Understanding the traffic flow in different types of freeway tunnels based on car-following behaviors analysis

被引:5
作者
Shang, Ting [1 ]
Lu, Jiaxin [1 ]
Luo, Ying [2 ]
Wang, Song [1 ]
He, Zhengyu [3 ]
Wang, Aobo [4 ]
机构
[1] Chongqing Jiaotong Univ, Coll Traff & Transportat, Chongqing 400074, Peoples R China
[2] Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
[3] Columbia Univ, Fu Fdn Sch Engn & Appl Sci, New York, NY 10027 USA
[4] Univ Nevada, Coll Engn, Reno, NV 89557 USA
基金
中国国家自然科学基金;
关键词
Continuous car -following behavior; Different tunnel types; Full velocity difference model; Traffic flow stability; Traffic flow safety; MODEL; CONGESTION; AGE;
D O I
10.1016/j.tust.2023.105494
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Tunnels are an engineering solution that has gained prominence for constructing freeways in mountainous regions. The length of the tunnels can vary, depending on the geological conditions, engineering requirements, and budget constraints. Car-following is the predominant driving behavior observed in tunnels, and understanding how drivers follow each other in different types of tunnels is crucial for ensuring smooth traffic flow and safety. Each type of tunnel environment can uniquely impact car-following behavior, which allows for targeted studies to optimize traffic management. In this research, natural driving data in freeway tunnels were collected through a driving experiment conducted on the Baomao Freeway in Chongqing, China. Then, the correlations and differences in car-following data between various tunnels and sections were analyzed. Finally, car-following models were developed considering various tunnel scenarios, and the influence of tunnel types on traffic flow was analyzed by simulation. The study revealed notable variations in car-following behavior across different types of tunnels, as well as within consecutive sections of the same tunnel. As tunnel length increased, the driving stability of following vehicles decreased, but the level of driving safety risk was not positively correlated with tunnel length. Significant vehicle trajectory oscillation was observed within the inner sections of long and extra-long tunnels, and a significant relationship between the acceleration of following vehicles and the location within the tunnel section was found. Additionally, the longer the tunnel, the greater the fluctuations in traffic flow, and the negative impact of the tunnel environment on traffic flow stability increased periodically downstream. These findings offer valuable insights for understanding and modeling car-following behavior in freeway tunnels, which ultimately facilitate traffic safety and mobility.
引用
收藏
页数:17
相关论文
共 38 条
  • [31] Operation analysis of freeway mixed traffic flow based on catch-up coordination platoon
    Yang, Xiaoxue
    Zou, Yajie
    Chen, Lei
    ACCIDENT ANALYSIS AND PREVENTION, 2022, 175
  • [32] Calibration and validation of the rule-based human driver model for car-following behaviors at roundabout using naturalistic driving data
    Choi, Junhee
    Kim, Dong-Kyu
    ASIAN TRANSPORT STUDIES, 2024, 10
  • [33] Uncertainty Analysis of Rear-End Collision Risk Based on Car-Following Driving Simulation Experiments
    Xue, Qingwan
    Yan, Xuedong
    Li, Xiaomeng
    Wang, Yun
    DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2018, 2018
  • [34] Location-based analysis of car-following behavior during braking using naturalistic driving data
    Tawfeek, Mostafa H.
    El-Basyouny, Karim
    CANADIAN JOURNAL OF CIVIL ENGINEERING, 2020, 47 (05) : 498 - 505
  • [35] Review on car-following sensor based and data-generation mapping for safety and traffic management and road map toward ITS
    Talal, Mohammed
    Ramli, Khairun Nidzam
    Zaidan, A. A.
    Zaidan, B. B.
    Jumaa, Fawaz
    VEHICULAR COMMUNICATIONS, 2020, 25
  • [36] Analysis of rear-end crash potential and driver contributing factors based on car-following driving simulation
    Bumrungsup, Lerdmanus
    Kanitpong, Kunnawee
    TRAFFIC INJURY PREVENTION, 2022, 23 (05) : 296 - 301
  • [37] Blending of Floating Car Data and Point-Based Sensor Data to Deduce Operating Speeds under Different Traffic Flow Conditions
    Del Serrone, Giulia
    Cantisani, Giuseppe
    Peluso, Paolo
    EUROPEAN TRANSPORT-TRASPORTI EUROPEI, 2023, (91):
  • [38] Understanding different types of consumers: A multi-group analysis based on convenience food-related lifestyle
    Liang, Austin Rong-Da
    Lim, Wai Mun
    Tung, Wei
    Lin, Shu-Ying
    AIMS AGRICULTURE AND FOOD, 2023, 8 (02): : 374 - 390