A multi-objective optimization method generates privacy-preserving synthetic daily mobility trajectories from aggregated OD data, dwell-travel quantiles, and the universal visit-frequency law that match real distributions in Tokyo and Fukuoka.
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Privacy-Preserving Synthetic Dataset of Individual Daily Trajectories for City-Scale Mobility Analytics
A multi-objective optimization method generates privacy-preserving synthetic daily mobility trajectories from aggregated OD data, dwell-travel quantiles, and the universal visit-frequency law that match real distributions in Tokyo and Fukuoka.