ABSTRACT
Coronavirus, which emerged in China towards the end of 2019 and subsequently influenced the whole world, has changed the daily lives of people to a great extent. In many parts of the world, in both cities and rural areas, people have been forced to stay home weeks. They have only been allowed to leave home for fundamental needs such as food and health needs, and most started to work from home. In this period, very few people, including essential workers, had to leave their homes. Avoiding social contact is proven to be the best method to reduce the spread of the novel Coronavirus. Because of the COVID-19 pandemic, people are adapting their behavior to this new reality, and it may change the type of public events people perform and how people go to these activities. Consumer behaviors have been altered during the pandemic. While people try to avoid gatherings, they also stayed away from mass transport modes and turned to private modes of transportation more -- private cars, private taxis and bike-sharing systems;even walking became more popular. In this study, the authors attempt to analyze how the use of bicycling has changed -- pre- and post-pandemic -- using open data sources and investigating how socio-economics characteristics affect this change. The results showed that average income, average education level, and total population are the most crucial variables for the Pandemic to Transition period and the Transition to the Normalization period.
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.