Comparison of Multivariate Regression Models and Artificial Neural Networks for Prediction Highway Traffic Accidents in Spain: A Case Study

摘要

In recent years Spain shows the great reduction in the accident rate that has been achieved and the improvement of the behavior of road users, despite this, there is still a need to improve many areas. In 2016 for the first time since the last 13 years, the number of deaths increased by 7% concerning to the previous year. In this paper, analysis, and prediction of road traffic accidents (RTAs) of high accident locations highways in Spain, were undertaken using Artificial Neural Networks (ANNs), which can be used for policymakers, this paper contributes to the area of transportation safety and researchers. ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to forecast future trends.There are many ANN models for predicting the number of accidents on highways that were developed using 4 years of data for accident counts on the Spain freeway roads from 2014 to 2017. The best ANN model was selected for this task and the model variables involved highway sections, years, section length (km), annual average daily traffic (AADT), the average horizontal curve radius, Slope gradient, traffic accidents with the number of heavy vehicles. In the ANN model development, the sigmoid activation function was employed with the Levenberg-Marquardt algorithm and the different number of neurons.The model results indicate the estimated traffic accidents, based on appropriate data are close enough to actual traffic accidents and so are dependable to forecast traffic accidents in Spain. However, it demonstrates that ANNs provide a potentially powerful tool in analyzing and predicting traffic accidents. The performance of the model was in comparison to the multivariate regression model developed for the same purpose. The results prove that the ANN model stronger forecasted model which produced estimates fairly close to forecast future highway traffic accidents with Spanish conditions.