Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic

Fan Zuo, Jingqin Gao, Abdullah Kurkcu, Hong Yang, Kaan Ozbay, Qingyu Ma

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: The rapidly evolving COVID-19 pandemic has dramatically reshaped urban travel patterns. In this research, we explore the relationship between “social distancing,” a concept that has gained worldwide familiarity, and urban mobility during the pandemic. Understanding social distancing behavior will allow urban planners and engineers to better understand the new norm of urban mobility amid the pandemic, and what patterns might hold for individual mobility post-pandemic or in the event of a future pandemic. Methods: There are still few efforts to obtain precise information on social distancing patterns of pedestrians in urban environments. This is largely attributed to numerous burdens in safely deploying any effective field data collection approaches during the crisis. This paper aims to fill that gap by developing a data-driven analytical framework that leverages existing public video data sources and advanced computer vision techniques to monitor the evolution of social distancing patterns in urban areas. Specifically, the proposed framework develops a deep-learning approach with a pre-trained convolutional neural network to mine the massive amount of public video data captured in urban areas. Real-time traffic camera data collected in New York City (NYC) was used as a case study to demonstrate the feasibility and validity of using the proposed approach to analyze pedestrian social distancing patterns. Results: The results show that microscopic pedestrian social distancing patterns can be quantified by using a generalized real-distance approximation method. The estimated distance between individuals can be compared to social distancing guidelines to evaluate policy compliance and effectiveness during a pandemic. Quantifying social distancing adherence will provide decision-makers with a better understanding of prevailing social contact challenges. It also provides insights into the development of response strategies and plans for phased reopening for similar future scenarios.

Original languageEnglish (US)
Article number101032
JournalJournal of Transport and Health
Volume21
DOIs
StatePublished - Jun 2021

Keywords

  • COVID-19
  • Close contact
  • Computer vision
  • Deep learning
  • Pedestrian
  • Social distancing

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Transportation
  • Pollution
  • Safety Research
  • Health Policy
  • Public Health, Environmental and Occupational Health

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