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Travel order quantity prediction via attention-based bidirectional LSTM networks

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Abstract

Traffic flow prediction is a very challenging task in traffic networks. Travel order quantity prediction is of great value to the analysis of traffic flow. However, the number of travel orders is closely related to time, leading to its short-term proximity and long-term cyclical dependence. Therefore, it is difficult to predict travel order quantity using traditional methods. To capture both the long-term and short-term correlations, in this paper, we propose an attention-based bidirectional long short-term memory network (AT-BLSTM) model, which consists of bidirectional long short-term memory (BLSTM) layer and attention layer. The BLSTM layer contains forward and backward long short-term memory (LSTM), which uses a novel method to combine forward and backward output. The attention layer utilizes novel self-attention algorithm to assign different weights according to the correlation between features. In this case, AT-BLSTM can predict travel order quantity more accurately than other time series models. For example, AT-BLSTM achieves the lowest MAE (0.0647), the lowest RMSE (0.0836) and the lowest MAPE (0.1239) among all the methods on dataset Xiuying. Extensive experiments on real-world travel datasets offer evidence that the proposed approach matches or outperforms state-of-the-art methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61772386), and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (Grant No. KF-2020-05-014).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Fei Yang, Huyin Zhang & Shiming Tao

  2. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, China

    Huyin Zhang

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  1. Fei Yang
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  2. Huyin Zhang
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Correspondence to Huyin Zhang.

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Yang, F., Zhang, H. & Tao, S. Travel order quantity prediction via attention-based bidirectional LSTM networks. J Supercomput 78, 4398–4420 (2022). https://doi.org/10.1007/s11227-021-04032-8

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