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Multi-city traffic flow forecasting via multi-task learning

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Abstract

Traffic flow forecasting or prediction plays an important role in the traffic control and management of a city. Existing works mostly train a model using the traffic flow data of a city and then test the trained model using the data of the same city. It may not be truly intelligent as there are many cities around us and there should be some shared knowledge among different cities. The data of a city and its knowledge can be used to help improve the traffic flow forecasting of other cities. To address this motivation, we study building a universal deep learning model for multi-city traffic flow forecasting. In this paper, we exploit spatial-temporal correlations among different cities with multi-task learning to approach the traffic flow forecasting tasks of multiple cities. As a result, we propose a M ulti-city T raffic flow forecasting N etwork (MTN) via multi-task learning to extract the spatial dependency and temporal regularity among multiple cities later used to improve the performance of each individual city traffic flow forecasting collaboratively. In brief, the proposed model is a quartet of methods: (1) It integrates three temporal intervals and formulates a multi-interval component for each city to extract temporal features of each city; (2) A spatial-temporal attention layer with 3D Convolutional kernels is plugged into the neural networks to learn spatial-temporal relationship; (3) As traffic peak distributions of different cities are often similar, it proposes to use a peak zoom network to learn the peak effect of multiple cities and enhance the prediction performance on important time steps in different cities; (4) It uses a fusion layer to merge the outputs from distinct temporal intervals for the final forecasting results. Experimental results using real-world datasets from DIDI show the superior performance of the proposed model.

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Notes

  1. https://gaia.didichuxing.com

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61976247 and 61572407) and the National Key Technology Research and Development Program (No. 2015BAH19F02). The datasets used in the experiments are from DIDI Chuxing GAIA Initiative. Hao Wang would like to thank a grant from Project Funded by China Postdoctoral Science Foundation (No. 2020M681960).

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Authors and Affiliations

  1. Southwest Jiaotong University, Chengdu, China

    Yiling Zhang, Yan Yang, Wei Zhou & Xiaocao Ouyang

  2. Zhejiang Lab, Zhejiang, China

    Hao Wang

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  1. Yiling Zhang
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  2. Yan Yang
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  3. Wei Zhou
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  4. Hao Wang
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Correspondence to Yan Yang.

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Zhang, Y., Yang, Y., Zhou, W. et al. Multi-city traffic flow forecasting via multi-task learning. Appl Intell 51, 6895–6913 (2021). https://doi.org/10.1007/s10489-020-02074-8

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