Spatial dynamics of crash hotspots under autonomous vehicle adoption scenarios
通过微观仿真和空间统计方法，研究不同自动驾驶渗透率下碰撞热点的空间分布变化，发现热点概率随自动化水平非线性变化，交叉口和冲突区域最危险，为混合交通规划提供依据。
• Integrated framework linking microsimulation, conflicts and spatial analysis. • Crash-equivalent risk hotspots identified using spatial statistical methods. • Hotspot probability changes nonlinearly with increasing automation penetration. • Intersections and crossing conflicts show the highest probability of hotspots. • Automation reduces hotspot probability on short, high-speed, low-capacity segments. In road environments with large Autonomous Vehicle (AV) fleets and higher SAE automation levels, reliable crash data are often unavailable, making direct safety assessment infeasible. In such cases, traffic simulation offers a valuable alternative for evaluating safety. This study conducts a spatial modelling analysis to predict crash hotspot occurrences under different AV deployment scenarios. The study combines microsimulation-derived conflict data, a quantitative crash-risk formulation, validated using field crash data, based on Time-To-Collision (TTC) thresholds, and spatial statistical analysis using the Getis-Ord Gi* statistic to detect statistically significant hotspots of elevated crash risk. The resulting hotspots were further analysed using a binomial Generalised Additive Model (GAM) to quantify the impact of automation, roadway and spatial factors on the probability that a conflict event occurs within a hotspot area. Results show that automation significantly alters the spatial distribution of crash risk, leading to a gradual reduction and spatial diffusion of hotspots as AV penetration increases. However, a temporary rise in the probability that conflict events occur within hotspot areas occurs under moderate automation shares, highlighting the transitional instability of mixed-traffic conditions. Intersections and other high-interaction areas remained the most critical locations, while congested segments were associated with a higher probability that conflict events occur within hotspot areas. The proposed framework supports data-informed planning and policy decisions during the transition toward automated urban mobility.