Traffic Crash Severity Prediction with Deep Learning

Abstract

This paper is based on deep learning to explore the accuracy forecasting model of traffic crash severity. A prediction model in terms of convolutional neural networks is proposed in the paper. The first is to collect the basic information of all traffic crash data of the entire urban road network in Chicago from 2016 to 2020. Then using the PivotTable to observe the distribution of each variable, and filtering them. The required prediction model is based on Python and it mainly includes three parts, the input layer, the hidden layer, and the output layer. Besides, using SGD optimizer to accelerate the training of the network model is a need. Aiming to avoid problems such as over-fitting, the dropout technique is adopted, and its parameter value taking 0.1 is better. Then comparing and analyzing with three traditional machine learning models, the decision tree classification prediction model, the logistic regression prediction model and the support vector machine prediction model, it is obvious to show the better performance of prediction model based on the convolutional neural network method. After optimization, its highest accuracy is 89.2%.