Application and Comparison of Machine Learning Algorithms in Traffic Signals Prediction-Reference-Cited by-同舟云学术

Application and Comparison of Machine Learning Algorithms in Traffic Signals Prediction

Published:2022 Issue: Volume: Page:69-78
ISSN:2523-3440
Container-title:Present Approach to Traffic Flow Theory and Research in Civil and Transportation Engineering
language:
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Author:

Kunashko Anastasiia,Xie Feng,Naumann Sebastian,Gao Yin,Li Jun

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-93370-8_5

Reference8 articles.

1. Elsworth, S., Güttel, S.: Time Series Forecasting Using LSTM Networks: A Symbolic Approach (2020). https://arxiv.org/pdf/2003.05672.pdf

2. Bhattacharjya, D., Shanmugam, K., Gao, T., Mattei, N., Varshney, K., Subramanian, D.: Event-driven continuous time bayesian networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no 4, pp. 3259-3266 (2020)

3. Dabrowski, J.J., Zhang, Y., Rahman, A.: ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-step-Ahead Time-Series Forecasting. https://arxiv.org/abs/2002.04155

4. Shi, Q., et al.: Block hankel tensor ARIMA for multiple short time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no 4, pp. 5758-5766 (2020)

5. Zhang, S.Q., Zhou, Z.H.: Harmonic recurrent process for time series forecasting. In: Frontiers in Artificial Intelligence and Applications, vol. 325, pp. 1714-1721 (2020)

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