Efficiency Metrics Research Articles

QLTI-10. IMPACT OF A NEURO-ONCOLOGY HOSPITALIST MODEL ON INPATIENT CANCER CARE DELIVERY AT A SINGLE INSTITUTION

Abstract Patients with brain tumors are often admitted to the hospital at critical stages of their disease course: initial diagnosis, relapse, end-of-life, and for management of serious complications. Patients who develop neurologic complications from systemic cancers and their treatments also frequently require management in the hospital setting. Most neuro-oncologists have busy outpatient practices and limited inpatient responsibilities. Their experience with hospital workflows, bandwidth to see inpatients, and comfort level managing certain diagnoses such as immunotherapy neurotoxicities may be variable, even within a single institution. Recent literature supports the beneficial role of the Oncology Hospitalist, a physician who specializes in the care of hospitalized patients with cancer. Oncology Hospitalist care improves length of stay, cost effectiveness, and the patient and oncologist experience. To our knowledge, the role of a hospitalist who specializes in the care of patients with neuro-oncologic issues has not yet been investigated. Here we describe the responsibilities and examine the impact of a Neuro-Oncology Hospitalist at a single institution over a 2-year period, with a focus on metrics of quality and efficiency of care. Patients and cancer care systems benefit from the dedicated inpatient presence of a neuro-oncologist, supporting the development of a Neuro-Oncology Hospitalist model.

The brain’s first “traffic map” through Unified Structural and Functional Connectivity (USFC) modeling

The brain’s white matter connections are thought to provide the structural basis for its functional connections between distant brain regions but how our brain selects the best structural routes for functional communications remains poorly understood. In this study, we propose a Unified Structural and Functional Connectivity (USFC) model and use an “economical assumption” to create the brain’s first “traffic map” reflecting how frequently each segment of the brain structural connection is used to achieve the global functional communication system. The resulting USFC map highlights regions in the subcortical, default-mode, and salience networks as the most heavily traversed nodes and a midline frontal-caudate-thalamus-posterior cingulate-visual cortex corridor as the backbone of the whole brain connectivity system. Our results further revealed a striking negative association between structural and functional connectivity strengths in routes supporting negative functional connections, as well as significantly higher efficiency metrics and better predictive performance for cognition in the USFC connectome when compared to structural and functional ones alone. Overall, the proposed USFC model opens up a new window for integrated brain connectome modeling and provides a major leap forward in brain mapping efforts for a better understanding of the brain’s fundamental communication mechanisms.

Preparation of Vanadium (3.5+) Electrolyte by Hydrothermal Reduction Process Using Citric Acid for Vanadium Redox Flow Battery

In this study, vanadium (3.5+) electrolyte was prepared for vanadium redox flow batteries (VRFBs) through a reduction reaction using a batch-type hydrothermal reactor, differing from conventional production methods that utilize VOSO4 and V2O5. The starting material, V2O5, was mixed with various concentrations (0.8 M, 1.2 M, 1.6 M, 2.0 M) of citric acid (CA) as the reducing agent and stirred for 60 min at 90 °C using a hot plate to ensure complete dispersion in the solution. The resulting solution was subsequently subjected to a hydrothermal reduction reaction (HRR) furnace at 150 °C for 24 h to generate vanadium (3.5+). The mixed states of the produced vanadium (3+) and vanadium (4+) were confirmed using UV-vis spectroscopy. The electrochemical properties of the electrolyte were investigated through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), revealing that the optimal concentration of the CA was 1.6 M. The current efficiency, energy efficiency, and voltage efficiency of the electrolyte produced via the HRR process was compared with that prepared using VOSO4 in charge and discharge experiments. The results demonstrate that the HRR process yields an enhanced electrolyte across all efficiency metrics produced through the given improved performance in all efficiencies. These findings indicate that the HRR process using citric acid can facilitate the straightforward preparation of vanadium (3.5+) electrolyte, making it suitable for large-scale production.

Comprehensive redesign of a rehabilitation program at Runnymede Healthcare Centre.

The redesign of the rehabilitation program at Runnymede Healthcare Centre (RHC) was designed to enhance patient care, staff satisfaction, and rehabilitation capacity. Aligned with RHC's strategic vision to become a centre of excellence for ageing and wellness, the initiative introduced a range of initiatives such as group therapy models, specific, measurable, achievable, relevant and time-bound rehabilitation goals, and enhanced leadership practices under the healthy engagement of older adults in rehabilitation therapy philosophy. Evaluation was integral to the initiative, with patient satisfaction, staff engagement, and key performance indicators continuously measured. Patient satisfaction scores, operational efficiency metrics, and staff engagement scores improved considerably. This project exemplifies how structured change management and embedded evaluation can lead to sustained improvements in patient care, staff experience, and rehabilitation outcomes.

