Effects of built environment on bicycle wrong Way riding behavior: A data -driven approach

被引:20
作者
Luan, Sen [1 ]
Li, Meng [2 ]
Li, Xin [3 ]
Ma, Xiaolei [1 ,4 ]
机构
[1] Beihang Univ, Sch Transportat Sci & Engn, Beijing Key Lab Cooperat Vehicle Infrastruct Syst, Beijing 100191, Peoples R China
[2] Tsinghua Univ, Dept Civil Engn, Beijing 100084, Peoples R China
[3] Dalian Maritime Univ, Coll Transportat Engn, Dalian 116026, Peoples R China
[4] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp, Beijing 100191, Peoples R China
基金
国家重点研发计划;
关键词
Bicycle wrong way riding; Bike sharing; Built environment; Decision tree; PEDESTRIAN CRASH FREQUENCY; ROUTE CHOICE MODEL; SPATIAL-ANALYSIS; RISK; SAFETY; SEVERITY; LOCATION; COUNTS; SYSTEM;
D O I
10.1016/j.aap.2020.105613
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Bicycle wrong way riding (WWR) is a dangerous and often neglected behavior that engenders threats to traffic safety. Owing to the lack of exposure data, the detection of WWR and its relationship with the built environment (BE) factors remain unclear. Accordingly, this study fills the research gaps by proposing a WWR detection framework based on bike-sharing trajectories collected from Chengdu, China. Moreover, this study adopts Negative Binomial-based Additive Decision Tree to investigate the impacts of built environment on WWR frequencies. Results reveal that (1) WWR distribution is unaffected by different periods in a day; (2) road length is more influential than road level and road direction in WWR occurrence; (3) company, bus stop, subway station, residence, and catering facility are primary contributors affecting WWR behavior during peak hours, whereas education becomes an emerging influential variable during nonpeak hours; and most importantly, (4) these variables clearly present non-linear effects on the WWR frequencies. Therefore, geographically differentiated policies should be adopted for bicycle safety improvement.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] [Anonymous], TRANSPORTATION
  • [2] Cycling to work in Brazil: Users profile, risk behaviors, and traffic accident occurrence
    Bacchieri, Giancarlo
    Barros, Aluisio J. D.
    dos Santos, Janaina V.
    Gigante, Denise P.
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2010, 42 (04) : 1025 - 1030
  • [3] Bao J., 2017, ACM SIGKDD INT C KNO
  • [4] Breiman L., 1984, CLASSIFICATION REGRE, DOI [10.1201/9781315139470, DOI 10.1201/9781315139470]
  • [5] Where do cyclists ride? A route choice model developed with revealed preference GPS data
    Broach, Joseph
    Dill, Jennifer
    Gliebe, John
    [J]. TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 2012, 46 (10) : 1730 - 1740
  • [6] Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models
    Cai, Qing
    Lee, Jaeyoung
    Eluru, Naveen
    Abdel-Aty, Mohamed
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2016, 93 : 14 - 22
  • [7] Optimizing the location of stations in bike-sharing programs: A GIS approach
    Carlos Garcia-Palomares, Juan
    Gutierrez, Javier
    Latorre, Marta
    [J]. APPLIED GEOGRAPHY, 2012, 35 (1-2) : 235 - 246
  • [8] Evaluating the Safety Effects of Bicycle Lanes in New York City
    Chen, Li
    Chen, Cynthia
    Srinivasan, Raghavan
    McKnight, Claire E.
    Ewing, Reid
    Roe, Matthew
    [J]. AMERICAN JOURNAL OF PUBLIC HEALTH, 2012, 102 (06) : 1120 - 1127
  • [9] Built environment effects on bike crash frequency and risk in Beijing
    Chen, Peng
    Sun, Feiyang
    Wang, Zhenbo
    Gao, Xu
    Jiao, Junfeng
    Tao, Zhimin
    [J]. JOURNAL OF SAFETY RESEARCH, 2018, 64 : 135 - 143
  • [10] Effects of the built environment on automobile-involved pedestrian crash frequency and risk
    Chen, Peng
    Zhou, Jiangping
    [J]. JOURNAL OF TRANSPORT & HEALTH, 2016, 3 (04) : 448 - 456