The gateway in-street sign configuration has been demonstrated to be a low-cost method for increasing motorist yielding the right of way to pedestrians at crosswalks. It has previously been hypothesized that the gateway is effective because it visually narrows a travel lane. In the present study, gateway widths (i.e., distance between signs) were compared to determine whether there was a differential effect on motorist yielding. Experiment 1 was a parametric analysis of distance between the signs, varying in 2-ft intervals from 12 to 18 ft. The results showed that the percentage of motorists yielding increased as the distance between the signs decreased. Experiment 2 examined curb-top and gutter-pan placements of the edge signs at three different sites. Both placements produced substantial increases in yielding compared with baseline, though the difference between gutter-pan and curb-top placement was not significant at two of the three sites. Based on the distance between signs in these two configurations, the results at two of the sites aligned with those in Experiment 1, and one site demonstrated much higher yielding than would have been predicted. This suggests that small increases in the distance between signs may result in a minor decrease in yielding but may improve the survivability of the signs and reduce maintenance costs over time. The potential to combine this sign effect with other engineering treatments (e.g., curb extensions and bicycle lanes) was additionally explored. The results are discussed in relation to a perceived narrowing hypothesis, sign survival, cost effectiveness, and equity.

This study aimed to apply a journey mapping methodology to identify travel considerations and barriers for people with disabilities (PWDs) at each travel stage, from considering a trip through to arriving at the destination for their current modes of transportation, with the objective of understanding and avoiding "pain points" during a transition to autonomous driving systems. Twenty PWDs, including those with physical, visual, aural, cognitive, and combined physical/visual impairments, participated in a semistructured one-on-one interview. Descriptive statistics were used for demographic information, and qualitative content analysis was used to analyze the transcribed interviews and extract themes. Themes were further organized by the modes of transportation used. The top four themes in considering and planning a trip were third-party assistance availability (private vehicle, public transportation, and paratransit), finding an accessible or suitable parking space (private vehicle), access to a service location (public transportation and paratransit), and transportation schedules (public transportation and paratransit). The top four travel barriers to locating, entering, riding, and exiting transportation and arriving at the destination were vehicle ingress/egress (private vehicle and public transportation), concerns about wheelchair securement (public transportation and paratransit), requiring third-party assistance (private vehicle and public transportation), and accessibility to service locations (public transportation). The study suggests that to mitigate travel considerations and barriers for PWDs, vehicle-specific barriers and infrastructure issues should be addressed simultaneously. We anticipate that the findings will provide insights into the design and development of autonomous vehicles, to better accommodate the needs of PWDs.

The COVID-19 pandemic had a significant impact on travel mode choices in cities across the world. Driven by perceptions of risk and the fear of infection, the pandemic resulted in an increased preference for private vehicles and active modes and a reduced preference for public transit and ride-sourcing. As travel behavior and modal preferences evolve, a key question is whether the pandemic will result in long-term changes to travel mode choices. This study uses data from a web-based survey to examine the factors influencing mode choices for non-commuting trips in the post-pandemic era. Specifically, it uses stated preference data to develop a random parameter mixed logit model, which is used to compare the elasticity of key variables across different income and age groups. The results of the study highlight the influence of sociodemographic attributes and pre-pandemic travel habits on anticipated post-pandemic mode choices. Additionally, the results suggest that frequent users of private vehicles, public transit, and active modes are likely to continue to use these modes post-pandemic. Furthermore, the results highlight the potential for the perception of shared modes to influence post-pandemic mode choice decisions. The results of the study offer insights into policy measures that could be applied to address the increased use of private vehicles and reduced use of transit during the pandemic, while also emphasizing the need to ensure that certain segments of the population can maintain a sufficient level of mobility and access to transport.

The COVID-19 pandemic has had serious adverse impacts on public transport service providers. Most public transport lines exhibit reduced ridership levels while, at the same time, some of them may exhibit passenger demand levels beyond the pandemic-imposed capacity limitations. This study models the problem of bus dispatching time optimization within a periodic rolling horizon optimization framework that reacts to travel time and passenger demand variations. This model allows public transport service providers to adjust their bus schedules periodically to avoid in-vehicle crowding beyond the pandemic-imposed capacity limit. The proposed model is a mixed-integer linear program that considers the possible changes to vehicle schedules and tries to minimize the number of vehicles required to perform the service while adhering to the COVID-19 capacity restrictions. Case study results from the implementation of our model on bus Line 2 in the Twente region in the Netherlands are provided demonstrating the potential gains when rescheduling the trip dispatching times and vehicle schedules.

