C2SMART COVID-19 Data Dashboard

Welcome to the C2SMART COVID-19 Data Dashboard! This interactive data dashboard consolidates public data sources to track the impact of the pandemic on transportation systems as it unfolds. With social distancing policies in place, the COVID-19 outbreak has dramatically changed travel behavior in affected cities. The C2SMART research team has been investigating the impact of COVID-19 on mobility and sociability, including passenger travel trends, the effect of social distancing policies on transit use and mobility patterns, freight, logistics, and supply chain impacts and economic impacts on agencies’ operating/capital budgets in multiple cities. Through this interactive data dashboard, we are hoping to provide researchers, transportation authorities, and the general public with information on track the impact of the outbreak on transportation as it unfolds. This platform will update regularly and continue to evolve with the addition of new data, impact metrics, and visualizations. The newest issue of a white paper detailing this research was released on September 4th, 2020.


  • NEW! – Social Distancing Safety Rate and Temporal Distributions of Pedestrian, Car and Cyclist charts have been uploaded under “Mobility” Tab
  • NEW! – Taxi data is added under “Mobility” Tab
  • Most of the data and aggregated statistics will be updated weekly and are open for download


Data published on our platform are for visualization and COVID-19 decision support. Researchers and decision-makers are encouraged to use published aggregated data for COVID-19 research with proper citation of the dashboard based on suggested citations below. To request data files or reposting of data created by C2SMART on other dashboards or data archives, please email us (c2smart@nyu.edu).

  • C2SMART University Transportation Center (2020). C2SMART COVID-19 Data Dashboard, http://c2smart.engineering.nyu.edu/covid-19-dashboard/, accessed on [date]
  • Fan Zuo, Jingxing Wang, Jingqin Gao, Kaan Ozbay, Xuegang Jeff Ban, Yubin Shen, Hong Yang and Shri Iyer. (2020), An Interactive Data Visualization and Analytics Tool to Evaluate Mobility and Sociability Trends During COVID-19, UrbComp 2020: The 9th SIGKDD International Workshop on Urban Computing, San Diego, California, USA (accepted). https://arxiv.org/abs/2006.14882

Passenger Travel Impact

C2SMART researchers are collecting and processing transportation datasets from various sources in affected cities, including New York and Seattle to quantify the reduction in travel due to stay-at-home orders. Preliminary findings from New York 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. More updated details can be find in our white papers.

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.

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 the 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

Monthly Vehicle Traffic via MTA Bridges and Tunnels
Overall Vehicle Traffic via MTA Bridge and Tunnels

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Weekly Vehicle Traffic via MTA Bridges and Tunnels

MTA Subway Ridership Trends

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MTA Bus Ridership Trends
MTA Bus Speed

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Citi Bike Heat Map

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Citi Bike Trends
Corridor Travel Time

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Speed Map
Percentage of Crashes by Injury Type

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Reported Crashes
Speeding Tickets
Speeding Ticket Spatial Distribution

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Average GVW, Class 9

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QB Vehicle Hourly Speed
SIB Vehicle Hourly Speed
Average GVW, Class 6
Average Daily Truck Traffic (ADTT)
Average Daily Traffic (ADT)
MTA Metro North Railroad Ridership Trends

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MTA Long Island Rail Road Ridership Trends

MTA Access-A-Ride Ridership Trends

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Monthly Ridership for Yellow and Green

Yellow and Green Taxi Passenger Counts

Yellow, Green Taxi and HVFHS Trip

Yellow and Green Taxi Trip Distance

Monthly Ridership for FHV and HVFHS

User Guide:
Date Filter → Select one/multiple dates to show corresponding data on the graph. Selecting any date values would display the line graph of those selected dates.
Timestamp Filter → Select one/multiple date stamps separated hourly to visualize the filtered data on graph. If ‘Date Granularity’ is selected as Hour then data is displayed for the corresponding selected hourly values.
Date Granularity → Select date or hour as the data display mode. Day Granularity should be selected to view data in combination with Date Filter. Hour Granularity can be used along with Timestamp Filter for hourly visualization of data.

  1. Timeline → Shows daily data in the form of trend line
  2. Comparison → Specific date visualization between two Bound Volumes

Date Group → Useful in Comparison Mode to highlight single date data
Filter Down Arrow

  1. Include Values → The graph will represent the selected values from the filter
  2. Exclude Values → The graph will display values except the selected values from the filter

Comparison Mode Example:

To investigate crowd density and the effectiveness of social distancing strategies, C2SMART researchers have introduced a low-cost, AI-driven big data acquisition framework leveraging hundreds of traffic cameras along with a deep learning-based video processing method.

Object detection and distance approximation between pedestrian pairs are applied to traffic camera videos at multiple NYC and Seattle locations to analyze local social distancing patterns. This sociability board shows some examples of the application.

The methology can be found from the white paper issue 3

For real-time analysis, please contact us for more information.


This application may contain and/or utilize information which was originally compiled by the New York City Department of Transportation (DOT) for governmental purposes; the information may subsequently been modified by entity/entities other than DOT. DOT and the City of New York make no representation as to the accuracy or usefulness of the information provided by this application or the information’s suitability for any purpose and disclaim any liability for omissions or errors that may be contained therein. The public is advised to observe posted signage for compliance with applicable laws and regulations.

A glance of crowd density during COVID-19

2 Ave and E 14 St

Whitehall and Water St

8 AVE and 23 ST

9 AVE and 42 ST

Main St and Roosevelt Ave

6 AVE and 42 ST

Seattle: Broadway and Pike St

August Issue

July Issue

June Issue

May Issue

Whitepaper Issue 2 Photograph

April Issue

Photograph of White paper issue 1

COVID-19 Recovery and Congestion Pricing

The open-source, modular nature of the Multi-Agent Transport Simulation (MATSim) Virtual Testbed has allowed a team of researchers led by Dr. Joseph Chow and Yueshuai Brian He to add timely new simulation extensions: the impact of congestion pricing on transit behavior and MTA revenues, and the effects of the pandemic and an ensuing recovery on transit use.

A Pandemic Recovery Plan      

How will transit patterns change as New York City begins to reopen in stages?  Researchers recalibrated the simulation testbed to evaluate the impact of COVID-19 on mass transit ridership, demonstrating how MATSIM might be used to help policy-makers plan for reopening.

Using Apple Mobility Trends Reports, MTA Transit Data and NAICS industry codes, the research team recalibrated mode choice to fit updated ridership data and account for the shift to cars during the COVID-19 pandemic. The team will simulate a multi-stage recovery on the synthetic population by “reopening” manufacturing/construction industries while keeping schools and nonessential businesses close, mirroring governor Andrew Cuomo’s stated plan for reopening New York City.

Pre COVID-19