Unveiling Risk Reconfiguration in Freeway Merging Areas: A Spatiotemporal Framework for Conflict Prediction and Hotspot Migration in CAV Mixed Traffic

The transition to mixed traffic flows comprising Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HVs) induces a fundamental spatial reconfiguration of risk in freeway merging areas. This study proposes a novel spatiotemporal safety assessment framework to characterize the dynamic evolution of risk hotspots. Unlike traditional models, the framework integrates a conflict prediction model based on Negative Binomial regression with a high-resolution, grid-based risk mapping technique. By applying this framework to data from a microscopically simulated and carefully calibrated environment, we successfully identify a distinct migration pattern of risk hotspots: as CAV penetration increases, high-risk zones shift from the static geometric bottleneck at the ramp merge point to a dynamic interaction interface on the mainline. This paradigm shift is further quantified using a multi-dimensional indicator system. A case study demonstrates that increasing the CAV penetration rate from 10% to 50% can improve the safety grade of a merging area from D (Poor) to A (Excellent). The proposed framework provides a practical tool for refined safety diagnostics and offers insights for spatiotemporal risk analysis, informing the development of future cooperative control strategies in mixed traffic environments.