Share:


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

Abstract

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. https://doi.org/10.3846/transport.2020.13297
Published in Issue
Jun 22, 2021
Abstract Views
306
PDF Downloads
169
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Alhajyaseen, W. K. M. 2014. The development of conflict index for the safety assessment of intersections considering crash probability and severity, Procedia Computer Science 32: 364–371. https://doi.org/10.1016/j.procs.2014.05.436

Alhajyaseen, W. K. M. 2015. The integration of conflict probability and severity for the safety assessment of intersections, Arabian Journal for Science and Engineering 40(2): 421–430. https://doi.org/10.1007/s13369-014-1553-1

Allen, B. L.; Shin, B. T.; Cooper, P. J. 1978. Analysis of traffic conflicts and collisions, Transportation Research Record 667: 67–74.

Amundsen, F. H. 1977. Proceedings from the first Workshop on Traffic Conflicts. September 1977, Oslo, Norway. 138 p.

Autey, J.; Sayed, T.; Zaki, M. H. 2012. Safety evaluation of right-turn smart channels using automated traffic conflict analysis, Accident Analysis & Prevention 45: 120–130. https://doi.org/10.1016/j.aap.2011.11.015

Bai, L.; Liu, P.; Chen, Y.; Zhang, X.; Wang, W. 2013. Comparative analysis of the safety effects of electric bikes at signalized intersections, Transportation Research Part D: Transport and Environment 20: 48–54. https://doi.org/10.1016/j.trd.2013.02.001

Behbahani, H.; Nadimi, N.; Alenoori, H.; Sayadi, M. 2014. Developing a new surrogate safety indicator based on motion equations, Promet – Traffic & Transportation 26(5): 371–381. https://doi.org/10.7307/ptt.v26i5.1388

Cherry, C.; Cervero, R. 2007. Use characteristics and mode choice behavior of electric bike users in China, Transport Policy 14(3): 247–257. https://doi.org/10.1016/j.tranpol.2007.02.005

Dozza, M.; Bianchi Piccinini, G. F.; Werneke, J. 2016. Using naturalistic data to assess e-cyclist behavior, Transportation Research Part F: Traffic Psychology and Behaviour 41: 217–226. https://doi.org/10.1016/j.trf.2015.04.003

Guo, Y.; Liu, P.; Bai, L.; Xu, C.; Chen, J. 2014. Red light running behavior of electric bicycles at signalized intersections in China, Transportation Research Record: Journal of the Transportation Research Board 2468: 28–37. https://doi.org/10.3141/2468-04

Hayward, J. C. 1972. Near-miss determination through use of a scale of danger, Highway Research Record 384: 24–34.

Hyden, C. 1987. The development of a method for traffic safety evaluation: the Swedish traffic conflicts technique, Bulletin Lund Institute of Technology 70: 1–57.

Ismail, K.; Sayed, T.; Saunier, N. 2011. Methodologies for aggregating indicators of traffic conflict, Transportation Research Record: Journal of the Transportation Research Board 2237: 10–19. https://doi.org/10.3141/2237-02

Kim, J.-K.; Kim, S.; Ulfarsson, G. F.; Porrello, L. A. 2007. Bicyclist injury severities in bicycle–motor vehicle accidents, Accident Analysis & Prevention 39(2): 238–251. https://doi.org/10.1016/j.aap.2006.07.002

Langford, B. C.; Chen, J.; Cherry, C. R. 2015. Risky riding: naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders, Accident Analysis & Prevention 82: 220–226. https://doi.org/10.1016/j.aap.2015.05.016

Laureshyn, A.; De Ceunynck, T.; Karlsson, C.; Svensson, Å.; Daniels, S. 2017. In search of the severity dimension of traffic events: extended delta-V as a traffic conflict indicator, Accident Analysis & Prevention 98: 46–56. https://doi.org/10.1016/j.aap.2016.09.026

MIIT. 2018. Safety Technical Specification for Electric Bicycle. Ministry of Industry and Information Technology (MIIT) of the People’s Republic of China. Available from Internet: http://www.miit.gov.cn (in Chinese).

