The surge in global electric bicycle ownership has exerted immense pressure on bicycle infrastructure. Theoretically, there's a need to reassess the risk factors associated with multiple bike lane users. Based on this, there's a practical need to re-evaluate the safety and quality of outdated infrastructure. This paper aims to reconsider risk factors related to bicycle infrastructure safety in the context of electric bicycles sharing lanes with traditional bicycles. Moreover, many countries lack precise spatial data concerning bicycle infrastructure. This study introduces a mobile sensing method based on bicycles, aiming to acquire daytime and nighttime bike lane datasets in a cost-effective, efficient, and large-scale manner. A computer vision-based bicycle risk factor assessment model was established, and the distribution of bicycle safety risk factors was visually analyzed. Research data was collected from a representative 59.5-kilometer bicycle lane area in Beijing. The results confirm the significant impact of the surge in electric bicycles, with electric bike users accounting for 72.1% of cyclists, 32.3% wearing helmets, and 8.4% riding against traffic. During the day, the highest-ranking risk factors include the type of bicycle lanes (half lacking dedicated lanes or being shared), roadside parking, and subpar road conditions. At night, insufficient street lighting are notable concerns. The research methodology is easily replicable and can be extended to new multi-user coexistence cycling environments or countries without bicycle spatial data, offering insights for bicycle safety policies and road design.

The unprecedented COVID-19 pandemic has brought drastic changes in our daily activities. One of these essential activities is grocery shopping. In compliance with the recommended social distancing standards, many people have switched to online grocery shopping or curbside pickup to minimize possible contagion. Although the shift to online grocery shopping is substantial, it is not clear whether this change would last in the long term. This study examines the attributes and underlying attitudes that may influence individuals' future decisions on online grocery shopping. An online survey was conducted in May 2020 in South Florida to collect data for this study. The survey contained a comprehensive set of questions related to respondents' sociodemographic attributes, shopping and trip patterns, technology use, as well as attitudes toward telecommuting and online shopping. A structural equation model (SEM) was applied to examine the intervening effects of observed as well as latent attitude variables on the likelihood of online grocery shopping after the outbreak. The results indicated that those with more experience in using online grocery shopping platforms were more likely to continue purchasing their groceries online. Individuals with positive attitudes toward technology and online grocery shopping in terms of convenience, efficiency, usefulness, and easiness were more likely to adopt online grocery shopping in the future. On the other hand, pro- driving individuals were less likely to substitute online grocery shopping for in-store shopping. The results suggested that attitudinal factors could have substantial impacts on the propensity toward online grocery shopping.

The COVID-19 crisis has upset the way of life of our society. The objective of this study was to apprehend the consequences of public health policies on mobility through the lens of gender. The analyses are based on a representative sample of 3000 people living in France. Travel behaviour was quantified using three mobility indicators (number of daily trips, daily distance travelled and daily travel time) that we regressed on individual and contextual explanatory variables. Two periods were studied: lockdown (March 17, 2020 until May 11, 2020), and post-lockdown (a curfew period: January-February 2021). For the lockdown period, our results show: (i) a statistically significant gender difference for the three mobility indicators. On average, women made 1.19 daily trips versus 1.46 for men, travelled 12 km whereas versus 17 km for men and spent less time on travel (23 min) than men (30 min); (ii) the degree of mobility was particularly sensitive to access to a car, according to a gender difference. For the post-lockdown period, our results reveal that: (i) women were more likely than men to make a higher number of daily trips (OR = 1.10, 95% CI = [1.04-1.17]); (ii) having only one or no car in the household impacted the mobility of women during the post-lockdown period; (iii) women regained some mobility but without reaching the pre-lockdown level. A better understanding of the factors influencing mobility behaviour, in lockdown and curfew periods, can provide some pathways to improve transport planning and help public authorities while tackling gender inequalites.