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown-including on the transport sector-to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra- and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making.

Bikesharing is a popular transportation mode for people to commute, for leisurely travel, or for recreation purposes in their daily tasks. Throughout 2020, the COVID-19 pandemic had significant impacts on bikeshare usage in the United States. Previous studies show that the pandemic negatively affected bikeshare activity patterns. To examine the effects of the pandemic on bikeshare behavior across membership types, this study investigated trip volume- and trip duration patterns of both members and nonmembers of five bikeshare systems across the United States. The results showed that member ridership significantly decreased throughout the pandemic, but nonmember ridership tended to be stable. It was also found that trip durations increased across both groups throughout the pandemic. Additionally, inferences were made to determine the level of support for a reversion to prepandemic normality as the pandemic progressed and reopening occurred in phases. The findings from this study could benefit bikeshare agencies in developing postpandemic recovery strategies.

This paper presents a study in capturing the impacts of the mandatory pandemic-induced telework practice on workers' perceptions of the benefits, challenges, and difficulties associated with telecommuting and how those might influence their preference for telework in the future. Data was collected through an online survey conducted in South Florida in May 2020. Survey data showed that telework indices (either measured through actual behavior or stated preference) before, during, and after the pandemic were heterogeneous across socio-economic, demographic, and attitudinal segments. Before the outbreak, males, full-time students, those with PhD degrees, and high-income people showed higher percentages of involvement in jobs with a telework option. They also had higher pro-technology, pro-online education, workaholic, and pro-telework attitudes. During the pandemic, professional/managerial/technical jobs as well as jobs with lower physical-proximity measures showed the highest telework frequency. In view of future telework preferences, our analysis showed that those who were more pro-telework, pro-technology, and showed less dislike of telework dislike preferred higher telework frequency. A structural equation model was developed to assess the impacts of different predictors on telework behavior before the pandemic and preferences after the pandemic. While telework frequency before the pandemic was highly affected by the pro-telework attitude, the after-pandemic preferences were influenced by several other attitudes such as dislike telework, enjoy interaction, workaholic, as well as productivity factors. This might confirm the assumption that the mandatory practice through the pandemic has provided employees more experiences with work-from-home arrangements, which could reshape decisions and expectations around telework adoption in the future.

The COVID-19 pandemic had a dramatic impact on aviation in 2020, and the industry's future is uncertain. In this paper, we consider scenarios for recovery and ongoing demand, and discuss the implications of these scenarios for aviation emissions-related policy, including the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) and the EU Emissions Trading Scheme (ETS). Using the Aviation Integrated Model (AIM2015), a global aviation systems model, we project how long-term demand, fleet, and emissions projections might change. Depending on recovery scenario, we project cumulative aviation fuel use to 2050 might be up to 9% below that in scenarios not including the pandemic. The majority of this difference arises from reductions in relative global income levels. Around 40% of modeled scenarios project no offset requirement in either the CORSIA pilot or first phases; however, because of its more stringent emissions baseline (based on reductions from year 2004-2006 CO, rather than constant year-2019 CO), the EU ETS is likely to be less affected. However, if no new policies are applied and technology developments follow historical trends, year-2050 global net aviation CO is still likely to be well above industry goals, including the goal of carbon-neutral growth from 2019, even when the demand effects of the pandemic are accounted for.

COVID-19 has radically transformed urban travel behavior throughout the world. Agencies have had to provide adequate service while navigating a rapidly changing environment with reduced revenue. As COVID-19-related restrictions are lifted, transit agencies are concerned about their ability to adapt to changes in ridership behavior and public transit usage. To aid their becoming more adaptive to sudden or persistent shifts in ridership, we addressed three questions: To what degree has COVID-19 affected fixed-line public transit ridership and what is the relationship between reduced demand and -vehicle trips? How has COVID-19 changed ridership patterns and are they expected to persist after restrictions are lifted? Are there disparities in ridership changes across socioeconomic groups and mobility-impaired riders? Focusing on Nashville and Chattanooga, TN, ridership demand and vehicle trips were compared with anonymized mobile location data to study the relationship between mobility patterns and transit usage. Correlation analysis and multiple linear regression were used to investigate the relationship between socioeconomic indicators and changes in transit ridership, and an analysis of changes in paratransit demand before and during COVID-19. Ridership initially dropped by 66% and 65% over the first month of the pandemic for Nashville and Chattanooga, respectively. Cellular mobility patterns in Chattanooga indicated that foot traffic recovered to a greater degree than transit ridership between mid-April and the last week in June, 2020. Education-level had a statistically significant impact on changes in fixed-line bus transit, and the distribution of changes in demand for paratransit services were similar to those of fixed-line bus transit.

