Traffic Flow Prediction during the Holidays Based on DFT and SVR

Xianglong Luo,Shengrui Zhang,Danyang Li

doi:10.1155/2019/6461450

Abstract

With the implementation of the freeway free policy during the holidays, traffic congestion in the freeway becomes a common phenomenon. In order to alleviate traffic pressure, traffic flow prediction during the holidays has become a problem of great concern. This paper proposes a hybrid prediction methodology combining discrete Fourier transform (DFT) with support vector regression (SVR). The common trend in the traffic flow data is extracted using DFT by setting an appropriate threshold, which is predicted by extreme extrapolation of the historical trend. The SVR method is applied to predict the residual series. The experimental results with measured data collected from the toll stations in Jiangsu province of China show that the proposed algorithm has higher accuracy compared with the traditional method, and it is an efficient method for traffic flow prediction during the holidays.

Highlights

With the rapid development of the social economy, the present massive road infrastructure still fails to meet people’s traveling demands [1,2,3,4,5] and traffic congestion has become a common phenomenon in the freeway
Several standard evaluation measurements are adopted in the experiments to evaluate the proposed method, including the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE)
It is apparent that the proposed discrete Fourier transform (DFT) and support vector regression (SVR) model has the best prediction performance, and the prediction value of the proposed method is identical to the real value during most of the hours, especially during the rush hours

Summary

Introduction

With the rapid development of the social economy, the present massive road infrastructure still fails to meet people’s traveling demands [1,2,3,4,5] and traffic congestion has become a common phenomenon in the freeway. To solve the problem of traffic congestion during the holidays, intelligent transportation systems (ITS) have been widely implemented. The implementation of some policies results in more dramatic changes in traffic flow, such as free charge for cars under 7 seats during important holidays in China, which makes traffic flow prediction more difficult. People’s travel is more for leisure and tourism during the holidays, which makes some changes in the composition of traffic flow. Traffic flow around scenic spots is increasing dramatically, and occasional traffic congestions often occur, which results in traffic flow data becoming more stochastic. Traffic flow distribution during the holidays is obviously different from those during workdays. To improve transportation operation efficiency, accurate traffic flow prediction during the holidays has become a problem of great concern

Methods

Results

Conclusion