Analysis of Spatial Concentrations of Large-Truck Crashes Using Data Mining Methods

Abstract

In recent years, the number of studies on crashes involving large-trucks has increased due to its importance to the economy and the higher chance of fatalities. However, none of the previous studies has given attention to the spatial concentrations of large-truck crashes. Moreover, the literature lacks exploration of granular level land use and urban design factors. The current study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method to identify the spatial concentrations of crashes involving large-trucks. Additionally, the study explored housing, population, employment, and road network density attributes along with the crash characteristics, roadway attributes, location type, traffic conditions, driver’s action and behavior, and environmental factors. The association rule analysis was employed to discover the contributory factors that lead to no injury, non-severe and severe injuries at the spatial concentrations of crashes involving large-trucks. The findings indicated that the rear-end collisions involving drunk drivers often lead to severe injuries in large-truck crashes. Non-interstate roads, speed limit from 40 to 80 kilometers per hour, high road network density, medium and high population density are frequent conditions of non-severe injuries. Lastly, collisions between large-trucks and fixed objects, sideswipe same direction collisions, snowy roads, clear weather, medium road network and employment density are likely to facilitate no injury crashes involving large-trucks. Road traffic authorities can use these insights to reduce the frequency and severity of crashes involving large-trucks at their spatial concentrations.