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 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.

The COVID-19 pandemic has disrupted day-to-day lives and infrastructure across the United States, including public transit systems, which saw precipitous declines in ridership beginning in March 2020. This study aimed to explore the disparities in ridership decline across census tracts in Austin, TX and whether demographic and spatial characteristics exist that are related to these declines. Transit ridership data from the Capital Metropolitan Transportation Authority were used in conjunction with American Community Survey data to understand the spatial distribution of ridership changes caused by the pandemic. Using a multivariate clustering analysis as well as geographically weighted regression models, the analysis indicated that areas of the city with older populations as well as higher percentages of Black and Hispanic populations were associated with less severe declines in ridership, whereas areas with higher unemployment saw steeper declines. The percentage of Hispanic residents appeared to affect ridership most clearly in the center of Austin. These findings support and expand on previous research that found that the impacts of the pandemic on transit ridership have emphasized the disparities in transit usage and dependence across the United States and within cities.

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.

This research examines the impacts of COVID-19 on the mobility of resource-poor women and its linkage with livelihoods in urban South Asia, and how gender-responsive transport measures could be adopted. The study, conducted in Delhi between October 2020 and May 2021, used a mixed methods, multi-stakeholder, and reflexive approach. A literature review was conducted on the gender and mobility context in Delhi, India. Quantitative data were collected through surveys with resource-poor women, while qualitative research methods consisted of in-depth interviews with them. Different stakeholders were engaged through round tables and key informant interviews before and after data collection to share the findings and recommendations. The sample survey (n = 800) revealed that only 1.8% of working resource-poor women have access to a personal vehicle, making them dependent on public transport. While 81% of their trips are by bus, 57% of their peak hour trips are by paratransit, despite free travel on buses. Only 10% of the sample have access to a smart phone, which restricts their access to digital initiatives based on smart phone applications. The women expressed concerns such as poor bus frequencies and buses not stopping for them under the free ride scheme. These were consistent with issues faced before the COVID-19 pandemic. These findings highlight the need for targeted strategies for resource-poor women to achieve equity in gender-responsive transport. These include a multimodal subsidy, short messaging service to obtain real-time information, increased awareness on filing complaints, and an effective grievance redressal system.

The paper reports evidence related to peoples\' perceptions and attitude during the early stages of COVID-19 related lockdown in India in four major aspects: strategies and preventive measures, long-distance travel, essential services, and post-lockdown travel. A five-stage survey instrument was designed and circulated through various online modes to make it convenient to the respondents and also to achieve a greater geographical coverage within a short period of time. The survey responses were analyzed using statistical tools and the results are translated into potential policy recommendations which may be useful to implement effective interventions during future pandemics of similar nature. The findings highlight a high rate of awareness among the people about the COVID-19, lack of supply of protective equipment such as masks, gloves, and personal protective equipment kits during the early stage of lockdown in India. However, several heterogeneities were also observed across a few socio-economic groups which emphasize the need for targeted campaigns in a diverse country such as India. The findings also suggest the need for arranging safe and hygienic long-distance trips for a section of the society when such lockdown measures are extended for long periods. The observations related to mode choice preferences during the post-lockdown recovery period indicate a potential shift of public transport patronage to the personal modes.

The COVID-19 pandemic has had far-reaching impacts on public health and safety, economics, and the transportation system. To reduce the spread of this disease, federal and local governments around the world have introduced stay-at-home orders and other restrictions on travel to \"non-essential\" businesses to implement social distancing. Preliminary evidence suggests substantial variability in the impacts of these orders in the United States, both across states and over time. This study examines this issue using daily county-level vehicle miles traveled (VMT) data for the 48 continental U.S. states and the District of Columbia. A two-way random effects model is estimated to assess changes in VMT from March 1 to June 30, 2020 as compared with baseline January travel levels. The implementation of stay-at-home orders was associated with a 56.4 percent reduction in VMT on average. However, this effect was shown to dissipate over time, which may be attributable to \"quarantine fatigue.\" In the absence of full shelter-in-place orders, travel was also reduced where restrictions on select businesses were introduced. For example, restrictions on entertainment, indoor dining, and indoor recreational activities corresponded to reductions in VMT of 3 to 4 percent while restrictions on retail and personal care facilities showed 13 percent lower traffic levels. VMT was also shown to vary based on the number of COVID case reports, as well as with respect to other characteristics, including median household income, political leanings, and how rural the county was in nature.

In the absence of a vaccine, nonpharmaceutical interventions such as social distancing and travel reductions were the only strategies for slowing the spread of the COVID-19 pandemic. Using survey data from Hawaii (n = 22,200) collected in March through May of 2020 at the onset of the pandemic, the differences between traveler spreaders who brought the disease into the state and community spreaders were investigated. In addition to describing the demographic attributes and comparing them with attributes of those who were vulnerable to COVID-19, logit models explaining travel behaviors were developed and tested. Traveler spreaders were likely to be male, younger, and returning students. Community spreaders were more likely to be male, essential workers, first responders, and medical personnel at the highest risk of exposure. Using spatial statistics, clusters and hotspot locations of high-risk individuals were mapped. As transportation researchers are in a position to combine their critical analytical capabilities and experience with relevant databases on mobility and the spread of infectious diseases, this analysis could support efforts to respond to and slow the spread of the pandemic.

To prevent the pandemic spread of human-to-human transmitted diseases such as COVID-19, governments commonly resort to countrywide or regional lockdown strategies. Such lockdowns, whenever and wherever implemented, curtail the movement of persons and vehicles, and drastically alter traffic conditions. This study focuses on the effect of drastic and sudden changes in the traffic conditions, during the COVID-19 lockdown in the State of Maharashtra in India, in March-June 2020, on the numbers of motor vehicle accidents (MVAs), and the resultant fatalities and injuries. Content analysis of police-reported first information reports (FIRs) of MVAs is performed, and these lockdown trends are compared with archival data from corresponding previous (normal) periods. The statistical analysis shows that, during the lockdown, while the total number of MVAs fall drastically, they are more severe and have a much higher fatality rate per MVA. Also, the pattern of vehicles involved in MVAs, and resultant pattern of fatalities, also changes during lockdowns. The paper explores the reasons for these changed patterns and provides suggestions to reduce these negative externalities of pandemic related lockdowns.

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the \"protection factor\"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.