Application of Protection Motivation Theory to Quantify the Impact of Pandemic Fear on Anticipated Postpandemic Transit Usage.

The COVID-19 pandemic had an unprecedented impact on transit usage, primarily owing to the fear of infection. Social distancing measures, moreover, could alter habitual travel behavior, for example, using transit for commuting. This study explored the relationships among pandemic fear, the adoption of protective measures, changes in travel behavior, and anticipated transit usage in the post-COVID era, through the lens of protection motivation theory. Data containing multidimensional attitudinal responses about transit usage at several pandemic stages were utilized for the investigation. They were collected through a web-based survey in the Greater Toronto Area, Canada. Two structural equation models were estimated to examine the factors influencing anticipated postpandemic transit usage behavior. The results revealed that people taking relatively higher protective measures were comfortable taking a cautious approach such as complying with transit safety policies (TSP) and getting vaccinated to make transit trips. However, the intention to use transit on vaccine availability was found to be lower than in the case of TSP implementation. Conversely, those who were uncomfortable taking transit with caution and who were inclined to avoid travel and rely on e-shopping were most unlikely to return to transit in the future. A similar finding was observed for females, those with vehicle access, and middle-income individuals. However, frequent transit users during the pre-COVID period were more likely to continue to use transit after the pandemic. The study's findings also indicated that some travelers might be avoiding transit specifically because of the pandemic, implying they are likely to return in the future.

What happens when post-secondary programmes go virtual for COVID-19? Effects of forced telecommuting on travel demand of post-secondary students during the pandemic.

The outbreak of coronavirus disease 2019 (COVID-19) spreads globally, disrupting every aspect of everyday activities. Countermeasures during the pandemic, such as remote working and learning, proliferated tele-activities worldwide during the COVID -19 pandemic. The prevalence of telecommuting could lead to new activity-travel patterns. It is in the interest of transport demand modellers to capture this developing trend of telecommuting using state-of-art travel demand forecasting techniques. This study develops a modelling framework using activity-based and agent-based microsimulation to forecast activity-travel demand considering telecommuting and the pandemic. For empirical application, the modelling framework investigates changes in travel behaviours in post-secondary students when all major post-secondary institutions in the Greater Toronto Area (GTA), Canada, decided to go virtual during the pandemic. The empirical investigation reveals that enforced telecommuting and the pandemic caused significant mobility drops and shifts in students' trip starting time patterns. While only considering the influence of telecommuting, the empirical exercise reveals noteworthy dynamics between telecommuting and the overall travel demand. Telecommuting could simultaneously reduce the need to commute but also induce discretionary travel. When telecommuting is enforced, students' overall trip rates drop by 14.2%, despite increasing trip rates for all discretionary activities except shopping/market. Moreover, the study demonstrates that it is beneficial to model at-home productive and maintenance episodes when telecommuting is prominent.

How has the COVID-19 pandemic affected the use of ride-sourcing services? An empirical evidence-based investigation for the Greater Toronto Area.

The ongoing COVID-19 pandemic has fundamentally changed the nature of day-to-day life in cities worldwide. In the transportation sector, COVID-19 appears to have impacted modal preferences. In particular, people seem to be less willing to use modes where they may encounter strangers (such as public transit) and modes that involve coming into contact with shared surfaces (such as ride-sourcing). Given the transformative impact that ride-sourcing services had on urban mobility before the pandemic, it is crucial to understand the effects of COVID-19 on the use of ride-sourcing moving forward. Using data from a web-based survey, this study combines descriptive analysis with the application of a two-stage ordered logit model framework to investigate the impacts of COVID-19 on the utilization of ride-sourcing services in the Greater Toronto Area, including how often ride-sourcing is used and the earliest stage of the pandemic that a person would consider using ride-sourcing. Generally speaking, the use of ride-sourcing has decreased since the start of the pandemic, however, there are also people who are using ride-sourcing more often than they did before the pandemic. The results indicate that the perception of risk, the tendency to take precautions when leaving home, and socio-economic factors influence the earliest stage of the pandemic where a person would consider using ride-sourcing. Overall, it appears that ride-sourcing usage will gradually increase as restrictions are lifted; however, it is unlikely to return to pre-pandemic levels until COVID-19 is no longer considered a public health threat.

Exploring the impacts of the COVID-19 pandemic on modality profiles for non-mandatory trips in the Greater Toronto Area.

The ongoing COVID-19 pandemic has drastically altered daily life in cities across the world. To slow the spread of COVID-19, many countries have introduced mobility restrictions, ordered the temporary closure of businesses, and encouraged social distancing. These policies have directly and indirectly influenced travel behaviour, particularly modal preferences. The purpose of this paper to explore modality profiles for non-mandatory trips and analyze how they have changed in response to the pandemic and pandemic-related public health policies. The data used for this study were collected from web-based surveys conducted in the Greater Toronto Area. Modality profiles were identified through the application of latent class cluster analysis, with six modality profiles being identified for both the pre-pandemic and pandemic periods. The results indicate that the importance of public transit has declined during the pandemic, while the roles of private vehicles and active modes have become more prominent. However, individuals' changes in modal preferences vary based on their pre-pandemic modality profile. In particular, it appears that pre-pandemic transit users with access to a private vehicle have substituted public transit for travel by private vehicle, while those without private vehicle access are continuing to use public transit for non-mandatory trips. Consequently, pandemic-related transportation policies should consider those who do not have access to a private vehicle and aim to help those making non-mandatory trips using transit or active modes comply with local public health guidelines while travelling. The results highlight how the changes in modal preferences that occurred due to the pandemic differ among different segments of the population.

Exploring the spatiotemporal factors affecting bicycle-sharing demand during the COVID-19 pandemic.

This study investigates the roles of the socio-economic, land use, built environment, and weather factors in shaping up the demand for bicycle-sharing trips during the COVID-19 pandemic in Toronto. It uses "Bike Share Toronto" ridership data of 2019 and 2020 and a two-stage methodology. First, multilevel modelling is used to analyze how the factors affect monthly station-level trip generation during the pandemic compared to pre-pandemic period. Then, a geographically weighted regression analysis is performed to better understand how the relationships vary by communities and regions. The study results indicate that the demand of the service for commuting decreased, and the demand for recreational and maintenance trips increased significantly during the pandemic. In addition, higher-income neighborhoods are found to generate fewer weekday trips, whereas neighbourhoods with more immigrants experienced an increase in bike-share ridership during the pandemic. Moreover, the pandemic trip generation rates are more sensitive to the availability of bicycle facilities within station buffers than pre-pandemic rates. The results also suggest significant spatial heterogeneity in terms of the level of influence of the explanatory factors on the demand for bicycle-sharing during the pandemic. Based on the findings, some neighbourhood-specific policy recommendations are made, which inform decisions regarding the locations and capacity of new stations and the management of existing stations so that equity concerns about the usage of the system are adequately accounted for.