According to NHTSA, more than 400,000 truck accidents occurred in 2009 and approximately 7,800 of those were fatal crashes. Compared with extensive studies conducted on freeway truck safety, the research on arterial streets is considerably disproportionate. Making the connections between truck traffic generators, arterial streets are key links in door-to-door deliveries. There is an urgent need to study truck safety on arterial streets because of the strong growth of truck traffic. Truck-related crashes are expected to be reduced through careful planning of the location, design, and operation of driveways, median openings, street connections, and street sections. Through the collection of extensive data on selected arterial corridors that are heavily used by trucks, contributing factors to truck crash frequency and severity were identified with a negative binomial model and multinomial logit model. Corridor truck miles traveled, annual average daily traffic, signal density, shoulder width, and pavement serviceability index and its standard deviation are significant factors for crash frequency prediction. The multinomial logit model identified 12 causal factors for crash severity, such as posted speed limit, lane width, number of lanes, pavement condition index, and undivided roadway portion. Subsequently, a crash severity index for truck arterial corridors was developed. The findings from the study not only will benefit state and local agencies in planning, design, and management of a safer truck arterial corridor, but will also help carriers to optimize their routes from a safety perspective.
According to NHTSA, more than 400,000 truck-related crashes occurred in 2009; approximately 7,800 of those were fatal. Truck-related crashes undermine the truck's remarkable contribution to the U.S. economy. Truck safety research on arterial streets is considerably disproportionate when compared with the extensive studies of truck safety on freeways. Identifying critical factors that contribute to truck-related crashes and developing remedial and preventive strategies to reduce truck-related crashes and their consequences on arterials are imperative. Truck-related crashes can be mitigated through careful planning of the location, design, and operation of driveways, median openings, and street connections. In this study, access-related data were collected manually in addition to roadway geometric characteristics. The augmented data offered more explanation and prediction power for truck crashes. The standard deviation of commercial driveway throat width, commercial driveway throat width with flare and its standard deviation, and the proportion of divided commercial driveway, signal density, and shoulder width were significant factors for crash frequency prediction. A generalized negative binomial model was used to identify sources of data overdispersion. This study found that some previously significant variables were no longer significant after access parameters were added; this finding demonstrated the impact of access parameters on truck-related crashes on arterials. This noticeable change in the statistical models composed of different variables is a reminder that a spurious relationship can form if a causal relationship is nonexistent.
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