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ConvGCN-RF: A hybrid learning model for commuting flow prediction considering geographical semantics and neighborhood effects

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Abstract

Commuting flow prediction is a crucial issue for transport optimization and urban planning. However, the two existing types of solutions have inherent flaws. One is traditional models, such as the gravity model and radiation model. These models rely on fixed and simple mathematical formulas derived from physics, and ignore rich geographic semantics, which makes them difficult to model complex human mobility patterns. The other is the machine learning models, most of which simply leverage the features of Origin-Destination (OD), ignoring the topological nature of the interaction network and the spatial correlation brought by the nearby areas. In this paper, we propose a ‘preprocessing-encoder-decoder’ hybrid learning model, which can make full use of geographic semantic information and spatial neighborhood effects, thereby significantly improving the prediction performance. Specifically, in the preprocessing part, we divide the study area into grids, and then incorporates features such as location, population, and land use types. The second step of the encoder designs a convolutional neural network (CNN) to achieve the fusion of neighborhood features, constructs a spatial interaction network with the grids as nodes and the flows as edges, and then uses the graph convolutional network (GCN) to extract the embeddings of the nodes. In the last step of the decoder, a random forest regressor is trained to predict the commuting flow based on the learned embedding vectors. An empirical study on a commuter dataset in Beijing shows that our proposed model is approximately 20% better than XGBoost (state-of-the-art), thus proving its effectiveness.

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Data availability

The commuting flow data is provided by Amap company, and they have not given their permission for researchers to share their data. Data requests can be made to Amap company (https://lbs.amap.com/). The land use data is publicly available online at http://data.ess.tsinghua.edu.cn/.

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Acknowledgements

We thank the editors and reviewers for their suggestions to improve the quality of this paper. This research was supported by grants from the National Natural Science Foundation of China (41830645, 41971331).

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Authors and Affiliations

  1. Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, 100871, China

    Ganmin Yin, Zhou Huang, Yi Bao, Han Wang & Yi Zhang

  2. Beijing Key Lab of Spatial Information Integration & Its Applications, Peking University, Beijing, 100871, China

    Ganmin Yin, Zhou Huang, Yi Bao, Han Wang & Yi Zhang

  3. Department of Geography, California State University, Long Beach, California, USA

    Linna Li

  4. School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China

    Xiaolei Ma

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  1. Ganmin Yin
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Correspondence to Zhou Huang.

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Yin, G., Huang, Z., Bao, Y. et al. ConvGCN-RF: A hybrid learning model for commuting flow prediction considering geographical semantics and neighborhood effects. Geoinformatica 27, 137–157 (2023). https://doi.org/10.1007/s10707-022-00467-0

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