Transportation Resources Research Articles

Predicting Ride-Hailing Demand with Consideration of Social Equity: A Case Study of Chengdu

In the realm of shared autonomous vehicle ride-sharing, precise demand prediction is vital for optimizing resource allocation, improving travel efficiency, and promoting sustainable transport solutions. However, existing studies tend to overlook social attributes and demographic characteristics across various regions, resulting in disparities in prediction fairness between areas with plentiful and limited transportation resources. In order to achieve more accurate and fair prediction, an innovative Social Graph Convolution Long Short-Term Memory framework is proposed, incorporating demographic, spatial, and transportation accessibility information into multiple functional graphs, including functional similarity, population structure, and historical demand graphs. Furthermore, Mean Percentage Error indicators are employed in the loss function to balance prediction accuracy and fairness. The findings indicate that there is an enhancement in both prediction accuracy and fairness by at least 8.9% and 12.9%, respectively, compared to base models. Additionally, the predictions for rush hours in both privileged and underprivileged regions exhibit greater precision and rationality, supporting sustainable transport practices. The proposed framework effectively captures the demands of diverse social groups, thereby contributing to the advancement of social equity and long-term sustainability in urban mobility.

Revealing the multiscale impact of external factors on dockless bike sharing and metro integration: Evidence from Shenzhen

Dockless bike sharing (DBS) and metro integration offer innovative solutions for the first-mile and last-mile challenges of urban transportation. However, accurately identifying transfer passenger volume, analyzing DBS and metro integration characteristics, and understanding the spatial heterogeneity of factors affecting this volume remain areas to explore. This study, using Shenzhen as a case, constructs a framework to identify transfer passenger volume by considering built environment elements around metro stations and applying a Thiessen polygon. Transfer passenger volumes are classified into six categories, and their spatio-temporal characteristics are analyzed. A multiscale geographically weighted regression (MGWR) model investigates the impacts of socio-economic attributes, built environment, and transportation-related factors on transfer passenger volume, revealing spatial heterogeneity. Results show that the demand for DBS and metro integration is higher in the morning peak than in the evening peak, with access transfer passenger volume peaks occurring earlier than egress peaks. Spatially, transfer passenger volume shows heterogeneity, with higher demand in the urban core than in the suburbs. Commercial and residential and enterprises have the most significant impact on various types of transfer passenger volume, showing the highest spatial heterogeneity in enterprises. In contrast, science education and culture positively influence evening peak access transfer volume and exhibit the weakest spatial heterogeneity. Population density, sports and leisure, shopping and entertainment, and tourist attractions display localized positive or negative impacts. These findings can guide operators and planners in optimizing public transportation resources to enhance DBS and metro integration.

Joint scheduling of hybrid flow-shop with limited automatic guided vehicles: A hierarchical learning-based swarm optimizer

Transportation system in workshop is essential for high-efficient production scheduling. Due to the limited transportation resources, the joint scheduling of production and transportation has emerged as a pivotal issue in modern manufacturing. This paper investigates a joint scheduling of hybrid flow-shop with limited automatic guided vehicles (HFSP-LAGV), which extends the classical hybrid flow-shop scheduling by considering the limited number of the AGVs on the transportation resources. To solve such problem, a mixed integer linear programming (MILP) model is firstly built to formulate HFSP-LAGV. Then, a hierarchical learning-based swarm optimizer (HLSO) is proposed. An encoding and decoding method based on three dispatch rules is proposed. The framework of HLSO comprises a pyramid-based layering strategy, an inter-layer learning and an intra-layer learning. The pyramid-based layering strategy divides the swarm into several layers. In the inter-layer learning, the individuals in higher layers guide the evolution of individuals in lower layers to achieve the exploration of global area. In the intra-layer learning, an offline Q-learning-based local search is designed to implement the self-learning of elite individuals in higher layer to intensify the exploitation of the local area. A Q-learning model that has been pre-trained offline is used to guide the selection of appropriate operator of local search. Experimental results reveal the effectiveness of the designs and the superiority of HLSO over several well-performing methods on solving HFSP-LAGV.