The COVID-19 pandemic outbreak brought significant changes in the travel behavior and operational characteristics of transportation systems. Express lanes (ELs) are among the transportation facilities that are affected by this pandemic. These facilities are built adjacent to existing general-purpose lanes (GPLs), providing drivers additional lanes that are dynamically priced in response to changing traffic conditions. This research investigated the impacts of COVID-19 on the operational performance of ELs and GPLs based on field data from a 5.5 mi corridor on I-95 in Miami, Florida, U.S. The traffic flow parameters, which include speed, traffic flow, and occupancy, were used to describe the traffic conditions before and during COVID-19 (i.e., March-June 2019 and March-June 2020, respectively). The travel time reliability measures, coefficient of variation of travel time, and planning time index, were used to measure user satisfaction. These metrics were derived from a multivariate Bayesian additive regression model that was developed to calibrate the traffic conditions on the study corridor. Overall, the model results indicated that both ELs and GPLs have less variation in travel time, thus making the travel time more reliable during COVID-19 than before. This may be attributed to the decline in the traffic volume observed during the pandemic. The results further showed that COVID-19 had more impact on the GPLs compared with the ELs. The results from this research could assist transportation agencies in understanding the impacts of the COVID-19 pandemic on ELs and GPLs in relation to traffic operations.

The pandemic arising from the 2019 coronavirus disease has significantly affected all facets of human life across the world, including economies and transportation systems, thereby changing people's travel behaviors. This research was aimed at exploring the relationship between socio-economic factors and e-scooter trip durations before and during the pandemic. We developed a hazard-based duration approach and estimated multiple spatial and non-spatial models on the basis of 2019 and 2020 dockless e-scooter data collected from the City of Austin's Open Data Portal. The results indicated an overall increase in e-scooter trip durations after the pandemic. Moreover, analysis of variables revealed potential changes in users' behavior before and during the pandemic. In particular, whereas e-scooter trip durations were found to be positively associated with aggregate travel time to work before the pandemic, this trend was reversed during the pandemic. In addition, during the pandemic, e-scooter travel time was positively correlated with the ratio of individuals with bachelor's degrees or greater to those with associate degrees or lower. However, no specific pattern was observed before the pandemic. Lastly, the results showed the presence of disparities within the study area; therefore, it is vital to extend e-scooter service areas to cover underserved communities.

The COVID-19 pandemic increased the risk of financial distress, bankruptcy, or both, in the airline industry. Whether airlines can survive or not during and/or after the pandemic is closely related to their decisions and actions which will enable their success by increasing their resilience. In crisis periods such as COVID-19, the decisions taken by airlines are strategically important for achieving sustainable success. Thus, it is critical to understand which factors are more important for airlines to shape their actions and make correct decisions. This paper investigates the sustainable success factors on which airlines should focus to provide resilience during the COVID-19 pandemic crisis. It provides a robust model using the interval type-2 fuzzy analytic hierarchy process (IT2FAHP) and interval type-2 fuzzy Decision Making Trial and Evaluation Laboratory (IT2FDEMATEL) to identify and rank success factors. The findings indicate that financial and operational factors are extremely important to ensure resilience for airlines. In addition, the results of the study reveal that operational factors and information sharing factors have an impact on financial factors and customer satisfaction.

The objective of this research was to understand key levers that enabled city, regional, and national governments to improve non-motorized transport (NMT) infrastructure during the lockdowns necessitated by the COVID-19 pandemic. The research focused primarily on cycling and adopted a case study approach focusing on three cities: Bengaluru (India), Bogota (Colombia), and London (UK). The selected cities were chosen for diversity across geographies, country income levels, and the scale of interventions. Eight key levers were identified to understand how cycling interventions can be supported, implemented, sustained, and scaled up. These included institutional and organizational arrangements; technical capacity; financing; leadership; policy and regulatory framework; plans, strategies, and technical resources; role of civil society; and communications, messaging, and outreach. The research used secondary literature reviews and key informant interviews, which were validated through an online round table. Research revealed that certain levers were necessary in initiating and continuing successful NMT interventions. These included supportive leadership, participative civil society, and adequate financial and technical capacity. Communications and outreach helped bring behavioral change amongst residents while a coordinated institutional framework and plans and strategies were necessary to sustain momentum. This research contributes to urban mobility and public administration literature in understanding processes and enablers of sustainable mobility interventions. It is relevant for cities in low- and middle-income countries beginning to focus on NMT interventions to combat climate change and public health challenges.

