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Traffic forecasting is crucial for intelligent transportation systems.
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Barredo-Arrieta, A., I. Laña, and J. Del Ser, What lies beneath: A note on the explainability of black-box machine learning models for road traffic forecasting. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) , IEEE, 2019, pp. 2232–2237
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Ranjan, N., S. Bhandari, H. P. Zhao, H. Kim, and P. Khan, City-wide traffic congestion prediction based on CNN, LSTM and transpose CNN. IEEE Access , Vol. 8, 2020, pp. 81606–81620
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Shi, X., S. Xue, K. Wang, F. Zhou, J. Zhang, J. Zhou, C. Tan, and H. Mei, Language models can improve event prediction by few-shot abductive reasoning. Advances in Neural Information Processing Systems , Vol. 36, 2024
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Gruver, N., M. Finzi, S. Qiu, and A. G. Wilson, Large language models are zero-shot time series forecasters. Advances in Neural Information Processing Systems , Vol. 36, 2024
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Da, L., M. Gao, H. Mei, and H. Wei, Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning. In Proceedings of the AAAI Conference on Artificial Intelligence , 2024, Vol. 38, pp. 82–90
2024
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