Comparison of traffic accident injury severity prediction models with explainable machine learning

ABSTRACT

Traffic 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 prediction
• explainable machine learning
• shapley numbers
• decision trees
• Naive Bayes classifier
• case-based reasoning
• support vector classifier
• neural network

Disclosure statement

No potential conflict of interest was reported by the authors.