COVID-19 and Travel: How Our Out-of-home Travel Activity, In-home Activity, and Long-Distance Travel Have Changed

How has COVID-19 impacted customer perceptions and demand for delivery services: An exploratory analysis

The novel coronavirus (COVID-19) pandemic created an environment where nearly all aspects of mobility changed to ensure the health and safety of the public. The Centers for Disease Control and Prevention (CDC) recommended that people quarantine for 14 days if they were potentially exposed to the virus, stay at least six feet apart from others, and stay at home as much as possible. Delivery via third-party restaurant app, grocery, and package delivery quickly became an essential service. This study assesses customer's changes in use and perceived quality of delivery services in Southwest Virginia, via an online stated-preference survey (n = 423). The responses were analyzed using ordered logit and generalized ordered logit models to identify which population segments had changing delivery behavior and perceptions due to the pandemic. Findings include that before the pandemic, only households with an income greater than $100,000 had a significantly higher demand for package delivery services than those making less than $25,000. During the pandemic, all income brackets had a significantly higher demand for package delivery "weekly" than households with less than a $25,000 income, with a 19.50%, 22.54%, and 45.59% greater chance of use for income levels $25,000 to $50,000, $50,000 to $100,000, and over $100,000, respectively. This trend highlights that package delivery became necessary during the pandemic. Respondents who lived within town limits were statistically significantly more likely to use third-party restaurant delivery apps at least once a week before (3.10%), during (9.20%), and after (4.50%) the pandemic compared to those outside town limits. The results also found people who lived within town limits were 7.77% more likely to be satisfied with delivery services in general than those who lived outside town limits. The findings from this paper identify expanding delivery equity gaps within the population and provide recommendations for policymakers and delivery agencies. Some limitations include that low sample size did not allow for fully segmented models and meant that the study should be considered exploratory in nature.

COVID-19: Modeling Out-of-Home and In-Home Activity Participation during the Pandemic

Understanding the interaction between in-home and out-of-home activity participation decisions is important, particularly at a time when opportunities for out-of-home activities such as shopping, entertainment, and so forth are limited because of the COVID-19 pandemic. The travel restrictions imposed as a result of the pandemic have had a massive impact on out-of-home activities and have changed in-home activities as well. This study investigates in-home and out-of-home activity participation during the COVID-19 pandemic. Data comes from the COVID-19 Survey for assessing Travel impact (COST), conducted from March to May in 2020. This study uses data for the Okanagan region of British Columbia, Canada to develop the following two models: a random parameter multinomial logit (RPMNL) model for out-of-home activity participation and a hazard-based random parameter duration (HRPD) model for in-home activity participation. The model results suggest that significant interactions exist between out-of-home and in-home activities. For example, a higher frequency of out-of-home work-related travel is more likely to result in a shorter duration of in-home work activities. Similarly, a longer duration of in-home leisure activities might yield a lower likelihood for recreational travel. Health care workers are more likely to engage in work-related travel and less likely to participate in personal and household maintenance activities at home. The model confirms heterogeneity among the individuals. For instance, a shorter duration of in-home online shopping yields a higher probability for participation in out-of-home shopping activity. This variable shows significant heterogeneity with a large standard deviation, which reveals that sizable variation exists for this variable.

Travel behaviour changes and risk perception during COVID-19: A case study of Malaysia

The COVID-19 pandemic has disrupted travel behaviours due to the need for movement restrictions. The restrictions adversely affected various aspects of health and the economy. This study aimed to investigate factors affecting trip frequency during the recovery period of the COVID-19 pandemic in Malaysia. An online national cross-sectional survey was conducted to collect data in conjunction with different movement restriction policies. The questionnaire includes socio-demographics, experience with COVID-19, risk perception of COVID-19, and trip frequency on several activities during the pandemic. Mann Whitney U was conducted to determine whether there were statistically significant differences between the socio-demographic factors for the respondents in the first and second surveys. Results show no significant difference in socio-demographic factors except for the level of education. The results indicate that the respondents from both surveys were comparable. Next, Spearman correlation analyses were conducted to find significant correlations between trip frequencies toward socio-demographics, experience with COVID-19 and risk perception. There was a correlation between the frequency of travel and risk perception for both surveys. Regression analyses were performed based on the findings to investigate trip frequency determinants during the pandemic. Perceived risk, gender, and occupation influenced the trip frequencies for both surveys. By understanding the influence of risk perception on the frequency of travel, the government can identify the appropriate policy during a pandemic or health emergency to avoid impeding normal travel behaviour. Thus, people's mental and psychological well-being are not negatively affected.

