COVID-19’s Effect on Transportation: Developing a Public COVID-19 Data Dashboard

Overview

The COVID-19 outbreak has dramatically changed travel behavior in cities across the world. With changed travel demand, economic activity, and social-distancing/stay-at-home policies, transportation systems have experienced an unprecedented shift in demand and usage. Since the start of the pandemic, the C2SMART research team has been collecting data and investigating the impact of COVID-19 on mobility and sociability, including:

Passenger travel and freight traffic trends
Mode shift and usage based on various policies
Effect of social distancing policies on transit use and emissions
Sidewalk, crosswalk, and intersection crowd density
Effect of COVID-19 Policies on Transportation Systems

This project was made possible by collaboration from University of Washington (Jingxing Wang, Yanyan Chen, Sai Sarath Chandra Pavuluri Venkata, Xuegang (Jeff) Ban); Rutgers University (Chaekuk Na, Hani Nassif); Old Dominion University (Hong Yang, Qingyu Ma); Cornell University (H. Oliver Gao, Mohammad Tayarani); and Ulteig (Abdullah Kurkcu).

Research Objectives

Leveraging open data from multiple sources, this project developed a Sociability Data Dashboard which features both traditional and innovative techniques, such as data mining and visualization, agent-based traffic simulation model and real-time computer vision technique, to help researchers and transportation authorities understand and observe the impact of the pandemic on transportation.

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.

Deliverables

view project deliverables

Details

Project Title
COVID-19's Effect on Transportation: Developing a Public COVID-19 Data Dashboard
Universities
New York University
Principal Investigator
Kaan Ozbay
PI Contact Information
kaan.ozbay@nyu.edu
Co-Principal Investigators
Joseph Chow, Jingqin Gao
Funding Source(s) and Amounts Provided (by each agency or organization)
$53,975 (C2SMART: $35,983; Cost Share from NYU: $17,992)
Total Project Cost
$53,975
USDOT Award #
69A3551747124
Start and End Date
4/1/2020-9/30/2022
Brief Description of Research Project
The COVID-19 outbreak has dramatically changed travel behavior in cities across the world. With changed travel demand, economic activity, and social-distancing/stay-at-home policies, transportation systems have experienced an unprecedented shift in demand and usage. Since the start of the pandemic, the C2SMART research team has been collecting data and investigating the impact of COVID-19 on mobility and sociability, including: passenger travel and freight traffic trends; mode shift and usage based on various policies; effect of social distancing policies on transit use and emissions; sidewalk, crosswalk, and intersection crowd density; and effect of COVID-19 Policies on Transportation Systems. Leveraging open data from multiple sources, this project features both traditional and innovative techniques, such as data mining and visualization, agent-based traffic simulation model and real-time computer vision technique, to help researchers and transportation authorities understand and observe the impact of the pandemic on transportation.
Describe Implementation of Research Outcomes (or why not implemented)
An all-in-one data dashboard was built with interactive analytics and visualization of various mobility data sources.This 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. C2SMART researchers have also 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.
Impacts/Benefits of Implementation (actual, not anticipated)
This framework allows researchers to capture critical data on pedestrian, cyclist, and vehicle flows and densities without any additional infrastructure investment. It also provides data that assist with answering both traditional transportation planning questions, as well as novel questions such as how often pedestrians maintained the recommended “6 feet” of social distance during the COVID-19 pandemic.