Various factors such as speed, variability of speed, traffic flows and the proportion of trucks affect the probability of truck-involved crashes. Numerous attempts have been made to identify the causal factors of truck-involved crashes, such as traffic volume, speed characteristics and geometric characteristics. Most of the research focused on identifying the causal factors or establishing models to represent the relationship between crashes and the identified factors. However, few studies have compared the differences in the impact of a coefficient by the type of region. This study aims to analyse the differences in the causal factors of truck-involved crashes in rural and urban areas. The applicability of the count models is examined owing to the low number of trucks involved in the crashes. The models for each area type are established using zero-inflated Poisson regression and negative binomial regression model for rural and urban areas, respectively. Our results indicate that sight distance is the single factor responsible for truck-involved crashes in rural areas, whereas annual average daily traffic, shoulder width and alignment are the contributors to truck-involved crashes in urban areas.
count model, Poisson regression, truck-involved crashes, zero inflated regression
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