The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Deep learning for short-term traffic flow prediction
Open accessAbstract
Abstract We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers. The challenge of predicting traffic flows are the sharp nonlinearities due to transitions between free flow, breakdown, recovery and congestion. We show that deep learning architectures can capture these nonlinear spatio-temporal effects. The first layer identifies spatio-temporal relations among predictors and other layers model nonlinear relations. We illustrate our methodology on road sensor data from Interstate I-55 and predict traffic flows during two special events; a Chicago Bears football game and an extreme snowstorm event. Both cases have sharp traffic flow regime changes, occurring very suddenly, and we show how deep learning provides precise short term traffic flow predictions.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5590 · Cited by 1,005
Cited by
LSTM-based traffic flow prediction with missing data
Tian, Y., Zhang, K., Li, J., Lin, X., & Yang, B. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.08.067 · Cited by 544
DeepPF: A deep learning based architecture for metro passenger flow prediction
Liu, Y., Liu, Z., & Jia, R. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2019.01.027 · Cited by 426
Short-term traffic flow prediction with Conv-LSTM
Liu, Y., Zheng, H., Feng, X., & Chen, Z. (2017) · 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2017.8171119 · Cited by 328
Survey on traffic prediction in smart cities
Nagy, A. M., & Simon, V. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.07.004 · Cited by 312
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
Metadata
- Authors
- Polson, N. G., & Sokolov, V. O.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 79
- Pages
- 1-17
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2593182953
- Cited by
- 1,015
- References
- 3
- FWCI
- 103.46
- Type
- journal article
- Language
- en
- Open access
- green
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- Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2017.02.024
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