The global COVID-19 pandemic has given rise to a plethora of ideas for modifying and redesigning public transportation and shared mobility vehicles to protect workers and riders from contracting the disease while traveling. This research seeks to inventory these strategies, and to organize and distill them in a way that enables researchers, policymakers, and public transport and mobility service operators to more systematically and efficiently evaluate them. Through literature search and analysis, the COVID-19 risk-mitigating vehicle design (CRVD) typology was developed, articulating 12 categories of strategies (e.g., Seating Configuration, Barriers) and 12 mechanisms (e.g., physical distancing, physical separation) by which the strategies may reduce COVID-19 spread. A secondary contribution of this research is to gather opinions of experts in fields related to COVID-19 and its transmission, about the identified CRVD strategies and mitigation mechanisms. The typology and expert opinions serve as a launching point for further innovation and research to evaluate the effectiveness of CRVD strategies and their relationship to user preferences and travel behavior, within and beyond the current context. Public transport and shared mobility service operators can use the CRVD typology as a reference, in conjunction with industry guidance and emerging research on strategy effectiveness, to aid decision-making in their continued response to the pandemic as well as for future planning.

The spread of COVID-19 has been a major disruptive force in people's everyday lives and mobility behavior. The demand for on-demand ride services, such as taxis and ridehailing, has been specifically affected given both restrictions in service operations and users' concerns about virus transmission in shared vehicles. In the early months of the pandemic, demand for these modes decreased by as much as 80%. This study examines intentions to use on-demand ride services in the early lockdown stage of the pandemic in the United States, a period of unprecedented mobility reductions, changing household routines and transforming travel behaviors. Using data from a survey disseminated in June 2020 to 700 U.S. respondents, we use multigroup MIMIC (Multiple Indicator Multiple Cause) models to investigate the stated shift in intentions to use on-demand modes of travel. By using group-based segmentation we control for variation in ridership intentions according to personal, household, attitudinal factors, and pandemic experiences. The results point to a reduction across the board in the likelihood of using on-demand mobility associated with a significant COVID-19 effect. Beyond this general decrease, several groups are found to have more positive intentions, including younger adults, urban residents, graduate-degree holders, and people of Hispanic, Latino, Asian, and Pacific Islander ethnicities/races. The attitudinal effect of "tech-savviness" drives higher user intentions, revealing indirect effects of gender, education, and age. Multigroup analysis provides further evidence of potential COVID-triggered shifts in on-demand ridership intentions. The most significant drops in likelihood are observed for younger respondents (below 45), Black compared with all other racial/ethnic status, and for past users of on-demand mobility. This latter result is somewhat surprising, as riders who are younger and more experienced with on-demand travel are more likely to have been users in the past, but also more likely to reduce use during the pandemic. To conclude, we discuss the need to investigate pandemic experiences, risk attitudes, and circumstances to understand evolving mobility behavior and specific service model impacts.

More than a year after COVID-19 was declared a pandemic by the World Health Organization, the U.S.A. and Mexico rank first and fourth, respectively, with regard to the number of deaths. From March 2020, nonessential travelers were not allowed to cross the border into the U.S.A. from Mexico via international land ports of entry, which resulted in a more than 50% decrease in the number of people crossing the border. However, border communities still face a higher number of cases and faster community spread compared with those without international land ports of entry. This paper established an econometric model to understand the effects of cross-border mobility and other socioeconomic parameters on the speed of spread. The model was developed at the U.S. county level using data from all 3,141 counties in the U.S.A. Additionally, a follow-up U.S. county comparative analysis was developed to examine the significance of having a border crossing between the U.S.A. and Mexico for U.S. counties. The findings of the analysis revealed that the variables having a significant effect are as follows: population density; number of people per household; population in the 15-65 age group; median household income; mask use; number of visits to transit stations; number of visits to workplace; overall mobility; and having a border crossing to Mexico within county limits. The comparative analysis found that U.S. counties with border crossings have an average of 123 cases per 1,000 population whereas their counterparts without border crossings only have 90 cases per 1,000 population.

