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Multi-weighted graph 3D convolution network for traffic prediction

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Abstract

Predicting future traffic state (e.g., traffic speed, volume, travel time, etc.) accurately is highly desirable for traffic management and control. However, network-wide traffic flow has complicated spatial-temporal dependencies, making it challenging to predict. This study proposes a multi-weighted graph 3D convolution network (MWG3D) to predict future network-wide traffic speed, considering the spatial-temporal heterogeneous effects of multiple external factors (i.e., points of interests (POIs), roadway physical characteristics and incidents). The network is composed of a Graph-3D convolution (G3D) module and an incident impact module. In G3D module, a weighted graph convolution is developed first, which extracts complex spatial dependencies of traffic flow considering heterogeneous effects of POIs and roadway physical characteristics. These external factors have great influence on the periodicity of human daily activities, which in turn cyclically affect traffic flow. The weighted graph convolution is further connected with 3D convolutions to extract temporal dependencies of traffic flow, accounting for temporal heterogeneous effects of these external factors. An incident impact module is separately developed to account for spatial-temporal heterogeneous effects of incidents. These external factors could lead to abrupt and temporary changes in traffic flow. The proposed network is evaluated on two real-world datasets. The results show that MWG3D outperforms a selection of the state-of-the-art models. Furthermore, the spatial-temporal heterogeneous effects of external factors are crucial to prediction accuracy.

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Data availability

The datasets generated during and analyzed during the current study are not publicly available due to privacy restrictions but are available from the corresponding author on reasonable request.

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Acknowledgements

This research is supported by the National Key R & D Program of China (2018YFE0102700) and the National Natural and Science Foundation of China (71971061).

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Authors and Affiliations

  1. School of Transportation, Southeast University, Nanjing, JiangSu, 211198, China

    Yuqing Liu, Chen Wang, Sixuan Xu, Wei Zhou & Yuzhi Chen

  2. Purple Mountain Laboratories, Nanjing, JiangSu, 211111, China

    Chen Wang & Wei Zhou

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  1. Yuqing Liu
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  2. Chen Wang
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  3. Sixuan Xu
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  4. Wei Zhou
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  5. Yuzhi Chen
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Contributions

All authors contributed to the study conception and design. Datasets used in this study were developed by Y.L. and Y.C. Experiments were performed by Y.L. Result analysis was performed by Y.L., S.X., W.Z. and Y.C. The first draft of the manuscript was written by Y.L., and all authors commented on previous versions of the manuscript. Supervision was provided by C.W. All authors read and approved the final manuscript.

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Correspondence to Chen Wang.

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Liu, Y., Wang, C., Xu, S. et al. Multi-weighted graph 3D convolution network for traffic prediction. Neural Comput & Applic 35, 15221–15237 (2023). https://doi.org/10.1007/s00521-023-08519-8

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