The COVID-19 pandemic is a public health crisis that also fuels the pervasive social inequity in the United States. Existing studies have extensively analyzed the inequity issues on mobility across different demographic groups during the lockdown phase. However, it is unclear whether the mobility inequity is perennial and will continue into the mobility recovery phase. This study utilizes ride-hailing data from Jan 1st, 2019, to Mar 31st, 2022, in Chicago to analyze the impact of various factors, such as demographic, land use, and transit connectivity, on mobility inequity in the different recovery phases. Instead of commonly used statistical methods, this study leverages advanced time-series clustering and an interpretable machine learning algorithm. The result demonstrates that inequity still exists in the mobility recovery phase of the COVID-19 pandemic, and the degree of mobility inequity in different recovery phases is varied. Furthermore, mobility inequity is more likely to exist in the census tract with more families without children, lower health insurance coverage, inflexible workstyle, more African Americans, higher poverty rate, fewer commercial land use, and higher Gini index. This study aims to further the understanding of the social inequity issue during the mobility recovery phase of the COVID-19 pandemic and help governments propose proper policies to tackle the unequal impact of the pandemic.

The COVID-19 pandemic has presented numerous, significant challenges for elderly in their daily life. In order to reach a deeper understanding of the feelings and thoughts of the elderly related to their possibilities to travel and engage in activities during the pandemic, this study takes a qualitative approach to exploring the views of the elderly themselves. The study focuses on experiences during the COVID-19 pandemic. A number of in-depth semi-structured interviews with elderly aged 70 and above, were conducted in June 2020. Applied Thematic Analysis (ATA) was applied, as a first stage, to investigate meaningful segments of data. In a second stage these identified segments were combined into a number of themes. This study reports the outcome of the ATA analysis. More specifically we report experiences, motivations and barriers for travel and activity participation, and discuss how these relate to the health and well-being of elderly, and vice versa. These findings highlight the strong need to develop a transport system that to a higher extent addresses the physical as well as the mental health of old people, with a particular focus on facilitating social interactions.

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea - a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators - from perspectives of trip frequency, spatial diversity, and travel range - to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors' mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors' transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

COVID-19, which has spread since late 2019, has caused drastic changes in transportation use. A few studies have already addressed the relationships between COVID-19 and transportation mode choice. However, in most cases, the analysis has been based on transit ridership during the early phases of the COVID-19 pandemic. In addition, few studies have focused on public bike use before and after COVID-19. This study examines the effect of COVID-19 on the ridership of public bikes and various determining factors of public bike use. An origin-destination (OD) analysis and spatial regression models were used with public bike ridership data from Seoul, Korea. The findings of the analysis can be summarized as follows. First, this study confirms that public parks have significantly influenced the increase in public bike ridership since the COVID-19 outbreak. This finding indicates that outdoor spaces such as riverside parks have played important roles in public bike ridership during the pandemic period. Second, this study finds that accessibility to subway stations strongly impacts the increase in public bike ridership. This means that the demand for public bikes as a connected transportation mode has increased since COVID-19. Third, access to bike lanes has had a significant impact on the increase in public bike ridership. This finding indicates the importance of expanding the public bike infrastructure network. Finally, this study makes policy proposals to promote public bike ridership during the COVID-19 pandemic.

Transport workers were among the most affected by the COVID-19 crisis. In several countries, public transport and delivery drivers were considered essential workers during the pandemic, while the demand changed dramatically. In this context, little is known about the actual effects of the pandemic on the lives of drivers, and whether those effects depend on the type and formality of the corresponding job. In this paper, we analyse the impact of the pandemic on the daily jobs of public transport, ride-hailing, and delivery app drivers: we study changes on working time and income, pandemic-related concerns, and deterioration of job satisfaction, through a survey applied to drivers during the first peak of the pandemic in Santiago, Chile. Probit regressions on job satisfaction identify the main COVID-related experiences that explain variations in subjective perceptions. We then discuss the implications for post-pandemic job relationships, drivers' working conditions and urban mobility. We show that the unstable characteristics of app-based jobs sharpened during the pandemic: Public transport drivers have kept their jobs, with a similar income as in the pre-pandemic situation and keep their social security, whereas ride-hailing and delivery app drivers do not have social security. Several ride-hailing drivers lost their jobs without any compensation, while delivery drivers earn less money per hour, are more exhausted, and express the greatest concerns and largest decrease in their job satisfaction. The COVID-19 crisis has emphasized that the sustainability of post-pandemic passenger and delivery on-demand services needs to rely on formal job regulation and worker protection.

