It is known that the demographic, socioeconomic and traffic characteristics of crash locations have been used as important factors in macroscopic crash analysis. In this study, we focus on the residential ZIP code areas of drivers who were involved in crashes. The objective of this study is to identify the origin’s characteristics of drivers involved in traffic crashes so better targeted education and awareness could be designed and delivered. Various characteristic factors of the postal ZIP code area of a driver’s residence were used in the study. The postal codes were collected from police crash reports for the year 2006 and demographic, socioeconomic and travel pattern data were retrieved from US Census Bureau. Several negative binomial (NB) models were estimated for specific types of crashes such as, total number of crashes (for at-fault drivers), severe crashes (for at-fault drivers), pedestrian crashes, and bicycle crashes. It was found that demographic characteristics such as gender, ethnic group, socioeconomic characteristics including family income and unemployment, and travel patterns as commute mode and travel time to work are significant factors for specific types of crashes. The findings from the study implied that several demographic, socioeconomic and traffic factors of zones can influence the crash frequency of the resident. In the planning phases we can forecast the crash frequency with these models and predicted independent factors in the future. From operational perspective, the results from the study can be used to identify zones that have residents with higher chances to be involved in crashes, thus we can concentrate on these specific zones for education and stricter enforcement. (Author/publisher)
Abstract