Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
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- 1 November 2003
- journal article
- Published by American Society of Civil Engineers (ASCE) in Journal of Transportation Engineering
- Vol. 129 (6), 664-672
- https://doi.org/10.1061/(asce)0733-947x(2003)129:6(664)
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.Keywords
This publication has 11 references indexed in Scilit:
- Reducing bias in probe-based arterial link travel time estimatesTransportation Research Part C: Emerging Technologies, 2002
- Use of sequential learning for short-term traffic flow forecastingTransportation Research Part C: Emerging Technologies, 2001
- Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX ModelingTransportation Research Record: Journal of the Transportation Research Board, 2001
- Should we use neural networks or statistical models for short-term motorway traffic forecasting?International Journal of Forecasting, 1997
- Introduction to Time Series and ForecastingPublished by Springer Science and Business Media LLC ,1996
- Joint Estimation of Model Parameters and Outlier Effects in Time SeriesJournal of the American Statistical Association, 1993
- Forecasting time series with outliersJournal of Forecasting, 1993
- Exponential smoothing: The state of the artJournal of Forecasting, 1985
- Dynamic prediction of traffic volume through Kalman filtering theoryTransportation Research Part B: Methodological, 1984
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978