Mohammed Almannaa, Md Nabil Zawad, May Moshawah, Haifa Alabduljabbar
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Investigating the effect of road condition and vacation on crash severity using machine learning algorithms.
Investigating the contributing factors to traffic crash severity is a demanding topic in research focusing on traffic safety and policies. This research investigates the impact of 16 roadway condition features and vacations (along with the spatial and temporal factors and road geometry) on crash severity for major intra-city roads in Saudi Arabia. We used a crash dataset that covers four years (Oct. 2016 - Feb. 2021) with more than 59,000 crashes. Machine learning algorithms were utilized to predict the crash severity outcome (non-fatal/fatal) for three types of roads: single, multilane, and freeway. Furthermore, features that have a strong impact on crash severity were examined. Results show that only 4 out of 16 road condition variables were found to be contributing to crash severity, namely: paints, cat eyes, fence side, and metal cable. Additionally, vacation was found to be a contributing factor to crash severity, meaning crashes that occur on vacation are more severe than non-vacation days.
期刊介绍:
International Journal of Injury Control and Safety Promotion (formerly Injury Control and Safety Promotion) publishes articles concerning all phases of injury control, including prevention, acute care and rehabilitation. Specifically, this journal will publish articles that for each type of injury: •describe the problem •analyse the causes and risk factors •discuss the design and evaluation of solutions •describe the implementation of effective programs and policies The journal encompasses all causes of fatal and non-fatal injury, including injuries related to: •transport •school and work •home and leisure activities •sport •violence and assault