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.
| ID | pj287 |
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