Comparative analysis for traffic flow forecasting models with real-life data in Beijing

Abstract

Rational traffic flow forecasting is essential to the development of advanced intelligent transportation systems. Most existing research focuses on methodologies to improve prediction accuracy. However, applications of different forecast models have not been adequately studied yet. This research compares the performance of three representative prediction models with real-life data in Beijing. They are autoregressive integrated moving average, neutral network, and nonparametric regression. The results suggest that nonparametric regression significantly outperforms the other models. With Wilcoxon signed-rank test, the root mean square errors and the error distribution reveal that the nonparametric regression model experiences superior accuracy. In addition, the nonparametric regression model exhibits the best spatial-transferred application effect.