Research on the effectiveness of a intersection risk warning system based on driving simulation experiment

被引:0
|
作者
Yiwen Zhou
Fengxiang Guo
Wenchen Yang
Huasen Wan
机构
[1] Kunming University of Science and Technology,School of Transportation Engineering
[2] Broadvision Engineering Consultants,National Engineering Laboratory for Surface Transportation Weather Impacts Prevention
[3] Yunnan Key Laboratory of Digital Communications,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
In order to improve the traffic safety condition of intersections, a real-time traffic conflict risk warning system (RTCRWS) is proposed for uncontrolled intersections. To evaluate the effectiveness of this system, a driving simulation experiment was designed and conducted. In this study, a virtual experimental scene including static road, traffic environment and dynamic traffic flow was constructed, and 45 drivers were recruited to complete the driving simulation experiment at 13 intersections. Three different data analysis methods were employed: (1) descriptive analysis of driving behavior characteristics; (2) descriptive analysis of physiological and psychological reactions of drivers; (3) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) of RTCRWS. The results show that RTCRWS can effectively control the vehicle speed and reduce the driver's tension. In addition, the following conclusions are also drawn: (1) The early warning signs with better warning effect among the two types signs of RTCRWS were compared; (2) Among the elderly and young and middle-aged drivers, RTCRWS has a better warning effect on the elderly drivers. (3) Among the male and female drivers, RTCRWS has a better warning effect on female drivers.
引用
收藏
相关论文
共 50 条
  • [21] Simulation Research on the Driving Factors of Clean Production Based on the System Dynamics
    Wang Lin-xiu
    Ma Hai-song
    2009 INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT, INNOVATION MANAGEMENT AND INDUSTRIAL ENGINEERING, VOL 2, PROCEEDINGS, 2009, : 266 - 271
  • [22] Experimental research on the effectiveness of speed reduction markings based on driving simulation: A case study
    Ding, Han
    Zhao, Xiaohua
    Rong, Jian
    Ma, Jianming
    ACCIDENT ANALYSIS AND PREVENTION, 2013, 60 : 211 - 218
  • [23] Research on Risk Warning System of Distribution Network
    Zhang, Yu
    Zhang, Tianshu
    Song, Xiaohui
    Sheng, Wanxing
    Meng, Xiaoli
    2014 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON), 2014, : 1509 - 1514
  • [24] Impact of the Rural Intersection Active Warning System (RIAWS) on driver speed: A driving simulator study
    Meuleners, Lynn B.
    Fraser, Michelle L.
    Roberts, Paul
    ACCIDENT ANALYSIS AND PREVENTION, 2020, 141
  • [25] An early warning system for loan risk assessment based on rare event simulation
    Zhou, Hong
    Qiu, Yue
    Wu, Yueqin
    ASIASIM 2007, 2007, 5 : 85 - 94
  • [26] Research on Fatigue Driving Pre-warning System Based on Multi-information Fusion
    Zhao, Xuyang
    Ye, Wenwu
    6TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2018), 2018, 1967
  • [27] Risk perception and the warning strategy based on microscopic driving state
    Zhao, Xiaomei
    Li, Qian
    Xie, Dongfan
    Bi, Jun
    Lu, Rongqin
    Li, Chao
    ACCIDENT ANALYSIS AND PREVENTION, 2018, 118 : 154 - 165
  • [28] Research on data fusion in ballistic warning simulation system
    Cai Zhihao
    Zheng Hongtao
    Peng Xiaoyuan
    SENSORS, AUTOMATIC MEASUREMENT, CONTROL, AND COMPUTER SIMULATION, PTS 1 AND 2, 2006, 6358
  • [29] Simulation and experiment research of fatigue tester system based on neural network
    Pei Zhongcai
    Guo Feiyu
    Liu Wei
    Wang Zhanlin
    2005 IEEE International Conference on Industrial Technology - (ICIT), Vols 1 and 2, 2005, : 767 - 771
  • [30] The Research on Risk Management and Early Warning Based on Information System Data Mining
    Chen, Long
    2024 INTERNATIONAL CONFERENCE ON INFORMATICS EDUCATION AND COMPUTER TECHNOLOGY APPLICATIONS, IECA 2024, 2024, : 176 - 180