Uma análise de redes complexas da mobilidade urbana ferroviária de São Paulo a partir de dados de bilhetagem eletrônica de 2019 e 2020

Abstract

This study analyses rail-based urban mobility in the São Paulo Metropolitan Region using anonymized electronic ticketing records from 2019 and 2020. The study addresses a common limitation of smart card data, which generally records only passenger boarding events without explicit information on alighting locations. To overcome this limitation, an origin-destination inference procedure based on the Transaction Sequence Number (TSN) is proposed, allowing the reconstruction of successive trips and the construction of daily mobility networks. Based on these networks, structural metrics from complex network theory were analysed, together with distance distributions, station-level entropy, ranking dynamics, and spatial comparisons between periods. The results show that the COVID-19 pandemic was associated with a strong contraction in demand and significant changes in the structural organization of the network. A reduction of 41.2% in the total volume of trips was observed, accompanied by a 44.2% decrease in average network strength and a 44.0% reduction in global efficiency. Conversely, the average path length increased by 64.0%, together with a 9.0% growth in the number of communities and a spatial reorganization of flows between stations and system corridors. These findings indicate that the pandemic affected not only travel demand but also the way rail mobility was organized across the metropolis. Therefore, this study demonstrates that electronic ticketing data, when combined with origin-destination inference methods and complex network analysis, can contribute to the continuous monitoring of urban mobility and support the planning and management of public transportation systems.