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Determinants influencing risks in e-bike cyclists under mix traffic condition: a partially constrained random
Yuntong Zhou1, Xin Gu1, Mohamed Abdel-Aty2
1Beijing University of Technology, Beijing Key Laboratory of Traffic Engineering, Beijing 100124, China.
Accident; Analysis and Prevention
|December 29, 2025
Summary
Electric bike (e-bike) adoption impacts urban mobility and safety. Mixed traffic conditions significantly affect e-bike cyclist braking behavior, influencing risk perception and avoidance strategies.
Area of Science:
- Transportation Science
- Traffic Safety Engineering
- Human Factors in Cycling
Background:
- Global e-bike adoption enhances urban mobility but poses traffic safety challenges.
- Cyclist braking behavior is critical for safety, particularly in mixed traffic environments.
- Existing research often focuses on crash severity, neglecting specific behavioral risks in varied traffic conditions.
Purpose of the Study:
- To investigate factors influencing e-bike cyclists' braking behavior under mixed and non-mixed traffic conditions.
- To analyze the impact of traffic conditions on cyclist risk perception and avoidance.
- To provide a nuanced understanding of e-bike cyclist safety through behavioral analysis.
Main Methods:
- Utilized field experiment data to capture real-world cyclist behavior.
- Employed a partially constrained random parameters logit model with heterogeneity in the means.
- Controlled for cyclist characteristics, behavioral factors, and roadway characteristics.
Main Results:
- Mixed traffic conditions demonstrably affect e-bike cyclist safety.
- Head-turning and handlebar turning behaviors indicate varying risk perception and avoidance.
- Unobserved heterogeneity and cross-condition correlations significantly influence braking behavior.
Conclusions:
- Traffic infrastructure design and cyclist characteristics must consider the impact of mixed traffic.
- Understanding cyclist behavioral responses is crucial for improving e-bike safety.
- Accounting for unobserved factors provides deeper insights into e-bike cyclist braking patterns.
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