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.

In this work, we investigate a new paradigm for dock-less bike sharing. Recently, it has become essential to accommodate connected and free-floating bicycles in modern bike-sharing operations. This change comes with an increase in the coordination cost, as bicycles are no longer checked in and out from bike-sharing stations that are fully equipped to handle the volume of requests; instead, bicycles can be checked in and out from virtually anywhere. In this paper, we propose a new framework for combining traditional bike stations with locations that can serve as free-floating bike-sharing stations. The framework we propose here focuses on identifying highly centralized k-clubs (i.e., connected subgraphs of restricted diameter). The restricted diameter reduces coordination costs as dock-less bicycles can only be found in specific locations. In addition, we use closeness centrality as this metric allows for quick access to dock-less bike sharing while, at the same time, optimizing the reach of service to bikers/customers. For the proposed problem, we first derive its computational complexity and show that it is NP-hard (by reduction from the 3-SATISFIABILITY problem), and then provide an integer programming formulation. Due to its computational complexity, the problem cannot be solved exactly in a large-scale setting, as is such of an urban area. Hence, we provide a greedy heuristic approach that is shown to run in reasonable computational time. We also provide the presentation and analysis of a case study in two cities of the state of North Dakota: Casselton and Fargo. Our work concludes with the cost-benefit analysis of both models (docked vs. dockless) to suggest the potential advantages of the proposed model.

The electrification of traditional transit route networks has been a promising option for urban public transit. Compared with fast chargers, energy storage (ES) technology benefits the planner in relation to cost savings and avoiding high electricity demand charges. Although many studies ignored the potentiality of utilizing ES chargers, this study uses this concept with demand charges to design an electric transit route network by proposing a bi-level optimization model. The upper-level model aims to minimize the total travel times of passengers along with the total number of indirect trips. In comparison, the lower-level problem plans to minimize the total costs of purchasing two types of fast and ES chargers and demand charges for the transit network that entirely operates with battery electric buses (BEBs). Because of the NP-hard nature of the proposed model, the discrete multi-objective grey wolf optimizer algorithm is suggested in a discrete space to search for a feasible solution by providing the Pareto frontier of two objective functions. Also, the initial route set generation algorithm has been applied to generate a new set of transit routes. To validate the proposed model, our model has been tested on a benchmark network of Mumford0 in a small and a Fargo–Moorhead area in a medium size. Our results confirmed that considering ES chargers could save 4.8% and 76.5% (for the user cost), 2.3%, and 111.9% (for the operator cost) of the total network and demand charges.

There is a lack of research into the impact of road roughness on ride quality and route choice. The scarcity of ride roughness data for local and urban roads is likely one reason for the lack of such studies. Existing methods of obtaining ride roughness data are expensive and require expert practitioners and laborious data processing by trained personnel. Sensors in most current vehicles provide an alternative source for road roughness data. This study emulated the data needed from vehicle sensors by using the accelerometer and gyroscope of a smartphone. The authors used data collected from two different bus routes to classify segments of roads into objectively distinct roughness clusters. The output enables map service applications to suggest better routing options based on expected ride quality and also quantifies road roughness consistently to enable optimized maintenance planning and decision-making for roadway assets.

This paper presents a data-driven approach to address the state of good repair (SGR) in small urban and rural transit systems in the U.S. by predicting the service life of rolling stock vehicles. Achieving and maintaining public transportation rolling stock in SGR is crucial to providing safe and reliable services to riders, particularly for transit agencies utilizing federal grants that mandate asset maintenance at a full level of performance. In this context, an intelligent predictive model is proposed to analyze the transportation rolling stock, determine their backlog and current condition, predict their replacement or rehabilitation needs, and estimate the funding required for future replacements, ensuring SGR. The model utilizes historical data from retired revenue vehicles in the National Transit Database and employs machine learning techniques, specifically random forest regression and gradient boosting regression, to develop the predictive tool. By addressing the backlog of vehicles beyond their service lives and estimating replacement costs, the model provides valuable decision support for effective transit asset management. Transit agencies in small urban and rural transit systems, lacking analytical tools for service life prediction, can greatly benefit from this simple yet powerful predictive model. The approach offers a practical resource to address SGR needs, optimize investment prioritization, and enhance operational efficiency in public transit systems.