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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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      <title>Drone pre-positioning for cardiac arrest response : A feature-driven distributionally robust chance constrained programming</title>
      <link>https://trid.trb.org/View/2713611</link>
      <description><![CDATA[Cardiac arrest is a leading cause of mortality worldwide, with survival critically dependent on treatment within the strict 4–6 min golden time window. Conventional emergency medical service (EMS) relying on ground transport often faces delays from traffic congestion and geographic barriers. To address this challenge, we propose a feature-driven distributionally robust chance-constrained program (DRCCP) that determines drone base locations and assignments to minimize worst-case response time. A feature-driven Wasserstein ambiguity set is constructed to capture travel time uncertainty arising from wind speed and direction. To ensure reliable response within the golden window, we further develop two target-oriented DRCCPs, including a risk-oriented model that minimizes the violation probability and a robustness-oriented model that maximizes the admissible level of distributional ambiguity for a prespecified travel-time target. We solve these models by deriving tractable reformulations and designing a bisection-search algorithm. Numerical experiments demonstrate that the proposed DRCCP consistently outperforms benchmark models, yielding lower expected response times, improved tail-risk performance, and a reduced probability of exceeding critical thresholds. The two target-oriented DRCCP models achieve not only lower violation probabilities within the golden time window, but also smaller violation magnitudes when the target is violated. Moreover, our proposed algorithms significantly outperform state-of-the-art commercial solvers in computational efficiency.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713611</guid>
    </item>
    <item>
      <title>How do non-emergency medical transportation services influence patients’ subjective well-being?</title>
      <link>https://trid.trb.org/View/2732952</link>
      <description><![CDATA[Subjective well-being (SWB) related to travel experience has been a growing area of interest in transportation research, particularly through the mediating effects of travel satisfaction. While prior studies have extensively examined this relationship in the context of commuting and leisure travel, the role of non-emergency medical transportation (NEMT) service quality in shaping SWB remains underexplored. These trips present unique challenges such as physical vulnerability, time sensitivity, and emotional stress. This study investigates how user perceptions of NEMT service quality influence service satisfaction and SWB using survey data collected among rural patients in Illinois. The results indicate that reliability is the most influential factor affecting service satisfaction; reservation technology, customer service, accessibility, and driver behavior also contribute significantly to satisfaction with NEMT, and subsequently SWB. These findings underscore the critical importance of reliable and user-friendly NEMT services in supporting patients’ well-being and shed light on how to improve NEMT systems.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732952</guid>
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    <item>
      <title>Evaluating the equity of location and availability of healthcare infrastructure as a function of accessibility</title>
      <link>https://trid.trb.org/View/2682177</link>
      <description><![CDATA[The accessibility of healthcare facilities is an essential indicator for evaluating the welfare of a territory’s population. Living close to a hospital or reaching it easily reduces indirect costs (travel, the need to stay away from home, etc.) for patients and their families; poor accessibility, on the other hand, produces high indirect costs, sometimes limiting the possibility of receiving care. A problem of equity may emerge, considering that some segments of the population may have good accessibility of health facilities and others may not have the same levels of accessibility. When considering how and where to invest resources in building new health facilities or expanding and improving existing ones, assessing equity is important, given that the resources come from public funds. In this paper, we propose a model to evaluate the equity of the location of healthcare facilities. By applying the proposed methodology to a real case, the Campania Region, we calculate a Gini index of 0.2208, which is deemed acceptable but can be improved.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2682177</guid>
    </item>
    <item>
      <title>Equity Evaluation of Residents’ Transit+Walk Accessibility to Sacramento Healthcare Facilities</title>
      <link>https://trid.trb.org/View/2683211</link>
