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MmgFra: A multiscale multigraph learning framework for traffic prediction in smart cities

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Abstract

Traffic prediction is an important part of smart city projects. Due to the complex topology of urban road network and the dynamic change of traffic data, establishing a spatio-temporal model to accurately predict traffic volume remains a challenging task at present. Recently, Graph Convolution Networks (GCN) have been widely used to extract features from non-grid data, and time sequence models have been used to learn temporal features of traffic distributions. However, current GCN based methods only make use of the natural structure of road network, while ignoring the information of administrative units, neighborhood units and other hierarchical structures of spatial interaction. Therefore, traditional models are typically developed under one single scale, which are far from the prediction of multi-scale systems. To address this issue, we propose a novel framework, named MmgFra, to merge multi-scale information for high-precision urban traffic flow prediction. MmgFra consists of three components: a spatial feature extraction module, a feature clustering fusion module, and a temporal feature extraction module. Specifically, we use stacking GCNs to extract spatial structure information of the city road network, administrative unit road network, and neighborhood units, and employ a DIFFPOOL module to cluster and fuse the above information. Finally, we introduce GRU to capture temporal features. We evaluated its performance on a real city dataset using different time scales. The experimental results indicate that compared with state-of-the-art neural network models and GCN-based variant models, our model exhibits higher predictive accuracy. MmgFra improves by approximately 8.4%-29.5% and 7.5%-30.6% in terms of RMSE and MAPE metrics, respectively.

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The data that supports the findings of this study are available in [figshare.com] with the identifier (https://figshare.com/s/794b68474b6e83626c1c).

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Funding

The work was supported by the National Natural Science Foundation of China [42071442]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG170640].

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Authors and Affiliations

  1. School of Geography and Information Engineering, China University of Geosciences, Wuhan, China

    Wenhao Yu, Shangyou Wu & Mengqiu Huang

  2. National Engineering Research Center for Geographic Information System, China University of Geosciences, Wuhan, China

    Wenhao Yu

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  1. Wenhao Yu
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  2. Shangyou Wu
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  3. Mengqiu Huang
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Contributions

Wenhao Yu and Shangyou Wu contributed to the study conception and design. Wenhao Yu put forward the main ideas, Wenhao Yu and Shangyou Wu wrote the main manuscript text, Shangyou Wu wrote the main experimental code, Mengqiu Huang revised the manuscript text, and Shangyou Wu searched for the required literature. All authors reviewed the manuscript.

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Correspondence to Wenhao Yu.

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Communicated by: H. Babaie

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Yu, W., Wu, S. & Huang, M. MmgFra: A multiscale multigraph learning framework for traffic prediction in smart cities. Earth Sci Inform 16, 2727–2739 (2023). https://doi.org/10.1007/s12145-023-01068-7

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