Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers |
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| Institution: | 1. School of Transportation, Southeast University, Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, PR China;2. School of Transportation, Southeast University, Jiangsu Key Laboratory of Urban ITS, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, 2 Sipailou, Nanjing 210096, PR China;3. Urban Planning Group, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands;4. School of Transportation, Dalian Maritime University, PR China;1. Center for Transportation Research, The University of Tennessee, 600 Henley Street, Knoxville, TN 37996, USA;2. Department of Civil & Environmental Engineering, The University of Tennessee, 72 Perkins Hall, Knoxville, TN 37996, USA;1. College of Engineering, Civil Engineering Department University of Wasit, Kut, Iraq;2. Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall, West Lafayette, IN 47907-2051, USA;1. USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States;2. USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3261, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States;1. University Transportation Research Center, City College of New York, New York, NY 10031, United States;2. Department of Civil, Construction, & Environmental Engineering, San Diego State University, CA 92182, United States;3. Leidos, Inc., 11251 Roger Bacon Drive, Reston, VA 20190, United States;4. Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China;1. Department of Civil Engineering, University of South Alabama, 150 Jaguar Drive, Shelby Hall, Suite 3142, Mobile, AL 36688, United States;2. Department of Civil, Construction & Environmental Engineering, University of Alabama, Box 870205, Tuscaloosa, AL 35487, United States |
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| Abstract: | Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned. |
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| Keywords: | Truck-involved fatal crash Risk factors Crash severity Latent class clustering Partial proportional odds model |
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