      <description><![CDATA[Access to healthcare is a key component of public health equity, yet many U.S. communities remain dependent on private vehicles for medical travel. This study introduces an integrated framework, the Transit+Walk Score (TWS), to evaluate how effectively residents of Sacramento, California, can reach healthcare facilities via public transit and how safely, comfortably, and conveniently they can walk from transit stops to those facilities. The research aims to inform planners, transit agencies, and policy makers in designing equitable and multimodal transportation systems. The research team combined geospatial network modeling and field-based walkability audits to assess 123 healthcare facilities across Sacramento, with 56 sites selected for detailed evaluation using the Pedestrian Environment Data Scan (PEDS) tool. The TWS integrated modeled transit travel times with on-the-ground walkability data to identify areas of high and low accessibility. Findings revealed that Downtown, Midtown, and East Sacramento exhibited the highest multimodal accessibility, while northern Arden-Arcade and outer suburban areas demonstrated the lowest. Educational attainment emerged as the strongest predictor of accessibility, highlighting structural inequities in pedestrian and transit infrastructure. The study concludes that equitable healthcare access requires both reliable transit and safe, continuous pedestrian networks. The TWS offers a scalable, data-driven tool to guide future investments that advance health equity, sustainable transportation, and inclusive community design.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:12:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683211</guid>
    </item>
    <item>
      <title>Creating User Profile of NEMT Travelers for Kanagawa Prefecture, Japan using K-Means Clustering Algorithm</title>
      <link>https://trid.trb.org/View/2655502</link>
      <description><![CDATA[In Japanese society, the elderly population is steadily increasing, which creates a growing demand for long-term medical care. Non-Emergency Medical Transportation (NEMT) plays a key role for individuals who face mobility barriers due to physical or mental conditions. In 2022, NEMT service was launched in Kanagawa Prefecture, driven by demographic trends indicating market expansion potential. However, efforts should be made to ensure that this service is affordable and efficient. To that end, this study takes first step. This study aims to profile NEMT travelers in Kanagawa to better understand their trip characteristics and needs using K-Means clustering. The results revealed two distinct clusters based on trip distance: short trips (average 5 km) and long trips (average 28 km). Both clusters primarily consist of travelers over 60 years old, with long trips often requiring special medical devices. Short trips typically involve hospital-to-hospital transfers, while long trips exhibit more varied origin-destination patterns.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655502</guid>
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    <item>
      <title>Alaska’s Frozen Front Line: Mastering search and rescue in the Last Frontier</title>
      <link>https://trid.trb.org/View/2668463</link>
      <description><![CDATA[In Alaska, even the simplest search and rescue (SAR) missions can become anything but routine. The author describes a nighttime medical evacuation in Cold Bay that should have been straightforward but was complicated by unexpected snow squalls, deteriorating visibility, and risky mountainous terrain. After retrieving the patient, the return trip required every system on the aircraft—radar to identify terrain, forward-looking infrared to detect unseen obstacles, and deicing systems working overtime. Alaskan SAR operations are poised to face increasing challenges as the transformation of the Arctic accelerates. The retreat of sea ice is opening new trade and fishing routes, leading to a surge in vessel traffic through the Bering Sea. This heightened activity will necessitate an expanded Coast Guard presence and enhanced SAR capabilities to meet the demands of a rapidly evolving operational landscape.]]></description>
      <pubDate>Wed, 18 Feb 2026 13:22:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2668463</guid>
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    <item>
      <title>Quintessential Kodiak: A complex, long-distance Arctic rescue</title>
      <link>https://trid.trb.org/View/2663579</link>
      <description><![CDATA[On August 26, 2023, United States Coast Guard Air Station Kodiak completed the medical evacuation of a man from a fishing vessel in the Bering Sea, more than 200 nautical miles northwest of St. Paul Island, Alaska. The rescue required an H-60 helicopter, a C-130 plane, crew and backup crew for each aircraft, plus a self-rescue H-60 crew and helicopter. The harsh Alaska environment brought extra complexities which included planned and unplanned refueling stops; adding a cold-weather kit of fuel additives, plugs and tie-downs; avoiding the cloud of ash from the eruption of Mount Shishaldin, and coping with low visibility from heavy fog. When all assets finally returned home, the air station crews had accumulated 29.4 flight hours, covering more than 1,400 nautical miles across four locations in just three days under the most challenging of conditions.]]></description>
      <pubDate>Tue, 03 Feb 2026 10:07:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663579</guid>
    </item>
    <item>
      <title>Identifying Gaps in Southwest Oklahoma NEMT Transit Networks</title>
      <link>https://trid.trb.org/View/2562077</link>
