Veterans living in rural areas of the United States face various health challenges that demand timely access to care to improve their well-being and quality of life. Telehealth (i.e., the use of telecommunications technology to connect people with care providers remotely) has become vital in addressing the accessibility gap for people constrained by vehicle ownership, income, geographic isolation, and limited access to specialists. This study aims to examine the current evidence on rural veterans’ use of telehealth for their healthcare needs, evaluates the cost savings associated with telehealth, as well as veterans’ use of telehealth during COVID-19. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted on three databases (Google Scholar, PubMed, and Scopus) to select relevant articles published from 2017 to 2023. A total of 36 articles met the inclusion criteria and were categorized into three objectives: veterans’ medical conditions managed through telehealth (n = 24), veterans’ transportation cost savings using telehealth (n = 4), and telehealth use during the COVID-19 pandemic (n = 8). The results indicated that telehealth is a viable option for managing various medical conditions of rural veterans, including complex ones like diabetes and cancer. Additionally, telemedicine was a useful platform in bridging the healthcare accessibility gap during disasters or pandemics like COVID-19 evident from its increased usage during the pandemic. Lastly, telehealth was associated with cost and time savings between USD 65.29 and USD 72.94 per visit and 2.10 and 2.60 h per visit, respectively. However, the feasibility of telehealth for veterans’ medical conditions such as rheumatism, cancer, HIV, and diabetes is underexplored and calls for further investigation post-COVID-19. Lastly, the limited literature on rural veterans’ transportation cost savings using different mobility options—taxi, Uber, public transportation, and rides from friends and family—is another critical gap.

This paper investigates the nexus of transit and rural livability as demonstrated by case studies in the North Dakota communities of Valley City and Dickinson. While there are many factors that influence the livability of a rural community, transit is believed to be an important contributor. For each of the two North Dakota communities considered, public/resident surveys, local transit rider surveys, and stakeholder interviews were conducted to understand differing opinions on livability and how transit contributes to livability. In both Valley City and Dickinson, surveys of residents showed that they believe affordable housing, low crime, quality healthcare, overall cost of living, quality public schools, and available jobs are the most important factors contributing to the livability of a small community. While transit was not among the top factors, survey respondents expressed considerable support for providing transit services and funding transit through various sources. Residents in both cities expressed the opinion that transit should be provided in their community as a transportation option for seniors, people with disabilities, those who choose not to drive, and those who cannot afford to drive. Transit riders in both cities indicated that transit is important to their quality of life, and stakeholders from both communities expressed the sentiment that transit is a critical lifeline for people who are elderly and/or have a disability, individuals with no vehicle, and those who cannot drive.

This study investigates the effects of transportation, specifically public transit, the built environment, and sociodemographic factors on community quality of life (QOL) and individuals’ overall life satisfaction (OLS) in the United States. Using data from the 2017 National Community Livability Survey (NCLS), which includes responses from 994 adults across all 50 states, structural equation modeling (SEM) was applied to examine four latent constructs: the built environment, access to amenities, transportation quality, and support for transit funding. Additional observed variables included perceived importance of public transit, public transit need, ease of travel, and key sociodemographic indicators. Results show that the built environment and transportation quality significantly enhance community QOL but do not have a direct effect on individual OLS. In contrast, support for transit funding has a significant positive effect on individual OLS but not on community QOL. Public transit and walking access to amenities such as parks and recreation facilities, grocery stores, healthcare services, etc., improve both the built environment’s perceptions and community QOL. Interestingly, the perceived importance of public transit negatively influences community QOL, while perceived transit need negatively impacts individual OLS. Ease of travel is the only factor with a significant positive effect on both outcomes. Sociodemographic factors such as gender, age, income, health, education, and race are also found to significantly influence individual life satisfaction. These findings suggest that enhancing perceptions of the built environment, improving transportation quality—particularly public and active transit—and ensuring ease of travel can contribute to improved quality of life. Policies that integrate transportation planning with broader livability and well-being goals may offer more inclusive benefits at both the community and individual levels.

