Traffic injury prevention · 2026
A conflict-based framework for proactive risk assessment of electric bicycle-motor vehicle interactions at signalized intersections.
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He Yina, Mao Chengyuan, Lei Fangling, Huang Yiwen
Аннотация
OBJECTIVE: The proposed research uses a novel surrogate safety outcome called Maximum Residual Reaction Time (MRRT) to design a comprehensive risk assessment model for analyzing interactions between electric bikes and motor vehicles (EBs-MVs) with regards to signalized intersections. The overall aim is to enhance pro-active injury prevention by improving short-term risk detection and estimation for vulnerable road users.
METHODS: Data were collected from video recordings at six signalized intersections, including 2,732 EB-MV interaction trajectories. The study proposes MRRT to measure maximum evasive time, using kinematic data from both vehicles. Enhancing this assessment, two derivative metrics are introduced: time-exposed MRRT (TE-MRRT) and time-integrated MRRT (TI-MRRT). Also, to facilitate autonomous assessment, a scheme has been proposed to identify four types of EB-MV conflicts. Risk thresholds were established via hierarchical clustering and validated by 400 manually observed conflict events.
RESULTS: Conflicts with higher approach speeds and smaller minimum passing distances showed distinctly higher risk levels based on MRRT. The combined TE-MRRT and TI-MRRT model outperformed traditional indicators such as time-to-collision (TTC) and post-encroachment time (PET) in terms of 10-15% higher precision and up to 40% greater accuracy in identifying dangerous conflicts. Distinct thresholds for all four conflict types were successfully established, reflecting realistic variations in EB-MV behavior.
CONCLUSIONS: The MRRT-based framework offers a comprehensive and transferable scheme for estimating the risk of a near-crash event and mitigating prospective injuries for e-bicyclists in mixed-traffic situations. It makes safety management more effective by integrating both road-engineering and public health-oriented approaches.
METHODS: Data were collected from video recordings at six signalized intersections, including 2,732 EB-MV interaction trajectories. The study proposes MRRT to measure maximum evasive time, using kinematic data from both vehicles. Enhancing this assessment, two derivative metrics are introduced: time-exposed MRRT (TE-MRRT) and time-integrated MRRT (TI-MRRT). Also, to facilitate autonomous assessment, a scheme has been proposed to identify four types of EB-MV conflicts. Risk thresholds were established via hierarchical clustering and validated by 400 manually observed conflict events.
RESULTS: Conflicts with higher approach speeds and smaller minimum passing distances showed distinctly higher risk levels based on MRRT. The combined TE-MRRT and TI-MRRT model outperformed traditional indicators such as time-to-collision (TTC) and post-encroachment time (PET) in terms of 10-15% higher precision and up to 40% greater accuracy in identifying dangerous conflicts. Distinct thresholds for all four conflict types were successfully established, reflecting realistic variations in EB-MV behavior.
CONCLUSIONS: The MRRT-based framework offers a comprehensive and transferable scheme for estimating the risk of a near-crash event and mitigating prospective injuries for e-bicyclists in mixed-traffic situations. It makes safety management more effective by integrating both road-engineering and public health-oriented approaches.