Forecasting traffic congestion status in terminal areas based on support vector machine

Abstract

This article researches on a traffic congestion status forecasting method to improve the real-time monitoring and controlling of air traffic in terminal areas. First, a traffic congestion status evaluation method was introduced based on a fuzzy C-means clustering algorithm, as well as several traffic congestion status evaluation metrics. And then, a traffic congestion status forecasting model was proposed based on support vector machine. Finally, a real case study from a terminal area in China was provided to test and verify the proposed evaluation method and forecasting model. The evaluation results show that traffic congestion status of the terminal area can be classified into five levels: free, smooth, slightly congested, moderately congested, and severely congested. The forecasting results show that the mean absolute error and the cluster accuracy are 0.041% and 92.2%, respectively, which indicate that the forecasting model is very effective and accurate. In addition, it is also found that the parameters of forecasting period and size of training set have some influence on forecasting results, and the optimal results can be found when the two parameters values are 15 and 3, respectively.