Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

Billy M. Williams · L A Hoel

2,210 citations0/0 files storedJournal of Transportation Engineering

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Traffic Prediction and Management Techniques Traffic control and management Forecasting Techniques and Applications

Abstract

This article presents the theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes. This foundation rests on the Wold decomposition theorem and on the assertion that a one-week lagged first seasonal difference applied to discrete interval traffic condition data will yield a weakly stationary transformation. Moreover, empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis. Conclusions are given on the implications of these assertions and findings relative to ongoing intelligent transportation systems research, deployment, and operations.

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References

2002Introduction to Time Series and Forecasting 1985Exponential smoothing: The state of the art 1984Dynamic prediction of traffic volume through Kalman filtering theory 2002Comparison of parametric and nonparametric models for traffic flow forecasting 1996Introduction to Time Series and Forecasting 2001Use of sequential learning for short-term traffic flow forecasting 1993Forecasting time series with outliers 1993INTEGRATED APPROACH TO VEHICLE ROUTING AND CONGESTION PREDICTION FOR REAL-TIME DRIVER GUIDANCE

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