Collaborative supply chain network design under demand uncertainty: A robust optimization approach

This paper studies a collaborative robust supply chain network design (CRSCND) problem aimed at maximizing economic and social benefits by enabling enterprises to jointly address demand uncertainties. Through strategies including joint inventory replenishment, shared distribution centers (DCs), and pooled transportation resources, the CRSCND problem seeks to optimize plant and DC locations and the allocation of DCs to customers under a collaborative framework. To address this, we develop two robust optimization models incorporating a budget uncertainty set, each model representing a distinct risk-pooling policy. These models are then reformulated into solvable linear programming structures. Results from numerical experiments confirm the cost-reduction benefits of collaboration and robust optimization. Sensitivity analysis reveals that factors like violated probability and high demand volatility minimally impact cost savings enabled by collaboration and robustness. Moreover, each robust model shows distinct suitability depending on specific scenario parameters. Finally, we test three cost-saving allocation mechanisms, finding that only the Shapley value method yields best allocations in cases involving overlapping demand.

Fresh Milk Supply Chain Analysis in Kelompok Ternak Mandiri Sejahtera using Food Supply Chain Network Approach

Supply chain management plays a role in ensuring the availability of raw materials from suppliers to the final product reaching consumers. Kelompok Ternak Mandiri Sejahtera (KTMS) is a cooperative that acts as a link between farmers and dairy processing consumers. However, research on theapplication of the supply chain concept at KTMS has not yet been explored. One commonly used approach to analyze supply chain conditions is the Food Supply Chain Network (FSCN). Therefore, the aim of this study is to analyze the fresh milk supply chain conditions at KTMS using the FSCNapproach to comprehensively understand the supply chain conditions. The research was conducted at the Kelompok Ternak Mandiri Sejahtera (KTMS) from June to July 2024. The method used was qualitative descriptive with observations and interviews using questionnaires relevant to the FoodSupply Chain Network (FSCN) framework with 17 respondents. The results of the study indicate that the fresh milk supply chain conditions at Kelompok Ternak Mandiri Sejahtera (KTMS), based on the Food Supply Chain Network framework approach, have been running smoothly, although transportation resources and technology resources remain weaknesses for both the farmers and KTMS.

The Cloud People of Naupallacta: Middle Horizon Camelid Management in the Hinterland of the Chicha Soras Valley

Archaeological, ethnographic, and rural development work over the past 50 years in the Chicha Soras area has revealed that the region was first intensively used from the Middle Horizon Epoch 2 (AD 680/700–900) onward. An introduced population here had its seven occupation sites focused on the southern part of the Chicha valley and supported agriculture and textile production, while settlement in the upper Rio Yanamayo and west of the Rio Huayllaripa comprising the Naupallacta complex, served the management of camelid herds, which provided transport, fiber, and meat resources. The interrelationship between the arable farmers and camelid pastoralists produced a mutually supportive resource base for the local agropastoral community, and tribute produced for distant elite Wari administrators. This is confirmed by the interdisciplinary data sets presented here, including demographic, architectural, artefactual, archaeozoological, paleoenvironmental and aDNA results.

Shared Mobility's Role in Sustainable Mobility: Past, Present, and Future

This article reviews shared mobility, a prominent urban transportation concept with considerable potential to contribute to more sustainable urban mobility. Shared passenger mobility spans diverse services, often leveraging technological advances and disruptions such as smartphones and data analytics to optimize transport resources. Given the broad range of services, a shared mobility taxonomy is proposed, accommodating evolving services. Key challenges for delivering efficient and effective shared mobility services with lower environmental impacts are also identified. Finally, the International Transport Forum transport demand models are used to analyze policy implications and potential effects quantitatively. This article presents a possible scenario for the global evolution of these services to 2050. Results emphasize shared mobility's role in transport decarbonization in the present and future and show that shared mobility may reduce resource use and mobility externalities (e.g., CO2, local pollutants, congestion, urban space use) but that the uptake will differ between Global South and Global North cities.

A Distributed VMD-BiLSTM Model for Taxi Demand Forecasting with GPS Sensor Data.

With the ubiquitous deployment of mobile and sensor technologies in modes of transportation, taxis have become a significant component of public transportation. However, vacant taxis represent an important waste of transportation resources. Forecasting taxi demand within a short time achieves a supply-demand balance and reduces oil emissions. Although earlier studies have forwarded highly developed machine learning- and deep learning-based models to forecast taxicab demands, these models often face significant computational expenses and cannot effectively utilize large-scale trajectory sensor data. To address these challenges, in this paper, we propose a hybrid deep learning-based model for taxi demand prediction. In particular, the Variational Mode Decomposition (VMD) algorithm is integrated along with a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the prediction process. The VMD algorithm is applied to decompose time series-aware traffic features into multiple sub-modes of different frequencies. After that, the BiLSTM method is utilized to predict time series data fed with the relevant demand features. To overcome the limitation of high computational expenses, the designed model is performed on the Spark distributed platform. The performance of the proposed model is tested using a real-world dataset, and it surpasses existing state-of-the-art predictive models in terms of accuracy, efficiency, and distributed performance. These findings provide insights for enhancing the efficiency of passenger search and increasing the profit of taxicabs.

