To perform a spatial analysis of Road Traffic Crashes (RTCs) and assess road safety issues from the perspective of road users. Although many initiatives have been taken to reduce the occurrence and severity of RTCs, they continue to persist. Existing research often investigates the spatial occurrence of RTCs or the perception of road safety issues from the road user. In doing this, only a limited number of factors that contribute to RTCs can be revealed, whereas in most RTC occurrences a multitude of factors plays a role. A more integrated approach combining both knowledge areas can contribute to improving road safety. RTCs that occurred from 2018 to 2020 in Rotterdam, in the Netherlands, were spatially analyzed. This was performed using Network Kernel Density Estimation (NKDE) analysis. Two zones within the study area were selected to understand road users' perceptions of road safety through a survey. Furthermore, opinions toward possible recommendations for improving road safety were also collected through key informant interviews. NKDE resulted in a hot-spot map of the road segments in the study area that showed the frequency of RTCs using different colors. The road segments were classified based on the number of RTCs from 2018 to 2020, ranging from zero to 17.9 RTCs per kilometer. This led to the selection of a hot and cold spot zone for further analysis. The road user perception survey resulted in the discovery of qualitative responses that can be used to improve road safety in future and the possible recommendations would be well received by them. The key-informant interviews acted as a backup to the opinions given by the road users and provided insights on what is being done in the study area to improve road safety. The synthesis of findings unveiled why road users perceive some areas as dangerous and which road policies need to be revised to improve road safety in Rotterdam.

Automated Vehicles (AVs) are being developed with the aim to reduce the occurrence and severity of Road Traffic Crashes (RTCs). Studies suggest AVs may improve the safety of Vulnerable Road Users (VRUs), particularly on road crossings. However, exposure to novel technology over time may lead to behavioural adaptation. Thus, understanding VRUs’ behavioural intentions towards AVs is crucial for their safe integration into traffic. We investigate four external factors pedestrians consider when crossing a road in front of an AV. An online questionnaire with 281 participants assessed crossing intentions, focusing on road gradient, weather, pedestrian–AV distance, and AV type. Personality traits and self-reported behaviour were measured. Anderson’s experimental protocol revealed all factors significantly influenced crossing decisions. Using hierarchical clustering followed by K-means clustering, the participants were classified into three different profiles: risk-averse, resolute, and indecisive pedestrians. We provide evidence of a strong link between crossing decisions, reported behaviours and psychological facets while interacting with an AV at crossings. Pedestrian profiling allows targeting preventative measures for groups based on unique characteristics, maximising efficiency thereof. Furthermore, pedestrian profiling can inform AV’s driving style to support safer road interactions. This is salient for resolute pedestrians, who take more risks, which may lead to severe RTCs.

Automated vehicles (AVs) are expected to enhance road safety through capabilities such as self-navigation, quick decision-making by sensing the environment, and adherence to traffic rules (Hulse 20...