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Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic
Journal of Transport & Health ( IF 3.2 ) Pub Date : 2021-03-18 , DOI: 10.1016/j.jth.2021.101032
Fan Zuo 1 , Jingqin Gao 1 , Abdullah Kurkcu 2 , Hong Yang 3 , Kaan Ozbay 4 , Qingyu Ma 3
Affiliation  

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.



中文翻译:


COVID-19 大流行期间基于无参考视频到真实距离近似的城市社交距离分析


 介绍


迅速发展的 COVID-19 大流行极大地改变了城市出行模式。在这项研究中,我们探讨了“社交距离”这一全世界都熟悉的概念与大流行期间的城市流动性之间的关系。了解社交距离行为将使城市规划者和工程师更好地了解大流行期间城市流动的新规范,以及大流行后或未来发生大流行时个人流动可能存在的模式。

 方法


目前仍没有多少努力来获取有关城市环境中行人社交距离模式的准确信息。这很大程度上归因于在危机期间安全部署任何有效的现场数据收集方法带来的巨大负担。本文旨在通过开发一个数据驱动的分析框架来填补这一空白,该框架利用现有的公共视频数据源和先进的计算机视觉技术来监测城市地区社会距离模式的演变。具体来说,所提出的框架开发了一种带有预先训练的卷积神经网络的深度学习方法,以挖掘在城市地区捕获的大量公共视频数据。以纽约市(NYC)收集的实时交通摄像头数据作为案例研究,证明使用所提出的方法来分析行人社交距离模式的可行性和有效性。

 结果


结果表明,微观行人社交距离模式可以通过使用广义真实距离近似方法来量化。人与人之间的估计距离可以与社交距离准则进行比较,以评估大流行期间政策的合规性和有效性。量化社交距离的遵守情况将使决策者更好地了解当前的社交接触挑战。它还提供了有关针对未来类似情况分阶段重新开放的响应策略和计划的制定的见解。

更新日期:2021-03-19
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