Understanding travel behavior adjustment under COVID-19

Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration, China

The outbreak and spreading of COVID-19 since early 2020 have dramatically impacted public health and the travel environment. However, most of the studies are devoted to travel behavior from the macro perspective. Meanwhile, few researchers pay attention to intercity travel behavior. Thus, this study explores the changes in the travel behavior of intercity high-speed railway travelers during the COVID-19 pandemic from the perspective of the individual. Using the smartphone data, this study first extracts the trip chains by proposing a novel method including three steps. The trip chain can describe the whole process of traveling, including individual characteristics, travel time, travel distance, travel mode, etc. Then, a Multinomial Logit model is applied to analyze the trip chains which verified the validity by using studentized residual error. The study finds that intercity travel behavior has changed in gender, age, travel mode choice, and travel purpose by comparing the trip chains between May 2019 and May 2021 in the Beijing-Tianjin-Hebei urban agglomeration. The method proposed in this study can be used to assess the impact of any long-term emergency on individual travel behavior. The findings proposed in this study are expected to guide public health management and travel environment improvement under the situation of normalized COVID-19 prevention and safety control.

Estimating road traffic impacts of commute mode shifts

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.

The Contribution of Cognitive Factors to Compulsive Buying Behaviour: Insights from Shopping Habit Changes during the COVID-19 Pandemic

The last decade has seen an increase in compulsive behaviours among young adults worldwide, particularly in 2020, during restrictions due to the COVID-19 pandemic. Importantly, even if shopping is an ordinary activity in everyday life, it can become a compulsive behaviour for certain individuals. The aim of this study was to investigate the role of working memory and decision-making style in compulsive behaviour. A total of 105 participants (65 F, 40 M) were recruited online from May 2020 to December 2020. They completed a series of questionnaires to measure shopping compulsive behaviour, decision-making styles, deficits in working memory and online shopping habits. The results show that during the COVID-19 pandemic, people spent much more time shopping online, made more purchases and spent more money than prior to the pandemic. Moreover, both higher working memory deficits and spontaneous decision-making style predicted a greater tendency to engage in compulsive buying. These results suggest the need to develop specific training programs to improve cognitive aspects related to compulsive shopping behaviour.

Physical Activity Behavior, Motivation and Active Commuting: Relationships with the Use of Green Spaces in Italy

Many benefits of physical activity (PA) are observed with weekly average volumes of 150–300 min at moderate intensity. Public parks may be an attraction for many people living in the city and could help to achieve the recommended dose of PA. The present study aims to understand the motivation that drives people to a park and evaluate the amount of PA practiced by park-goers. A questionnaire was anonymously administered to 383 voluntary visitors to the Arcoveggio park (Bologna), aged 18–70 years. Sixty-one percent of participants practiced outdoor PA. Differences in park use between sexes and age groups were found. PA was higher in men than in women and in the 18–30 age group than in other age groups. Most participants travelled to the park in an active way (86.4%), resulting in easier attainment of the recommended amount of PA (64.5%). The main motivations for using the park were related to relaxation, performing PA, or both. According to a multiple regression model, the time per week spent at the park, the method of getting there, and the kind of PA were significant explanatory variables of the amount of PA practiced. In particular, the highest number of minutes of PA was achieved by those who travelled to the park by running, while those using vehicles presented the lowest number. All initiatives to promote active commuting and activities in the urban park represent an important strategy to improve health, supporting adults to lead an active lifestyle.

Development of a Method for Evaluating Social Distancing Situations on Urban Streets during a Pandemic

In the New Normal era of “Living with COVID-19”, we need a measure of the safety of street spaces. Social distancing during a pandemic is considered an effective safety measure, but the current binary threshold approach to social distancing is clearly inadequate for evaluating and monitoring the risk of infection on urban streets. This study is to propose a social distancing indicator that can quantitatively evaluate the level of exposure to viral infection for pedestrians using urban streets during a pandemic, and to develop a statistical model to estimate the proposed indicator from simulations of pedestrian activity on urban streets. We assumed that the risk of infection on urban streets has a direct relationship with distance between pedestrians. The social distancing indicator was based largely on the findings of past studies. We developed a statistical model to relate the proposed indicator to three other explanatory variables: pedestrian density, clumpiness, and directional heterogeneity. We used pedestrian simulation to generate the raw data for these explanatory variables. The social distancing indicator demonstrated a statistically significant relationship with input variables and can be used to evaluate pedestrians’ social distancing on urban streets. We measured the relationship between different levels of pedestrian density, clumpiness, and directional heterogeneity and related the results to the potential level of exposure to viral infection. Health agencies can use the findings to develop appropriate policies for monitoring and improving the social distance between pedestrians on urban streets during a pandemic.