The primary goal of Transportation systems is to provide transportation accessibility to opportunities. Equitable access to essential destinations encompassing social, recreational, educational, and civic opportunities needs to be more consistent across different social groups. This study evaluates the disparities in social justice using social equity as a measure of transit access and walk access to non-work amenities. These non-work amenities include grocery stores, personal services, retail outlets, recreational venues, entertainment centers, and healthcare facilities in the U.S. Logistic regression models are developed using the 2017 National Community Livability Survey data. The results indicate regressive public transit access for socially disadvantaged groups, including older citizens, non-drivers, Medicare/Medicaid beneficiaries, and non-metropolitan residents. Walk access inequities similarly affect older individuals, non-drivers, the physically disabled, the unemployed, students, women, and non-metropolitan residents. This research emphasizes the importance of addressing transit and walk-access inequities to non-work amenities within transportation systems. By acknowledging the disparities in transportation equity, decision-makers and communities can foster more inclusive and equitable access to essential destinations, thereby promoting social cohesion and overall community well-being.

While the automobile industry has moved rapidly toward automation, the U.S. public transit industry began investigating automation in bus operations comparatively recently, and adoption pathways differ sharply by community size. This study establishes a national baseline of transit agency perspectives on bus transit automation technologies—ranging from advanced driver assistance systems (ADAS) to fully automated shuttles—captured immediately before the wave of demonstration deployments and the COVID-19 pandemic reshaped the industry. A national survey of U.S. transit agencies conducted through the Small Urban and Rural Transit Center yielded 258 responses (157 rural, 67 small-urban, and 34 urban agencies). The results show a pronounced awareness gradient by community size: 65% of rural agencies were unfamiliar with SAE automation levels, compared with 23% of urban agencies. Agencies across all community sizes believed automation at all SAE levels (1–5) can promote safety, and safety was simultaneously the leading motivation for adoption and the leading identified research need. However, agencies expressed that SAE Level 4–5 vehicles would still require an onboard operator or agent for customer support and environment monitoring—particularly for ADA paratransit and demand-response services. Collision avoidance, curb avoidance, and lane-keep assist emerged as the technologies most favored for near-term adoption, while interest in fully automated shuttles concentrated in circulator, campus, and first-mile/last-mile applications. Rural agencies identified distinct barriers, including state-mediated procurement, missing digital and physical infrastructure, and clientele requiring personalized assistance. The findings provide a benchmark against which post-pandemic shifts in transit automation readiness can be measured and offer guidance for technology developers, planners, and policymakers targeting communities of different sizes. We recommend that outreach and technical assistance be differentiated by community size, that vehicle developers design for attended rather than driverless operation in paratransit and demand-response contexts, and that state departments of transportation be engaged directly as a leverage point for rural adoption, given that rural vehicle procurement is state-mediated.

Passenger Car Equivalent (PCE) factors are widely used to convert heterogeneous traffic streams into equivalent homogeneous flow rates for the design and analysis of roads and intersections. In developing countries, mixed traffic conditions differ substantially from those in developed contexts due to variations in vehicle composition, operating characteristics, roadway parameters, and environmental conditions. Consequently, PCE values are highly context-specific and require periodic updates to accurately represent prevailing traffic conditions. However, such updates are often infrequent because conventional PCE estimation relies on extensive field data collection through time-consuming and costly traffic surveys, as well as the availability of experienced experts to conduct and validate the analyses. In Sri Lanka, the currently adopted PCE factors are more than two decades old and no longer reflect existing traffic conditions. Although several recent studies have estimated PCE values for mid-block roadway sections of various facility types (e.g., four-lane roads, two-lane roads, and freeways), no study has comprehensively addressed intersections, which are critical for signal timing and geometric design. This study aims to develop a systematic methodology for estimating intersection-specific PCE factors using drone-based video data. Traffic data were collected at selected intersections using an unmanned aerial vehicle to obtain an accurate bird’s-eye view of vehicle movements. The methodology compares the area occupancy of different vehicle categories under varying traffic compositions with that of a passenger-car-only traffic stream operating at the same average speed. Using the extracted traffic parameters, the basic headway method was applied to establish a framework for calculating PCE factors. PCE values were estimated for ten vehicle categories, and the results reveal significant deviations, particularly for three-wheelers, motorcycles, and commercial vehicles, when compared with values currently in use. A high-level comparison with studies from other developing countries in the South Asian region indicates notable differences in vehicle impacts at signalized intersections in Sri Lanka. Furthermore, the proposed methodology provides a practical, economical, and less labour-intensive approach for estimating PCE factors, enabling more frequent updates without requiring extensive field surveys or specialized expertise. Because it relies on a straightforward headway-based framework and drone-derived traffic data, the methodology can be readily adapted to different roadway facilities, including highways, rural roads, and intersections, making it suitable for application across diverse geographical regions.