Robustness Analysis of Public Transportation Systems in Seoul Based on General Multi-Layer Networks

Abstract

Public transportation systems play a vital role in modern cities, enhancing the quality of life and fostering sustainable economic growth. Modeling and understanding the complexities of these transportation networks are crucial for effective urban planning and management. Traditional models often fall short in capturing the intricate interactions and interdependencies in multi-modal public transportation systems. To address this challenge, recent research has embraced multi-layer network models, offering a more sophisticated representation of these networks. However, there is a need to explore and develop robustness analysis techniques tailored to these general multi-layer networks to fully assess their complexities in real-world scenarios. In this paper, we employ a general multi-layer network model to comprehensively analyze a real-world multi-modal transportation network in Seoul, South Korea. We leverage a large volume of traffic data to model, visualize, and evaluate the city's mobility patterns. Additionally, we introduce two novel methodologies for robustness analysis, one based on random walk coverage and the other on Eigenvalue, specifically designed for general multi-layer networks. Extensive experiments using the large volume of real-world data sets demonstrate the effectiveness of the proposed approaches.