Abstract: Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of massive taxi trip data of Shanghai, we built spatially-embedded networks to model the intra-city spatial interactions and introduced network science methods into the issue. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long-and short-distance trips, we reveal a two-level hierarchical polycentric city structure of Shanghai. Further explorations on sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential in local traffic. By incorporating the land use of centers from the travel pattern perspective, we investigate sub-region formation and center-local places interaction patterns. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in applying urban and transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than other structures, such as administrative divisions.
Using taxi trajectory data from Shanghai, Liu et al. REF built spatial networks to model intra-city spatial interactions, revealing the hierarchical and polycentric structure of Shanghai.
Revealing travel patterns and city structure with taxi trip data
Physics Computer Science