DEVELOPMENT OF AN ACCIDENT PREDICTOR INTERFACE FOR PREDICTING ACCIDENT POTENTIALS OF TRANSPORT VEHICLES IN NIGERIA

Chukwunonso Nweze Nwogu; Ama Agwu Anya; Bethrand Nduka Nwankwojike

doi:10.29121/shodhai.v1.i1.2024.15

Authors

Chukwunonso Nweze Nwogu Senior Lecturer, Department of Mechanical Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
Ama Agwu Anya Master’s Student, Department of Mechanical Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
Bethrand Nduka Nwankwojike Professor, Department of Mechanical Engineering, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria

DOI:

https://doi.org/10.29121/shodhai.v1.i1.2024.15

Keywords:

Accident, Accident Potential, Accident Data, Vehicle Crash, Accident Prediction

Abstract

In this work, an accident prediction model was developed for determining the accident potential of a vehicle while on transit. The developed model identifies Human factors (HF), Mechanical factors (MF) and Environmental factors (EF) as the main factors responsible for vehicle crashes. The accident data used for this research was obtained from the Nigerian Federal Road Safety Corps (FRSC). Analysis of the accident data showed that HF contributes 0.846 accident probability, MF contributes 0.138 accident probability while EF contributes 0.016 accident probability. Also, driver’s age, distance of travel and maintenance frequency of the vehicle were considered in the development of the model, since they also play significant roles in determining accident probabilities. The model gives results ranging from 0-1. When the value is close to 0, it signifies low accident probability while values close to 1 signify high accident probability. An accident predictor interface was developed by combining all the possible accident cause factors. The accident predictor interface gave the least accident probability value of 0.2892 for the following combination: driver age of 60+ years, regular maintenance frequency and travel distance of C ≤ 250 Km. Application of the results of this model will significantly reduce the rate of road accident occurrences. Finally, transport companies and fleet owners are encouraged to embrace and use this innovation for safer operations.

References

Abdulhafedh, A., (2017). Road Crash Prediction Models: Different Statistical Modeling Approaches. Journal of Transportation Technologies, 2017, 7:190-205

Beirness, D. J. and Beasley, E., (2011). A Comparison of Drug- and Alcohol-Involved Motor Vehicle Driver Fatalities. Canadian Centre on Substance Abuse, Ottawa, 2011

Brualdi, Richard A., (2010). Introductory Combinatorics (5th Ed.), Pearson Prentice Hall, 2010, ISBN 978-0-13-602040-0.

FRSC and Balogun Y. (2006). Research and Development Opinions, Federal Road Maintenance Agency January/February 2006 Pg. 13-16

FRSC, (2016). FRSC Annual Report 2016 ; www.frsc.gov.ng/publications, Retrieved on April 8, 2018

Gupta, B.L. and Gupta A., (2009). Different Modes of Transportation : Roads, Railways, Bridges, Tunnels & Harbour. Dock Engineering, 2009. Chapter 1; pp 1.2-1.4.

Hermitte, T., (2012). Review of Accident Causation Models used in Road Accident Research of the EC FP7 project DaCoTA, 2012

Kalyoncuoğlu, F.S, & Tığdemir, M., (2014). The Effects of Daily Driven Distance and Age Factor on the Traffic Accidents. Transportation Department, Engineering Faculty, Civil Engineering, 2014 https://doi.org/10.7307/ptt.v26i3.1306

Oyedepo, O. J., (2010). Accident Prediction Models for Akure - Ondo Carriageway, Ondo State Southwest Nigeria : Using Multiple Linear Regressions. An International Multi-Disciplinary Journal, Ethiopia Vol. 4 April, 2010, ISSN 2070-0083. https://doi.org/10.4314/afrrev.v4i2.58286

Oyetunji, M.O., Oladeji, F.A., Falana, P.A., & Idowu, P.A., (2017). Prediction of Road Traffic Accident in Nigeria Using Naïve Baye's Approach. Advances in Multidisciplinary & Scientific Research Journal, volume 3(1), 2017, Pp 23-30. .https://doi.org/10.4236/jtts.2017.72014