Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price

This study aimed to explore the impacts of COVID-19 on car and bus usage and their relationships with land use and land price. Large-scale trip data of car and bus usage in Daejeon, South Korea, were tested. We made a trip-chain-level data set to analyze travel behavior based on activity-based travel volumes. Hexagonal cells were used to capture geographical explanatory variables, and a mixed-effect regression model was adopted to determine the impacts of COVID-19. The modeling outcomes demonstrated behavioral differences associated with using cars and buses amid the pandemic. People responded to the pandemic by reducing their trips more intensively during the daytime and weekends. Moreover, they avoided crowded or shared spaces by reducing bus trips and trips toward commercial areas. In terms of social equity, trips of people living in wealthier areas decreased more than those of people living in lower-priced areas, especially trips by buses. The findings contribute to the previous literature by adding a fundamental reference for the different impacts of pandemics on two universal transportation modes.     

Unravel the impact of COVID-19 on the spatio-temporal mobility patterns of microtransit

Shared mobility is an essential component of the larger sharing economy. Ride-hailing, bike-sharing, e-scooters, and other types of shared mobility continue to grow worldwide. Among these services is microtransit, a new transport mode that extends transit coverage within a region. Mobile devices enable microtransit services, aggregating riders and using real-time routing algorithms to group customers traveling in similar directions. Meanwhile, the newly emerged coronavirus, COVID-19, has radically reshaped the ridership behavior of all transit services, including microtransit. While existing research evaluates the performance of microtransit pilot programs before the pandemic, there is no information concerning the spatio-temporal pattern of microtransit activities under the impact of COVID-19. The purpose of this paper is to apply eigendecomposition and k-clique percolation methods to uncover the spatio-temporal patterns of microtransit trips. Further, we used these approaches to identify underlying communities using data from a pilot program in Salt Lake City, Utah. The resulting research offers insight into how COVID-19 altered travel behavior. Specifically, eigendecomposition delineated the homogeneity and heterogeneity of travel patterns across temporal dimensions. We identified first mile/last mile trips as a major source of variance in both pre- and post-COVID periods and that transit-dependent users prove to be inelastic despite the threat of COVID-19. The k-clique percolation method detected possible community formations and tracked how these communities evolved during the pandemic. In addition, we systematically analyzed overlapping communities and the network structure around shared nodes by using a clustering coefficient. The workflow developed in this research broadly is generalizable and valuable for understanding the unique spatio-temporal patterns of microtransit. The framework can also help transit agencies with performance evaluation, regional transport strategies, and optimal vehicle dispatching.     

Estimating the impact of COVID-19 on air travel in the medium and long term using neural network and Monte Carlo simulation

The COVID-19 pandemic has had a substantial impact on the airline industry. Air travel in the United States declined in 2020 with significantly lower domestic and international flights. The dynamic change and uncertainty in the trend of COVID-19 have made it difficult to predict future air travel. This paper aims at developing and testing neural network models that predict domestic and international air travel in the medium and long term based on residents' daily trips by distance, economic condition, COVID-19 severity, and travel restrictions. Data in the United States from various sources were used to train and validate the neural network models, and Monte Carlo simulations were constructed to predict air travel under uncertainty of the pandemic and economic growth. The results show that weekly economic index (WEI) is the most important predictor for air travel. Additionally, daily trips by distance play a more important role in the prediction of domestic air travel than the international one, while travel restrictions seem to have an impact on both. Sensitivity analysis results for four different scenarios indicate that air travel in the future is more sensitive to the change in WEI than the changes in COVID-19 variables. Additionally, even in the best-case scenario, when the pandemic is over and the economy is back to normal, it still takes several years for air travel to return to normal, as before the pandemic. The findings have significant contributions to the literature in COVID-19's impact on air transportation and air travel prediction.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

Does COVID-19 affect metro use in Taipei?

