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The extensive scale of holiday travel exerts a substantial influence on intercity transportation systems, the magnitude and spatio-temporal distribution of such travel hold pivotal significance in the realm of transportation planning. In this paper, we conducted an in-depth study of Baidu Migration data using Non-negative Matrix Factorization and complex network theory. We revealed the spatio-temporal patterns of intercity mobility during the 2023 May Day holiday in China and characterized the network features of intercity mobility under different patterns, in order to provide reliable decision support for inter-city traffic management measures. The results are demonstrated below: 1) NMF identified four distinct mobility patterns during the May Day holiday: the travel period pattern, the mid-journey mobility period pattern, return period pattern, and daily intercity commuting pattern. Different types of cities exhibit varying spatial characteristics in these patterns, with noticeable spatial symmetry observed in the travel period and return period patterns. 2) Utilizing complex network theory proves effective in measuring intercity mobile network characteristics. During the travel period, the population's travel focus shifts towards the central and western regions. However, during the return period and daily intercity commuting, the old first-tier cities and their surrounding cities regain central control. 3) Calculating various centrality indices facilitates the exploration of hub and gateway functions in intercity mobility. Provincial capitals often serve as hub cities, controlling passenger flow within their respective provinces. During holidays, there are special gateway cities that facilitate communication between different provinces, and in some cases, serve as gateways for both land and sea connections.
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