Citation

Ihueze CC, Onwurah UO. Accid. Anal. Prev. 2018; 112: 21-29.

Affiliation

Department of Industrial and Production Engineering, Nnamdi Azikiwe University, Awka, Nigeria. Electronic address: debest2006@yahoo.com.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.aap.2017.12.016

PMID

29306685

Abstract

One of the major problems in the world today is the rate of road traffic crashes and deaths on our roads. Majority of these deaths occur in low-and-middle income countries including Nigeria. This study analyzed road traffic crashes in Anambra State, Nigeria with the intention of developing accurate predictive models for forecasting crash frequency in the State using autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modelling techniques. The result showed that ARIMAX model outperformed the ARIMA (1,1,1) model generated when their performances were compared using the lower Bayesian information criterion, mean absolute percentage error, root mean square error; and higher coefficient of determination (R-Squared) as accuracy measures. The findings of this study reveal that incorporating human, vehicle and environmental related factors in time series analysis of crash dataset produces a more robust predictive model than solely using aggregated crash count. This study contributes to the body of knowledge on road traffic safety and provides an approach to forecasting using many human, vehicle and environmental factors. The recommendations made in this study if applied will help in reducing the number of road traffic crashes in Nigeria.

Copyright © 2017 Elsevier Ltd. All rights reserved.

Language: en

Keywords

ARIMA model; ARIMAX model; Anambra State; Forecasting; Road traffic crashes; Time series analysis