Thursday, February 21, 2019

Download and Read Urban Spatiotemporal Analysis Using Mobile Phone Data: Case Study of Medium- and Large-sized Korean Cities Book PDF

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Urban Spatiotemporal Analysis Using Mobile Phone Data: Case Study of Medium- and Large-sized Korean Cities

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Synopsis

Abstract: Recent advanced information and communication technologies can provide more accurate and comprehensive information. In particular, mobile phone data provide a new data source for urban structures and mobility. It is important for urban and transportation planners to be able to use data containing valuable location information that is not easily obtainable from traditional datasets, such as expensive household surveys, extensive traffic counts, or aggregated socioeconomic statistics. This study explored the potential of using mobile phone data to characterize and compare urban activity and mobility patterns from daily and hourly mobile phone records across 10 cities in Korea. We compared the internal and external mobility of phone users, and calculated urban attractiveness and home-based trip length frequency distribution for comparisons of different sized cities. The spatiotemporal evolution of urban activity within a day was examined, and the spatiotemporal extension for each city was calculated, showing the degree of spatial dispersion of residences and other activity locations. We also identified urban activity subcenters and hotspots based on density and time persistence criteria, as well as their compactness. Policy makers can expect to see more applications using mobile phone data, and this study helps demonstrate the potential of such data. We hope that spatiotemporal activity analysis can provide a foundation for future research that will help improve urban and transportation policies. Highlights: Comparing urban characteristics using mobile phone data. Assessing spatial-temporal activity patterns. Examining hourly activity patterns and spatial-temporal extension for each city. Identifying hourly locations of sub-centers and hotspots. Implications for better transportation policies, e.g., late night buses, flexible transit operations.

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