Investigating Crash Risks and Driving Performance Among the Oil and Gas Industry Workers

Moffis, Katlin T.; Martinez, Joseph A.; Choi, Hoon

doi:10.1177/1071181322661345

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…However, tailgating as a cause of collisions and running signs and signals both increased injury rates. Previous studies have identified the violation of traffic signals and signs as significant contributors to accidents [48,51,52]. Moreover, closely following another vehicle with a short braking distance was found to be a critical factor impacting injuries [53].…”

Section: Cause Of Assumption Factormentioning

confidence: 95%

Big Data Analytics with the Multivariate Adaptive Regression Splines to Analyze Key Factors Influencing Accident Severity in Industrial Zones of Thailand: A Study on Truck and Non-Truck Collisions

Seefong,

Wisutwattanasak,

et al. 2023

BDCC

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Machine learning currently holds a vital position in predicting collision severity. Identifying factors associated with heightened risks of injury and fatalities aids in enhancing road safety measures and management. Presently, Thailand faces considerable challenges with respect to road traffic accidents. These challenges are particularly acute in industrial zones, where they contribute to a rise in injuries and fatalities. The mixture of heavy traffic, comprising both trucks and non-trucks, significantly amplifies the risk of accidents. This situation, hence, generates profound concerns for road safety in Thailand. Consequently, discerning the factors that influence the severity of injuries and fatalities becomes pivotal for formulating effective road safety policies and measures. This study is specifically aimed at predicting the factors contributing to the severity of accidents involving truck and non-truck collisions in industrial zones. It considers a variety of aspects, including roadway characteristics, underlying assumptions of cause, crash characteristics, and weather conditions. Due to the fact that accident data is big data with specific characteristics and complexity, with the employment of machine learning in tandem with the Multi-variate Adaptive Regression Splines technique, we can make precise predictions to identify the factors influencing the severity of collision outcomes. The analysis demonstrates that various factors augment the severity of accidents involving trucks. These include darting in front of a vehicle, head-on collisions, and pedestrian collisions. Conversely, for non-truck related collisions, the significant factors that heighten severity are tailgating, running signs/signals, angle collisions, head-on collisions, overtaking collisions, pedestrian collisions, obstruction collisions, and collisions during overcast conditions. These findings illuminate the significant factors influencing the severity of accidents involving trucks and non-trucks. Such insights provide invaluable information for developing targeted road safety measures and policies, thereby contributing to the mitigation of injuries and fatalities.

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