      <description><![CDATA[In rural areas like Southwest Oklahoma, gaps in Non-Emergency Medical Transportation (NEMT) access pose critical challenges for Medicaid clients who rely on these services for essential healthcare. This study examines the availability and limitations of NEMT services in three key counties—Jackson, Kiowa, and Tillman from Southwest Oklahoma—using data from 2021 to 2023. Open records from ModivCare, demographic analysis, and semi-structured interviews with six local transit agencies were utilized to identify factors influencing ride denials, cancellation rates, and complaint trends. The data highlighted that 24% of service types, particularly dental care, account for over 80% of denials, with access issues compounded by distance restrictions in rural areas. The analysis also revealed that cancellation trends often mirrored statewide complaint trends, suggesting underlying systemic factors influencing service reliability. Transit agencies cited challenges such as maintaining financial and scheduling independence, as well as the need for better-coordinated communication and resource sharing. Future work includes expanding data collection efforts and integrating client feedback to validate findings and refine solutions further.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562077</guid>
    </item>
    <item>
      <title>Transportation Challenges to Healthcare: Evaluating the Transportation Needs of Patients at Saban Community Clinic</title>
      <link>https://trid.trb.org/View/2608508</link>
      <description><![CDATA[People without adequate transportation can often have trouble getting to medical appointments and miss or delay their care (Syed et al., 2013). In 2017, 5.8 million people delayed or missed medical appointments due to a lack of transportation options (Wang 2021; Wolfe et al. 2020). This report evaluates the transportation challenges faced by patients seeking care at one of the Saban Community Clinic (SCC) locations. SCC is a Federally Qualified Health Center (FQHC) that provides healthcare to patients who are underinsured or without insurance to a predominantly Latinx population. This report explored SCC patient transportation needs by examining the spatial patterns of patient residential locations, surveying patient transportation needs, and evaluating an SCC effort to reduce transportation barriers by offering free Lyft rides to patients. Findings reveal that unreliable transportation options in addition to lack of affordability and limited accessibility results in transportation difficulties for patients at SCC. Despite these challenges, many patients continue to seek care at SCC because they value the quality of service. Increasing flexibility around appointments, diversifying transportation funding, expanding care to patients living further away from current SCC sites, and working with local transportation providers are strategies SCC can pursue to address patients’ transportation challenges. The research provides insights into how healthcare and insurance providers and transportation agencies can best improve access to healthcare for patients similar to those served by SCC.]]></description>
      <pubDate>Tue, 11 Nov 2025 09:24:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608508</guid>
    </item>
    <item>
      <title>Investigation of Transportation Barriers to Healthcare Services in Rural Illinois</title>
      <link>https://trid.trb.org/View/2608112</link>
      <description><![CDATA[Transportation is a fundamental necessity for accessing healthcare services. However, transportation barriers are a primary reason for appointment cancelations or delays, particularly in rural areas, adversely affecting individual health and increasing healthcare system costs. In this study, we conducted face-to-face interviews with individuals across rural areas in Illinois to investigate barriers they face when using non-emergency medical transportation (NEMT) to access healthcare facilities. We used thematic analysis to systematically organize themes by analyzing conversational notes in QSR NVivo software. The thematic analysis identified four key areas: (1) NEMT service provision, including service reliability, coverage, availability, the reservation system, and provider code of conduct; (2) travel experience, encompassing long journeys to healthcare centers, in-vehicle crowding, safety concerns, and travel costs; (3) transportation infrastructure; and (4) non-transportation issues. The results show that the unreliability of public transportation (both fixed-route and on-demand services) and NEMT significantly contributes to late arrivals and canceled medical appointments. Additionally, the closure of local hospitals during the COVID-19 pandemic has forced patients to travel longer distances for medical care, particularly affecting rural residents. Furthermore, a lack of awareness or understanding of insurance coverage for transportation costs results in out-of-pocket expenses, disproportionately affecting low-income individuals. These findings highlight the need for improved integration of transportation providers with the healthcare system, increased awareness of NEMT availability and insurance coverage, and modernization of reservation systems.]]></description>
      <pubDate>Sun, 12 Oct 2025 17:08:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608112</guid>
    </item>
    <item>
      <title>Transportation Barriers to Vision Care for the Visually Impaired</title>
