Cyclist behavioral persistence across consecutive intersections: Re-ID based analysis and intention prediction.

The safety risks associated with cyclists at signalized intersections are often analyzed within isolated units, an approach that fails to account for the inherent behavioral dependencies across consecutive intersections. Ignoring such cross-intersection persistence results in a fragmented understanding of cyclist decision-making and hinders the early identification of risky behaviors. This study investigates cyclists' behavioral persistence across consecutive signalized intersections using high-resolution trajectory data collected from unmanned aerial vehicles and roadside cameras. A re-identification (Re-ID) method is applied to match cyclists across intersections, enabling the analysis of individual-level behavioral persistence. Based on this, a downstream intention prediction framework is developed, incorporating upstream behavioral information as behavioral priors. The results show that cyclists' behaviors are not independent across intersections. While aggregate-level analysis reveals moderate associations, individual-level analysis demonstrates clear persistence in behavior, especially for decision-related variables such as crossing decision and rolling behavior. Incorporating upstream behavioral information significantly improves downstream intention prediction, with the largest improvement observed when individual-level behavioral priors are utilized via Re-ID. The findings highlight the importance of modeling cyclist behavior from a corridor-level perspective. By leveraging cross-intersection behavioral persistence, the proposed framework enables earlier and more reliable identification of risky behaviors, providing practical support for proactive traffic safety management at signalized intersections.