Weighted Multi-attribute Framework to Identify Freeway Incident Hot Spots in a Spatiotemporal Context

The present work used the analytical hierarchy process supported by a machine learning technique (namely random forest variable importance) in a geospatial environment to identify traffic-incident hot spots where seven incident attributes (incident severity level, vehicle type, number of vehicles involved, number of weather condition factors, number of lanes blocked, time period, and incident duration) and incident frequency were considered. Using a frame of raster representation, each factor was ranked and weighted based on the literature, expert surveys, and random forest technique to perform hot spot analysis and generate a critical road index map. Statistical analysis of spatial clustering and hot spot spatial densities was carried out based on Moran’s I method of spatial autocorrelation, Getis–Ord Gi* statistics, and point kernel density. Based on weighted attributes of more than 130,000 incidents from Harris County, Texas, for the period between 2004 and 2013, the method successfully identified critical road segments and highlighted factors that contribute to criticality. The results show that high-severity segments are located in the downtown area during the period of 2004 to 2010, and shifted northwest of the downtown area during the period of 2010 to 2013. The random forest variable importance revealed that the number of lanes blocked along with time periods was the most critical factor affecting the severity of traffic incidents. Though the application of the method was demonstrated using incident database from Harris County, it can be generalized to other cities. Traffic agencies can use the suggested approach to garner and visualize reliable information that ensures better traffic management schemes.