      <link>https://trid.trb.org/View/2576318</link>
      <description><![CDATA[Transportation barriers significantly hinder healthcare access for individuals with disabilities, particularly in rural areas. This study examines these challenges in Nebraska and Kansas, focusing on individuals with visual impairments who require specialized low-vision care. Using a mixed-methods approach, the research integrates qualitative interviews with low-vision patients, caregivers, and clinicians alongside aspatial and spatial analyses to identify and characterize transportation barriers to vision care and areas with high unmet demand for non-emergency medical transportation (NEMT). Qualitative data were gathered through semi-structured interviews with patients and caregivers receiving low-vision rehabilitation services at the University of Nebraska Medical Center’s Weigel Williamson Center for Visual Rehabilitation, as well as with clinicians providing low-vision care in the region. These interviews explored transportation challenges, coping strategies, and the impact of mobility constraints on healthcare access. The quantitative component utilized data from the 2022 National Household Travel Survey (NHTS) and the 2018–2022 American Community Survey (ACS) to analyze sociodemographic factors associated with travel-limiting disabilities. Areas with latent demand for NEMT were identified by combining a Disability Index—developed using NHTS data to measure the prevalence of travel-limiting conditions—with ACS Disability Status data and driving time calculations to healthcare facilities. Census tracts with both a high prevalence of disability and significant travel times to healthcare services were classified as having unmet NEMT demand. Findings highlight the significant reliance on informal transportation networks, limited access to public and specialized transportation services, and the burden of long-distance travel for rural patients. Clinicians confirm that these barriers contribute to missed appointments and worsened health outcomes. The study recommends expanding NEMT services, improving coordination between healthcare and transportation providers, and enhancing community awareness of available mobility resources to promote equitable healthcare access for individuals who are blind or have low vision in underserved areas.]]></description>
      <pubDate>Fri, 08 Aug 2025 08:50:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2576318</guid>
    </item>
    <item>
      <title>Wheels to Care: Navigating NEMT Across America: An NEMT Profile for Each of the 50 States and D.C.</title>
      <link>https://trid.trb.org/View/2571775</link>
      <description><![CDATA[Medicaid is a joint federal and state program that provides health coverage for individuals and families with limited incomes and resources. Medicaid non-emergency medical transportation (NEMT) is an important benefit for Medicaid beneficiaries who need to get to and from medical services and have no other means of transportation. Within broad national guidelines established by federal statutes, regulations, and policies, each state establishes its own eligibility standards; determines the type, amount, duration, and scope of Medicaid services; sets the rate of payment for services; and administers its own Medicaid program, including its own NEMT program. The state role means there are differences in how healthcare is administered, and in differences in state-to-state for how NEMT is provided. This document presents an NEMT profile for each of the 50 states and the District of Columbia. The set of profiles provides information for each state in the following sequence: (1) NEMT model; (2)  map of the state and the corresponding Centers for Medicaid and Medicare Services (CMS) region; (3) general description of the state’s Medicaid structure and NEMT model; (4) Medicaid recipient NEMT protocol and requirements to access transportation; (5) transportation provider information and requirements in the provision of NEMT; (6) public transit’s role in the provision of NEMT; and (7) profile date.]]></description>
      <pubDate>Mon, 14 Jul 2025 12:53:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2571775</guid>
    </item>
    <item>
      <title>Modeling and variable selection of healthcare trip behaviours using statistical learning techniques</title>
      <link>https://trid.trb.org/View/2554432</link>
      <description><![CDATA[In an increasingly complex and fast-paced world, understanding healthcare-related travel behavior has become a critical challenge in transportation planning. Traditional models, including the Four-Step Transportation Model (FSTM), require further refinements to better capture evolving travel patterns, particularly the growing share of home-based other trips, which include shopping, leisure, and healthcare-related journeys. Among these, healthcare trips require special attention due to the increasing proportion of the aging population and the essential nature of medical accessibility.This study aims to identify key variables influencing healthcare-related travel behaviors using variable selection techniques, specifically Least Absolute Shrinkage and Selection Operator (Lasso) and Elastic Net (ENet). The analysis is based on zonal-level aggregated data derived from household-based hospital travel surveys conducted in Eskişehir City. The