Abstract
As an essential part of the urban public transport system, taxi has been the necessary transport option in the social life of city residents. The research on the analysis and prediction of taxi demands based on the taxi trip records tends to be one of the important topics recently, which is of great importance to optimize the taxi dispatching, minimize the wait-time for passengers and drivers, reduce the time and distances of vacant driving, as well as improve the quality of taxi operation and management. In this paper, we propose the CNN-BiLSTM-Attention model, which consists of Convolutional Neural Networks (CNNs), Bidirectional Long Short Term Memory (BiLSTM) neural networks and the Attention mechanism, to predict the taxi demands at some certain regions. Then we compare the prediction performance of CNN-BiLSTM-Attention model with the baselines. The results show that this model can outperform other models in predicting the taxi demands, which also proves that our CNN-BiLSTM-Attention model is capable of capturing the spatial and temporal features more effectively, and has a better prediction accuracy.
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Guo, X. (2020). Prediction of Taxi Demand Based on CNN-BiLSTM-Attention Neural Network. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_28
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DOI: https://doi.org/10.1007/978-3-030-63836-8_28
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