Validity and reliability of short-form travel behavior questionnaire during COVID-19 pandemic (TBQ-Cov19)

Introduction

Avoiding unnecessary travel and reducing the number of essential travels are among the effective strategies for preventing the spread of COVID-19 during the pandemic. Given that it is impossible to avoid essential travel, health protocols should be observed to prevent disease transmission. The extent to which health protocols are observed during the trip should be accurately assessed by a valid questionnaire. Therefore, this study aims to develop and validate a questionnaire to assess compliance with COVID-19 prevention protocols during travel.

Methods

In this cross-sectional study, 285 individuals were selected from six provinces using the cluster sampling method in May and June 2021. The Content Validity Ratio (CVR) and Content Validity Index (CVI) were calculated using the comments of 12 external experts. The exploratory factor analysis (EFA), with principal component factor in the extraction method and Varimax rotation, were applied to determine construct validity. Cronbach's alpha was employed to assess internal consistency and the Spearman-Brown correlation coefficient was computed for test-retest reliability.

Results

In the content validity phase, the I-CVIs for all items were acceptable, but due to a low CVR value (below 0.56), one question was eliminated. As a result of EFA for construct validity, two factors were extracted that justified 61.8% of the variance. Cronbach's alpha coefficient of the questionnaire, with 10 items, was 0.83. The Spearman-Brown correlation coefficient was 0.911 which confirmed the stability of the questionnaire at an excellent level.

Conclusions

This questionnaire is a valid tool with good validity and reliability for assessing compliance with health protocols in travel during the COVID-19 pandemic.

The impact of COVID-19 and related containment measures on Bangkok's public transport ridership

The COVID-19 pandemic and related measures used to contain its spread affected public transport ridership in cities around the world. In Thailand, the government issued 41 Royal Decrees between April 2020 and December 2021 to mitigate the spread of the pandemic. In this study, we investigate how Bangkok's public transport services (bus, metro, and boat) have been affected during this period by analyzing the daily ridership data, confirmed COVID-19 cases, and aggregated travel trends by trip destinations using from Google mobility reports. The results show that public transport ridership decreased as daily COVID cases increased and the levels of restraining measures became higher. However, other factors, such as relative strictness compared to earlier measures and sequencing of the measures seems to have had an impact on the ridership. Moreover, the impact on ridership trends is unique for each of the three modes. Bus and metro ridership appear to be more sensitive to the changes in restrictions than the boats. Bus and metro ridership also shows similar changes in the travel trends concerning the place of visit. The findings reported here provide first insights into how Bangkok's public transport systems were affected and suggest the rationale of why different public transport modes were affected differently. These results can be useful for researchers and for decision-makers who plan and design policies and measures for public transport services.

Effects of COVID-19 on China's civil aviation passenger transport market

COVID-19 has had an unprecedented impact on the civil aviation transportation sector. This study uses COVID-19 statistics and socioeconomic development data for various cities in China, combined with the Moran index and econometric models to analyse the spatial distribution of China's epidemic and civil aviation passenger throughput, as well as the impact of COVID-19 and socioeconomic factors on the civil aviation passenger transport market. We find that COVID-19 spread from Wuhan to the surrounding cities, showing a positive spatial autocorrelation; civil aviation passenger throughput decreased significantly in cities where the virus continued to spread and recurred frequently. Due to the effective prevention and control of the pandemic in China, the domestic passenger transport industry recovered rapidly, while international air passenger services continued to suffer; major cities with high social and economic development still show a large decline in passenger transport, but they are again undertaking most passenger transport services. COVID-19's impact on tourism has affected the civil aviation industry significantly, whose recovery depends on the tourism industry's recovery; this, in turn, is led by regions with a high degree of social and economic development.

Examining the potential environmental and travel time saved benefits of remote working hubs

Since the coronavirus pandemic hit in early 2020 many workers around the world, that can, have begun to work remotely. Many studies have been completed on the success or otherwise of this new trend in remote working and postulate that this trend will continue beyond the pandemic. One of the other trends that has been spoken about significantly with this renewed interest in remote working is the development of remote working hubs (RWHs). These are locations outside of main cities that are used by workers from different companies to work remotely in a flexible way. The research conducted in this paper examines several of these RWHs that are located on the periphery of Dublin city centre. The motivation for the research was to establish the potential emissions and travel time savings for commuters using RWHs. The study collected survey data from 514 participants that are currently using RWHs and questioned them on their travel and work habits. The analysis showed that users of RWHs were driving on average 60 km less per day and the majority were able to depart for work later. In the sample, 34% would have driven to their normal place of work and whereas 12% drove to their RWH. The results also point to substantial travel time and emissions savings from using RWHs. The findings suggest that on average those that drive alone could save 1.126 tonnes of CO2 from working at a RWH 3 days a week for a year.

