Retracted: A Mechanistic Model for Predicting Accident Potential of Vehicles Transiting in Nigerian Roads

Main Article Content

Ama Agwu
Nwogu Chukwunoso
Nwankwojike Bethrand

Abstract

An accident prediction model was developed for determining the accident potential of a vehicle while on transit. The model identifies the various factors responsible for vehicle crashes. With the help of accident data obtained from the database of the Nigerian Federal Road Safety Corps (FRSC), the percentage contribution of each factor is calculated. These accident-cause factors were further grouped into three distinct classes: Human factors (HF), Mechanical factors (MF) and Environmental factors (EF). Analysis of the accident data showed that HF is the chief cause of most road accidents recorded, followed by MF and EF with probabilities of 0.846, 0.138 and 0.016 respectively. Also, driver age, travel distance and maintenance frequency of the vehicle were considered in the development of the model. The model gives an output ranging from 0-1. Values close to 0 mean low accident probability while values close to 1 signify high accident probability. Application and adherence to this model will significantly reduce the frequency of road accidents. Finally, transport companies and fleet operators are therefore encouraged to embrace and use this innovation for safer operations.

Retraction Notice: This paper has been retracted from the journal. This journal is determined to promote integrity in research publication. This retraction is in spirit of the same. After formal procedures editor(s) and publisher have retracted this paper on 19th October 2019. Related policy is available here: http://goo.gl/lI77Nn

Keywords:
Accident, accident potential, accident data, vehicle crashes, accident-cause factors.

Article Details

How to Cite
Agwu, A., Chukwunoso, N., & Bethrand, N. (2019). Retracted: A Mechanistic Model for Predicting Accident Potential of Vehicles Transiting in Nigerian Roads. Advances in Research, 19(5), 1-10. https://doi.org/10.9734/air/2019/v19i530136
Section
Original Research Article