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MSRFormer: Road Network Representation Learning using Multi-scale Feature Fusion of Heterogeneous Spatial Interactions
Created by
Haebom
Author
Jian Yang, Jiahui Wu, Li Fang, Hongchao Fan, Bianying Zhang, Huijie Zhao, Guangyi Yang, Rui Xin, Xiong You
Outline
This paper proposes MSRFormer, a novel road network representation learning framework that integrates multi-scale spatial interactions to overcome the limitations of existing methods that transform road network data into vector representations using deep learning. Considering the heterogeneity and hierarchical nature of road networks, we utilize spatial flow convolutions to extract small-scale features from large trajectory datasets and identify scale-dependent spatial interaction regions. We effectively capture complex multi-scale spatial dependencies using graph transformers and fuse spatial interaction features through residual connections to derive the final road network representation. Validation results using two real-world datasets demonstrate that MSRFormer outperforms existing methods on two road network analysis tasks, achieving up to 16% performance improvement over the best-performing existing method, particularly for complex road network structures. We demonstrate that integrating trajectory data is advantageous for traffic-related tasks and highlight the interplay between scale effects and flow heterogeneity in spatial interactions.
Takeaways, Limitations
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Takeaways:
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We present MSRFormer, a road network representation learning framework that considers multi-scale spatial interactions.
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Leveraging trajectory data to improve the performance of traffic-related tasks.
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Performance improvement (up to 16%) over existing methods in complex road network structures.
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Provides insight into the interaction patterns between scale effects and flow heterogeneity.
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Providing a practical framework for developing task-independent road network representation models.
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Limitations:
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Generalization performance needs to be verified on datasets other than the two presented real datasets.
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Further analysis of the computational complexity and efficiency of MSRFormer is needed.
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Further research is needed on its applicability to various types of road network analysis tasks.
