Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City

Sentiment Analysis on Multimodal Transportation during the COVID-19 Using Social Media Data

This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020–January 2022). Building on these data, we develop travel mode classifiers, adapted from natural language processing (NLP) models, to determine whether individual tweets are related to some travel mode (subway, bus, bike, taxi/Uber, and private vehicle). Sentiment analysis is performed to understand people’s attitudinal changes about mode choices during the pandemic. Results show that a majority of people had a positive attitude toward buses, bikes, and private vehicles, which is consistent with the phenomenon of many commuters shifting away from subways to buses, bikes and private vehicles during the pandemic. We analyze negative tweets related to travel modes and find that people were worried about those who did not wear masks on subways and buses. Based on users’ demographic information, we conduct regression analysis to analyze what factors affected people’s attitude toward public transit. We find that the attitude of users in the service industry was more easily affected by MTA subway service during the pandemic.

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis

The objective of this systematic review with PRISMA guidelines is to discover how population movement information has epidemiological implications for the spread of COVID-19. In November 2022, the Web of Science and Scopus databases were searched for relevant reports for the review. The inclusion criteria are: (1) the study uses data from Apple Mobility Trends Reports, (2) the context of the study is about COVID-19 mobility patterns, and (3) the report is published in a peer-reviewed venue in the form of an article or conference paper in English. The review included 35 studies in the period of 2020-2022. The main strategy used for data extraction in this review is a matrix proposal to present each study from a perspective of research objective and outcome, study context, country, time span, and conducted research method. We conclude by pointing out that these data are not often used in studies and it is better to study a single country instead of doing multiple-country research. We propose topic classifications for the context of the studies as transmission rate, transport policy, air quality, re-increased activities, economic activities, and financial markets.

Viability of compact cities in the post-COVID-19 era: subway ridership variations in Seoul Korea

COVID-19 exposed the vulnerability of compact cities against shock events. As the impact of COVID-19 not only persists, but also expands throughout the world, this study questions whether the compact city model would be sustainable in the post-COVID-19 era. As such, this study examines the dynamics among major COVID-19 outbreak events, government interventions, and subway ridership in two compact cities, Seoul and New York City. Then, to gain thorough understanding of the impact of risks on compact urban form, it narrows the scope to Seoul in comparing subway ridership patterns in 2019 and 2020, and identifying characteristics that affect the volatility of subway ridership levels. The results affirm that individual mobility, COVID-19 outbreaks, and government interventions are closely related, and reveal that the extent of social distancing measures in compact cities is limited. This finding aligns with existing literature that link diseases transmission with dense population and mixed land use, accentuating the vulnerability of the compact city model against shocks. As a result, a multidimensional urban planning approach that incorporates polycentric and decentralized urban form is recommended to effectively and sustainably control disease outbreaks in compact cities.

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

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.