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Published on: June 9, 2020
Cyclist behavioral persistence across consecutive intersections: Re-ID based analysis and intention prediction
Haojie Li1, Ziqian Zhang2, Gang Ren1
1School of Transportation, Southeast University, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China.
Accident; Analysis and Prevention
|June 23, 2026
Summary
Cyclists exhibit persistent behaviors across multiple intersections, not just isolated ones. Analyzing this cross-intersection persistence improves prediction of risky cycling actions for better traffic safety.
Area of Science:
- Traffic Safety
- Behavioral Science
- Data Science
Background:
- Cyclist safety at signalized intersections is often studied in isolation.
- This approach overlooks behavioral dependencies across consecutive intersections.
- Fragmented analysis hinders early detection of risky cyclist behaviors.
Purpose of the Study:
- To investigate cyclist behavioral persistence across consecutive signalized intersections.
- To develop an intention prediction framework using upstream behavioral data.
- To enhance proactive traffic safety management.
Main Methods:
- Utilized high-resolution trajectory data from UAVs and roadside cameras.
- Applied a re-identification (Re-ID) method to track individual cyclists.
- Developed a prediction framework incorporating behavioral priors.
Main Results:
- Cyclist behavior is not independent across intersections.
- Individual-level analysis revealed significant behavioral persistence, especially in crossing and rolling decisions.
- Upstream behavioral information, particularly individual-level priors via Re-ID, substantially improved intention prediction.
Conclusions:
- Modeling cyclist behavior from a corridor-level perspective is crucial.
- Cross-intersection behavioral persistence enables earlier and more reliable identification of risky behaviors.
- The findings support proactive traffic safety management strategies.
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