Sustainability of Indonesia's transportation sector energy and resources demand under the low carbon transition strategies

– The transportation sector is Indonesia's largest energy demand sector and has rapidly increased over the last decade, consuming more than 2.4 million TJ in 2022. With most of this energy coming from fossil fuels, the government has set policies to reduce the sector's climate impacts. This study examines the impact of these policies on the future energy demand, the resources needed, and the limiting factors to the implementation. Our results show that the policies can decrease energy demand from the transportation sector, but there will be shifts in the primary energy mix. Coal will be more in demand than gasoline in the future even under the 31 % renewables goal. The demand for critical minerals would also be substantial following the increase in renewables. While Indonesia has one of the largest nickel reserves, it would still require other critical minerals that could threaten Indonesia's energy security. Furthermore, the massive land use required to meet the renewables goals could be a limiting factor in achieving the set goals. This finding serves as valuable information for the policymakers when planning energy and resource supply to meet the emission target.

Optimizing the order picking and delivery process to the final recipient

The aim of this study is to analyse and evaluate the order picking process and the transport of products to customers, taking into account the specific characteristics and potential of small and medium-sized industrial enterprises. Scientific standards and selected indicators for measuring the efficiency and quality of transport and order picking processes were used to achieve the intended objectives. The research was conducted in a small, family-owned manufacturing and trading company specialising in the production and sale of wrought iron finishing elements and fences. The analysis covered both internal and external transport processes. It was shown that the order picking process in the studied company is largely based on manual procedures, which, despite high precision, generates time-consuming activities related to the movement of warehouse employees. External transport efficiency indicators showed high efficiency and appropriate use of transport resources. The high quality of deliveries, as measured by on-time and reliability indicators, confirms customer satisfaction. The study was conducted in one company, which is a limitation. Future studies should include more companies from the same sector. Proposed improvements need to be verified sometime after implementation and solutions resulting from data analysis can help SMEs in similar industries to improve their logistics processes. The work brings a new approach to the analysis of order picking and transport processes in the context of SMEs, providing practical solutions and indicating areas for further research. It is aimed at logistics managers, researchers and practitioners interested in optimising logistics processes in the industrial sector.

Investigating the performance of PBL parametrizations in WRF model for front enhanced SES simulation in istanbul megacity

Sea-effect snow (SES) is a meteorological phenomenon resulting from cold air moving over warmer waters. Accurate prediction of SES is vital for emergency management, transportation, and water resource planning. A thundersnow event in Istanbul from 17–19 February 2015 caused significant disruptions, with traffic and flights affected, highways temporarily closed, and trees falling due to heavy snowfall. This study investigates the influence of different parameterization schemes in the Weather Research and Forecasting (WRF) model on SES simulations. Six distinct PBL parameterization schemes were used in a series of WRF simulations. In addition, the following factors pivotal to SES event have also been investigated: 1000–500 hPa thickness, total and latent heat fluxes, radar and satellite analyses, temperature gradients, wind shear, inversion levels, and atmospheric stability indices. Additionally, the formation of SES during the cold front transition further contributed to these elements in the Black Sea region. The simulations displayed notably high total heat flux and latent heat flux values, particularly following the passage of the cold front. Furthermore, the northeast-southwest oriented SES cloud, distinguished by its banded structure, was successfully validated using radar and satellite imagery. However, it's worth noting that the model positioned it farther west than its actual location. This study highlights the challenges in precise prediction and analysis of such convective activities. In this thundersnow event, the local closure schemes, particularly MYNN in first place and second MYJ, demonstrated superior performance compared to non-local schemes within the parameterization options.

Management of Patients With Cardiac Arrest Requiring Interfacility Transport: A Scientific Statement From the American Heart Association.

