Abstract
Traffic signals prediction based on machine learning plays a vital role in future vehicle to infrastructure communication in intelligent transport systems, since they can help vehicles avoid waiting and reduce energy consumption with a higher communication security compared with traditional radio-based pattern. In this work, Baseline Model, Linear Model, Dense Model, Convolutional Neural Network, Long Short Term Memory (LSTM) and Autoregressive LSTM are applicated to forecast the traffic signals based on the historical time series. After comparison, LSTM outperforms other methods with a high potential to be extended further for a better accuracy.
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Kunashko, A., Xie, F., Naumann, S., Gao, Y., Li, J. (2022). Application and Comparison of Machine Learning Algorithms in Traffic Signals Prediction. In: Macioszek, E., Sierpiński, G. (eds) Present Approach to Traffic Flow Theory and Research in Civil and Transportation Engineering. TSTP 2021. Lecture Notes in Intelligent Transportation and Infrastructure. Springer, Cham. https://doi.org/10.1007/978-3-030-93370-8_5
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DOI: https://doi.org/10.1007/978-3-030-93370-8_5
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