Citation

Xu Z, Zheng N, Logan DB, Vu HL. Accid. Anal. Prev. 2023; 191: e107194.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.aap.2023.107194

PMID

37402331

Abstract

Animosity between drivers and cyclists has existed on urban road networks for many years. Conflicts between these two groups of road users are exceptionally high in the shared right-of-way environments. Benchmarking methods of conflict assessments are mostly based on statistical analysis with limited data sources. The actual crash data would be valuable to understand the features of bike-car collisions, however the available data are spatially and temporally sparse. To this end, this paper proposes a simulation-based bicycle-vehicle conflict data generation and assessment approach. The proposed approach uses a three-dimensional visualization and virtual reality platform, integrating traffic microsimulation to reproduce a naturalistic driving/cycling-enabled experimental environment. The simulation platform is validated to reflect the human-resembled driving/cycling behaviors under different infrastructure designs. Comparative experiments are carried out on bicycle-vehicle interactions under different conditions, with data collected from a total of 960 scenarios. Based on the results of the surrogate safety assessment model (SSAM), the obtained key insights include: (1) scenarios of a high conflict probability do not lead to actual crashes, which suggests that the classic SSM-based measurements such as TTC or PET values may not sufficiently reflect real cyclist-driver interactions; (2) the major cause of conflicts is variation in vehicle acceleration, which suggests that drivers are considered to be the main party responsible for bicycle-vehicle conflict/crash occurrence; (3) the proposed approach is able to generate near-miss events and reproduce interaction patterns between cyclists and drivers, facilitating experiments and data collections which would be typically unavailable for this type of study.

Language: en

Keywords

Virtual reality; Bicycle-vehicle conflicts; Data generation; Surrogate safety measurements