Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction
Pages 573 - 576
Abstract
Large-scale commuting flow prediction is an essential task to estimate the commuting origin-destination (OD) demand within within a prefecture or the whole nation using multiple auxiliary data. Considering ranked structures of metropolitan areas and increased number of geographical units that need to be maintained, we develop a heterogeneous graph-based model to generate meaningful region embeddings at multiple spatial resolutions for predicting different types of inter-level OD flows. To demonstrate the effectiveness of the proposed method, extensive experiments were conducted using real-world aggregated mobile phone datasets collected from Shizuoka Prefecture, Japan. The results indicate that our proposed model outperforms existing models in terms of a uniform urban structure. We extend the understanding of predicted results using reasonable explanations to enhance the credibility of the model.
References
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Index Terms
- Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction
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Published In
October 2024
743 pages
ISBN:9798400711077
DOI:10.1145/3678717
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Publication History
Published: 22 November 2024
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- Short-paper
- Research
- Refereed limited
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- JSPS KAKENHI Grant
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SIGSPATIAL '24
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SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems
October 29 - November 1, 2024
GA, Atlanta, USA
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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%
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