The Use of Information Communication Technologies in a South African Deaf Older Adult Population Living in a Residential Care Home to Communicate with Emigrant Family Members

South Africa has experienced considerable international outward migration in the last half century, which has had a severe psychological impact on members of families affected by this phenomenon. Older parents who remain behind may experience feelings of loss and isolation. Information Communication Technologies (ICTs) are useful in maintaining relationships between family members separated by migration and increasingly allow migrant families to experience a virtual co-presence despite geographical separation. However, the process may be challenging, especially for older people with hearing difficulties. This article reports on a qualitative study exploring the perceptions of a group of older adults who have difficulty hearing and who live in a residential care home about using ICTs to communicate with family abroad. Interview data were analysed using thematic analysis. Most of the participants used either a fixed line telephone or a mobile phone. They reported challenges in communicating with family members abroad arising from their deafness, as well as difficulties using technological devices together with their hearing aids. These challenges resulted in feelings of helplessness and frustration. Although the data collection took place prior to the COVID-19 pandemic, these findings may be of particular relevance to situations such as those during the pandemic when many older adults became more reliant on technology to communicate with family members because of restrictions on direct contact. Accordingly, suggestions are made to address challenges in communication between older adults and loved ones who are geographically separated.

Assessing the Compensatory Potentiality of Hot Spring Tourism in the COVID-19 Post-Pandemic Environment

Considering the public health crisis induced by the COVID-19 disease, hot spring tourism has attracted more people who want to compensate for this themselves and seek restoration of health. Research regarding consumer experience and their psychological restoration from compensatory travel activities is lacking. To address this gap, a conceptual model is developed that links the compensatory experience quality and the perceived restorative value. The model was assessed using a sample of 631 tourists who visited hot spring resorts in the post-pandemic environment. Our findings confirm the positive influence of the quality of compensatory experience (CEQ) on perceived restorativeness (PR). In particular, the cognitive image and affective image partially mediated the effect of CEQ on PR. These research findings provide both theoretical contributions and managerial implications on hot spring destination management and marketing.

How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model

Integrating occupant behavior with residential energy use for detailed energy quantification has attracted research attention. However, many of the available models fail to capture unseen behavior, especially in unprecedented situations such as COVID-19 lockdowns. In this study, we adopted a hybrid approach consisting of agent-based simulation, machine learning and energy simulation techniques to simulate the urban energy consumption considering the occupants' behavior. An agent-based model is developed to simulate the in-home and out-of-home activities of individuals. Separate models were developed to recognize physical characteristics of residential dwellings, including heating equipment, source of energy, and thermostat setpoints. The developed modeling framework was implemented as a case study for the Central Okanagan region of British Columbia, where alternative COVID-19 scenarios were tested. The results suggested that during the pandemic, the daily average in-home-activity duration (IHD) increased by approximately 80%, causing the energy consumption to increase by around 29%. After the pandemic, the average daily IHD is expected to be higher by approximately 32% compared with the pre-pandemic situation, which translates to an approximately 12% increase in energy consumption. The results of this study can help us understand the implications of the imposed COVID-19 lockdown with respect to energy usage in residential locations.

Aggravated social segregation during the COVID-19 pandemic: Evidence from crowdsourced mobility data in twelve most populated U.S. metropolitan areas

The notion of social segregation refers to the degrees of separation between socially different population groups. Many studies have examined spatial and residential separations among different socioeconomic or racial populations. However, with the advancement of transportation and communication technologies, people's activities and social interactions are no longer limited to their residential areas. Therefore, there is a growing necessity to investigate social segregation from a mobility perspective by analyzing people's mobility patterns. Taking advantage of crowdsourced mobility data derived from 45 million mobile devices, we innovatively quantify social segregation for the twelve most populated U.S. metropolitan statistical areas (MSAs). We analyze the mobility patterns between different communities within each MSA to assess their separations for two years. Meanwhile, we particularly explore the dynamics of social segregation impacted by the COVID-19 pandemic. The results demonstrate that New York and Washington D.C. are the most and least segregated MSA respectively among the twelve MSAs. Since the COVID-19 began, six of the twelve MSAs experienced a statistically significant increase in segregation. This study also shows that, within each MSA, the most and least vulnerable groups of communities are prone to interacting with their similar communities, indicating a higher degree of social segregation.

