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International Journal of Intelligent Transportation Systems Research
Published in:

21-09-2021

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

Authors: Md. Aminul Islam, Md. Bashirul Haque, Md. Jahid Hasan, Md. Ruhul Amin

Published in: International Journal of Intelligent Transportation Systems Research | Issue 1/2022

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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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Appendix
Available only for authorised users
Footnotes
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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Metadata
Title
Frequency Modelling and Risk Evaluation of Road Crashes in Sylhet Region of Bangladesh
Authors
Md. Aminul Islam
Md. Bashirul Haque
Md. Jahid Hasan
Md. Ruhul Amin
Publication date
21-09-2021
Publisher
Springer US
Published in
International Journal of Intelligent Transportation Systems Research / Issue 1/2022
Print ISSN: 1348-8503
Electronic ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-021-00275-0

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A Data-Driven Approach for Vehicle Relocation in Car-Sharing Services with Balanced Supply-Demand Ratios

Stability Analysis and Control of an Extended Car-Following Model under Honk Environment

Improving the Accuracy of Traffic Accident Prediction Models on Expressways by Considering Additional Information

A Model for Detecting Drowsiness Based on the Data Analysis of Drive Recorder

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