Traffic crash prediction model in Kano State, Nigeria: a multivariate LSTM approach

Abstract

Accurate traffic crash prediction is crucial for implementing effective road safety measures. This study compares the performance of Long Short-Term Memory (LSTM) and Multivariate LSTM (MLSTM) models in forecasting total crash count data in Kano State, Nigeria. Human and vehicle factors, including speed violation, tire burst, brake failure, sign light violation, and phone use while driving, are incorporated as covariates in the MLSTM model. An ARIMAX model is employed to investigate the effects of the covariates. The MLSTM model outperforms both the basic LSTM model and individual covariate models, emphasizing the synergistic effect of considering a broad range of factors. The ARIMAX model results reveal that speed violation is significantly positively correlated with total crashes, while other covariates show positive correlations but do not reach the statistical significance. The findings underscore the importance of a multivariate approach in enhancing traffic crash prediction. The MLSTM model's superior performance highlights the value of considering a comprehensive range of factors that influence crash occurrence to achieve more accurate predictions. Practical applications of these models could involve leveraging them for proactive traffic safety measures, which include increased enforcement of traffic rules, targeted driver education and campaigns, and improvements to road infrastructure.