Travel Behaviour of Vulnerable Social Groups: Pre, during, and Post COVID-19 Pandemic

Exploring the Influence of Summer Temperature on Human Mobility During the COVID-19 Pandemic in the San Francisco Bay Area

Studies on the relationship between temperature and local, small scale mobility are limited, and sensitive to the region and time period of interest. We contribute to the growing mobility literature through a detailed characterization of the observed temperature-mobility relationship in the San Francisco Bay Area at fine spatial and temporal scale across two summers (2020-2021). We used anonymized cellphone data from SafeGraph's neighborhood patterns data set and gridded temperature data from gridMET, and analyzed the influence of incremental changes in temperature on mobility rate (i.e., visits per capita) using a panel regression with fixed effects. This strategy enabled us to control for spatial and temporal variability across the studied region. Our analysis suggested that all areas exhibited lower mobility rate in response to higher summer temperatures. We then explored how several additional variables altered these results. Extremely hot days resulted in faster mobility declines with increasing temperatures. Weekdays were often more resistant to temperature changes when compared to the weekend. In addition, the rate of decrease in mobility in response to high temperature was significantly greater among the wealthiest census block groups compared with the least wealthy. Further, the least mobile locations experienced significant differences in mobility response compared to the rest of the data set. Given the fundamental differences in the mobility response to temperature across most of our additive variables, our results are relevant for future mobility studies in the region.

A study on the factors that influenced the choice of transport mode before, during, and after the first lockdown in Milan, Italy

COVID-19 measures implied many changes to travel behaviour and transport mode choice during the pandemic. This study seeks to understand what individual characteristics and travel attributes are related to transport mode choice before, during, and after the first lockdown in Italy. Based on an online survey (carried out in May 2020 in Milan), three multinomial regression models are presented. The results show that and in which measure parameters regarding distance and duration of daily travel are markedly related to transport mode before the lockdown. However, these factors are less significantly associated with the transport mode during and after the lockdown. Meanwhile, factors such as Preferences and Worry about using public transport have more significant relationship with the modal choice during the pandemic. Regarding individual characteristics, women are more likely to use active mode during and after the lockdown. Additionally, two personality traits of Agreeableness, and Openness to experiences are related to transport mode during and after the lockdown, respectively. Overall, this study reveals that in addition to socio-demographic factors, other variables such as worry about using public transport, preference, and personality are associated with the choice of transport mode during the lockdown.

Understanding the travel challenges and gaps for older adults during the COVID-19 outbreak: Insights from the New York City area

The COVID-19 pandemic has greatly impacted lifestyles and travel patterns, revealing existing societal and transportation gaps and introducing new challenges. In the context of an aging population, this study investigated how the travel behaviors of older adults (aged 60+) in New York City were affected by COVID-19, using an online survey and analyzing younger adult (aged 18-59) data for comparative analysis. The purpose of the study is to understand the pandemic's effects on older adults' travel purpose and frequency, challenges faced during essential trips, and to identify potential policies to enhance their mobility during future crises. Descriptive analysis and Wilcoxon signed-rank tests were used to summarize the changes in employment status, trip purposes, transportation mode usage, and attitude regarding transportation systems before and during the outbreak and after the travel restrictions were lifted. A Natural Language Processing model, Gibbs Sampling Dirichlet Multinomial Mixture, was adopted to open-ended questions due to its advantage in extracting information from short text. The findings show differences between older and younger adults in telework and increased essential-purpose trips (e.g., medical visits) for older adults. The pandemic increased older adults' concern about health, safety, comfort, prices when choosing travel mode, leading to reduced transit use and walking, increased driving, and limited bike use. To reduce travel burdens and maintain older adults' employment, targeted programs improving digital skills (telework, telehealth, telemedicine) are recommended. Additionally, safe, affordable, and accessible transportation alternatives are necessary to ensure mobility and essential trips for older adults, along with facilitation of walkable communities.

