Traffic conflict identification of e-bikes at signalized intersections

    Zhaowei Qu Affiliation
    ; Yuhong Gao Affiliation
    ; Xianmin Song Affiliation
    ; Yingji Xia Affiliation
    ; Lin Ma Affiliation
    ; Ronghan Yao Affiliation


The increase of e-bikes has raised traffic conflict concerns over past decade. Numerous conflict indicators are applied to measure traffic conflicts by detecting differences in temporal or spatial proximity between users. However, for traffic environment with plenty of e-bikes, these separate space-time approaching indicators may not be applicable. Thus, this study aims to propose a multi-variable conflict indicator and build a conflict identification method for e-bikes moving in the same direction. In particular, by analysing the conflict characteristics from e-bikes trajectories, a multi-variable conflict indicator utilizing change of forecast post encroachment time, change of relative speed and change of distance is derived. Mathematical statistics and cluster discriminant analyses are applied to identify types of conflict, including conflict existence identification and conflict severity identification. The experimental results show: in mixed traffic environments with many e-bikes, compared with time-to-collision and deceleration, accuracy of identifying e-bike conflict types based on proposed method is the highest and can reach more than 90%; that is, multi-variable indicator based on time and space are more suitable for identifying e-bike conflicts than separate space-time approaching indicators. Furthermore, setting of dividing strip between motor vehicle and non-motorized vehicle has significant influence on number and change trend of conflict types. The proposed method can not only provide a theoretical basis and technical support for automated conflict detection in mixed transportation, but also give the safety optimization sequence of e-bikes at different types of intersections.

First published online 22 October 2020

Keyword : traffic safety, conflict identification, cluster discriminant analysis, e-bikes, trajectory extraction, signalized intersection

How to Cite
Qu, Z. ., Gao, Y., Song, X., Xia, Y., Ma, L., & Yao, R. (2021). Traffic conflict identification of e-bikes at signalized intersections. Transport, 36(2), 185-198.
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Jun 22, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.


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