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Pseudo-PFLOW: Development of nationwide synthetic open dataset for people movement based on limited travel survey and open statistical data

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arxiv 2205.00657 v1 pith:67Y52VDY submitted 2022-05-02 physics.soc-ph cs.CY

classification physics.soc-phcs.CY
keywords datapeopledatasettravelsurveyareascoveringdevelopment
verification ladder T0 review T1 audit T2 compute T3 formal
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People flow data are utilized in diverse fields such as urban and commercial planning and disaster management. However, people flow data collected from mobile phones, such as using global positioning system and call detail records data, are difficult to obtain because of privacy issues. Even if the data were obtained, they would be difficult to handle. This study developed pseudo-people-flow data covering all of Japan by combining public statistical and travel survey data from limited urban areas. This dataset is not a representation of actual travel movements but of typical weekday movements of people. Therefore it is expected to be useful for various purposes. Additionally, the dataset represents the seamless movement of people throughout Japan, with no restrictions on coverage, unlike the travel surveys. In this paper, we propose a method for generating pseudo-people-flow and describe the development of a "Pseudo-PFLOW" dataset covering the entire population of approximately 130 million people. We then evaluated the accuracy of the dataset using mobile phone and trip survey data from multiple metropolitan areas. The results showed that a coefficient of determination of more than 0.5 was confirmed for comparisons regarding population distribution and trip volume.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Embarrassingly Simple Rule-based Visiting Circulation Approach to Trip Destination Prediction

    cs.LG 2026-07 accept novelty 6.0 of 10

    A deterministic rule-based method that chains each person's same-day trips into a round trip achieved 0.43838 accuracy and second place in the IEEE Big Data Cup 2022 trip destination prediction challenge.

  2. Deciphering Delivery Mobility: A City-Scale, Path-Reconstructed Trajectory Dataset of Instant Delivery Riders

    physics.soc-ph 2025-07 conditional novelty 6.0 of 10

    The paper presents an open dataset of 79,648 reconstructed delivery rider trajectories in Beijing, generated by connecting order stops with map-API cycling paths and validated at r=0.92 for distance.

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