Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data

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The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility.
Publisher
PUBLIC LIBRARY SCIENCE
Issue Date
2018-02
Language
English
Article Type
Article
Keywords

MODELING NETWORK AUTOCORRELATION; TRIP DISTRIBUTION; GRAVITY MODEL; OPPORTUNITY MODEL; COMMUTING TIME; URBAN FORM; TRAVEL; MIGRATION; DISTANCE; IDENTIFICATION

Citation

PLOS ONE, v.13, no.2

ISSN
1932-6203
DOI
10.1371/journal.pone.0192698
URI
http://hdl.handle.net/10203/240616
Appears in Collection
GCT-Journal Papers(저널논문)
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