Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).

This paper presents a GIS approach based on spatial autocorrelation analysis of pedestrian-vehicle crash data for identification and ranking of unsafe bus stops. Instead of crash counts, severity indices are used for analysis and ranking. Moran's I statistic is employed to examine spatial patterns of pedestrian-vehicle crash data. Getis-Ord Gi* statistic is used to identify the clustering of low and high index values and to generate a pedestrian-vehicle crash hot spots map. As recent studies have shown strong correlations between pedestrian-vehicle crashes and transit access, especially bus stops, bus stops in pedestrian-vehicle crash hot spots are then selected and ranked based on the severity of pedestrian-vehicle crashes in their vicinities. The proposed approach is evaluated using 13 years (1996–2008) of pedestrian-vehicle crash data for the Adelaide metropolitan area. Results show that the approach is efficient and reliable in identifying pedestrian-vehicle crash hot spots and ranking unsafe bus stops.