机器学习
人工智能
朴素贝叶斯分类器
计算机科学
人工神经网络
支持向量机
分类器(UML)
特征工程
多层感知器
预测建模
深度学习
作者
Elif Çiçek,Murat Akın,Furkan Uysal,ReyhanMerve Topcu Aytas
标识
DOI:10.1080/19427867.2023.2214758
摘要
ABSTRACTABSTRACTTraffic accidents are still the main cause of fatalities, injuries and significant delays in highways. Understanding the accident contributing factor is imperative to increase safety in a traffic network. Recent research confirms that predictive modeling is an important tool to comprehend accident contributing factors. However, little effort has been put forward to explain complex machine learning models and their feature effects in accident prediction models. Thus, this study aims to build predictive models based on different machine learning methods and tries to explain the most contributing factors by using Shapley values which was developed based on game theory. Decision Trees, Neural Networks with Multilayer Perceptron (MLP), Support Vector Classifier, Case-Based Reasoning and Naive Bayes Classifier were used to predict the injury severity in accidents. Belt usage, alcohol consumption and speed violations were found as the most effective features and MLP gave the highest accuracy among all the applied predictive models.KEYWORDS: Accident predictionexplainable machine learningshapley numbersdecision treesNaive Bayes classifiercase-based reasoningsupport vector classifierneural network Disclosure statementNo potential conflict of interest was reported by the authors.
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