The global COVID pandemic of 2020, affected travel patterns across the world. The level of impact was influenced not only by the virus itself, but also by the nature, extent, and duration of governmental restriction on commerce and personal activity to limit its spread. This paper focuses on the interaction between COVID-19 transmission and traffic volume and further explores the impact of traffic control policies on the interaction. Roadway traffic volume was used to quantify and assess the Chinese response to the pandemic; specifically, the relationship between government restrictions, travel activity, and COVID-19 progression across 29 provinces. Space and time distributions of traffic volume across China during the first half of 2020, were used to quantity the response and recovery of travel during the critical initial onset period of the virus. Most revealing of these trends were the impact of the Chinese restriction policies on both travel and the virus as well as the relationship of traffic trends during the closure period with the speed and extent of the recovery "bounce" across individual provinces based on location, economic activity, and restriction policy. These suggest that the most significant and rapid declines in traffic volume during the restriction period resulted in the most pronounced returns to normal (or more) demand levels. Based on these trends a Susceptible Infection Recovery model was created to simulate a range of outbreak and restriction policies to examine the relationship between COVID-19 spread and traffic volume in China.

Throughout the COVID-19 pandemic, public transport has been one of the hardest hit transport modes, losing ridership due to fear of contagion. This can partially be explained by the lack of preparedness in the sector to a pandemic scenario, as only few cities had epidemic contingency plans for the transport sector. To anticipate disruptions caused by future crises, we look at the preparedness and the response to COVID-19 by the public transport sector in Belgium. We interview all public transport operators in Belgium and analyze the interviews through the disaster management framework. We also aim to distill the lessons that can be learned from the pandemic to increase resilience in future public transport planning. We find that no operator in Belgium had contingency plans ready for a pandemic scenario, but that other plans were deployed to adapt their offer to COVID-19 conditions. Although all operators lost a significant part of ridership, their offer was maintained throughout the crisis, albeit at a decreased level for some operators. The availability of reliable and real-time data is identified as an important learning by the operators, as well as the ability to identify a core response team in case of a crisis. COVID-19 was seen by the operators as a learning platform to face future crises and highlighted the need to increase reactivity through better preparedness and data availability. We recommend the structural use of foresight methods through for example scenario planning to increase the preparedness of operators in the case of future disruptions.

Consumer reactions to COVID-19 pandemic disruptions have been varied, including modifications in spending frequency, amount, product categories and delivery channels. This study analyzes spending data from a sample of 720 U.S. households during the start of deconfinement and early vaccine rollout to understand changes in spending and behavior one year into the pandemic. This paper finds that overall spending is similar to pre-pandemic levels, except for a 28% decline in prepared food spending. More educated and higher income households with children have shifted away from in-person spending, whereas politically conservative respondents are more likely to shop in-person and via pickup.

Daily commuting characteristics were highly affected by the COVID-19 pandemic, since restriction of the movement of people was one of the main preventive measures adopted. Understanding of the effects that the pandemic had on mobility is essential to help in mitigating the problems arising from this crisis, while also providing an opportunity for the implementation of sustainable policies in the post-pandemic period. Therefore, the aim of this study was to identify the impacts of the pandemic on the profile of travel behavior and mobility preferences in Brazil, using a case study of cities located in the state of Rio Grande do Sul. The data obtained from an online survey were modeled using exploratory factor analysis, resulting in the extraction of 15 main factors that explain behavioral changes in mobility due to the effects of the pandemic, as well as future perspectives. In the pandemic period, the use of private vehicles grew as the main mode of transport to the principal activity. Conversely, the use of public transport decreased drastically, due to compulsory measures taken by the health authorities to prevent the spread of the new virus. There was also greater receptivity to the adoption of active mobility, especially the bicycle, although it is necessary to provide better conditions for use of this transport mode. The findings support the development of public policies to reduce urban mobility problems and to provide guidelines for sustainable planning in the post-pandemic period.

