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Analysis of the Characteristics and Number of Bicycle–Passenger Conflicts at Bus Stops for Improving Safety

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  • Xingchen Yan

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China)

  • Tao Wang

    (School of Architecture and Transportation, Guilin University of Electronic Technology, Jinji Road 1#, Guilin 541004, China)

  • Jun Chen

    (School of Transportation, Southeast University, Dongnandaxue Road 2#, Jiangning Development Zone, Nanjing 211189, China)

  • Xiaofei Ye

    (School of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China)

  • Zhen Yang

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China)

  • Hua Bai

    (China Design Group Co., Ltd., Ziyun Road 9#, Nanjing 210014, China)

Abstract

This study aimed to analyze the characteristics of bicycle–passenger conflicts at bus stops and develop a model to predict the number of conflicts accurately. This paper investigated the traffic flow operation at bus stops by video recording. Duration and distribution characteristics of bicycle–passenger conflicts were statistically analyzed. Then four types of conflicts defined based on evasive behavior (cyclist yielding as Type 1, cyclist bypassing as Type 2, passenger yielding as Type 3, and passenger bypassing as Type 4) were compared. A generalized event count (GEC) model was established for bicycle–passenger conflict estimation and analysis. The main results indicated that: (1) The average conflict duration was 1.716 s, whilst 60.9% of conflicts existed near the accesses of bus stops in longitudinal direction; (2) Type 1 conflict was significantly different from Type 2, 3, and 4 conflicts in duration, whilst the three had no significant difference; (3) the proposed GEC model showed good performance in predicting bicycle–passenger conflicts, with 15.71% of mean-absolute-percentage-error and 0.8772 of R 2 ; and (4) bicycle volume, bus passenger volume, and passenger crossing time were critical factors impacting the number of bicycle–passenger conflicts. Finally, transport agencies may consider installing separations and crosswalks to improve the safety of the stop area.

Suggested Citation

  • Xingchen Yan & Tao Wang & Jun Chen & Xiaofei Ye & Zhen Yang & Hua Bai, 2019. "Analysis of the Characteristics and Number of Bicycle–Passenger Conflicts at Bus Stops for Improving Safety," Sustainability, MDPI, vol. 11(19), pages 1-14, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5263-:d:270551
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    References listed on IDEAS

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    1. Qingyu Luo & Tianyao Zheng & Wenjing Wu & Hongfei Jia & Jin Li, 2018. "Modeling the effect of bus stops on capacity of curb lane," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 29(03), pages 1-20, March.
    2. Cherry, Christopher & Cervero, Robert, 2007. "Use characteristics and mode choice behavior of electric bike users in China," Transport Policy, Elsevier, vol. 14(3), pages 247-257, May.
    3. Pan, Yingjiu & Chen, Shuyan & Li, Tiezhu & Niu, Shifeng & Tang, Kun, 2019. "Exploring spatial variation of the bus stop influence zone with multi-source data: A case study in Zhenjiang, China," Journal of Transport Geography, Elsevier, vol. 76(C), pages 166-177.
    4. Lee, Lung-Fei, 1986. "Specification Test for Poisson Regression Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 27(3), pages 689-706, October.
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    Cited by:

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    2. Xiaohong Jiang & Mingyu Dong & Yuewei He & Jiayi Shen & Wenqi Jing & Nan Yang & Xiucheng Guo, 2020. "Research on the Design of and Preference for Collection Modes of Reusable Takeaway Containers to Promote Sustainable Consumption," IJERPH, MDPI, vol. 17(13), pages 1-18, July.
    3. Joseph V. Terza, 2025. "Standard Errors for Regression-Based Causal Effect Estimates in Economics Using Numerical Derivatives," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 69-89, January.
    4. Xiaofei Ye & Yi Zhu & Tao Wang & Xingchen Yan & Jun Chen & Bin Ran, 2022. "Level of Service Model of the Non-Motorized Vehicle Crossing the Signalized Intersection Based on Riders’ Perception Data," IJERPH, MDPI, vol. 19(8), pages 1-17, April.
    5. Yongqiang Zhang & Zhuang Hu & Min Zhang & Wenting Ba & Ying Wang, 2022. "Emergency Response Resource Allocation in Sparse Network Using Improved Particle Swarm Optimization," IJERPH, MDPI, vol. 19(16), pages 1-11, August.

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