C2SMART Mobility Data Dashboard

An all-in-one data dashboard was built with interactive analytics and visualization of various mobility data sources. The mobility board presents multi-data views in terms of vehicular traffic volume, corridor travel time, transit ridership, freight traffic by gross vehicle weight (GVW), and risk indicators like reported crashes, pedestrian and cyclist fatalities, and speeding tickets. The dashboard supports scenario analyses with these metrics in timeseries or comparisons to understand the temporal and spatial aspects of the data. This platform is updated regularly and continues to evolve with the addition of new data, metrics, and visualizations.

Suggested Citations

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).

  • Zuo, F., Wang, J., Gao, J., Ozbay, K., Ban, X., Shen. Y., Yang, H. and Iyer, S. (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. Access here.

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

Public Sociability Board Based on Image Recognition

C2SMART researchers have developed a data-driven analytical framework that leverages public video data sources and advanced computer vision techniques to monitor social distancing patterns in urban areas. The approach was used on real-time traffic cameras in NYC to observe pedestrian social distancing patterns and calculate real-distance approximations. The approximations is presented in the public sociability board and can be used to estimate social distances among individuals and compared to public health guidelines.