People who experience out-of-hospital cardiac arrest often require care at a regional center for continued treatment after resuscitation, but many do not initially present to the hospital where they will be admitted. For patients who require interfacility transport after cardiac arrest, the decision to transfer between centers is complex and often based on individual clinical characteristics, resources at the presenting hospital, and available transport resources. Once the decision has been made to transfer a patient after cardiac arrest, there is little direct guidance on how best to provide interfacility transport. Accepting centers depend on transferring emergency departments and emergency medical services professionals to make important and nuanced decisions about postresuscitation care that may determine the efficacy of future treatments. The consequences of early care are greater when transport delays occur, which is common in rural areas or due to inclement weather. Challenges of providing interfacility transfer services for patients who have experienced cardiac arrest include varying expertise of clinicians, differing resources available to them, and nonstandardized communication between transferring and receiving centers. Although many aspects of care are insufficiently studied to determine implications for specific out-of-hospital treatment on outcomes, a general approach of maintaining otherwise recommended postresuscitation care during interfacility transfer is reasonable. This includes close attention to airway, vascular access, ventilator management, sedation, cardiopulmonary monitoring, antiarrhythmic treatments, blood pressure control, temperature control, and metabolic management. Patient stability for transfer, equity and inclusion, and communication also must be considered. Many of these aspects can be delivered by protocol-driven care.

Smart Public Art: Leveraging IoT and Digital Technology in Ganzhou’s Public Spaces

Public areas can have Internet of Things (IoT)-enabled sensing devices placed to keep track of possible security risks such as suspicious activity or misplaced possessions. In public areas, devices have the ability to gather information, but, in some cases, people must explicitly check out if they wish their data to be taken. This research presents an analysis of the IoT environment on Ganzhou’s public transportation, suggests a new IoT-based intelligent public transportation system framework, and shows how the communication network’s components are being applied. In this study, we detail the deployment of IoT sensors through the collection of data. We proposed a novel Salp Swarm weighted recurrent neural network (SS- WRNN) for prediction in Ganzhou’s public transport flow. The data were extracted using linear discriminate analysis (LDA) from high-dimensional sensor data into a lower-dimensional space. For resolving the dynamic bus scheduling and controlling issues, decision support algorithms are suggested. It can help decision-makers decrease passenger trip times, boost scheduling effectiveness, and raise the rate at which transportation resources are utilized.

Coordinated planning model for multi-regional ammonia industries leveraging hydrogen supply chain and power grid integration: A case study of Shandong

With the advancement of green hydrogen production technology, power-to-ammonia (P2A) has emerged as a pivotal approach for consuming large-scale renewable resources. However, the inherent intermittency of wind and solar resources leads to a spatial and temporal mismatch between supply and demand, rendering effective integration within a single region challenging and necessitating cross-regional collaborative planning. To address this, we propose a coordinated planning model for a multi-regional ammonia industrial system (MAIS) that leverages the integration of a hydrogen supply chain (HSC) and power grid (PG), termed HSC-PG-MAIS. During the modeling of the HSC, we developed a novel hydrogen truck transportation model that allows trucks to serve as both hydrogen storage and transportation resources. Furthermore, considering the cooperative relationships among ammonia industrial systems in different regions, we proposed a Nash bargaining method based on the magnitude of interactive value contributions to equitably allocate the cooperation costs and benefits of the MAIS. Finally, real data from Shandong Province in China is used to validate the effectiveness and significance of the proposed model and strategy. The results indicate that the proposed model can effectively reduce the overall operating costs of the system and enhance its flexibility, providing valuable insights into exploring the collaborative potential of large-scale renewable resources across regions.

Access to Care among Hispanic/Latin American Patients in Neuropsychology: Addressing Potential Barriers to Appointment Adherence

Abstract Objective We will review barriers impacting access to care among Hispanic/Latin American patients, focusing on factors contributing to no-shows and possible interventions. Method We implemented an appointment management system to improve attendance using native Spanish speakers when indicated. We then recorded demographic data and attendance from 211 consecutive Hispanic/Latin American patients (123 females/88 males; mean age = 55 years, SD = 17.3; mean years formal education = 8.7, SD = 4.5). These were from a study of 537 patients in which we conducted MANOVA assessing the relationship between attendance and age, education, proxy income, and prior no-show rate, and Chi-Squares assessing associations between appointment status and gender, payor, race/ethnicity, MyChart Activation, and availability of collaterals. Among Hispanic/Latin American patients, we assessed associations between appointment status and language, MyChart activation, and availability of collaterals. Results Our system improved attendance, though no-shows remained high (17% among Latin American/Hispanic patients) with significant associations between attendance and language (χ2 = 9.884, p = 0.007), MyChart access and language (χ2 = 11.315 p = 0.004), and availability of collaterals and language (χ2 = 8.785, p = 0.012). Being monolingual Spanish speaking was associated positively with collateral availability (p < 0.05), while the relationships between language and attendance and between language and MyChart activation were complex. In the larger sample, older age, higher prior no-show rates, and no MyChart activation were associated with poorer appointment attendance; education and availability of collaterals were not. Conclusion There are potentially modifiable targets to improve attendance and access to care. We are now developing a health-fair model to teach patients the importance of attendance, assisting them with MyChart, transportation, and other resources.