After successfully inhibiting the first wave of COVID-19 transmission through a city lockdown, Wuhan implemented a series of policies to gradually lift restrictions and restore daily activities. Existing studies mainly focus on the intercity recovery under a macroscopic view. How does the intracity mobility return to normal? Is the recovery process consistent among different subareas, and what factor affects the post-pandemic recovery? To answer these questions, we sorted out policies adopted during the Wuhan resumption, and collected the long-time mobility big data in 1105 traffic analysis zones (TAZs) to construct an observation matrix (). We then used the nonnegative matrix factorization (NMF) method to approximate as the product of two condensed matrices (). The column vectors of matrix were visualized as five typical recovery curves to reveal the temporal change. The row vectors of matrix were visualized to identify the spatial distribution of each recovery type, and were analyzed with variables of population, GDP, land use, and key facility to explain the recovery driving mechanisms. We found that the "staggered time" policies implemented in Wuhan effectively staggered the peak mobility of several recovery types ("staggered peak"). Besides, different TAZs had heterogeneous response intensities to these policies ("staggered area") which were closely related to land uses and key facilities. The creative policies taken by Wuhan highlight the wisdom of public health crisis management, and could provide an empirical reference for the adjustment of post-pandemic intervention measures in other cities.

The COVID-19 pandemic has affected people's lives throughout the world. Governments have imposed restrictions on business and social activities to reduce the spread of the virus. In the US, the pandemic response has been largely left to state and local governments, resulting in a patchwork of policies that frequently changed. We examine travel behavior across income and race/ethnic groups in Los Angeles County over several stages of the pandemic. We use a difference-in-difference model based on mobile device data to compare mobility patterns before and during the various stages of the pandemic. We find a strong relationship between income/ethnicity and mobility. Residents of low-income and ethnic minority neighborhoods reduced travel less than residents of middle- and high-income neighborhoods during the shelter-in-place order, consistent with having to travel for work or other essential purposes. As public health rules were relaxed and COVID vaccines became available, residents of high-income and White neighborhoods increased travel more than other groups, suggesting more discretionary travel. Our trip purpose model results show that residents of low-income and ethnic minority neighborhoods reduced work and shopping travel less than those of White and high-income neighborhoods during the shelter-in-place order. Results are consistent with higher-income workers more likely being able to work at home than lower-income workers. In contrast, low-income/minorities apparently have more constraints associated with work or household care. The consequence is less capacity to avoid virus risk. Race and socioeconomic disparities are revealed in mobility patterns observed during the COVID-19 pandemic.

High-speed railways (HSRs) greatly decrease transportation costs and facilitate the movement of goods, services, and passengers across cities. In the context of the Covid-19 pandemic, however, HSRs may contribute to the cross-regional spread of the new coronavirus. This paper evaluates the role of HSRs in spreading Covid-19 from Wuhan to other Chinese cities. We use train frequencies in 1971 and 1990 as instrumental variables. Empirical results from gravity models demonstrate that one more HSR train originating from Wuhan each day before the Wuhan lockdown increases the cumulative number of Covid-19 cases in a city by about 10 percent. The empirical analysis suggests that other transportation modes, including normal-speed trains and airline flights, also contribute to the spread of Covid-19, but their effects are smaller than the effect of HSRs. This paper's findings indicate that transportation infrastructures, especially HSR trains originating from a city where a pandemic broke out, can be important factors promoting the spread of an infectious disease.

Since the beginning of the COVID-19 pandemic, authorities around the world explored ways to slowdown the spread of the disease while maintaining the physical and mental health of individuals. They redistributed the street space to promote physical activity and non-motorized travel while meeting the social distancing requirements. Although the statistics showed significant increases in walking and bicycling trips during the pandemic, we have limited knowledge about the associations between built environment characteristics, COVID-19 infection risk perception while traveling, and subjective well-being. This study assesses the impacts of the built environment on subjective well-being and infection risk perception while traveling during the pandemic. It uses data collected from the residents of Columbus, Ohio, through a multi-wave survey conducted at different time points during the COVID-19 outbreak. By employing a structural equation modeling approach, it explores the associations between residential neighborhood characteristics, individuals' subjective well-being, and perceived infection risk while using non-motorized modes and shared micromobility. The findings show that those living in more compact, accessible, and walkable neighborhoods are less likely to perceive active travel and shared micromobility as risky in terms of COVID-19 infection. Our results also show that built environment characteristics have an indirect positive effect on the subjective well-being of individuals. The findings of our study demonstrate that built environment interventions can help promote physical activity and support mental health of individuals at this critical time. Our study also indicates that designing compact neighborhoods will be a crucial element of pandemic resilient cities in the post-COVID-19 era.