How Drivers of Seasonality in Respiratory Infections May Impact Vaccine Strategy: A Case Study in How Coronavirus Disease 2019 (COVID-19) May Help Us Solve One of Influenza's Biggest Challenges

Vaccines against seasonal infections like influenza offer a recurring testbed, encompassing challenges in design, implementation, and uptake to combat a both familiar and ever-shifting threat. One of the pervading mysteries of influenza epidemiology is what causes the distinctive seasonal outbreak pattern. Proposed theories each suggest different paths forward in being able to tailor precision vaccines and/or deploy them most effectively. One of the greatest challenges in contrasting and supporting these theories is, of course, that there is no means by which to actually test them. In this communication we revisit theories and explore how the ongoing coronavirus disease 2019 (COVID-19) pandemic might provide a unique opportunity to better understand the global circulation of respiratory infections. We discuss how vaccine strategies may be targeted and improved by both isolating drivers and understanding the immunological consequences of seasonality, and how these insights about influenza vaccines may generalize to vaccines for other seasonal respiratory infections.

Dynamic activity chain pattern estimation under mobility demand changes during COVID-19

During the coronavirus disease 2019 pandemic, the activity engagement and travel behavior of city residents have been impacted by government restrictions, such as temporary city-wide lockdowns, the closure of public areas and public transport suspension. Based on multiple heterogeneous data sources, which include aggregated mobility change reports and household survey data, this paper proposes a machine learning approach for dynamic activity chain pattern estimation with improved interpretability for examining behavioral pattern adjustments. Based on historical household survey samples, we first establish a computational graph-based discrete choice model to estimate the baseline travel tour parameters before the pandemic. To further capture structural deviations of activity chain patterns from day-by-day time series, we define the activity-oriented deviation parameters within an interpretable utility-based nested logit model framework, which are further estimated through a constrained optimization problem. By incorporating the long short-term memory method as the explainable module to capture the complex periodic and trend information before and after interventions, we predict day-to-day activity chain patterns with more accuracy. The performance of our model is examined based on publicly available datasets such as the 2017 National Household Travel Survey in the United States and the Google Global Mobility Dataset throughout the epidemic period. Our model could shed more light on transportation planning, policy adaptation and management decisions during the pandemic and post-pandemic phases.

Will modal shift occur from subway to other modes of transportation in the post-corona world in developing countries?

Developing countries are more fragile in the face of the crippling Covid-19 pandemic. Transportation is one of the major industries that has been hardly hit worldwide, and it is more worrying for these countries that already have challenges such as high modal share of private cars, air pollution, and high fatalities due to car accidents. This paper is one of its first kinds that studies the impact of this pandemic on the transportation of Tehran, the capital of Iran, which is one of the forefronts of the battle. In the first step and to get better insights from the travel behavior of passengers due to the pandemic, an online questionnaire is developed and distributed. Priorities for mode choice before and during the pandemic decrease and increase in the share of different modes and the impact of having a high-risk person in the family is studied. Subway had the most decrease and private cars had the highest increase. Hence, two logit models are developed to explain the variables that affect shifting away from the subway and shifting to private cars. Based on the results, a follow-up survey some months later and ridership trends of public transportation during the pandemic, four scenarios are envisaged for the post-corona world, the most probable one is highlighted and policies are recommended to better manage the situation.

Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

The Post-Pandemic Recovery of Transport Activity: Emerging Mobility Patterns and Repercussions on Future Evolution

The expectations for post-COVID recovery of transport activity point towards a gradual return to normality, once the pandemic is under control and mobility restrictions end. The shock to society and economy has, however, caused a number of behavioural changes that can influence the evolution of the transport sector. We analyse the main factors that can influence future supply and demand and explore how they may affect trip generation, distribution and modal split in passenger transport. We combine several conventional and innovative data sources with a detailed strategic transport model at the EU level, in order to present quantitative estimates under various scenarios. New remote work patterns or personal risk avoidance attitudes can lead to increased levels of car ownership and use. Public policy priorities in the aftermath of the pandemic would need to address the emerging challenges and adopt measures that can sustain the shift to active travel, support public transport, railways and aviation and stimulate innovation in transport technologies and services.