This paper provides the first evidence of the causal effect of COVID-19 on metro use using real-time data from the Taipei Metro System in Taiwan. In contrast to other cities or countries, Taiwan did not enforce strict social lockdowns or mandatory stay-at-home orders to combat COVID-19. The major prevention strategies to the pandemic in Taiwan include promoting social distancing, mandating the wearing of face masks in public areas, and requiring all international arrivals to quarantine for 14 days. Using administrative data on confirmed cases of COVID-19 and ridership from metro stations with the difference-in-differences model, we find that an additional new confirmed case of COVID-19 reduces metro use by 1.43% after controlling for local socio-demographic variables associated with ridership and the number of international arrivals to Taiwan. This result implies that the reduction in metro trips is attributable to decreases in residents' use of public transportation due to perceived health risks. Furthermore, the effect of COVID-19 on metro use disproportionally impacts stations with different characteristics. The effect is more pronounced for metro stations connected to night markets, shopping centers, or colleges. Although decreases in metro ridership lower the revenue of the Taipei Metro System, our results indicate a tradeoff between increased financial burdens of public transportation systems and reducing medical expenses associated with COVID-19.     

Impact of COVID-19 on the number of days working from home and commuting travel: A cross-cultural comparison between Australia,South America and South Africa

The COVID-19 pandemic has changed the way we go about our daily lives in ways that are unlikely to return to the pre-COVID-19 levels. A key feature of the COVID-19 era is likely to be a rethink of the way we work and the implications on commuting activity. Working from home (WFH) has been the ‘new normal’ during the period of lockdown, except for essential services that require commuting. In recognition of the new normal as represented by an increasing amount of WFH, this paper develops a model to identify the incidence of WFH and what impact this could have on the number of weekly commuting trips. Using data collected in eight countries (Argentina, Australia, Brazil, Chile, Colombia, Ecuador, Peru and South Africa), we developed a Poisson regression model for the number of days individuals worked from home during the pandemic. Simulated scenarios quantify the impact of the different variables on the probability of WFH by country. The findings provide a reference point as we continue to undertake similar analysis at different points through time during the pandemic and after when restrictions are effectively removed.     

The impact of the COVID-19 pandemic on people's mobility: A longitudinal study of the U.S. from March to September of 2020

This paper examines changes in people's mobility over a 7-month period (from March 1st to September 30th, 2020) during the COVID-19 pandemic in the U.S. using longitudinal models and county-level mobility data obtained from people's anonymized mobile phone signals. It differentiates two distinct waves of the study period: Wave 1 (March–June) and Wave 2 (June–September). It also analyzes the relationships of these mobility changes with various social, spatial, policy, and political factors. The results indicate that mobility changes in Wave 1 have a V-shaped trend: people's mobility first declined at the early stage of the COVID-19 pandemic (March–April) but quickly recovered to the pre-pandemic mobility levels from April to June. The rates of mobility changes during this period are significantly associated with most of our key variables, including political partisanship, poverty level, and the strictness of mobility restriction policies. For Wave 2, there was very little mobility decline despite the existence of mobility restriction policies and the COVID-19 pandemic becoming more severe. Our findings suggest that restricting people's mobility to control the pandemic may be effective only for a short period, especially in liberal democratic societies. Further, since poor people (who are mostly essential workers) kept traveling during the pandemic, health authorities should pay special attention to these people by implementing policies to mitigate their high COVID-19 exposure risk.     

How did COVID-19 impact air transportation? A first peek through the lens of complex networks

The current outbreak of COVID-19 is an unprecedented event in air transportation. This is probably the first time that global aviation contributed to the planet-wide spread of a pandemic, with casualties in over two hundred countries. As of August 23rd, 2020, the number of infected cases has topped 23 million, reportedly relating to more than 800,000 deaths worldwide. However, there is also a second side of the pandemic: it has led to an unmatched singularity in the global air transportation system. In what could be considered a highly uncoordinated, almost chaotic manner, countries have closed their borders, and people are reluctant/unable to travel due to country-specific lock-down measures. Accordingly, aviation is one of the industries that has been suffering most due to the consequences of the pandemic outbreak, despite probably being one of its largest initial drivers. In this study, we investigate the impact of COVID-19 on global air transportation at different scales, ranging from worldwide airport networks where airports are nodes and links between airports exist when direct flights exist, to international country networks where countries are contracted as nodes, and to domestic airport networks for representative countries/regions. We focus on the spatial-temporal evolutionary dynamics of COVID-19 in air transportation networks. Our study provides a comprehensive empirical analysis on the impact of the COVID-19 pandemic on aviation from a complex system perspective using network science tools.     

