Abstract With the development of information communication technologies, it becomes easier to access to the spatio-temporal data such as smart card data, GPS-enabled taxi data and social media data, which gives fresh impetus to research on commuting behavior and urban spatial structure. What's more, compared with the traditional data like questionnaires, this kind data has advantage of good continuity, wide coverage and dynamic update. At the same time, it is the impact of the urban space on commuting behavior that is the important topic of urban geography, urban planning and urban traffic study. And most people choose to travel by subway because of its convenience in many large cities worldwide. Therefore, this research aims to analyze commuting behavior and employment center based on subway smart card data.The subway smart card data used in this study was collected by the fare collection system for one week in April, 2015 in Shanghai. In this paper, we designedthe Trip-Chain data model corresponding to the passengers' travels in one day. According to the rules of identifying commuting built, wegraphically illustrated the spatial relation between residence and employment in Shanghai. Also the commuting pattern is depicted clearly.Meanwhilethe analysis outcomedemonstrates that the average commuting time in Shanghai is 35 minutes and the mainstream of commuting is centripetal. More interesting, the average commuting time increases concentrically from the inner city to the fringe area. In addition, the place of employment gathers in the core area while the place of residence in the suburbs. On the other hand, we identified the passenger-attracting scope of the 13 employmentcenters in the downtownseparately and they could be divided into three types: distance attenuation mode, enclave attracting mode and composite mode. Based on the number of the employment center's passenger-attracting, we summary all the passenger-attracting scopes in one map. And the identification results indicate the polycentric structure can adjust and decentralize the employment spatially, which improves the efficiency of the commuting integrally.
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2017, Vol. 32 
