It has long been a research interest for understanding human mobility and the urban spatial structure in transport geography. Due to the development of information and sensing technologies, the mobility pattern and the underlying city structure can be depicted by the massive realistic residents’ travel flow. It is quite different from the knowledge that we know from urban plan and design. Considering its importance for the planning and management of the city, transportation system and urban infrastructure, many researchers have made efforts in this field recently. However, few studies focus on revealing the mobility pattern and the urban spatial structure on the basis of multi-source travel data. To fill this gap, in this study, the differences of the mobility patterns between the taxi, metro and bike sharing system of Shanghai were detected by network science method and statistical approach during weekdays and weekends. Using the community detection method, the data sets were analyzed to uncover the urban structure of three transportation modes. The characteristics of the spatial structure revealed by each mode were dissected at the same time. Furthermore, this study illustrated the effect of each transportation mode have on the evolution of the urban spatial structure under four-level travel distance.

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