2024-03-21T13:35:11 n93133

An alternative source of Bangladesh road crash data

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Road traffic injuries are one of the primary reasons for death, especially in developing countries like Bangladesh. Safety in land transport is one of the major concerns for road safety authorities and other policymakers. For this reason, contributory factors identification associated with crashes is necessary for reducing road crashes and ensuring transportation safety. This paper presents an analytical approach to identifying significant contributing factors of Bangladesh road crashes by evaluating the road crash data, considering three different severity levels (non-fetal, severe, and extremely severe).

Generally, official crash databases are compiled from police-reported crash records. Though the official datasets are focusing on compiling a wide array of attributes, an assorted number of unreported issues can be observed that demands an alternative source of crash data. Therefore, this proposed approach considers compiling crash data from newspapers in Bangladesh which could be complimentary to the official crash database.

To conduct the analysis, first, we filtered the useful features from compiled crash data using three popular feature selection techniques: chi-square, Two-way ANOVA, and Regression analysis. Then, we employed three machine learning classifiers: Decision Tree, Random Forest, and Naïve Bayes over the extracted features. A confusion matrix was considered to evaluate the proposed model, including classification accuracy, sensitivity, and specificity. The predictive machine learning model, namely, Random Forest using Label Encoder with chi-square and Two-way ANOVA feature selection process, seems the best option for crash severity prediction that provides high prediction accuracy. The resulting model highlights nine out of fourteen independent features as responsible factors. Significant features associated with crash severities include driver characteristics (gender, license type, seat belts), vehicle characteristics (vehicle type), road characteristics (road surface type, road classification), environmental conditions (day of crash occurred, time of crash), and injury localisation. This outcome may contribute to improving traffic safety of Bangladesh.

In this study, we have compiled 441 crash records reported in three newspapers of Bangladesh for the year 2019. We opted for the most famous and oldest newspapers among several newspapers: Daily Prothom Alo, Daily Jugantor, and Bdnews24. The detailed information on different crashes reported in this newspaper is collected and compiled from the e-archive of these newspapers. The crash severity was reported in the database as a three-point severity scale variable: non-fatal injury, severe injury, and extremely severe injury.

Access rights

Free access for research purposes.

Geographical area of data collection

kmlPolyCoords
92.680115,26.633914 88.008614,26.633914 88.008614,20.379400 92.680115,20.379400 92.680115,26.633914

Publications

Bhuiyan, H., Ara, J., Hasib, K.M. et al. Crash severity analysis and risk factors identification based on an alternate data source: a case study of developing country. Sci Rep 12, 21243 (2022). http://dx.doi.org/https://doi.org/10.1038/s41598-022-25361-5

Research areas

Traffic safety
Crash severity
Random Forest
Gaussian Naïve Bayes
Decision Tree
Multinomial Naïve Bayes
Machine learning classifiers
Risk factors

Cite this collection

Bhuiyan, Hanif; Ara, Jinat; Khan, Md. Hasib; Showrov, Md Imran Hossain ; Karim, Faria Benta; Sik-Lanyi, Cecilia; Governatori, Guido; Rakotonirainy, Andry; Yasmin, Shamsunnahar; (2023): An alternative source of Bangladesh road crash data. Queensland University of Technology. (Dataset) https://doi.org/10.25912/RDF_1710991761039

Data file types

Microsoft Excel (recommended version 2016 or later).

Licence


Creative Commons Attribution 4.0 (CC-BY)
http://creativecommons.org/licenses/by/4.0/

Copyright

© Queensland University of Technology, 2005.

Dates of data collection

From 1-1-2019 to 31-12-2019

Connections

Has association with
Andry Rakotonirainy  (Researcher)

Contacts

Other

Date record created:
2023-02-07T09:54:32
Date record modified:
2024-03-21T13:35:11
Record status:
Published - Open Access