Analysis of Spatiotemporal Factors Affecting Traffic Safety Based on Multisource Data Fusion

Abstract

Traffic state information, road network structure characteristics, and built environment characteristics are factors influencing traffic safety, which will alleviate or aggravate traffic safety problems. This paper analyzes the relationship between these factors and traffic accidents involving either property damage only (PDO) crashes or killed and severe injury (KSI) crashes. The spatiotemporal distribution of the two types of accidents was analyzed. Abundant explanatory variables were extracted from accident data, license plate recognition (LPR) data, OpenStreetMap (OSM) data, and point of interest (POI) data based on complex network methods and information entropy theories. Geographical and temporal weighted regression (GTWR), geographically weighted regression (GWR), and ordinary least squares (OLS) models were used to analyze the influencing factors of traffic safety, respectively. The results demonstrate that the GTWR model performs best in modeling spatiotemporal heterogeneity data. Traffic state information, road network structure, and built environment factors all have significant effects on traffic accidents, and traffic state information have the highest correlation with traffic accidents among all factors. The greater the traffic volume, the more likely are traffic accidents. The strongest correlation is between PDO crashes and traffic state in the morning peak, in the evening peak, and at night. For a road network divided into grids, the more important the intersections in the grid, the greater is the street circuity, and the more chaotic the street direction, the more likely PDO crashes are to occur in the grid. Furthermore, the diversity of land use is positively correlated with traffic accidents in urban areas, whereas the correlation is negative in suburban areas, which reflects the spatial heterogeneity.