A Comprehensive Study of Single and Multiple Truck Crashes Using Violation and Crash Data

Around 4,000 people died in crashes involving trucks in 2016 alone in the U.S., with 21 percent of these fatalities involving only single-unit trucks. Many studies have identified the underlying factors for truck crashes. However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety. An injury/fatal crash was defined as a crash that results in an injury or fatality. The first approach investigated the contributory factors that increased the odds of injury/fatal single truck and multiple vehicle crashes with involvement of at least one truck. The literature has indicated that previous violations can be used to predict future violations and crashes. Therefore, the second approach used violations related to driver actions that could result in truck crashes. The analysis for the first approach indicated that driving on dry-roadway surfaces, driver distraction, and rollover/jackknife types of truck crashes, speed compliance failure, and higher posted speed limits are some of the factors that increased the odds of injury/fatal single and multiple vehicle crashes. With the second approach, the violations related to risky driver actions, which were underlying causes of truck crashes, were identified and analyses were run to identify the groups at increased risk of truck involved crashes. The results of violations indicated that being nonresident, driving off peak hours, and driving on weekends could increase the risk of truck involved crashes.

[1]  Khaled Ksaibati,et al.  Utilizing crash and violation data to assess unsafe driving actions , 2017 .

[2]  Mohamed Abdel-Aty,et al.  Predicting Freeway Crashes from Loop Detector Data by Matched Case-Control Logistic Regression , 2004 .

[3]  Suren Chen,et al.  Injury severities of truck drivers in single- and multi-vehicle accidents on rural highways. , 2011, Accident; analysis and prevention.

[4]  Heike Martensen,et al.  Comparing single vehicle and multivehicle fatal road crashes: a joint analysis of road conditions, time variables and driver characteristics. , 2013, Accident; analysis and prevention.

[5]  Daniel Blower,et al.  The large truck crash causation study , 2002 .

[6]  Peter J. Cooper,et al.  Driver accident risk in relation to the penalty point system in British Columbia , 1995 .

[7]  A. Dobson,et al.  Women drivers' behaviour, socio-demographic characteristics and accidents. , 1999, Accident; analysis and prevention.

[8]  Peter T. Savolainen,et al.  Driver Injury Severity Resulting from Single-Vehicle Crashes along Horizontal Curves on Rural Two-Lane Highways , 2009 .

[9]  Brenda Lantz,et al.  Development and implementation of a driver safety history indicator into the roadside inspection selection system. , 2005, Journal of safety research.

[10]  Ahmed E. Radwan,et al.  Modeling traffic accident occurrence and involvement. , 2000, Accident; analysis and prevention.

[11]  A. Khattak,et al.  Injury Effects of Rollovers and Events Sequence in Single-Vehicle Crashes , 2000 .

[12]  Avinash Unnikrishnan,et al.  Analysis of large truck crash severity using heteroskedastic ordered probit models. , 2011, Accident; analysis and prevention.

[13]  Khaled Ksaibati,et al.  Impact of traffic Enforcement on Traffic Safety , 2017 .

[14]  Xiaoyu Zhu,et al.  A comprehensive analysis of factors influencing the injury severity of large-truck crashes. , 2011, Accident; analysis and prevention.

[15]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[16]  S. Dissanayake,et al.  Analysis of Severity of Young Driver Crashes: Sequential Binary Logistic Regression Modeling , 2002 .

[17]  A. Khattak,et al.  RISK FACTORS IN LARGE TRUCK ROLLOVERS AND INJURY SEVERITY: ANALYSIS OF SINGLE-VEHICLE COLLISIONS , 2003 .

[18]  Khaled Ksaibati,et al.  Factors associated with crash severity on rural roadways in Wyoming , 2016 .

[19]  Wei Zou,et al.  Truck crash severity in New York city: An investigation of the spatial and the time of day effects. , 2017, Accident; analysis and prevention.

[20]  Roderick J. A. Little,et al.  Persistence of Violation and Crash Behavior Over Time , 2000 .

[21]  S P Baker,et al.  Prior crash and violation records of pilots in commuter and air taxi crashes: a case-control study. , 1994, Aviation, space, and environmental medicine.

[22]  Khaled Ksaibati,et al.  Developing a tool to help highway patrol in allocating resources to crashes , 2016 .

[23]  C J O'Donnell,et al.  Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice. , 1996, Accident; analysis and prevention.

[24]  Jasmine Pahukula,et al.  A time of day analysis of crashes involving large trucks in urban areas. , 2015, Accident; analysis and prevention.

[25]  A Gains,et al.  The national safety camera programme - four-year evaluation report - December 2005 , 2004 .

[26]  Khaled Ksaibati,et al.  An Investigation of Influential Factors of Truck Crashes on Two-Lane Downgrades in Wyoming: A Logistic Regression Approach , 2018 .

[27]  Gudmundur F. Ulfarsson,et al.  Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender. , 2013, Accident; analysis and prevention.

[28]  A. S. Al-Ghamdi Using logistic regression to estimate the influence of accident factors on accident severity. , 2002, Accident; analysis and prevention.

[29]  David W Soole,et al.  Effects of average speed enforcement on speed compliance and crashes: a review of the literature. , 2013, Accident; analysis and prevention.

[30]  William L. Weber,et al.  Productivity and efficiency in the trucking industry: Accounting for traffic fatalities , 2004 .

[31]  T L Bunn,et al.  Sleepiness/fatigue and distraction/inattention as factors for fatal versus nonfatal commercial motor vehicle driver injuries. , 2005, Accident; analysis and prevention.