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Frequency Modelling and Risk Evaluation of Road Crashes in Sylhet Region of Bangladesh

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Abstract

Road traffic accident is a concerning issue for many low to middle-income countries (LMICs). An appropriate accident prevention strategy is thus necessary for ensuring a safer road that requires scientific investigation of accident risk and accident prediction. Therefore, this study aims for accident hazard risk analysis of Sylhet district of Bangladesh to identify the vulnerable zones of accident occurrence. Highly vulnerable areas are found geographically connected. To explore the driving factors of accident occurrence, accident frequency prediction models are developed. The negative binomial regression model is found to outperform over the Poisson regression model indicating the overdispersion of the data. The estimated model confirms a significant level of association of accident frequency with zone level sociodemographic, roadway and traffic characteristics such as population, number of commercial activities, length of the road, width of the road and average annual daily traffic. The findings of this study can be useful for relevant policy analysis to minimize the road crash severity and frequency in the cities of LMICs that share similar characteristics of the study area Sylhet.

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Notes

  1. This is the police recorded data that only considered died at the crash scene. This figure may largely underestimate the true number of deaths. For example, Bangladesh Health and Injury Survey 2003 estimated the annual number of deaths to be 13, 200 whereas police recorded death was around 3160 (World Health Organization 2009).

  2. The LR is calculated using the equation, \(LR=-2\left[LL\left({\beta }_{a}\right)-{LL(\beta }_{n})\right]\). Where LL(βa) is the log-likelihood for the larger model and LL(βn) is the log-likelihood of the smaller model. The LR can be compared to a critical value from a \({\chi }_{n}^{2}\) distribution with n degrees of freedom, where n = Ka - Kn, with Ka and Kn are the number of estimated parameters in the larger model and smaller model respectively.

  3. \(RMSE=\sqrt{\frac{1}{N}\sum _{i=1}^{N}({\frac{{O}_{i}-{P}_{i}}{{O}_{i}})}^{2}}\) and \(MAE=\frac{1}{N}\sum _{i=1}^{N}\frac{\left|{O}_{i}-{P}_{i}\right|}{{O}_{i}}\), where Oi and Pi represent observed and predicted values respectively. N is the number of observations.

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Acknowledgements

The authors would like to acknowledge the Sylhet district police division and Road and Highway (RHD) Division of Bangladesh for their contribution to this research work by allowing data access.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh

    Md. Aminul Islam, Md. Bashirul Haque, Md. Jahid Hasan & Md. Ruhul Amin

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  1. Md. Aminul Islam
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  2. Md. Bashirul Haque
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  3. Md. Jahid Hasan
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Correspondence to Md. Bashirul Haque.

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Appendices

Appendix

Table 4 Collinearity statistics
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Appendix

Table 5 Model Estimated for 60 %, 80 and 100 % of data
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Islam, M.A., Haque, M.B., Hasan, M.J. et al. Frequency Modelling and Risk Evaluation of Road Crashes in Sylhet Region of Bangladesh. Int. J. ITS Res. 20, 90–102 (2022). https://doi.org/10.1007/s13177-021-00275-0

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