Functional Embedding of Urban Areas in Human Mobility Patterns Based on Road Network Information

Functional embedding in urban areas is the basis for human beings to move towards the construction of smart cities, and human aggregation and movement information can reflect urban functions. For any region, capturing human movement patterns and extracting the corresponding spatio-temporal features can effectively characterize the function of the city region and carry out city region function embedding. In our work, we consider the advantages of multi-graph attention network feature extraction and propose a new grid-based node-level multi-graph feature extraction method and capture road network features with N-level residual blocks, which in turn performs regional function embedding, called Multi-graph attention feature fusion segmentation network (MGAF-SegNet). MGAF-SegNet is a new multi-graph fusion residual network, in which the multi-graph attention mechanism can effectively capture the information within and between grids to mine the functional characteristics of urban areas. Considering the limitation of road network on urban functional division, incorporating the road network information, extracting the semantic relevance of the region, and realizing the division of urban functional regions from nodes to irregular regions through the semantic segmentation algorithm. The effectiveness of the proposed network is also demonstrated by taking Beijing as an example.