interval Design at Urban Signalized Intersections[J]. Urban Transport of China, 2010, 8(4):67-72.
[3] GAZIS D, HERMAN R, MARADUDIN A. The Problem of the Amber Signal Light in Traffic Flow[J]. Operations Research, 1960, 8(1):112-132.
[4] PRASHKER J N, MAHALEL D. The Relationship Between an Option Space and Drivers' Indecision at Signalized Intersection Approaches[J]. Transportation Research Part B, 1989, 23(6):401-413.
[5] TANG K S, ZHU S F, XU Y Q, et al. Modeling Drivers' Dynamic Decision-making Behavior During the Phase Transition Period:An Analytical Approach Based on Hidden Markov Model Theory[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(1):206-214.
[6] WANG F, TANG K S, XU Y Q, et al. Modeling Risky Driver Behavior Under the Influence of Flashing Green Signal with Vehicle Trajectory Data[J]. Transportation Research Record, 2016(2562):53-62.
[7] TANG K S, XU Y Q, WANG P F, et al. Impacts of Flashing Green on Dilemma Zone Behavior at High-speed Intersections:Empirical Study in China[J]. Journal of Transportation Engineering, 2015, 141(7):04015005.
[8] DATONDJI S R E, DUPUIS Y, SUBIRATS P, et al. A Survey of Vision-based Traffic Monitoring of Road Intersections[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10):2681-2698.
[9] KI Y K, LEE D Y. A Traffic Accident Recording and Reporting Model at Intersections[J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(2):188-194.
[10] KHATTAK A, LIU, WANG X. Supporting Instantaneous Driving Decisions Through Trajectory Data[C]//TRB. Proceedings of the 94th Transportation Research Board Annual Meeting. Washington DC:TRB, 2015, DOI:10.13140/RG.2.1.2758.4245.
[11] GATES T J, NOYCE D A, LARACUENTE L. Analysis of Dilemma Zone Driver Behavior at Signalized Intersections[C]//TRB. Transportation Research Board 2007 Annual Meeting. Washington DC:TRB, 2007:1-25.
[12] TAK S, KIM S, YEO H. Development of a Deceleration-based Surrogate Safety Measure for Rear-end Collision Risk[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(5):2435-2445.
[13] BAGDADI O, VÁRHELYI A. Development of a Method for Detecting Jerks in Safety Critical Events[J]. Accident Analysis & Prevention, 2013, 50:83-91.
[14] KIM H, OH J S, JAYAKRISHNAN R. Application of Activity Chaining Model Incorporating a Time Use Problem to Network Demand Analysis[J]. Transportation Research Record, 2006(1977):214-224.
[15] KIM E, CHOI E. Estimates of Critical Values of Aggressive Acceleration from a Viewpoint of Fuel Consumption and Emissions[C]//TRB. Proceedings of the 2013 Transportation Research Board Annual Meeting. Washington DC:TRB, 2013:1-16.
[16] DE VLIEGER I, DE KEUKELEERE D, KRETZS-CHMAR J G. Environmental Effects of Driving Behaviour and Congestion Related to Passenger Cars[J]. Atmospheric Environment, 2000, 34(27):4649-4655.
[17] HAYWARD J C. Near-miss Determination Through Use of a Scale of Danger[J]. Highway Research Record, 1972(384):24-34.
[18] 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. LU Jian, ZHANG Wen-jun, YANG Hai-fei, et al. Analysis of Rear-end Risk Based on the Indicator of Time to Collision[J]. Journal of Transport Information and Safety, 2014, 32(5):58-64.
[19] 林庆峰,成波,徐少兵,等.基于Logistic回归的危险认知模型与避撞时间模型的对比[J].中国公路学报,2012,25(6):123-128. LIN Qing-feng, CHENG Bo, XU Shao-bing, et al. Comparison of Risk Perception Model Based on Logistic Regression and Time-to-collision Model[J]. China Journal of Highway and Transport, 2012, 25(6):123-128.
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脚注
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基金
国家自然科学基金项目（61673302）
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