model incorporates regional socioeconomic variables, including household characteristics, vehicle ownership rates, employment ratios, and age group distributions. Model performance is evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE) as key success criteria.The results indicate that the ENet model achieved the lowest Mean Squared Error (MSE), reducing the error by approximately 37 % compared to the OLS model, while the Lasso model yielded the lowest Mean Absolute Error (MAE), reflecting a 38 % improvement. Both methods effectively performed variable selection, retaining 10 out of 17 predictors in the final model. Significant variables positively associated with healthcare travel frequency include the proportions of individuals aged 30–49, 50–64, and over 65, as well as family density. These results suggest that household accompaniment patterns and age-related healthcare needs increase the frequency of such trips. In contrast, negative associations were observed between healthcare travel frequency and the share of the 6–17 age group, employment ratio, average number of cars per family, and the number of healthcare centers.]]></description>
      <pubDate>Thu, 29 May 2025 09:22:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2554432</guid>
    </item>
    <item>
      <title>The role of emerging mobility solutions in shaping care-seeking behaviors in rural communities: A national survey with stated choice experiment</title>
      <link>https://trid.trb.org/View/2480446</link>
      <description><![CDATA[Transportation barriers significantly hinder access to medical appointments, particularly for minorities, patients with chronic conditions, and the economically disadvantaged. This study examines the relationship between ride-hailing transportation attributes and the likelihood of attending routine doctor appointments, using data from a national survey with a stated choice experiment. Mixed logistic regressions were applied to analyze correlations between sociodemographic profiles, transportation service attributes, and appointment adherence, with a focus on urban-rural differences. The findings show that flexible routes are crucial for rural patients, who often combine multiple activities in a single trip, making direct returns home impractical. Analysis of two subgroups, those who have delayed treatment and those who have not, revealed nuanced patterns. Patients who delayed treatment face greater time constraints due to being younger, employed, and lacking vehicles or comprehensive insurance. For this group, shorter booking times, flexible routes, and minimized ridesharing significantly increase the likelihood of attending appointments. In contrast, patients who have not delayed treatment are less influenced by transportation attributes, reflecting their higher vehicle ownership, shorter travel times, and greater likelihood of being retirees. These results emphasize the need for tailored, group- and region-specific strategies. This study emphasizes the importance of reevaluating cost-minimization policies in non-emergency transportation Medicaid and Medicare services to better meet patient needs. It advances the literature by quantifying how ride-hailing attributes influence appointment adherence, offering insights for reducing no-show rates and improving health outcomes.]]></description>
      <pubDate>Mon, 06 Jan 2025 14:37:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2480446</guid>
    </item>
    <item>
      <title>Mobile COVID-19 vaccination scheduling with capacity selection</title>
      <link>https://trid.trb.org/View/2453763</link>
      <description><![CDATA[Massive COVID-19 vaccination can significantly reduce both mild and severe infection rates. Some governments have adopted mobile vaccination vehicles, offering a more convenient and flexible service compared to static walk-in sites. This paper addresses a new scheduling problem arising from the mobile COVID-19 vaccination planning practice. Given a set of communities, each with a specific number of residents to vaccinate, the objective is to assign mobile vaccination vehicles to communities and determine each vehicle’s service capacity and routes, attempting to minimize the total operational cost. To the authors' knowledge, this is the first attempt to tackle the joint challenge of mass vaccination scheduling and routing. They formulate the problem as a mixed-integer nonlinear program model, which they linearize by treating each vehicle with multiple stations as separate units. Given that the problem is NP-hard, they then developed a tailored adaptive large neighborhood search (ALNS) approach that effectively solves practical-sized instances by utilizing the intrinsic structure of the problem. To illustrate the efficiency of the suggested model and solution methodologies, they conduct numerical experiments on instances of varying sizes. The results demonstrate the effectiveness of the developed ALNS algorithm in solving instances with realistic sizes, efficiently handling up to 100 communities and 14 vaccination vehicles. In addition, a case study shows that their method significantly reduces operational expenses compared to some experience-based greedy methods.]]></description>
      <pubDate>Mon, 30 Dec 2024 11:16:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2453763</guid>
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