Chosen risk level during car-following in adverse weather conditions

被引:29
作者
Hjelkrem, Odd Andre [1 ]
Ryeng, Eirin Olaussen [1 ]
机构
[1] Norwegian Univ Sci & Technol, Dept Civil & Transport Engn, NO-7491 Trondheim, Norway
关键词
Risk perception; Car-following; Adverse weather; GLM; VISIBILITY RELATED CRASHES; TIME; COLLISION; RAINFALL; HEADWAY; CANADA; SPEED; MODEL; FLOW;
D O I
10.1016/j.aap.2016.07.006
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
This study examines how precipitation, light conditions and surface conditions affect the drivers' risk perception. An indicator CRI (Chosen Risk Index) is defined, which describes the chosen risk level for drivers in a car-following situation. The dataset contains about 70 000 observations of driver behaviour and weather status on a rural road. Based on the theory of risk homeostasis and an assumption that driving behaviour in situations with daylight, dry road and no precipitation reflects drivers' target level of risk, generalised linear models (GLM) were estimated for cars and trucks separately to reveal the effect of adverse weather conditions on risk perception. The analyses show that both car and truck drivers perceive the highest risk when driving on snow covered roads. For car drivers, a snow covered road in combination with moderate rain or light snow are the factors which lowers the CRI the most. For trucks, snow cover and partially covered roads significantly lowers the CRI, while precipitation did not seem to impose any higher risk. Interaction effects were found for car drivers only. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:227 / 235
页数:9
相关论文
共 50 条
  • [31] How various urgencies and visibilities influence drivers' takeover performance in critical car-following conditions? A driving simulation study
    Lin, Zijian
    Chen, Feng
    TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2024, 104 : 303 - 317
  • [32] Modeling Car-following Behavior as Instantaneous Maximization of Drivers' Utility for Estimation of Traffic Level of Service
    Kasai, Makoto
    2012 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2012, : 1049 - 1054
  • [33] Effect of the Uncertainty Level of Vehicle-Position Information on the Stability and Safety of the Car-Following Process
    Zhang, Junjie
    Lu, Guangquan
    Yu, Haiyang
    Wang, Yunpeng
    Yang, Can
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (06) : 4944 - 4958
  • [34] Attribute selection for modelling driver's car-following behaviour in heterogeneous congested traffic conditions
    Aghabayk, Kayvan
    Sarvi, Majid
    Young, William
    TRANSPORTMETRICA A-TRANSPORT SCIENCE, 2014, 10 (05) : 457 - 468
  • [35] Analysis of Reaction Time During Car-following Process Based on Driving Simulation Test
    Xue, Qingwan
    Yan, Xuedong
    Zhang, Yuting
    Wang, Bin
    3RD INTERNATIONAL CONFERENCE ON TRANSPORTATION INFORMATION AND SAFETY (ICTIS 2015), 2015, : 207 - 212
  • [36] Modeling vehicle car-following behavior in congested traffic conditions based on different vehicle combinations
    Kong, Dewen
    List, George F.
    Guo, Xiucheng
    Wu, Dingxin
    TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH, 2018, 10 (05): : 280 - 293
  • [37] New fuzzy solution for determining anticipation and evaluation behavior during car-following maneuvers
    Ghaffari, Ali
    Khodayari, Alireza
    Kamali, Ali
    Tajdari, Farzam
    Hosseinkhani, Niloofar
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2018, 232 (07) : 936 - 945
  • [38] Modeling car-following behavior during queue discharge at signalized intersections with countdown timer
    Raveendran, Bijul
    Mathew, Tom V.
    Velaga, Nagendra R.
    TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH, 2024, 16 (01): : 1 - 13
  • [39] Fuel consumption optimization for smart hybrid electric vehicle during a car-following process
    Li, Liang
    Wang, Xiangyu
    Song, Jian
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 87 : 17 - 29
  • [40] Highly Automated Driving Impact on Drivers' Gaze Behaviors during a Car-Following Task
    Navarro, J.
    Osiurak, F.
    Ovigue, M.
    Charrier, L.
    Reynaud, E.
    INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, 2019, 35 (11) : 1008 - 1017