An Explainable Machine Learning Framework for Predicting Road Accident Intensity in Bangladesh

An Explainable Machine Learning Framework for Predicting Road Accident Intensity in Bangladesh | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 May 2025 V1 Latest version Share on An Explainable Machine Learning Framework for Predicting Road Accident Intensity in Bangladesh Authors : Fernaz Narin Nur 0000-0003-3273-0553 [email protected] , Muhammad Nazrul Islam 0000-0002-7189-4879 , and Rup Chowdhury 0009-0000-6899-3254 Authors Info & Affiliations https://doi.org/10.22541/au.174690728.88444148/v1 370 views 145 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Road traffic accidents present a major public health and economic challenge in Bangladesh, with thousands of lives lost annually due to poor road infrastructure, human error, and lack of effective safety measures. This study proposes a comprehensive machine learning-based framework for predicting accident intensity using real-world road crash data from 2007 to 2024, comprising 46,256 records obtained from four national sources. The dataset was preprocessed through imputation, feature engineering, and label encoding, followed by the application of four parallel modeling pipelines: traditional machine learning, ensemble learning, PyCaret-based automated modeling, and neural networks. Among all models, the Extra Trees classifier achieved the highest performance with 96% accuracy, outperforming existing approaches in both prediction and reliability. To enhance model transparency, explainable AI techniques such as SHAP and LIME were applied, revealing ’Location’, ’Month’, and ’Weather’ as the most influential features. The findings not only offer robust predictive insights but also support data-driven interventions for policymakers to identify high-risk zones and design targeted road safety strategies. Supplementary Material File (journal_eaid.pdf) Download 2.63 MB Information & Authors Information Version history V1 Version 1 10 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords machine learning neural network road safety x ai Authors Affiliations Fernaz Narin Nur 0000-0003-3273-0553 [email protected] Military Institute of Science and Technology View all articles by this author Muhammad Nazrul Islam 0000-0002-7189-4879 Military Institute of Science and Technology View all articles by this author Rup Chowdhury 0009-0000-6899-3254 Military Institute of Science and Technology View all articles by this author Metrics & Citations Metrics Article Usage 370 views 145 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Fernaz Narin Nur, Muhammad Nazrul Islam, Rup Chowdhury. An Explainable Machine Learning Framework for Predicting Road Accident Intensity in Bangladesh. Authorea . 10 May 2025. DOI: https://doi.org/10.22541/au.174690728.88444148/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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