Minikel, E. 2012. Cyclist safety on bicycle boulevards and parallel arterial routes in Berkeley, California, Accident Analysis & Prevention 45: 241–247. https://doi.org/10.1016/j.aap.2011.07.009

Ning, J.; Zhang, L.; Zhang, D.; Wu, C. 2012. Robust mean-shift tracking with corrected background-weighted histogram, IET Computer Vision 6(1): 62–69. https://doi.org/10.1049/iet-cvi.2009.0075

Pai, C.-W.; Jou, R.-C. 2014. Cyclists’ red-light running behaviours: An examination of risk-taking, opportunistic, and law-obeying behaviours, Accident Analysis & Prevention 62: 191–198. https://doi.org/10.1016/j.aap.2013.09.008

Räsänen, M.; Summala, H. 1998. Attention and expectation problems in bicycle–car collisions: an in-depth study, Accident Analysis & Prevention 30(5): 657–666. https://doi.org/10.1016/s0001-4575(98)00007-4

Saccomanno, F. F.; Cunto, F.; Guido, G.; Vitale, A. 2008. Comparing safety at signalized intersections and roundabouts using simulated rear-end conflicts, Transportation Research Record: Journal of the Transportation Research Board 2078: 90–95. https://doi.org/10.3141/2078-12

Sayed, T.; Zaki, M. H.; Autey, J. 2013. Automated safety diagnosis of vehicle–bicycle interactions using computer vision analysis, Safety Science 59: 163–172. https://doi.org/10.1016/j.ssci.2013.05.009

Sharizli, A. A.; Rahizar, R.; Karim, M. R.; Saifizul, A. A. 2015. New method for distance-based close following safety indicator, Traffic Injury Prevention 16(2): 190–195. https://doi.org/10.1080/15389588.2014.921913

Silvano, A. P.; Koutsopoulos, H. N.; Ma, X. 2016. Analysis of vehicle-bicycle interactions at unsignalized crossings: A probabilistic approach and application, Accident Analysis & Prevention 97: 38–48. https://doi.org/10.1016/j.aap.2016.08.016

Tageldin, A.; Sayed, T.; Wang, X. 2015. Can time proximity measures be used as safety indicators in all driving cultures? Case study of motorcycle safety in China, Transportation Research Record: Journal of the Transportation Research Board 2520: 165–174. https://doi.org/10.3141/2520-19

Wang, Y.; Nihan, N. L. 2004. Estimating the risk of collisions between bicycles and motor vehicles at signalized intersections, Accident Analysis & Prevention 36(3): 313–321. https://doi.org/10.1016/s0001-4575(03)00009-5

Wu, C.; Yao, L.; Zhang, K. 2012. The red-light running behavior of electric bike riders and cyclists at urban intersections in China: an observational study, Accident Analysis & Prevention 49: 186–192. https://doi.org/10.1016/j.aap.2011.06.001

Wu, Y.; Lu, J.; Chen, H.; Wan, Q. 2016. Modeling the frequency of cyclists’ red-light running behavior using Bayesian PG model and PLN model, Discrete Dynamics in Nature and Society 2016: 2593698. https://doi.org/10.1155/2016/2593698

Xu, C.; Yang, Y.; Jin, S.; Qu, Z.; Hou, L. 2016. Potential risk and its influencing factors for separated bicycle paths, Accident Analysis & Prevention 87: 59–67. https://doi.org/10.1016/j.aap.2015.11.014

Yang, H.; Ozbay, K. 2011. Estimation of traffic conflict risk for merging vehicles on highway merge section, Transportation Research Record: Journal of the Transportation Research Board 2236: 58–65. https://doi.org/10.3141/2236-07