Resilience analysis of cities' air accessibility under disruptions

Air transportation stands as an indispensable pillar of a city's economy. An effective and reliable air transport service plays an important role for the prosperity of a city. Moreover, in many cases, a city has multiple airports within its catchment area and the collaborative relationships under disruptions between these airport services have often been overlooked in prior studies. To bridge this gap, this paper firstly introduces the concept of “instant air accessibility” for a city and develops a resilience metric aimed at quantifying the impact of airport disruptions on a city's air accessibility, taking into account the perspective of a multi-airport system. We apply the metrics to a network with 48 cities at or above the second-tier level in China. Results of the air accessibility analysis show that some relatively small cities have high accessibility that are comparable to megacities in China, but some provincial capital cities have low accessibilities, although they maintain superior political and economic status among the cities. Most of the cities' accessibility are vulnerable to targeted disruptions. Additionally, we identify critical airports that wield significant influence over the overall accessibility performance of the entire network. The findings from this study offer valuable insights for the management of air transport resources and the enhancement of the resilience of cities' air accessibility.

Advancing Military Medical Planning in Large Scale Combat Operations: Insights From Computer Simulation and Experimentation in NATO's Vigorous Warrior Exercise 2024.

The ongoing conflict in Ukraine from Russian invasion presents a critical challenge to medical planning in the context of multi-domain battle against a peer adversary deploying conventional weapon systems. The potential escalation of preventable morbidity and mortality, reaching a scale unprecedented since World War II, underscores the paramount importance of effective phases of care from Point of Injury (PoI)/Point of Wounding (PoW) or Point of Exposure (PoE) to Role 1 (R1) and Role 2 (R2) echelons of care.The NATO Vigorous Warrior (VW) Live Exercise (LIVEX) serves as a strategic platform for NATO and its partners, providing an opportunity to challenge operational concepts, experiment, innovate life-saving systems, and foster best practices across the Alliance. This study delineates the strategic application of the VW LIVEX platform for the adaptation of the computational simulation software Simulation for the Assessment and Optimization of Medical Disaster Management (SIMEDIS) within the context of Large-Scale Combat Operations (LSCO). The SIMEDIS computer simulator plays a pivotal role by furnishing real-time insights into the evolving injury patterns of patients, employing an all-hazards approach. This simulator facilitates the examination of temporal shifts in medical timelines and the ramifications of resource scarcity against both morbidity and mortality outcomes. The VW LIVEX provides a unique opportunity for systematic validation to evaluate the results of the computer simulator in a realistic setting and identify gaps for future concepts of operations. We report the process and methodologies to be evaluated at the VW LIVEX in far forward and retrospective medical support operations. Using the SIMEDIS simulator, we can define battlefield scenarios for varied situations including artillery, drone strikes, and Chemical, Biological, Radiological, Nuclear, and explosive (CBRNe) attacks. Casualty health progressions versus time are dependent on each threat. Mortality is computed based on the concepts found in Tactical Combat Casualty Care (TCCC) of "self-aid"/"buddy-aid" factoring in the application or absence of definitive traumatic hemorrhage control and on the distribution policy of victims to medical treatment facilities through appropriate Command and Control (C2) ("Scoop and Run" versus "Stay and Play"). The number of medical supplies available along with the number of transport resources and personnel are set and are scalable, with their effect on both morbidity and mortality quantified.Concept of Medical Operations can be optimized and interoperability enhanced when shared data are provided to C2 for prospective medical planning with retrospective data. The SIMEDIS simulator determines best practices of medical management for a myriad of injury types and tactical/operational situations relevant to policy making and battlefield medical planning for LSCO. The VW LIVEX provides a Concept Development and Experimentation platform for SIMEDIS refinement and conclusive insights into medical planning to reduce preventable morbidity and mortality. Recommending further iterations of similar methodologies at other NATO LIVEXs for validation is crucial, as is information sharing across the Alliance and partners to ensure best practice standards are met.

