Spatial-Temporal Analysis of Crash Severity: Multisource Data Fusion Approach

High severity crashes are one of the negative consequences suburban transportation for a range factors. Fatalities, injuries, and medical costs, as well road car damage mental side effects, more important severe crashes. The goal this research is to figure out what factors contribute different crash levels, in order reduce likelihood such This study unique that it tries investigate capabilities various discrete choice methods explore which performs best given current database restrictions. Furthermore, data fusion approach allows take advantage wide characteristics influence severity. To achieve objective, used several types models, ordered logit (OL), multinominal (MNL), mixed (ML) examine influencing highway area. related traffic counters Khorasan Razavi province northeast Iran. Spatial-temporal analysis with has been conducted prepare multisource set spectrum independent variables acquire reliable results using models. Independent descriptive include geometric design, time-related, weather environmental conditions, land use, attributes, vehicle characteristics, driver characteristics. ML provided fit available when compared other techniques. In addition, all three coefficients conditions significant, demonstrating significance defining impacting