Are stay-at-home orders more difficult to follow for low-income groups?

In response to the COVID-19 pandemic, a growing number of states, counties and cities in the United States issued mandatory stay-at-home orders as part of their efforts to slow down the spread of the virus. We argue that the consequences of this one-size-fits-all order will be differentially distributed among economic groups. In this paper, we examine social distance behavior changes for lower income populations. We conduct a comparative analysis of responses between lower-income and upper-income groups and assess their relative exposure to COVID-19 risks. Using a difference-in-difference-in-differences analysis of 3140 counties, we find social distance policy effect on the lower-income group is smaller than that of the upper-income group, by as much as 46% to 54%. Our explorations of the mechanisms behind the disparate effects suggest that for the work-related trips the stay-at-home orders do not significantly reduce low income work trips and this result is statistically significant. That is, the share of essential business defined by stay-at-home orders is significantly negatively correlated with income at county level. In the non-work-related trips, we find that both the lower-income and upper-income groups reduced visits to retail, recreation, grocery, and pharmacy visits after the stay-at-home order, with the upper-income group reducing trips more compared to lower-income group.     

Airport user experience unpacked: Conceptualizing its potential in the face of COVID-19

Given the unprecedented challenges imposed on the aviation industry by the COVID-19 pandemic, this paper proposes a new perspective on airport user experience as a field of study to unlock its potential as a basis for strategic roadmapping. Through an integrative literature review, this study points out a dominant focus, in practice and research, on customer experience and service quality, as opposed to user experience, to help airports gain a competitive edge in an increasingly commoditized industry. The review highlights several issues with this understanding of experience, as users other than passengers, such as employees, working for the airport and its myriad stakeholders, as well as visitors, are largely omitted from study. Given the complexity of the system, operationally, passengers are generally reduced to smooth flows of a passive mass, which this study argues is both a missed opportunity and a vulnerability exacerbated by the COVID-19 pandemic. Major events apart from COVID-19 are used to show the negative effects this simplification of user experience has had. Based on solutions and models proposed in previous studies, a conceptual model has been developed to illustrate the postulated potential of a deeper and more holistic study of airport user experience to make airport systems generally more agile, flexible and future-proof. As such, the paper advocates to utilize the user experience as a basis for strategic planning to equip airports with the know-how to manage not just daily operations more effectively but also the aftermath of and recovery from major events like the COVID-19 pandemic. Moreover, with the user experience at the center of the strategic roadmap, airports can plan ahead to mitigate the impact of future scenarios. The importance of future research and the use of existing research are discussed.     

The relationship between ridehailing and public transit in Chicago: A comparison before and after COVID-19

As Transportation Network Companies (TNCs) have expanded their role in U.S. cities recently, their services (i.e. ridehailing) have been subject to scrutiny for displacing public transit (PT) ridership. Previous studies have attempted to classify the relationship between transit and TNCs, though analysis has been limited by a lack of granular TNC trip records, or has been conducted at aggregated scales. This study seeks to understand the TNC-PT relationship in Chicago at a spatially and temporally granular level by analyzing detailed individual trip records. An analysis framework is developed which enables TNC trips to be classified according to their potential relationship with transit: complementary (providing access to/from transit), substitutive (replacing a transit alternative), or independent (not desirably completable by transit). This framework is applied to both regular operating conditions and to early stages of the COVID-19 pandemic, to identify the TNC-PT relationship in these two contexts. We find that complementary TNC trips make up a small fraction of trips taken (approximately 2%), while potential independent trips represent 48% to 53% and potential substitution trips represent 45% to 50%. The percentage of substitution trips drops substantially following COVID-19 shutdowns (to around 14%). This may be attributed to a reduction in work-based TNC trips from Chicago's north side, indicated by changes in spatial distributions and flattening of trips occurring during peak hours. Furthermore, using spatial regression, we find that an increased tendency of TNC trips to substitute transit is related to a lower proportion of elderly people, greater proportion of peak-period TNC travel, greater transit network availability, a higher percentage of white population, and increased crime rates. Our findings identify spatial and temporal trends in the tendency to use TNC services in place of public transit, and thus have potential policy implications for transit management, such as spatially targeted service improvements and safety measures to reduce the possibility of public transit being substituted by TNC services.     

