An aggregation approach to short-term traffic flow prediction

Authors:
Man-Chun Tan;S. C. Wong;Jian-Min Xu;Zhan-Rong Guan;Peng Zhang
Affiliations:
Department of Mathematics, College of Information Science and Technology, Jinan University, Guangzhou, China;Department of Civil Engineering, University of Hong Kong, Hong Kong;College of Traffic and Communication, South China University of Technology, Guangzhou, China;Department of Mathematics, College of Information Science and Technology, Jinan University, Guangzhou, China;Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai, China
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2009

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Abstract

In this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/day over several years. The three relevant time series are a weekly similarity time series, a daily similarity time series, and an hourly time series, which can be directly generated from the source time series. The MA, ES, and ARIMA models are selected to give predictions of the three relevant time series. The predictions that result from the different models are used as the basis of the NN in the aggregation stage. The output of the trained NN serves as the final prediction. To assess the performance of the different models, the naïve, ARIMA, nonparametric regression, NN, and data aggregation (DA) models are applied to the prediction of a real vehicle traffic flow, from which data have been collected at a data-collection point that is located on National Highway 107, Guangzhou, Guangdong, China. The outcome suggests that the DA model obtains a more accurate forecast than any individual model alone. The aggregation strategy can offer substantial benefits in terms of improving operational forecasting.