Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
Abstrak
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.
Topik & Kata Kunci
Penulis (2)
Billy M. Williams
L. Hoel
Akses Cepat
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- 2003
- Bahasa
- en
- Total Sitasi
- 1759×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1061/(ASCE)0733-947X(2003)129:6(664)
- Akses
- Open Access ✓