Detecting Urban Community Based on Urban Mobility Patterns From the Public Transportation Trip-Chain Data: A Case Study of Seoul, South Korea

Abstract

Urban mobility has been studied in various areas of geography and related disciplines. Commuting patterns are highly linked to the spatial structure of functional districts in a city, which is substantially influenced by public transportation systems. Understanding urban mobility patterns are significant for revealing actual living districts and planning efficient transportation systems and infrastructure. This study aims to detect urban communities with spatially strong interaction based on similar mobility patterns. To extract homogeneous urban communities, we utilize a network community detection method, focusing on the multimodal transfer travels of the public transport system. As a case study, we examined urban communities in the Seoul metropolitan area of South Korea. The results showed newly detected living communities based on the actual mobility patterns of urban populations including multiple-choice behavior of transport mode. Such empirical findings depict more realistic dynamics of populations in large metropolitan areas and the significant influence of public transportation systems on urban mobility patterns.