Speed up for sustainable development: High-speed rail and corporate ESG performance

With sustainability becoming a fundamental concept in development, there has been a growing emphasis on corporate environmental, social, and governance (ESG) performance. This paper examines how the high-speed railway (HSR) network would affect enterprises' ESG performance by constructing an indicator of HSR network advantages based on social network analysis (SNA). We found that the HSR network advantages can enhance corporate ESG performance, and this effect is achieved by alleviating financing constraints and promoting corporate technological innovation. Meanwhile, the effect of HSR network advantages on corporate ESG performance is more pronounced in private firms, low-polluting industries, and cities with high initial transportation resource endowments. Furthermore, we further examine the impact of the sub-dimensional characteristics of the HSR network on corporate ESG performance. We found that degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC) all have a positive effect on enhancing firms' ESG performance, but closeness centrality (CC) produced the largest impact.

Root and rhizosphere traits for enhanced water and nutrients uptake efficiency in dynamic environments.

Modern agriculture's goal of improving crop resource acquisition efficiency relies on the intricate relationship between the root system and the soil. Root and rhizosphere traits play a critical role in the efficient use of nutrients and water, especially under dynamic environments. This review emphasizes a holistic perspective, challenging the conventional separation of nutrient and water uptake processes and the necessity for an integrated approach. Anticipating climate change-induced increase in the likelihood of extreme weather events that result in fluctuations in soil moisture and nutrient availability, the study explores the adaptive potential of root and rhizosphere traits to mitigate stress. We emphasize the significance of root and rhizosphere characteristics that enable crops to rapidly respond to varying resource availabilities (i.e. the presence of water and mobile nutrients in the root zone) and their accessibility (i.e. the possibility to transport resources to the root surface). These traits encompass for example root hairs, mucilage and extracellular polymeric substance (EPS) exudation, rhizosheath formation and the expression of nutrient and water transporters. Moreover, we recognize the challenge of balancing carbon investments, especially under stress, where optimized traits must consider carbon-efficient strategies. To advance our understanding, the review calls for well-designed field experiments, recognizing the limitations of controlled environments. Non-destructive methods such as mini rhizotron assessments and in-situ stable isotope techniques, in combination with destructive approaches such as root exudation analysis, are proposed for assessing root and rhizosphere traits. The integration of modeling, experimentation, and plant breeding is essential for developing resilient crop genotypes capable of adapting to evolving resource limitation.

Using Vascular Deserts as a Guide for Limb Preservation Outreach Programs Successfully Targets Underserved Populations

Vascular deserts, regions without vascular providers, previously described targets for limb salvage efforts. The Comprehensive Heart and Multidisciplinary Limb Preservation Outreach Networks (CHAMPIONS) programs targeted regions for outreach and evaluated the population using desert maps. At 2 events targeting underserved regions between 2022 and 2023, providers screened and educated participants on peripheral arterial and cardiovascular disease (PACD). Demographics and cardiovascular risk factors were collected. Using Arc geographic information system, vascular surgeons, and Vascular Quality Initiative (VQI) participating facilities were mapped with a 30-mile buffer. Participants were mapped with census data, and the healthy places index (HPI) was overlayed for population and social determinants of health data analysis in medical service study areas (MSSA), a geographical analysis unit. (Figure1) Results were compared to prior statewide deserts. Outreach program participants' mean age was 56 (range 6-88); 39% were male, and the majority were Hispanic (86%). 27% had no primary care provider (PCP). 30% had diabetes, 10% undiagnosed before the event, 38% had hypertension, 40% undiagnosed prior to the event, and 21% described intermittent claudication. 81% made <$30,000 annually, and 28% reported no health insurance. Similarities were observed when comparing program participant demographics to the population-level data from the targeted regions. Patients were more frequently Hispanic than other desert regions (68% vs. 36%, P<0.001). Compared to other vascular desert regions, the target population was more disadvantaged in all HPI domains, including economic (18 vs. 38%, P<0.001), education (21 vs. 39%, P<0.001), and transportation (30 vs. 40%, P<0.001). Worse education, financial, and transportation resources correspond to decreased care access due to poor literacy and travel burdens. CHAMPIONS programs successfully targeted populations needing care based on vascular care desert maps, demonstrating that at-risk populations can be successfully identified and screened for cardiovascular disease.