The COVID-19 pandemic has caused unprecedented disruptions to urban systems worldwide, but the extent and nature of these disruptions are not yet fully understood when it comes to transportation. In this work, we aim to explore how social distancing policies have affected passenger demand in urban mass transportation systems with the goal of supporting informed decisions in policy planning. We propose an approach based on complex networks and clustering time series with similar behavior, investigating possible changes in similarity patterns during pandemics and how they reflect into a regional scale. The methods shown here proved useful in detecting that lines in central or peripheral regions present different dynamics, that bus lines have changed their behavior during pandemic so that similarity relations have changed significantly, and that when social distancing started, there was an abrupt shock in the properties of daily passenger time series, and the system did not return to its original behavior until the end of the evaluated period. The approach allows to track evolution of the community structure in different scenarios providing managers with tools to reinforce or destabilize similarities if needed.
Keywords: Complex networks; Complex systems; Pandemics; Time series; Urban transportation; Visibility graph.
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