arXiv subjects

Jun'ichi Ozaki

Publications and source records attributed to Jun'ichi Ozaki.

7 recordsLinked to original sources

arXiv2026

Reliability-Aware Refinement of High-Resolution Human Mobility Origin-Destination Tables under Sparsity and Suppression

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.

physics.soc-ph↗

arXiv2025

Privacy-Preserving Synthetic Dataset of Individual Daily Trajectories for City-Scale Mobility Analytics

Urban mobility data are indispensable for urban planning, transportation demand forecasting, pandemic modeling, and many other applications; however, individual mobile phone-derived Global Positioning System traces cannot generally be shared with third parties owing to severe re-identification risks. Aggregated records, such as origin-destination (OD) matrices, offer partial insights but fail to capture the key behavioral properties of daily human movement, limiting realistic city-scale analyses. This study presents a privacy-preserving synthetic mobility dataset that reconstructs daily trajectories from aggregated inputs. The proposed method integrates OD flows with two complementary behavioral constraints: (1) dwell-travel time quantiles that are available only as coarse summary statistics and (2) the universal law for the daily distribution of the number of visited locations. Embedding these elements in a multi-objective optimization framework enables the reproduction of realistic distributions of human mobility while ensuring that no personal identifiers are required. The proposed framework is validated in two contrasting regions of Japan: (1) the 23 special wards of Tokyo, representing a dense metropolitan environment; and (2) Fukuoka Prefecture, where urban and suburban mobility patterns coexist. The resulting synthetic mobility data reproduce dwell-travel time and visit frequency distributions with high fidelity, while deviations in OD consistency remain within the natural range of daily fluctuations. The results of this study establish a practical synthesis pathway under real-world constraints, providing governments, urban planners, and industries with scalable access to high-resolution mobility data for reliable analytics without the need for sensitive personal records, and supporting practical deployments in policy and commercial domains.

cs.SI↗

arXiv2022

Approximation of human flow in urban areas by a network of electric circuits : Potential fields and fluctuation-dissipation relations

Owing to the big data the extension of physical laws on nonmaterial has seen numerous successes, and human mobility is one of the scientific frontier topics. Recent GPS technology has made it possible to trace detailed trajectories of millions of people, macroscopic approaches such as the gravity law for human flow between cities and microscopic approaches of individual origin-destination distributions are attracting much attention. However, we need a more general basic model with wide applicability to realize traffic forecasting and urban planning of metropolis fully utilizing the GPS data. Here, based on a novel idea of treating moving people as charged particles, we introduce a sophisticated method to map macroscopic human flows into currents on an imaginary electric circuit defined over a metropolitan area. Conductance is found to be nearly proportional to the maximum current in each location and synchronized human flows in the morning and evening are well described by the temporal changes of electric potential. Surprisingly, the famous fluctuation dissipation theorem holds, namely, the variances of currents are proportional to the conductivities akin to an ordinary material. Especially during the pandemic, such a tool may offer an invaluable insight for policy-making in managing human flows.

physics.soc-ph↗

arXiv2022

Daily-activity-dependency of effective reproduction number in COVID-19 pandemic: direct modelling from GPS data

During the COVID-19 pandemic, governments faced difficulties in implementing mobility restriction measures, as no clear quantitative relationship between human mobility and infection spread in large cities is known. We developed a model that enables quantitative estimations of the infection risk for individual places and activities by using smartphone GPS data for the Tokyo metropolitan area. The effective reproduction number is directly calculated from the number of infectious social contacts defined by the square of the population density at each location. The difference in the infection rate of daily activities is considered, where the `stay-out' activity, staying at someplace neither home nor workplace, is more than 28 times larger than other activities. Also, the contribution to the infection strongly depends on location. We imply that the effective reproduction number is sufficiently suppressed if the highest-risk locations or activities are restricted. We also discuss the effects of the Delta variant and vaccination.

physics.soc-ph↗

arXiv2015

Drag dynamics in one-dimensional Fermi systems

We study drag dynamics of several fermions in a fermion cloud in one-dimensional continuous systems, with particular emphasis on the non-trivial quantum many-body effects in systems whose parameters change gradually in real time. We adopt the Fermi--Hubbard model and the time-dependent density matrix renormalization group method to calculate the drag force on a trapped fermion cluster in a cloud of another fermion species with contact interaction. The simulation result shows that a non-trivial peak in the resistance force is observed in the high cloud density region, which implies a criterion of effective ways in diffusive transport in a fermion cloud. We compare the DMRG simulation result with a mean-field result, and it is suggested that some internal degrees of freedom have a crucial role in the excitation process when the cloud density is high. This work emphasizes the difference between the full-quantum calculation and the semiclassical calculation, which is the quantum effects, in slow dynamics of many-body systems bound in a fermion cloud.

cond-mat.quant-gas↗

arXiv2012

Collision of one-dimensional fermion clusters

We study cluster-cluster collisions in one-dimensional Fermi systems with particular emphasis on the non-trivial quantum effects of the collision dynamics. We adopt the Fermi-Hubbard model and the time-dependent density matrix renormalization group method to simulate collision dynamics between two fermion clusters of different spin states with contact interaction. It is elucidated that the quantum effects become extremely strong with the interaction strength, leading to the transmittance much more enhanced than expected from semiclassical approximation. We propose a concise model based on one-to-one collisions, which unveils the origin of the quantum effects and also explains the overall properties of the simulation results clearly. Our concise model can quite widely describe the one-dimensional collision dynamics with contact interaction. Some potential applications, such as repeated collisions, are addressed.

cond-mat.quant-gas↗

arXiv2011

Quantum effects on one-dimensional collision dynamics of fermion clusters

Recently, many experiments with cold atomic gases have been conducted from interest in the non-equilibrium dynamics of correlated quantum systems. Of these experiments, the mixing dynamics of fermion clusters motivates us to research cluster-cluster collision dynamics in one-dimensional Fermi systems. We adopt the one-dimensional Fermi-Hubbard model and apply the time-dependent density matrix renormalization group method. We simulate collisions between two fermion clusters of spin-up and spin-down, and calculate reflectance of the clusters R changing the particle number in each cluster and the interaction strength between two fermions with up and down spins. We also evaluate the quasi-classical (independent collision) reflectance R^{qc} to compare it with R. The quasi-classical picture is quantitatively valid in the limit of weak interaction, but it is not valid when interaction is strong.

cond-mat.quant-gas↗