The COVID-19 pandemic and the consequent travel restrictions have had an unprecedented impact on the air travel market. However, a rigorous analysis of the potential role of safety perceptions and attitudes towards COVID-19 interventions on future air passenger choices has been lacking to date. To investigate this matter, 1469 individuals were interviewed between April and September 2020 in four multi-airport cities (London, New York City, Sao Paulo, Shanghai). The core analysis draws upon data from a set of stated preference (SP) experiments in which respondents were asked to reflect on a hypothetical air travel journey taking place when travel restrictions are lifted but there is still a risk of infection. The hybrid choice model results show that alongside traditional attributes, such as fare, duration and transfer, attitudinal and safety perception factors matter to air passengers when making future air travel choices. The cross-national analysis points towards differences in responses across the cities to stem from culturally-driven attitudes towards interpersonal distance and personal space. We also report the willingness to pay for travel attributes under the expected future conditions and discuss post-pandemic implications for the air travel sector, including video-conferencing as a substitute for air travel.

Life, including working style and travel behaviour, has been severely disrupted by the COVID-19 pandemic. The unprecedented number of work-from-home (WFH) employees after the outbreak of COVID-19 has attracted much scholarly attention. As it is generally believed that WFH arrangements are not ephemeral, it is imperative to study the impacts of WFH on travel behaviour and its impact on sustainable transport in the post-pandemic era. In relation, this study uses a set of longitudinal GPS tracking data in Switzerland to examine changes in trip characteristics (i.e. travel distance, travel time), travel behaviours (i.e. travel frequency, peak hour departure, trip destination, travel mode), and activities (i.e. trip pattern diversity, trip purpose, and time spent at home). Two groups of participants (WFH and Non-WFH) are identified and compared through three periods (pre-COVID, during lockdown, and post lockdown) from September 2019 to October 2020. Results show that more significant reductions of trip distance, travel time, travel frequency, morning peak hours trips, trips to the CBD are observed among the WFH group. These changes helped to mitigate negative transport externalities. Meanwhile, active transport trips, trip pattern diversity, leisure trips, and time spent at home also increased more significantly for the WFH group when compared to their counterparts. Hence, promoting WFH may not only be beneficial to teleworkers but also to the wider community through more sustainable transport. Future research direction and policy implications are also discussed.

The need to understand the influence of the COVID-19 pandemic on the long-term travel behaviour of people has never been higher as a consequence of the second wave of pandemic. In this context, the current study aims to understand the willingness of people to use sustainable modes of transportation including shared modes of transport, and non-motorized transport, against non- shared modes of transport such as personal 2-wheelers and 4-wheelers in a post-vaccinated scenario. The study further models the willingness to choose public transportation under various COVID-19 preventive measures representing the perception of safety among people. An Integrated Choice and Latent Variable (ICLV) framework a employed in the modelling. The fear of contracting COVID-19 and the belief in remedial measures significantly influenced the mode choice of individuals. This highlighted a significant long-term impact of the pandemic on the travel behaviour of individuals. The study concludes by presenting different strategies that could be adopted to make the existing sustainable modes safer, and hence, more attractive.

This paper presents results of a unique stated choice (SC) experiment to uncover the determinants of grocery shopping channel choice during the first wave of COVID-19 infections, where the most restrictive containment measures were in place. The choice sets were framed under regular and pandemic conditions, allowing for the estimation of pandemic-specific effects for each of the choice attributes. Our results show a significant overall increase of about 13%-points in online grocery shopping under pandemic conditions. Shopping and delivery costs were found to be the major decision drivers in both experimental settings, while the waiting time in front of the grocery store and risk of infection only played an secondary role. The value of delivery time savings (VDTS) decreases from about 10.8 CHF/day in the regular to 7.4 CHF/day in the pandemic case, indicating that respondents show an increased patience when waiting for the delivery of the ordered groceries. However, choice attributes related to the shopping trip, i.e. travel time and cost, do not show any notable effects. The COVID-19 death risk was valued rather low by the respondents and the relatively unrestricted Swiss containment measures are in line with the respondents' average preferences, as shown by a relatively low value of statistical life (VSL) of about 800,000 CHF.