Abstract
There have been numerous studies on traffic accidents and their severity, particularly in relation to weather conditions and road geometry. In these studies, traditional statistical methods have been employed, such as linear regression, logistic regression, and negative binomial regression modeling, which are the most common linear and non-linear regression analysis methods. In this research, machine learning architecture was applied to this problem using the random forest, artificial neural network, and decision tree techniques to ascertain the strengths and weaknesses of these methods. Three data sets were used: road geometry data, precipitation data, and traffic accident data over nine years corresponding to the Naebu Expressway, which is located in Seoul, Korea. For the model evaluation, three measures were employed: the out-of-bag estimate of error rate (OOB), mean square error (MSE), and root mean square error (RMSE). The low mean OOB, MSE, and RMSE observed in the results obtained using the proposed random forest model demonstrate its accuracy.
Highlights
More than 3000 people die in road crashes every day worldwide, including approximately 1000 people in the United States
The model was developed by training the classifier using the training data set, and the performance of the classifier was tested on the test data set
Random forest, artificial neural networks (ANN), and decision tree algorithms were applied to road accident
Summary
More than 3000 people die in road crashes every day worldwide, including approximately 1000 people in the United States. Such accidents cost $518 billion globally and $230.6 billion per year in the United States. Substantial efforts have been made to address the main factors that cause traffic accidents, such as speeding, distracted driving, driver fatigue, road conditions, use of mobile phones, and poor weather conditions. Regarding bad road conditions due to the weather, previous studies have revealed that there is a strong correlation between road friction coefficient and traffic accident risk [1,2]. The friction coefficient can be influenced by road conditions such as ice or wetness caused by snow or rainfall. It has been shown that hydroplaning occurs at vehicle speeds of 80 km/h on thick water films when the water depth exceeds
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