arXiv subjects

Suzana Duran Bernardes

Publications and source records attributed to Suzana Duran Bernardes.

5 recordsLinked to original sources

arXiv2020

Initial Impacts of COVID-19 on Transportation Systems: A Case Study of the U.S. Epicenter, the New York Metropolitan Area

The novel Coronavirus COVID-19 spreading rapidly throughout the world was recognized by the World Health Organization (WHO) as a pandemic on March 11, 2020. One month into the COVID-19 pandemic, this white paper looks at the initial impacts COVID-19 has had on transportation systems in the metropolitan area of New York, which has become the U.S. epicenter of the coronavirus.

physics.soc-ph↗

arXiv2020

The Effects of the COVID-19 Pandemic on Transportation Systems in New York City and Seattle, USA

This paper continues to highlight trends in mobility and sociability in New York City (NYC), and supplements them with similar data from Seattle, WA, two of the cities most affected by COVID-19 in the U.S. Seattle may be further along in its recovery from the pandemic and ensuing lockdown than NYC, and may offer some insights into how travel patterns change. Finally, some preliminary findings from cities in China are discussed, two months following the lifting of their lockdowns, to offer a glimpse further into the future of recovery.

physics.soc-ph↗

arXiv2020

Toward the "New Normal": A Surge in Speeding, New Volume Patterns, and Recent Trends in Taxis/For-Hire Vehicles

Six months into the pandemic and one month after the phase four reopening in New York City (NYC), restrictions are lifting, businesses and schools are reopening, but global infections are still rising. This white paper updates travel trends observed in the aftermath of the COVID-19 outbreak in NYC and highlight some findings toward the "new normal."

physics.soc-ph↗

arXiv2020

NYC Recovery at a Glance: The Rise of Buses and Micromobility

New York City (NYC) is entering Phase 4 of the state's reopening plan, starting July 20, 2020. This white paper updates travel trends observed during the first three reopening phases and highlights the spatial distributions in terms of bus speeds and Citi Bike trips, and further investigates the role of micro-mobility in the pandemic response.

physics.soc-ph↗

arXiv2020

Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation model's results to predict the impact of proposed phased reopening strategies. It also introduces a real-time video processing method to measure social distancing through cameras on city streets.

cs.MA↗