Roadway-Related Truck Crash Risk Analysis: Case Studies in Texas

Zhao, Qun; Goodman, Tyrie; Azimi, Mehdi; Qi, Yi

doi:10.1177/0361198118794055

Cited by 8 publications

(7 citation statements)

References 1 publication

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“…Since 1994, Texas has had the highest number of fatal crashes involving large trucks in the U.S. [ 1 ].Among these large truck crashes, the AK level crashes (A is the incapacitating crash, and K is the fatal crash) often result in immense social and economic losses. To prevent and mitigate the occurrence of AK level truck crashes, factors that significantly contribute to such severe crashes need to be identified before appropriate countermeasures can be explored.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Factors Contributing to the Severity of Large Truck Crashes

Liu

et al. 2020

Entropy

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Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, we applied three tree-based machine learning (ML) techniques, i.e., random forest (RF), gradient boost decision tree (GBDT), and adaptive boosting (AdaBoost), to analyze the factors contributing to the severity of large truck crashes. Besides, a mixed logit model was developed as a baseline model to compare with the factors identified by the ML models. The analysis was performed based on the crash data collected from the Texas Crash Records Information System (CRIS) from 2011 to 2015. The results of this research demonstrated that the GBDT model outperforms other ML methods in terms of its prediction accuracy and its capability in identifying more contributing factors that were also identified by the mixed logit model as significant factors. Besides, the GBDT method can effectively identify both categorical and numerical factors, and the directions and magnitudes of the impacts of the factors identified by the GBDT model are all reasonable and explainable. Among the identified factors, driving under the influence of drugs, alcohol, and fatigue are the most important factors contributing to the severity of large truck crashes. In addition, the exists of curbs and medians and lanes and shoulders with sufficient width can prevent severe large truck crashes.

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Section: Introductionmentioning

confidence: 99%

“…*1 The significance level of the estimated correlation coefficient, *2 MDI denotes Mean Decrease Impurity. * 3 ML denotes Machine Learning.…”

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confidence: 99%

Analysis of Factors Contributing to the Severity of Large Truck Crashes

Liu

et al. 2020

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“…Another study analyzing the injury severity associated with work zones found that daytime crashes, higher speed limits, no traffic control during access, and crashes on rural principal arterials substantially increased the likelihood of severe crashes [20]. Traffic engineers and roadway designers should adopt better, safer, and more efficient geometric designs and traffic control strategies, as roadway-related variables are more controllable, to assist with reducing specific types of crashes [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Statistical and Spatial Analysis of Large Truck Crashes in Texas (2017–2021)

Billah,

Sharif,

Dessouky

2024

Sustainability

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Freight transportation, dominated by trucks, is an integral part of trade and production in the USA. Given the prevalence of large truck crashes, a comprehensive investigation is imperative to ascertain the underlying causes. This study analyzed 2017–2021 Texas crash data to identify factors impacting large truck crash rates and injury severity and to locate high-risk zones for severe incidents. Logistic regression models and bivariate analysis were utilized to assess the impacts of various crash-related variables individually and collectively. Heat maps and hotspot analysis were employed to pinpoint areas with a high frequency of both minor and severe large truck crashes. The findings of the investigation highlighted night-time no-passing zones and marked lanes as primary road traffic control, highway or FM roads, a higher posted road speed limit, dark lighting conditions, male and older drivers, and curved road alignment as prominent contributing factors to large truck crashes. Furthermore, in cases where the large truck driver was determined not to be at fault, the likelihood of severe collisions significantly increased. The study’s findings urge policymakers to prioritize infrastructure improvements like dual left-turn lanes and extended exit ramps while advocating for wider adoption of safety technologies like lane departure warnings and autonomous emergency braking. Additionally, public awareness campaigns aimed at reducing distracted driving and drunk driving, particularly among truck drivers, could significantly reduce crashes. By implementing these targeted solutions, we can create safer roads for everyone in Texas.

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“…Apart from the common modeling techniques, other statistical and innovative machine learning methods have been used to examine the injury severity and crash frequency of large truck-related crashes. Some of these examples consist of the classification tree model ( 19 ), hierarchical Bayesian random intercept model ( 20 ), skewed logistic model ( 21 ), and a risk analysis model dependent on collision diagram ( 22 ). Even though most of these studies concentrated on highway crashes, Qin et al analyzed the factors contributing to the severity and frequency of truck-related crashes at freeway diverging section with the use of NB and MNL models ( 23 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Uncovering Deep Structure of Determinants in Large Truck Fatal Crashes

Das¹,

Islam

Dutta

et al. 2020

Transportation Research Record

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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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Roadway-Related Truck Crash Risk Analysis: Case Studies in Texas

Cited by 8 publications

References 1 publication

Analysis of Factors Contributing to the Severity of Large Truck Crashes

Analysis of Factors Contributing to the Severity of Large Truck Crashes

Statistical and Spatial Analysis of Large Truck Crashes in Texas (2017–2021)

Uncovering Deep Structure of Determinants in Large Truck Fatal Crashes

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