# Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features
## 6 Conclusions  
We proposed a method for embedding urban mobility structures that incorporate multi-scale features, which is used to detect urban mobility structures and learn the distribution of urban functions. To begin with, we gathered road network data and population travel data to construct the urban mobility network. By designing a multi-scale contractive learning model, it generates the embedding vectors of the regions by learning both local population activity features and global spatial structure features. In addition, a correlation matrix is employed to explicitly encode the coordination and competition relationship of POI functions. By integrating the urban mobility structure embedding vectors with the learning of urban functional distribution, we established a nonlinear mapping relationship between population activities and urban functions. It helps evaluate the balance of functional distribution and population activities in urban regions. The experimental results in Haikou City demonstrate that the proposed method has achieved excellent performance in both the detection of urban mobility structure and the learning of functional distribution.  
Through the analysis of urban mobility structure, it was found that the urban structure of Haikou City has a central clustering characteristic. The central area attracts a large amount of surrounding mobility due to high-intensity development and multi-functional agglomeration, reflecting a clear functional zoning in urban space and the dominant role of the central area. However, this also leads to traffic congestion and excessive population concentration. The mobility structure on weekends shows a decentralized and diversified pattern, indicating that the suburbs are transitioning from a single residential function to a multi-functional one, with the addition of leisure, entertainment, and commercial facilities. It has eased the pressure on the central area and fostered a more balanced spatial structure. However, the functional development of the suburbs remains less advanced than that of the central area, and balanced development is still in its early stages. In urban planning, it is essential to optimize the functional layout of the central urban area, enhance suburban functions, and promote their diversified development. It will help reduce residents' dependence on the central area and support the balanced growth of urban  
Figure 11: Regional flows statistics, (**a–d**) represent the four regions in Figure 10.  
space. Owing to the limitations of the available data sources, this study exclusively utilizes DiDi data. For future work, we plan to expand our data scope to include micromobility data, such as shared scooters and bikes, as well as sensor-based urban quality metrics like noise and vibration levels. We also intend to adopt multimodal mobility data fusion techniques to more accurately capture and reflect the complex dynamics of modern urban environments.  
Conceptualization, J.L. ([PERSON]), C.L., and H.L.; Data curation, Q.L.; Formal analysis, C.L. and Q.L.; Methodology, J.L. ([PERSON]), H.L., and Q.L.; Project administration, H.L.; Validation, J.L. ([PERSON]), C.L., J.L. ([PERSON]), and D.Z.; Visualization, [PERSON] ([PERSON]) and D.Z.; Writing--original draft, [PERSON] ([PERSON]); Writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Foundation of Henan Province [Grant No. 41974213] and Science and Technology Research Project of Henan Province [Grant No. 252102321003].  
The data are sourced from [PERSON]'s Gaia data openness program; you can contact the corresponding author to obtain them.  
We appreciate the editors and reviewers for their constructive comments and suggestions.  
No potential conflicts of interest were reported by the authors.