The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis-initial post-disruption mobility index, changepoint on restoration of a "new normal," and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes.

This paper analyzes and compares patterns of U.S. domestic rail freight volumes during and after the disruptions caused by the 2007-2009 Great Recession and the COVID-19 pandemic in 2020. Trends in rail and intermodal (IM) shipment data are examined in conjunction with economic indicators, focusing on the extent of drop and recovery of freight volumes of various commodities and IM shipments, and the lead/lag time with respect to economic drivers. Impacts of the Great Recession and the rebound from it were slow to develop, whereas COVID-19 produced both profound disruptions in the freight market and rapid rebound, with important variations across commodity types. Demand for energy-related commodities (coal, petroleum, and fracking sand) dropped during the pandemic whereas demand for other commodities (grain products and lumber, and IM freight) rebounded rapidly and in some cases grew. Overall, rail freight experienced a rapid rebound following the precipitous drop in traffic in March and April, 2020, achieving a near-full recovery in 5 months. As the recovery proceeded through 2020, IM flow, containers moving by rail for their longest overland trips, rebounded strongly, some exceeding 2019 levels. In contrast, rail flows during the Great Recession changed slowly with the onset and recovery extending over multiple years. Pandemic response reflected the impacts of quick shutdowns and a rapid shift in consumer purchasing patterns. Results for the pandemic illustrate the resilience of the U.S. rail freight industry and the multifaceted role it plays in the overall logistics system. Amid a challenging logistic environment, freight rail kept goods moving when other methods of transport were constrained.

This article assesses the impact of the COVID-19 outbreak on the urban motorcycle taxi (MCT) sector in Sub-Saharan Africa (SSA). MCT operators in SSA provide essential transport services and have shown ingenuity and an ability to adapt and innovate when responding to different challenges, including health challenges. However, policymakers and regulators often remain somewhat hostile toward the sector. The article discusses the measures and restrictions put in place to reduce the spread of COVID-19 and key stakeholders' perspectives on these and on the sector's level of compliance. Primary data were collected in six SSA countries during the last quarter of 2020. Between 10 and 15 qualitative interviews with key stakeholders relevant to the urban MCT sector were conducted in each country. These interviews were conducted with stakeholders based in the capital city and a secondary city, to ensure a geographically broader understanding of the measures, restrictions, and perspectives. The impact of COVID-19 measures on the MCT and motor-tricycle taxi sector was significant and overwhelmingly negative. Lockdowns, restrictions on the maximum number of passengers allowed to be carried at once, and more generally, a COVID-19-induced reduction in demand, resulted in a drop in income for operators, according to the key stakeholders. However, some key stakeholders indicated an increase in MCT activity and income because of the motorcycles' ability to bypass police and army controls. In most study countries measures were formulated in a non-consultative manner. This, we argue, is symptomatic of governments' unwillingness to seriously engage with the sector.

The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents' daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study's findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents' daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control.

In this study, we used survey data ( = 6,000) to investigate the work trip patterns of Scottish residents at various points of the COVID-19 pandemic. We focused specifically on the reported patterns of weekly work trips made during the government-enforced lockdown and subsequent phases of restriction easing. This was of particular importance given the widespread changes in work trips prompted by COVID-19, including a significant rise in telecommuting and a reduction in public transport commuting trips. The survey data showed that the vast majority of respondents (∼85%) made no work trips during lockdown, dropping to ∼77% following the easing of some work-related restrictions. Zero-inflated hierarchical ordered probit models were estimated to determine the sociodemographic and behavioral factors affecting the frequency of work trips made during three distinct periods. The model estimation results showed that the socioeconomic characteristics of respondents influenced work trips made throughout the pandemic. In particular, respondents in households whose main income earner was employed in a managerial/professional occupation were significantly more likely to make no work trips at all stages of the pandemic. Those with a health problem or disability were also significantly more likely to make no work trips throughout the pandemic. Other interesting findings concern respondents' gender, as males were more likely to complete frequent work trips than females throughout the pandemic, and differences between densely populated areas and the rest of Scotland, as respondents from a large city (Edinburgh or Glasgow) were significantly more likely to make frequent work trips as restrictions were eased.