Timed up-and-go performance is associated with objectively measured life space in patients 3 months after ischemic stroke: a cross-sectional observational study

BACKGROUND

Stroke is a common cause of mobility limitation, including a reduction in life space. Life space is defined as the spatial extent in which a person moves within a specified period of time. We aimed to analyze patients' objective and self-reported life space and clinical stroke characteristics.

METHODS

MOBITEC-Stroke is a prospective observational cohort study addressing poststroke mobility. This cross-sectional analysis refers to 3-month data. Life space was assessed by a portable tracking device (7 consecutive days) and by self-report (Life-Space Assessment; LSA). We analysed the timed up-and-go (TUG) test, stroke severity (National Institutes of Health Stroke Scale; NIHSS), and the level of functional outcome (modified Rankin Scale; mRS) in relation to participants' objective (distance- and area-related life-space parameters) and self-reported (LSA) life space by multivariable linear regression analyses, adjusted for age, sex, and residential area.

RESULTS

We included 41 patients, mean age 70.7 (SD11.0) years, 29.3% female, NIHSS score 1.76 (SD1.68). We found a positive relationship between TUG performance and maximum distance from home (p = 0.006), convex hull area (i.e. area enclosing all Global Navigation Satellite System [GNSS] fixes, represented as a polygon linking the outermost points; p = 0.009), perimeter of the convex hull area (i.e. total length of the boundary of the convex hull area; p = 0.008), as well as the standard ellipse area (i.e. the two-dimensional ellipse containing approximately 63% of GNSS points; p = 0.023), in multivariable regression analyses.

CONCLUSION

The TUG, an easily applicable bedside test, seems to be a useful indicator for patients' life space 3 months poststroke and may be a clinically useful measure to document the motor rehabilitative process.

Personal infection prevention behaviors and campaign to encourage travel during COVID-19: A cross-sectional study

Introduction

The Go To Travel campaign in Japan was launched to subsidize travel and accommodation costs for tourists through vouchers that could be used at domestic destinations. Infection prevention behavior can lead to refraining from travel owing to infection concerns; conversely, taking preventive action can promote travel. There is a lack of information about the association between infection prevention behaviors and desire to travel. During a pandemic of infection, there is the difficult challenge of balancing the promotion of infection prevention behavior with economic revitalization. Thus, we examined the relationship between personal infection prevention behaviors and using Go To Travel.

Methods

We conducted a cross-sectional study of 26,637 workers who responded to a large-scale questionnaire survey about COVID-19 in Japan. We built multilevel logistic regression models adjusted for confounders to assess the association between each personal infection prevention behavior and using Go To Travel. We analyzed the seven infection prevention behavior individually: wearing a mask when among other people; disinfecting hands with alcohol before going indoors; washing hands after using the toilet; gargling upon returning home; opening a window to ventilate a room; carrying an alcohol sanitizer; and disinfecting hands after touching objects outside.

Results

Among the 26,637 participants, 7,959 (30%) used Go To Travel. For "wearing a mask in the presence of others" and "washing hands after using the toilet," the majority of respondents answered "almost always: 86.5 and 85.6% respectively. Action "carrying alcohol disinfectant" was the least implemented, with 36.9% of respondents saying "almost never." Two of the seven preventive behaviors ("disinfecting hands with alcohol before going indoors" and "carrying alcohol disinfectant") were positively related to using Go To Travel, that is, the more of these actions they took, the more they used Go To Travel (p for trend <0.001).

Conclusions

To balance pandemic preparedness with economic preparedness, it is also necessary to promote travel when the infection situation is calm. However, since human mobility can be a factor that exacerbates the infection situation, it is necessary to promote more infection prevention behaviors among individuals. We confirmed that Go To Travel users were basically doing a good infection prevention behaviors, but they tended not to wash their hands after touching things or carry alcohol sanitizer. It is necessary to encourage these measures to be taken when traveling.