Contributing Factors to Run-Off-Road Crashes Involving Large Trucks under Lighted and Dark Conditions

Al-Bdairi, Nabeel Saleem Saad; Hernández, Salvador; Anderson, Jason C.

doi:10.1061/jtepbs.0000104

Cited by 30 publications

(30 citation statements)

References 21 publications

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“…These large representations of crash statistics on roadway segments are in line with the findings by Dong et al ( 17 ), in which the authors investigated the effect of geometric design features and found a strong association of longer straight segment lengths to crash occurrences. The proportion distribution of several of the variables are also in line with other studies such as lighting condition ( 2 ), roadway gradient ( 17 ), posted speed limit ( 19 ), time of day ( 26 ), weather condition ( 24 ), number of vehicles involved ( 6 ), and types of collision ( 9, 21 ). Three person-level variables (previous accident record of the drivers, driver’s past license suspension record, and impaired driving) are associated with the driving patterns of the large-truck drivers associated with fatal crashes.…”

Section: Data Descriptionsupporting

confidence: 84%

“…The association of factors discerned in this cluster is in line with multiple studies found in the literature. Al-Bdairi et al found that speeding while driving in darker conditions can significantly increase the chances of more severe crashes on rural interstate highways ( 2 ). Trimble et al investigated the factors contributing to rear-end crashes involving large trucks and concluded that speeding, including acceleration and deceleration in anticipation of drivers’ attempted avoidance maneuvers, is a significant factor of crashes ( 29 ).…”

Section: Results and Findingsmentioning

confidence: 99%

“…Although most of the studies found in the literature investigated factors that contribute to large truck-related crashes, a few other studies only looked at the effect of specific factors such as lighting condition ( 2 ), time of day ( 26 ), geometric elements such as horizontal curvatures ( 27 ), roadway separation ( 28 ), and crash types—for example rear-end crashes ( 29 ), rollover crashes ( 30 ), run-off-road crashes ( 31 ), angle crashes ( 21 ), and driver age group ( 32 )—on crash occurrence or injury severity. Trimble et al conducted a GES analysis on rear-end crashes involving large trucks to determine the causation of this type of crash and to improve rear-signaling under a national project by NHTSA ( 29 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…A large truck is defined as a “medium or heavy truck having a gross vehicle weight rating (GVWR) of more than 10,000 lb, excluding busses and recreational vehicles (RVs)” ( 1 ). Because of their considerable weight and size, crashes involving large trucks are more likely to result in a fatality or severe injury ( 2, 3 ). Therefore, a specialized investigation of crash causation with fatality and injury severity analysis is necessary to identify the mechanisms involved in these crashes and to provide effective countermeasures.…”

mentioning

confidence: 99%

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Uncovering Deep Structure of Determinants in Large Truck Fatal Crashes

Das¹,

Islam

Dutta

et al. 2020

Transportation Research Record: Journal of the Transportation R

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The number of fatalities and severe injuries in large truck-related crashes has significantly increased since 2009. According to the safety experts, the recent increase in large truck-related crashes can be explained by the significant growth in freight tonnage all over the U.S. over the past few years. This notable freight-haul growth has allowed continuous day–night movement of freight on roads and highways, exposing the trucks to a greater number of potential crashes or near-crash scenarios. There are many ongoing research efforts that aim to identify the different factors that influence large truck crashes; however, further research with innovative approaches is still needed to better understand the relationship between crash-related factors. In this study, the project team applied taxicab correspondence analysis (TCA), a data-mining method known for dimension reduction, to large truck fatal crash data to investigate the complex interaction between multiple factors under a two-dimensional map. For this study, 6 years (2010–2015) of large truck fatal crash data from the Fatality Analysis Reporting System (FARS) were used. The study found five clusters of attributes that show patterns of association between different crash attributes such as two-lane undivided roadways, intersection types, posted speed limit, crash types, number of vehicles, driver impairment, and weather. The findings of this study will help the safety professionals, trucking industry, and policymakers to make decisions for safer road design, and improvement in truck driver training, and education.

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Section: Data Descriptionsupporting

confidence: 84%

Section: Results and Findingsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

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Uncovering Deep Structure of Determinants in Large Truck Fatal Crashes

Das¹,

Islam

Dutta

et al. 2020

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Direct and indirect evaluation methods are mainly adopted in traffic safety research, in which the former needs mass data based on historical accident data. However, direct evaluation method faces problems such as difficult accident data acquisition, long data accumulation time, and restricted data size [6,7]. Indirect evaluation method uses alternative indexes to evaluate traffic accidents.…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Influence Factors of Accident Severity: Evidence from Urban Rivercrossing Tunnels in Shanghai of China

He¹,

Zhang²,

Lu³

et al. 2018

JESTR

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To analyze various factors influencing the accident severity of urban river-crossing tunnels, 12 influence factors were selected according to the three traffic elements of vehicle, road, and environment, including accident-involved vehicle type, tunnel length, tunnel speed limit, accident occurrence time, and weather. These factors were based on the historical data of 14 urban river-crossing tunnels in Shanghai. A binary logistic regression model was also utilized to identify significant influence factors and analyze the influence degrees of their accident severity. Significance analysis reveals that accident occurrence place, accident-involved vehicle type, tunnel speed limit, number of vehicles involved, and accident type are significantly correlated with accident severity. Moreover, VISSIM traffic simulation model and conflict simulation analysis software called surrogate safety assessment model were utilized to simulate different urban rivercrossing tunnel scenes. The simulation explored the influence degrees of traffic volume, tunnel longitudinal slope, tunnel speed limit, and ratio of heavy trucks, which stand for three traffic elements (i.e., vehicle, road, and environment, respectively) on accident severity. Results show that the factors exerting the greatest influences on accident severity are traffic volume, number of vehicles involved, accident occurrence place, and accident type, followed by tunnel longitudinal slope, tunnel speed limit, and accident-involved vehicle type. Traffic volume, number of vehicles involved, accident type, tunnel longitudinal slope, and accident-involved vehicle type have a significantly positive correlation with accident severity, whereas accident occurrence position and tunnel speed limit have a significantly negative correlation with accident severity. The study results provide a new idea for researches on urban river-crossing tunnels and provide basis and method for tunnel entrance design, speed limit adjustment and regulation of traffic volume.

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