Understanding the usage of dockless bike sharing in Singapore

A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.     

Elderly mobility during the COVID-19 pandemic: A qualitative exploration in Kunming,China

The outbreak of COVID-19 in China started at the end of December 2019. This led to a series of containment measurements to control the spread of COVID-19. Despite of the widely reported effects of these measures, inadequate attention has gone to their social impacts. The elderly, as one of the most susceptible populations, has experienced a considerable reduction in mobility.This paper explores the role mobility played and how the social environment influenced elderly mobility in the first 2 months of the COVID-19 outbreak. We surveyed 186 families with a total of 248 elderly people in Kunming. The results show that mobility improves the quality of daily living, such as access to grocery shopping, maintenance of outdoor activities for health cultivation and preserving social networks even during the pandemic. Four themes relating to social environment emerged from the data as elements influencing elderly mobility during the pandemic: social pressure, practice of the virtue of Xiao, the social norm of respecting the aged and the impacts of technological advances. Among them, the virtue of Xiao enabled the elderly to stay in place in the early phase of COVID-19 by fulfilling their needs for daily necessities and social interactions, whilst being less technology-savvy further excluded them socially by restraining them from restoring mobility after the lifting of travel restrictions.     

Attitudes of ageing passengers to air travel since the coronavirus pandemic

The coronavirus pandemic has had a devastating impact on the demand for air transport. One passenger segment that has received relatively little attention is ageing passengers (defined as aged 65+), in spite of the fact that this group has been disproportionately affected by COVID-19, and in recent years has been viewed as a potential growth market. Therefore, the aim of this brief paper is to analyse the attitudes of ageing passengers by assessing air travel plans in the next 12 months, examining the factors influencing future flying decisions, and investigating the impact of the coronavirus pandemic on perceived risks and experiences associated with flying. The findings show that over 60% of ageing passengers are planning to travel by air in the next 12 months, although the nature of their trips may change. Factors such as flexible ticket booking and quarantine rules do not appear to be key drivers affecting travel decisions and within the different stages of the air journey, getting to/from the airport is perceived as the safest stage. The findings suggest that there are various COVID-19 implications for airlines and airports serving this market segment, ranging from the use of self-service technology, the generation of commercial/ancillary revenues and the design of surface access policies.     

Spatial and temporal analysis of bike-sharing use in Cologne taking into account a public transit disruption

This research analyzes the relationship between bike-sharing and public transit using bike-sharing data collected in Cologne, Germany. The selected system is one of very few in Germany that is organized as a free-floating system, which allows the generation of more detailed data. A construction site in the light rail network causing multiple disruptions in the public transit network offered the possibility to detect changes in bike-sharing usage that occur in the corresponding period. Applying negative binomial regression, spatial and temporal usage patterns are analyzed to identify connections to the public transit network and other factors influencing the usage of bike sharing. The analysis suggests the existence of a spatial relationship between bike-sharing and public transit. Therefore, an intermodal use of both means of transport can be assumed. The short-term changes in the public transit network caused by the construction site only have minor impacts on the usage patterns. Other factors that affect the usage structures could be identified. Proximity to universities as well as the number of certain points of interest nearby, such as food outlets and shops, promote bike-sharing use. Higher temperatures are also positively correlated, while rain reduces usage. The findings of the study can be beneficial to integrate bike-sharing into urban transport systems, especially regarding public transit.     

Exploring the dynamic impacts of COVID-19 on intercity travel in China

Many studies have explored the effects of transportation and population movement on the spread of pandemics. However, little attention has been paid to the dynamic impact of pandemics on intercity travel and its recovery during a public health event period. Using intercity mobility and COVID-19 pandemic data, this study adopts the gradient boosting decision tree method to explore the dynamic effects of the COVID-19 on intercity travel in China. The influencing factors were classified into daily time-varying factors and time-invariant factors. The results show that China's intercity travel decreased on average by 51.35% from Jan 26 to Apr 7, 2020. Furtherly, the COVID-19 pandemic reduces intercity travel directly and indirectly by influencing industry development and transport connectivity. With the spread of COVID-19 and changes of control measures, the relationship between intercity travel and COVID-19, socio-economic development, transport is not linear. The relationship between intercity travel and secondary industry is illustrated by an inverted U-shaped curve from pre-pandemic to post-pandemic, whereas that with tertiary industry can be explained by a U-shaped curve. Meanwhile, this study highlights the dynamic effect of the COVID-19 on intercity mobility. These implications shed light on policies regarding the control measures during public health events that should include the dynamic impact of pandemics on intercity travel.     

