Analysis of Accident Predictability and the Use of Driver Behaviour Questionnaire: A Systematic Review

Olusegun Austine Taiwo, Sitti Asmah Hassan, Rahmat Bin Mohsin and Norashikin Mahmud
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Olusegun Austine Taiwo: Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia
Sitti Asmah Hassan: Nigerian Institute of Transport Technology (NITT), Zaria
Rahmat Bin Mohsin: Faculty of Civil Engineering, Universiti Teknologi Malaysia
Norashikin Mahmud: School of Human Resource Development and Psychology, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 3, 2347-2366

Abstract: Road Traffic Accidents (RTAs) have continued to be a menace in the world with the rising increase in road crashes due to driver behaviours. Despite using the Driver Behaviour Questionnaire (DBQ) for examining driver behaviours, little is known about the variability in predicting accident. The present systematic literature review study investigated the variation of accident predictors through DBQ approach. The PRISMA procedure was used to extract the reviewed articles from Scopus database using the keywords, “Driver Behaviour Questionnaire†OR “Driver Behavior Questionnaire†OR “DBQ†AND “Accident prediction†OR “Accident Predictor†OR “Crash Prediction†OR “Crash Predictor†. Broadly, results from this review show that road traffic crash involvement is predicted by violations, car lobbying, gender, marital status, household size, daily driving time, driving skills, and time, while aberrant driving behaviours predictors are average monthly salary, driving experience, distance driven per year or annual mileage, previous crash involvement, religion beliefs, age, driving hour/day, attention regulation and impulsivity, and living without a partner. This review can aid researchers in a more detailed understanding of the variables that predict accidents, thus influencing necessary mitigation strategies from policymakers and aiding driver trainers with specialized training to alleviate accidents in drivers through training or retraining.

Date: 2024
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