Reliability-Aware Refinement of High-Resolution Human Mobility Origin-Destination Tables under Sparsity and Suppression
Abstract
High-resolution origin-destination (OD) tables derived from digital mobility traces can preserve fine-scale collective movement structure, but sampling, population expansion, and privacy-motivated low-count suppression create heterogeneous observational support across OD entries and make apparent zeros difficult to interpret. We develop a reliability-aware refinement framework that combines each released OD entry with a time-resolved, data-informed radiation baseline through a closed-form cell-wise Gamma-Poisson update, without smoothing neighboring OD pairs. Applied to a 125 m hourly Tokyo OD product, the method preserves concentrated directional flows while adding lower-intensity, baseline-supported links. The relative contribution of released and model-supported structure varies substantially across OD pairs and departure hours, including among positive-flow entries: released flows are most strongly retained at 7:00, followed by 17:00, whereas the radiation baseline contributes more at 13:00. The inferred characteristic radius broadens the movement range represented by the baseline and exhibits structured variation around major station areas and rail corridors. Tokyo and Fukuoka also show a common increase in data-baseline separation at long distances. Under controlled Poisson thinning, calibrated refinement yields lower realization-averaged mean absolute error than an independently resampled no-refinement table across the tested conditions. This experiment provides internal calibration under imposed degradation, not external validation against true unsuppressed flows. The framework thus provides a transparent way to preserve fine-scale empirical mobility structure while making the cell-wise contribution of structured model support explicit.
Related
- Progressive Reliability-Guided Refinement with Cross-Modal Consistency and Iterative Correction for Weakly Supervised PET/CT Tumor Segmentation
- BTSR-Net: Boundary–topology guided structural refinement for high-resolution remote sensing image segmentation
- Linearly Constrained Generalized Linear Models with Applications in Origin-Destination Estimation
- A Constraint-Aware Generative Framework for Synthetic Origin-Destination Demand in Logistics Networks
- Measuring destination image dissonance: A compositional data analysis of cross-media representations in Sanya’s Tourism
BibTeX
@misc{shida2026reliabilityaware,
title = {{Reliability-Aware Refinement of High-Resolution Human Mobility Origin-Destination Tables under Sparsity and Suppression}},
author = {Yohei Shida and Ryosuke Susuta and Takuhiro Moriyama and Jun'ichi Ozaki},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21626},
eprint = {2610.21626},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21626},
}
RIS
TY - UNPB
TI - Reliability-Aware Refinement of High-Resolution Human Mobility Origin-Destination Tables under Sparsity and Suppression
AU - Yohei Shida
AU - Ryosuke Susuta
AU - Takuhiro Moriyama
AU - Jun'ichi Ozaki
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21626
UR - https://arxiv.org/abs/2610.21626
AB - High-resolution origin-destination (OD) tables derived from digital mobility traces can preserve fine-scale collective movement structure, but sampling, population expansion, and privacy-motivated low-count suppression create heterogeneous observational support across OD entries and make apparent zeros difficult to interpret. We develop a reliability-aware refinement framework that combines each released OD entry with a time-resolved, data-informed radiation baseline through a closed-form cell-wise Gamma-Poisson update, without smoothing neighboring OD pairs. Applied to a 125 m hourly Tokyo OD product, the method preserves concentrated directional flows while adding lower-intensity, baseline-supported links. The relative contribution of released and model-supported structure varies substantially across OD pairs and departure hours, including among positive-flow entries: released flows are most strongly retained at 7:00, followed by 17:00, whereas the radiation baseline contributes more at 13:00. The inferred characteristic radius broadens the movement range represented by the baseline and exhibits structured variation around major station areas and rail corridors. Tokyo and Fukuoka also show a common increase in data-baseline separation at long distances. Under controlled Poisson thinning, calibrated refinement yields lower realization-averaged mean absolute error than an independently resampled no-refinement table across the tested conditions. This experiment provides internal calibration under imposed degradation, not external validation against true unsuppressed flows. The framework thus provides a transparent way to preserve fine-scale empirical mobility structure while making the cell-wise contribution of structured model support explicit.
ER -