Research on Vehicle Trajectory Deviation Characteristics on Freeways Using Natural Driving Trajectory Data

被引:6
|
作者
Dai, Zhenhua [1 ]
Pan, Cunshu [1 ]
Xiong, Wenlei [2 ]
Ding, Rui [1 ]
Zhang, Heshan [1 ]
Xu, Jin [1 ,3 ]
机构
[1] Chongqing Jiaotong Univ, Coll Traff & Transportat, Chongqing 400074, Peoples R China
[2] CCCC Second Highway Consultant Co Ltd, Wuhan 430056, Peoples R China
[3] Chongqing Jiaotong Univ, Chongqing Key Lab Human Vehicle Rd Cooperat & Saf, Chongqing 400074, Peoples R China
关键词
traffic engineering; traffic safety; driving behavior; geometric alignment; lane width; trajectory deviation; LANE WIDTH; SHOULDER WIDTH; TRAFFIC FLOW; MODEL;
D O I
10.3390/ijerph192214695
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Lateral driving behavior analysis is the foundation of freeway cross-section design and the focus of road safety research. However, the factors that influence vehicle lateral driving behavior have not been clearly explained. The dataset of the natural driving trajectory of freeways is used in this study to analyze vehicle lateral driving behavior and trajectory characteristics. As vehicle trajectory characteristic indicators, parameters such as preferred trajectory deviation and standard deviation are extracted. The effects of lane position, speed, road safety facilities, and vehicle types on freeway trajectory behavior are investigated. The results show that lane width and lane position significantly impact vehicle trajectory distribution. As driving speed increases, the lateral distance between vehicles in the inner lane and the guardrail tends to increase. In contrast, vehicles in the outside lane will stay away from the road edge line, and vehicles in the middle lane will stay away from the right lane dividing line when the speed increases. Statistical analysis shows that the preferred trajectory distribution of the same vehicle type in different lane positions is significantly different among groups (Cohen's d > 0.7). In the same lane, the lateral position characteristics of the center of mass of different vehicle types are basically the same (Cohen's d < 0.35). This work aims to explain what variables cause trajectory deviation behaviors and how to design traffic safety facilities (guardrail and shoulder) and lane width to accommodate various vehicle types and design speeds.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] A Scoring Method for Driving Safety Credit Using Trajectory Data
    Wang, Wenfu
    Yang, Yao
    Chen, An
    Pan, Zhijie
    2019 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI 2019), 2019, : 558 - 565
  • [22] Geometric optimisation of chicanes using driving simulator trajectory data
    Akgol, Kadir
    Gunay, Banihan
    Aydin, Metin Mutlu
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT, 2022, 175 (04) : 238 - 248
  • [23] Research on Intelligent Vehicle Trajectory Planning Based on Multimodal Trajectory Prediction
    Huang J.
    Liu X.
    Deng X.
    Chen R.
    Qiche Gongcheng/Automotive Engineering, 2024, 46 (06): : 965 - 974and1024
  • [24] Probability of Trajectory Deviation of Unmanned Aerial Vehicle in Presence of Wind
    Banerjee P.
    Corbetta M.
    Jarvis K.
    Smalling K.
    Turner A.
    Journal of Air Transportation, 2023, 31 (03): : 128 - 139
  • [25] Vehicles Trajectory Oscillation Characteristics and Passenger Cars' Lane Width for Freeways
    Zhuang J.-F.
    Li Z.-J.
    Ding R.
    Xiong W.-L.
    Zhang H.-S.
    Xu J.
    Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2023, 23 (01): : 324 - 336
  • [26] Risky Driving Behavior Recognition Based on Vehicle Trajectory
    Chen, Shengdi
    Xue, Qingwen
    Zhao, Xiaochen
    Xing, Yingying
    Lu, Jian John
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (23)
  • [27] Characteristics of Heavy Vehicle Discretionary Lane Changing Based on Trajectory Data
    Li, Gen
    Ma, Jianxiao
    Yang, Zhen
    TRANSPORTATION RESEARCH RECORD, 2022, 2676 (03) : 258 - 275
  • [28] Virtual Detection at Intersections using Connected Vehicle Trajectory Data
    Li, Howell
    Day, Christopher M.
    Bullock, Darcy M.
    2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2016, : 2571 - 2576
  • [29] Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding
    Ugan, Jorge
    Abdel-Aty, Mohamed
    Islam, Zubayer
    IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 5 : 16 - 28
  • [30] Research on vehicle trajectory fusion prediction based on physical model and driving intention recognition
    Sun, Ning
    Xu, Nan
    Guo, Konghui
    Han, Yulong
    Wang, Luyao
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2025, 239 (01) : 239 - 254