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Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
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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Cited by
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301922 · Cited by 3,123
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000
Predicting Taxi–Passenger Demand Using Streaming Data
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2262376 · Cited by 721
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991401 · Cited by 712
Koesdwiady, A., Soua, R., & Karray, F. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2585575 · Cited by 367
Metadata
- Authors
- Williams, B. M., & Hoel, L. A.
- Venue
- Journal of Transportation Engineering
- Published
- 2003-11
- Volume
- 129
- Issue
- 6
- Pages
- 664-672
- ISSN
- 0733-947X (ISSN-L)
- 1943-5436
- DOI
- 10.1061/(asce)0733-947x(2003)129:6(664
- OpenAlex
- W1973943669
- Cited by
- 2,164
- References
- 0
- FWCI
- 10.72
- Type
- journal article
- Language
- en
- Open access
- closed
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- Williams, B. M., & Hoel, L. A. (2003). Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results. Journal of Transportation Engineering. https://doi.org/10.1061/(asce)0733-947x(2003)129%3A6(664
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
