Abstract:
This study develops a high accident risk prediction model to analyze traffic accident data and prioritize intersection improvements. A traffic accident database was organized and analyzed, and an intersection accident risk prediction model based on different mechanical learning methods was created to estimate high accident risk locations for traffic management departments to use in planning countermeasures to reduce accident risk. This study found that road width, speed limit, and roadside markings are significant risk factors for traffic accidents. An accident risk prediction model was developed using Naïve Bayes, Decision tree C4.5, Bayesian Network, Multilayer perceptron (MLP), Deep Neural Networks (DNN), Deep Belief Network (DBN), and Convolutional Neural Network (CNN). This model can also identify high-risk intersection factors and help traffic management departments make intersection improvement decisions. Using high-risk intersection environmental characteristics as model inputs to estimate future risk can prevent traffic accidents. It can also inform intersection design and environmental improvements.
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