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- W2998043476 abstract "The recent availability of geospatial data on human movements and communications has provided new insights into the understanding of human mobility. This study explores a data set of people flow which contains diverse travel modes in the Tokyo metropolitan area. We constructed a spatially embedded network to model the mobility interactions in urban space and then utilized the community detection method to reveal the sub-regional structures in individual travel modes and as well as the aggregated one. Two patterns of hierarchical polycentric mobility structure were revealed, and then the similarity and difference in sub-regional structures between different travel modes was analyzed. Furthermore, we examined how the mobility structures under different travel models collectively form the overall urban mobility structure. This work sheds some light on improving our understanding of the spatial aggregation of mobility and is of potential value for transport planning." @default.
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- W2998043476 date "2018-12-01" @default.
- W2998043476 modified "2023-09-23" @default.
- W2998043476 title "Revealing the Spatial Structure of Human Mobility in Diverse Travel Modes" @default.
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- W2998043476 doi "https://doi.org/10.1109/csci46756.2018.00212" @default.
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