Predicting Truck At-Fault Crashes Using Crash and Traffic Offence Data

Abstract

Introduction:The number of truck-related injuries and deaths can be reduced by understanding the factors that contribute to the higher risk of truck-related crashes and violations. Truck drivers are at fault of more than 80% of all the truck crashes on Wyoming interstates, and the literature review indicated that in order to identify appropriate countermeasure to crashes, each crash type should be analyzed individually. The literature review also revealed that relationships exist between driving records and driver culpability in crashes.Method:This study employed two approaches to identify contributory factors to truck-at-fault fatal and injury crashes, and truck-related violations. Interstate 80, a Wyoming corridor in a mountainous area with one of the highest truck crash rates in the US, was selected as a case study. Only truck-at-fault crashes and specific types of truck-related violations were considered in this study. The analyses include two approaches. First, the logistic regression model was employed to explore vehicle, driver, crash, and environmental characteristics that contribute to truck-at-fault fatal and injury crashes. Second, truck violations were used as a proxy for truck crashes to examine the tendency to violate truck-related traffic laws in relation to driver and temporal characteristics. Based on the literature, only violations associated with higher risk of severe crashes were included in the analyses. The included violations accounted for more than 70% of all the violations.Result:This study considered more than 30 variables and found that only 10 variables impact truck-at-fault crashes. These factors included: gender, history of past violation, crashes involving multiple vehicles, exceeding the speed limit, occupant distraction, driver ejection, fatigued driving, non-seat belt usage, overturn, and head-on collision. Results of the second analysis indicated that both residency and time of crash are factors that impact truck-related violations. Results of the analysis also indicated that both residency and time of the crash are factors that impact truck-related violations.

Investigating the Effect of Urban New Technologies on the Iranian Lorry Drivers’ Behavior

“…e results show that there is a significant relationship between driving experience and driving offenses such as speeding and not fastening seat belts [2, 3, 5, 9, 18-20, 23, 24]. Other studies in this field have found that violations such as long-distance disobedience [9,24], seat belts [2,5], technical defect [5,23], alcohol abuse [3,5], and factors such as accident's history [2,5,24], violations records [25,26], and some parameters of Driving Behavior Questionnaire (DBQ) [18,19,24] have a significant impact on the occurrence of cargo fleet accidents.…”

A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads

“…One of the most important offenses identified in past studies as a major contributing factor to the occurrence of accidents is speeding [9-11, 13-15, 21-23]. Other studies in this field have found that offenses such as maintaining safe distance to front vehicle [21,23], fastening seat belt [14,22], technical defect [13,22], alcohol abuse [15,22], and other related factors such as a history of a crash [14,21,22], a history of misconduct [24][25][26], and some parameters of the Driving Behavior Questionnaire [9,11,21] have a significant impact on the occurrence of accidents for commercial fleets.…”

Freeway Truck Traffic Safety in Wyoming: Crash Characteristics and Prediction Models

“…Some other studies also investigated different geometrical factors that contribute to truck-related crashes (16,17), with similar findings on the contributions of traffic and roadway features. Some recent studies in Wyoming investigated truckrelated crashes along Interstate highways using traffic violation and crash data (18)(19)(20). Those studies employed two approaches to identify factors contributing to truckrelated crashes and truck-related violations using the logistic regression method.…”