{"title":"交通事故死亡率预测的可解释机器学习方法","authors":"Md. Asif Khan Rifat, Ahmedul Kabir, Armana Sabiha Huq","doi":"arxiv-2409.11929","DOIUrl":null,"url":null,"abstract":"Road traffic accidents (RTA) pose a significant public health threat\nworldwide, leading to considerable loss of life and economic burdens. This is\nparticularly acute in developing countries like Bangladesh. Building reliable\nmodels to forecast crash outcomes is crucial for implementing effective\npreventive measures. To aid in developing targeted safety interventions, this\nstudy presents a machine learning-based approach for classifying fatal and\nnon-fatal road accident outcomes using data from the Dhaka metropolitan traffic\ncrash database from 2017 to 2022. Our framework utilizes a range of machine\nlearning classification algorithms, comprising Logistic Regression, Support\nVector Machines, Naive Bayes, Random Forest, Decision Tree, Gradient Boosting,\nLightGBM, and Artificial Neural Network. We prioritize model interpretability\nby employing the SHAP (SHapley Additive exPlanations) method, which elucidates\nthe key factors influencing accident fatality. Our results demonstrate that\nLightGBM outperforms other models, achieving a ROC-AUC score of 0.72. The\nglobal, local, and feature dependency analyses are conducted to acquire deeper\ninsights into the behavior of the model. SHAP analysis reveals that casualty\nclass, time of accident, location, vehicle type, and road type play pivotal\nroles in determining fatality risk. These findings offer valuable insights for\npolicymakers and road safety practitioners in developing countries, enabling\nthe implementation of evidence-based strategies to reduce traffic crash\nfatalities.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction\",\"authors\":\"Md. Asif Khan Rifat, Ahmedul Kabir, Armana Sabiha Huq\",\"doi\":\"arxiv-2409.11929\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Road traffic accidents (RTA) pose a significant public health threat\\nworldwide, leading to considerable loss of life and economic burdens. This is\\nparticularly acute in developing countries like Bangladesh. Building reliable\\nmodels to forecast crash outcomes is crucial for implementing effective\\npreventive measures. To aid in developing targeted safety interventions, this\\nstudy presents a machine learning-based approach for classifying fatal and\\nnon-fatal road accident outcomes using data from the Dhaka metropolitan traffic\\ncrash database from 2017 to 2022. Our framework utilizes a range of machine\\nlearning classification algorithms, comprising Logistic Regression, Support\\nVector Machines, Naive Bayes, Random Forest, Decision Tree, Gradient Boosting,\\nLightGBM, and Artificial Neural Network. We prioritize model interpretability\\nby employing the SHAP (SHapley Additive exPlanations) method, which elucidates\\nthe key factors influencing accident fatality. Our results demonstrate that\\nLightGBM outperforms other models, achieving a ROC-AUC score of 0.72. The\\nglobal, local, and feature dependency analyses are conducted to acquire deeper\\ninsights into the behavior of the model. SHAP analysis reveals that casualty\\nclass, time of accident, location, vehicle type, and road type play pivotal\\nroles in determining fatality risk. These findings offer valuable insights for\\npolicymakers and road safety practitioners in developing countries, enabling\\nthe implementation of evidence-based strategies to reduce traffic crash\\nfatalities.\",\"PeriodicalId\":501301,\"journal\":{\"name\":\"arXiv - CS - Machine Learning\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Machine Learning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.11929\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11929","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction
Road traffic accidents (RTA) pose a significant public health threat
worldwide, leading to considerable loss of life and economic burdens. This is
particularly acute in developing countries like Bangladesh. Building reliable
models to forecast crash outcomes is crucial for implementing effective
preventive measures. To aid in developing targeted safety interventions, this
study presents a machine learning-based approach for classifying fatal and
non-fatal road accident outcomes using data from the Dhaka metropolitan traffic
crash database from 2017 to 2022. Our framework utilizes a range of machine
learning classification algorithms, comprising Logistic Regression, Support
Vector Machines, Naive Bayes, Random Forest, Decision Tree, Gradient Boosting,
LightGBM, and Artificial Neural Network. We prioritize model interpretability
by employing the SHAP (SHapley Additive exPlanations) method, which elucidates
the key factors influencing accident fatality. Our results demonstrate that
LightGBM outperforms other models, achieving a ROC-AUC score of 0.72. The
global, local, and feature dependency analyses are conducted to acquire deeper
insights into the behavior of the model. SHAP analysis reveals that casualty
class, time of accident, location, vehicle type, and road type play pivotal
roles in determining fatality risk. These findings offer valuable insights for
policymakers and road safety practitioners in developing countries, enabling
the implementation of evidence-based strategies to reduce traffic crash
fatalities.