Bikesharing Trip Patterns in New York City: Associations with Land Use,Subways, and Bicycle Lanes

As bikesharing systems have proliferated, few studies have examined the trips made on these systems. In this paper, we examine trips between origin-destination pairs during three months in 2015 on New York City’s Citi Bike system. Findings suggest considerable variation across user types, across months, and across times of day. Principal findings indicate that bikesharing is used for transit access and egress during rush hours, and that stations located along the same high-quality bicycle route see far more trips than do other station pairs. Casual users complement subscribers’ usage by using bicycles more frequently during midday and the evening, and between areas characterized by nearby recreational land uses. Loop trips to and from the same station also occur and are likely recreational trips. The data analyzed is essentially a form of “big data.” That is, large data sets that are ubiquitously collected. The analysis suggests that in this case, “big data” that lacks the socio-economic data commonly collected and used in travel analysis can provide useful insights to planners.     

Public transport trends in Australia during the COVID-19 pandemic: An investigation of the influence of bio-security concerns on trip behaviour

This paper draws on findings from an Australia-wide survey with data collected in three waves throughout 2020 to explore the impact of COVID-19 on public transport trends in metropolitan areas of Australia. Following consideration of the public transport sector response to the pandemic and the emerging literature context, we explore three principal questions: (i) How has weekly travel composition changed across the waves? (ii) How has level of concern with using public transport changed over the course of the pandemic given new bio-security concerns? and (iii) How has attitudes to risk been associated with the changes in PT use? A key finding is that concerns over bio-security issues around public transport are enduring, that concern about hygiene is significantly negatively related to public transport use and that those with higher concern about the hygiene of public transport also held higher concern about COVID-19 at work. Even as COVID-19 restrictions are eased, both concern about crowds and hygiene have a significant and negative correlation with public transport use. Concluding remarks are offered on what might need to happen for public transport patronage to start returning.     

Exogenous shocks and managerial preparedness: A study of U.S. airlines’ environmental scanning before the onset of the COVID-19 pandemic

Managerial preparedness is a constant concern for firm stakeholders. This concern is exacerbated during times of immense stress brought about by exogenous shocks. In this paper, we analyze the preparedness of U.S. commercial airline management teams to the largest systematic exogenous shock to date, namely the COVID-19 pandemic in 2019 and 2020. We do this by underpinning the paper with theory on environmental scanning and managerial dysfunction and then documenting the signals and actions of management around multiple public health scares. These include the SARS outbreak, the Swine Flu outbreak and the COVID-19 outbreak. Our results, based off of corporate filings with the SEC, is that airline management had multiple “dry runs” before the COVID-19 outbreak that should have lead them to prepare for financially catastrophic scenarios such as the one observed in 2020. Instead, management teams failed to learn from these, and other, prior shocks. Instead, they focused on other, less serious threats while diffusing their financial buffers through dividends and share buybacks.     

Modeling bike-sharing demand using a regression model with spatially varying coefficients

As an emerging mobility service, bike-sharing has become increasingly popular around the world. A critical question in planning and designing bike-sharing services is to know how different factors, such as land-use and built environment, affect bike-sharing demand. Most research investigated this problem from a holistic view using regression models, where assume the factor coefficients are spatially homogeneous. However, ignoring the local spatial effects of different factors is not tally with facts. Therefore, we develop a regression model with spatially varying coefficients to investigate how land use, social-demographic, and transportation infrastructure affect the bike-sharing demand at different stations to address this problem. Unlike existing geographically weighted models, we define station-specific regression and use a graph structure to encourage nearby stations to have similar coefficients. Using the bike-sharing data from the BIXI service in Montreal, we showcase the spatially varying patterns in the regression coefficients and highlight more sensitive areas to the marginal change of a specific factor. The proposed model also exhibits superior out-of-sample prediction power compared with traditional machine learning models and geostatistical models.