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.

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

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.

Disclaimer

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