Abstract
In this paper, LSTM is proposed to predict metro passengers flow to avoid traffic jams for the city governors. The model is validated by manual counted data and the results show that LSTM can report an instructive prediction.
This work was supported by the Natural Science Foundation for Young Scientists of Jiangsu Province, China (Grant NO. BK20160148 and BK20160147).
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Hu, Z., Zuo, Y., Xue, Z., Ma, W., Zhang, G. (2018). Predicting the Metro Passengers Flow by Long-Short Term Memory. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_97
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DOI: https://doi.org/10.1007/978-981-10-7605-3_97
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