Environmental Perceptions and Traffic Crashes

This study examines the relationship between the perceived built environment and traffic crashes, utilizing computer vision techniques and machine learning methods. Our results indicate that the perceived beauty, safety, and wealthiness of the built environment are negatively associated with non-motorist crash risk, whereas the perceived liveliness, depression, and boredom of the environment have positive correlations. The findings can shed insights into the mechanistic intersection of environmental perceptions and traffic safety.

Paper: Perceived built environment and non-motorist crashes: An exploration with street view imagery

In the Media: Emotional perceptions can be a tool for predicting street safety

Spatiotemporal Modeling of Traffic Crash Risk

Traditional crash risk assessment methods, especially those aiming for cluster detection, are subject to the modified temporal unit problem, referring to the temporal effects (i.e., aggregation, segmentation, and boundary) in cluster detection. This study employs an emerging hot spot detection approach—the prospective space-time scan statistic (STSS) method—to assess crash risk at a refined network scale and across multiple years in Hartford, Connecticut. By identifying the spatial and temporal clusters of crash risk, the study provides evidence to support neighborhood-level road safety management strategies in areas characterized by elevated crash risk.

Paper: Assessing network-based traffic crash risk using prospective space-time scan statistic method