Article2009

An Aggregation Approach to Short-Term Traffic Flow Prediction

Manchun Tan · S.C. Wong · Jianmin Xu · Zhan-Rong Guan · Peng Zhang

338 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Forecasting Techniques and Applications Air Quality Monitoring and Forecasting

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 naive, 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.

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References

1977Book Review: Time Series Analysis: Forecasting and Control 2003Time series forecasting using a hybrid ARIMA and neural network model 1969The Combination of Forecasts 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 1971Time Series Analysis Forecasting and Control 2008Introduction to time series analysis and forecasting 2000Out-of-sample tests of forecasting accuracy: an analysis and review 2002Comparison of parametric and nonparametric models for traffic flow forecasting

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2020Machine Learning-based traffic prediction models for Intelligent Transportation Systems 2018Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction 2019Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges

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