C2SMART Research on the Effect of COVID-19 on Transportation Systems


The outbreak of the novel coronavirus COVID-19 has brought profound changes to almost every aspect of transportation. C2SMART researchers have launched new initiatives to observe how transportation systems are being affected. Current research efforts have focused on:

  • Short-term passenger travel trends in affected cities during the pandemic
  • Long-term changes to mode choice and travel patterns during the recovery
  • The effect of social distancing policies on transit use and mobility patterns
  • Freight, logistics, and supply chain impacts in response to the pandemic
  • Economic impacts on agencies’ revenue collection and operating/capital budgets

The objective is to understand the short-term and long-term impact of the pandemic on behavior and travel trends, develop results-oriented approaches to aid decision-making, and provide actionable insights to enhance the resiliency of transportation systems in the face of future disease outbreaks.

Leveraging open data from multiple data sources, the C2SMART research team has put together a white paper based on information collected in March 2020, one month into the COVID-19 pandemic in the U.S. This white paper provides initial impacts of COVID-19 on transportation systems in metropolitan New York, the U.S. epicenter of the coronavirus, before and after issuance of stay-at-home orders.

This paper reflects the Center’s perspective as of April 3rd, 2020 based on data collected in March 2020. C2SMART researchers are continuing to collect data, including perishable mobility, safety, and behavior data, and will continue to monitor these trends and regularly update our findings as this crisis unfolds.

A line chart showing a significant drop in the number of NYC vehicles and turnstile counts as a result of COVID-19

Daily changes in the number of positive cases in NYC, subway turnstile entries and traffic on MTA bridges and tunnels

Passenger Travel Impact

C2SMART researchers are collecting and processing transportation datasets from various sources in both New York and Seattle to quantify the reduction in travel in both cities due to stay-at-home orders. The white paper above shows preliminary results from New York data. Findings include:

  • Traffic volumes and travel times have dropped dramatically, and system usage is well below system capacity
  • Commuting activity from the suburbs to New York City has drastically decreased
  • A reduction in crashes, as well as a temporary mode shift to bicycling

Researchers will continue to track data in near real-time based on guidance and directives from governments in both cities. This work will identify differences between local population reactions to the pandemic and also population response to government directives in different timescales, providing better data to plan for potential future scenarios

Long-Term Impact on Travel Trends

Prior to the full stay-at-home order, researchers observed a shift towards micromobility modes and non-mass transit away from densely crowded alternatives. Following the lifting of the stay-at-home order, even as travel trends stabilize, a long-term shift in mobility patterns might emerge. This might include:

  • An increase in non-shared modes of travel such as bike/scooter and a decrease in shared modes such as public transportation and ride-sharing
  • A net decrease in home-to-work trips due to increased adoption of working from home
  • A reduction in tourism
  • A reduction in travel due to systemic unemployment and economic slowdown.

C2SMART is studying how these major potential shifts could affect our transportation systems both in terms of usage as well as impact on agencies’ operating and capital budgets. Using C2SMART’s open-source agent-based simulation model, researchers can model various scenarios and their impact to transportation systems. C2SMART has already begun modeling various scenarios of transportation systems’ usage during the recovery to determine what modes or services are likely to be over- or under-utilized.

A map of New York City shows colored dots across the city

C2SMART’s open-source agent-based simulation model

Travel times on the 495 corridor during COVID-19

Freight, Logistics, & Supply Chain Impacts

The pandemic and resulting stay-at-home orders are also affecting the shipping and movement of goods. C2SMART is tracking truck volumes and weights from weigh-in-motion (WIM) systems installed on its Urban Roadway Testbed on the Brooklyn-Queens Expressway (BQE) to observe the pandemic effects on trucks moving through New York City. Preliminary data show:

  • After 3/13, total traffic dropped by 30% for the rest of March. However, truck traffic appears to have dropped less than all traffic with a reduction of only 15%.
  • The reduction in number of trips has increased average vehicle speeds by 11% to 24%.
  • WIM data does not show a large change to GVWs; however, the number of heavy trucks (>80 kips) as well as the maximum GVW appear to have gone down: 20% reduction for Queens Bound trips and 14% reduction for Staten Island Bound trips–there are fewer trips carrier lighter than normal loads.

C2SMART, together with its partners at the Intercep Center for Emergency Preparedness, are planning to launch an Emergency Logistics Innovation Task Force to ensure effective supply of essential medical supplies and food to the Metropolitan New York region during the COVID-19 crisis. It will take advantage of Intercep’s established Metropolitan Resilience Network of government and private companies to shift focus away from preparedness to reaction to the crisis. Its objectives are to:

  • Develop results-oriented approaches beyond traditional constraints
  • Enable fast paced adaptation to the changing operating environment
  • Develop effective strategies immediately actionable in the current environment
  • Build/coordinate relationships and resources necessary to enable implementation