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Article

URBAN FREIGHT CRASH ANALYSIS USING ORDINAL LOGIT AND ORDINAL PROBIT REGRESSION IN THE STATE OF ALABAMA

DOI: 10.7708/ijtte.2019.9(1).10


9 / 1 / 127-144 Pages

Author(s)

Mehrnaz Doustmohammadi - Department of Civil and Environmental Engineering, University of Alabama in Huntsville, Huntsville, AL, 35899, USA -


Abstract

Trucking is vital to the economic advancement of the nation as the majority of goods are transported on truck internally. This topic is critical to the state of Alabama since trucking is the most frequently selected travel mode for all goods shipped through the Port of Mobile and especially those necessary to support the automotive industry, one of the major sectors of the Alabama economy. Therefore, truck crashes have a significant impact on moving goods and delivery schedules vital to maintaining the operation of assembly plants. Truck crashes often cause severe delays and extra time is needed to move larger vehicles from roadways and transfer the carried good to other vehicles. Additionally, truck crashes are often severe due to the fact their increased size and weight tends to cause severe damage when crashes occur with passenger cars, tending to cause more injuries. This study examined urban, at-fault truck crashes where the driver operated using a commercial driver’s license. The crashes were obtained from Alabama cities between 2012 and 2016. A statistical analysis of crashes using a logit regression model and a probit regression model was performed and the significant variables that influence truck crashes were determined and analyzed. From the analysis, the variables that most likely increased the severity of urban truck at-fault crashes were fatigue and speed. The analysis demonstrated the ability of the two models to determine influences and the models were in strong agreement of the variables, with distracted driving, driving too fast for conditions and fatigue being the leading issues that increased the severity of crashes.


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Keywords:
ordinal regression; urban freight; crash analysis;


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