Abstract
The Covid-19 pandemic has abruptly changed well-established mobility patterns, as the need for social distancing and lockdown orders have driven citizens to reduce their movements and avoid crowded mass transit. In this context, we look at the case of New York City's bike sharing system, one of the largest in the world, to gain insights on the socio-economic variables behind urban mobility during a pandemic. We exploit several sources of Smart City data to analyze the relationship between bike sharing, public transport, and other modes of transportation, deriving interesting insights for future urban planning, both city-wide and at the neighborhood level. The New York City case study shows some of the most important trends during the lockdown, and the combination between mobility and socio-economic data can be used to understand the consequences of the pandemic on different communities, as well as the future directions of expansion and management of the bike sharing system and urban infrastructure.
Highlights
The Covid-19 pandemic that struck the world at the end of 2019 has fundamentally altered social practices and spaces in ways that we still do not fully understand: the current necessity for social distancing might still shape mobility patterns long after the end of the lockdown, and its ripples could have long-term, or even permanent, effects on urban planning and mobility [1]
We present New York City as a case study, analyzing ridership statistics from the Citi Bike bike sharing system along with public transport data and other socio-economic factors, highlighting how spatio-temporal graph-based analyses can be used for resilient urban planning with social distancing-compatible mobility
We can spot the same behavior if we look at the Global Clustering Coefficient (GCC), often used in the cluster analysis of bike sharing systems [31], which we define as α(t) = # of closed triplets in week t t ∈ {1, 2, 3, 4}, (1) Total # of triplets in week t and indicates the fraction of triplets of nodes which are closed within week t graph
Summary
The Covid-19 pandemic that struck the world at the end of 2019 has fundamentally altered social practices and spaces in ways that we still do not fully understand: the current necessity for social distancing might still shape mobility patterns long after the end of the lockdown, and its ripples could have long-term, or even permanent, effects on urban planning and mobility [1]. We present New York City as a case study, analyzing ridership statistics from the Citi Bike bike sharing system (which can serve as a proxy for general patterns in cyclist mobility) along with public transport data and other socio-economic factors, highlighting how spatio-temporal graph-based analyses can be used for resilient urban planning with social distancing-compatible mobility. As traffic sharply decreases only in downtown Manhattan, increasing the relative importance of the outer boroughs, this change in the trip duration distribution reflects the change in the usage of the bike sharing system: as short trips downtown become less frequent, longer trips connecting different boroughs make up a larger share of the whole. We note that the bridges on the East River have experienced increased flows, indicating a stronger inter-borough flow of bike sharing rides
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
