https://doi.org/10.1140/epjp/s13360-024-05142-x
Regular Article
Unveiling realistic mobility patterns with home–origin–destination data aggregation
1
Graduate School of Information Sciences, Tohoku University, 6-3-09 Aoba, Aramaki, Aoba, 980-8579, Sendai, Miyagi, Japan
2
Graduate School of Data Science, Shiga University, 1-1-1, Banba, 522-8522, Hikone, Shiga, Japan
3
PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, 332-0012, Kawaguchi, Saitama, Japan
4
Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, 277-8568, Kashiwa, Chiba, Japan
5
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, 153-8505, Meguro-ku, Tokyo, Japan
a
du.yunhan.q8@dc.tohoku.ac.jp
Received:
28
December
2023
Accepted:
29
March
2024
Published online:
10
May
2024
The availability of increasingly abundant mobility data in recent years has opened up new avenues for researchers to unravel human mobility patterns. Data aggregation methods have been introduced to gain a quantitative understanding of collective individual movements using these data. Nevertheless, the widely adopted origin–destination (OD) aggregation method for human mobility data lacks an essential piece of information: home location, which plays a vital role in characterizing individual movement patterns. In this study, we propose a novel data aggregation approach called home–origin–destination (HOD) with the aim of improving the accuracy of human mobility estimation. We compare the performance of various data aggregation methods for estimating population distribution. Our experimental results reveal more realistic mobility patterns when incorporating estimated home information, where individuals move out in the morning and return home before midnight. To further evaluate the effectiveness of the HOD approach, we conduct an entropy analysis to measure the unpredictability of human mobility. The HOD results exhibit lower entropy values than those in the other two cases, OD and home–destination (HD). These findings underscore the importance of incorporating home information in understanding and modeling human mobility. By leveraging the HOD data aggregation method, we can achieve more accurate population distribution estimates and capture the inherent dynamics of human movement
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjp/s13360-024-05142-x.
© The Author(s) 2024
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