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Assessment of Road Traffic Accidents on Southwest Nigeria Intercity Highways

Authors: Akinyemi OO, Adeyemi HO, Raheem WA, Adeaga OA, Ade-Ikuesan OO, Adelaja OE


Road traffic accidents have posed a threat to the safety of human life. As estimated, 1.2 million people die each year in road traffic accident and more than 50 million are injured worldwide. This study was aimed at identifying road traffic accident hazards and developing a model that predicts the occurrence of road traffic accident. Data were collected from a State Command of Nigeria’s Federal Road Safety Corps (FRSC) and analyzed using fault tree analysis (FTA) and Bayesian belief network (BBN). The fault tree identified and modeled 18 hazards. The fault tree developed is a system of OR gates for all connection of events. The model predicts the probability of road traffic accident to be 0.73. Important measure analysis was also established based on the weight of the hazards. The hazards with the greatest contribution to road traffic accident were sleeping on steering and overloading and the hazard with the least contribution was loss of concentration. BBN identified and modeled seven hazards. Bayesian influence diagram, conditional probability table and Bayesian probability expression were developed for the occurrence of road traffic accident. The model predicted that the probability of road traffic accident is 0.59. Bayesian diagnostic inference shows that lack of concentration has the greatest contribution to road traffic accident occurrence while loss of control has the least contribution. The model developed can be useful for advising government’s road safety agency on the likelihood of road traffic accident occurrence and the indicators to road safety preventive strategies.

Affiliations: Department of Mechanical Engineering, Olabisi Onabanjo University, Ago-Iwoye, Nigeria
Keywords: Bayesian, Fault Tree, Road Traffic Accident, Road Obstruction, Highways
Published date: 2019/06/30

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ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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