An alternative source of Bangladesh road crash data

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.

道路交通伤害是导致死亡的主要原因之一，尤其在如孟加拉国等发展中国家。陆地交通的安全性是道路安全管理部门及其他政策制定者关注的重大议题。鉴于此，识别与事故相关的促成因素对于减少道路交通事故并确保交通运输安全至关重要。本文提出了一种分析方法，通过评估道路交通事故数据，考虑了三种不同的严重程度级别（非致命、严重和极度严重），以识别孟加拉国道路交通事故的显著促成因素。通常，官方事故数据库是由警方报告的事故记录汇编而成。尽管官方数据集旨在汇编广泛的各种属性，但仍然存在大量未报告的问题，这要求有替代性的事故数据来源。因此，本研究提出的方案考虑了从孟加拉国报纸中汇编事故数据，这些数据可以补充官方事故数据库。为了进行该分析，首先，我们使用三种流行的特征选择技术——卡方检验、双向方差分析和回归分析——从汇编的事故数据中筛选出有用的特征。然后，我们针对提取的特征应用了三种机器学习分类器：决策树、随机森林和朴素贝叶斯。混淆矩阵被用于评估所提出的模型，包括分类准确率、敏感性和特异性。名为随机森林的预测机器学习模型，使用标签编码器和卡方检验及双向方差分析特征选择过程，似乎是事故严重程度预测的最佳选择，它提供了高预测准确率。该模型突出了十四个独立特征中的九个作为责任因素。与事故严重程度相关的显著特征包括驾驶员特征（性别、执照类型、安全带）、车辆特征（车辆类型）、道路特征（路面类型、道路等级）、环境条件（事故发生日、事故时间）以及伤害定位。这一成果可能有助于提升孟加拉国的交通安全水平。在本研究中，我们汇编了2019年孟加拉国三家报纸报道的441起事故记录。在众多报纸中，我们选择了最具知名度和历史悠久的报纸：每日《普罗托玛洛》、《每日朱加托尔》和《Bdnews24》。该报纸报道的不同事故的详细信息是从这些报纸的电子档案中收集和汇编的。事故严重程度在数据库中以三点严重程度量表变量报告：非致命伤害、严重伤害和极度严重伤害。