arXiv subjects

Yongsung Kwon

Publications and source records attributed to Yongsung Kwon.

4 recordsLinked to original sources

arXiv2026

Age structure alters optimal hospital allocation for reducing tuberculosis fatalities in South Korea

Optimal healthcare allocation requires accounting for spatial heterogeneity in disease burden, while demographic heterogeneity may further alter local vulnerability when outcomes vary strongly with age. Tuberculosis (TB) provides a useful case because treatment requires sustained access to care and fatality risk rises markedly with age. In South Korea, TB incidence remains relatively high, while regional incidence, mortality, and hospital data are systematically recorded, allowing these effects to be examined empirically. Finer spatial resolution provides more local observations, helping reveal regional heterogeneity and spatial patterns. However, due to privacy concerns, official district-level TB statistics are provided only in age-aggregated form, whereas age-specific statistics are available at province level. We therefore propose a method to reconstruct age-resolved TB cases and fatalities across 228 districts for 2014--2022 by combining province-level age distributions with district-level totals, using the finest available age and spatial information. Building on an existing hospital-allocation framework, we incorporate age-dependent vulnerability into the fatality-minimization objective. We find that age-aware and age-agnostic optimizations yield similar total minimized fatalities but distinct district-level allocations. The oldest age group's rescaled patient density is strongly associated with this difference and the direction of hospital redistribution. Age-weighting schemes clarify how countervailing contributions across age groups affect optimization.

physics.soc-ph↗

arXiv2025

Quantifying Traffic Patterns with Percolation Theory: A Case Study of Seoul Roads

Urban traffic systems are characterized by dynamic interactions between congestion and free-flow states, influenced by human activity and road topology. This study employs percolation theory to analyze traffic dynamics in Seoul, focusing on the transition point $q_c$ and Fisher exponent $\tau$. The transition point $q_c$ quantifies the robustness of the free-flow clusters, while the exponent $\tau$ captures the spatial fragmentation of the traffic networks. Our analysis reveals temporal variations in these metrics, with lower $q_c$ and lower $\tau$ values during rush hours representing low-dimensional behavior. Weight-weight correlations are found to significantly impact cluster formation, driving the early onset of dominant traffic states. Comparisons with uncorrelated models highlight the role of real-world correlations. This approach provides a comprehensive framework for evaluating traffic resilience and informs strategies to optimize urban transportation systems.

physics.soc-ph↗

arXiv2024

A Computational Analysis of Traffic Cluster Dynamics Using a Percolation-Based Approach in Urban Road Networks

Understanding the dynamics of traffic clusters is crucial for enhancing urban transportation systems, particularly in managing congestion and free-flow states. This study applies computational percolation theory to analyze the formation and growth of traffic clusters within urban road networks, using high-resolution taxi data from Chengdu, China. Presenting the road network as a time-dependent, weighted, directed graph, we identify distinct behaviors in traffic jam and free-flow clusters through the growth patterns of giant connected components (GCCs). A persistent gap between GCC size curves, especially during rush hours, highlights disparities driven by spatial traffic correlations. These are quantified through long-range weight-weight correlations, offering a novel computational metric for traffic dynamics. Our approach demonstrates the influence of network topology and temporal variations on cluster formation, providing a robust framework for modeling complex traffic systems. The findings have practical implications for traffic management, including dynamic signal optimization, infrastructure prioritization, and strategies to mitigate congestion. By integrating graph theory, percolation analysis, and traffic modeling, this study advances computational methods in urban traffic analysis and offers a foundation for optimizing large-scale transportation systems.

physics.soc-ph↗

arXiv2024

Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learning

Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data-driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared transport systems such as public bicycles have gained prominence due to urban constraints and environmental concerns. Predicting rental and return patterns at bicycle stations remains a formidable task due to the system's openness and imbalanced usage patterns across stations. In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. The cartogram approach facilitates the prediction of demand for newly installed stations with no training data as well as long-period prediction, which has not been achieved before. We apply this method to public bicycle rental-and-return data in Seoul, South Korea, employing a spatial-temporal convolutional graph attention network. Our improved architecture incorporates batch attention and modified node feature updates for better prediction accuracy across different time scales. We demonstrate the effectiveness of our framework in predicting temporal patterns and its potential applications.

cs.LG↗