Investigating spatiotemporal traffic dynamics toward conflict risk levels using trajectory data in heterogeneous traffic conditions

The study introduces a robust methodological approach that combined advanced machine learning and statistical modeling to understand complex spatiotemporal dynamics influencing traffic conflict risks under mixed traffic conditions. By capturing these interactions at fine resolution and demonstrating their impact on conflict severity, this research offers significant implications for developing real-time risk-alert systems. Such systems can proactively warn drivers, enabling safer and more informed driving decisions, and thus enhancing overall highway safety in heterogeneous traffic environments.