Fine-Grained Urban Flow Inference with Multi-scale Representation Learning

• URL: http://arxiv.org/abs/2406.09710v1
• Date: Fri, 14 Jun 2024 04:42:29 GMT
• Title: Fine-Grained Urban Flow Inference with Multi-scale Representation Learning
• Authors: Shilu Yuan, Dongfeng Li, Wei Liu, Xinxin Zhang, Meng Chen, Junjie Zhang, Yongshun Gong,
• Abstract summary: We propose an effective fine-grained urban flow inference model called UrbanMSR. It uses self-supervised contrastive learning to obtain dynamic multi-scale representations of neighborhood-level and city-level geographic information. We validate the performance through extensive experiments on three real-world datasets.
• Score: 14.673004628911443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-grained urban flow inference (FUFI) is a crucial transportation service aimed at improving traffic efficiency and safety. FUFI can infer fine-grained urban traffic flows based solely on observed coarse-grained data. However, most of existing methods focus on the influence of single-scale static geographic information on FUFI, neglecting the interactions and dynamic information between different-scale regions within the city. Different-scale geographical features can capture redundant information from the same spatial areas. In order to effectively learn multi-scale information across time and space, we propose an effective fine-grained urban flow inference model called UrbanMSR, which uses self-supervised contrastive learning to obtain dynamic multi-scale representations of neighborhood-level and city-level geographic information, and fuses multi-scale representations to improve fine-grained accuracy. The fusion of multi-scale representations enhances fine-grained. We validate the performance through extensive experiments on three real-world datasets. The resutls compared with state-of-the-art methods demonstrate the superiority of the proposed model.

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