Are Climate Change Strategies Effective in Managing Urban Water Resources? The Case of Portugal

This study examines the relationship between climate mitigation, adaptation strategies, and water management practices in Portugal from 2015 to 2021. Utilizing climate and water resource data from 2015 to 2021, including meteorological data (emperature, rainfall), wastewater treatment volumes, and energy efficiency metrics, the data are sourced from national agencies such as IPMA, ERSAR, APA, and Eurostat. The methodology employs correlation analysis to assess the relationships between climate variables (e.g., temperature, rainfall) and water resource indicators (e.g., reclaimed wastewater, energy efficiency). Despite notable reductions in greenhouse gas emissions and improvements in wastewater treatment efficiency, water resource stability remains a complex issue, particularly with regional disparities such as severe droughts in the Algarve. Additionally, the study evaluates the effectiveness of rainwater harvesting systems, reclaimed wastewater, and infiltration facilities, revealing a decline in reclaimed wastewater efficiency despite increased wastewater treatment. Rainwater harvesting systems (RWHSs) offer resilience, but their broader adoption is hindered by high costs and public perception challenges. Key recommendations include the development of resilient infrastructure, enhanced support for reclaimed water use, and increased investment in research to address water management challenges amid climate variability.

Redefining artificial lighting through spectral engineering of light sources for well-being

Light-emitting diodes (LEDs) have revolutionized artificial lighting, but also exposed the detrimental health effects that stem from insufficient exposure to natural light. Human-centric artificial lighting requires both visual quality and circadian lighting performance that mimics daylight’s evolving spectral power distribution (SPD). Here, we present a color-tunable LED-based light source that achieves SPDs similar to various conditions of daylight and incandescent lighting over the range of visible wavelengths. This light source is comprised of a linear combination of light converter channels containing dyes exhibiting thermally activated delayed fluorescence (TADF) fabricated through additive manufacturing, photoexcited by violet-emitting LEDs (VLED). This hybrid light source establishes a new benchmark for state-of-the-art artificial lighting at approximating daylight, with Illuminating Engineering Society (IES) color gamut index Rg values within 3%, IES color fidelity index Rf values within 7% through CCT values ranging from 4277 K to 22,333 K. We propose efficiency metrics to accurately quantify similarity between light sources and the respective reference daylight spectrum encompassing visual and circadian effects, facilitating WLED benchmarking. The efficiency metrics pertaining to circadian lighting performance remain within 10% over the same CCT range. These results advance lighting science to address simultaneously the grand challenges of health and sustainability.

Unlocking Insights: Using Machine Learning to Identify Wasting and Risk Factors in Egyptian Children Under 5

Introduction: Malnutrition, particularly wasting, continues to be a significant public health issue among children under five years in Egypt. Despite global advancements in child health, the prevalence of wasting remains a critical concern. This study employs machine learning techniques to identify and analyze the determinants of wasting in this population. Aim: To evaluate the prevalence of wasting among children under five years in Egypt and identify key factors associated with wasting using machine learning models.Methods: This study is based on secondary data sourced from the Demographic and Health Surveys (DHS), conducted in 2005, 2008, and 2014. Six machine learning classifiers (XGBoost, Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbor, and Decision Tree) were applied to the dataset. The study included children under five years of age, focusing on nutritional status, maternal health, and socio-economic factors. The dataset was cleaned, preprocessed, encoded using one-hot encoding, and split into training (70%) and test (30%) sets. Additionally, k-fold cross-validation and the StandardScaler function from Scikit-learn were used. Performance metrics such as accuracy, precision, recall, F1 score, and ROC-AUC were used to evaluate and compare the algorithms.Results: It was observed that 76.2% of the children in the dataset have normal nutritional status. Furthermore, 5.2% were found to be suffering from wasting (1.7% experiencing severe wasting and 3.5% moderate wasting), with notable regional disparities. The XGBoost model outperformed other models. Its efficiency metrics include an accuracy of 94.8%, precision of 94.7%, recall of 94.7%, F1 score of 94.7%, and an ROC-AUC of 99.4%. These results indicate that XGBoost was highly effective in predicting wasting.Conclusion: Machine learning techniques, particularly XGBoost, show significant potential for improving the classification of nutritional status and addressing wasting among children in Egypt. However, the limitations in simpler models highlight the need for further research to refine predictive tools and develop targeted interventions. Addressing the identified determinants of wasting can contribute to more effective public health strategies.

A Review of Comprehensive Guidelines for Computational Fluid Dynamics (CFD) Validation in Solar Chimney Power Plants: Methodology and Manzanares Prototype Case Study

This review provides a comprehensive examination of CFD modeling procedures for SCPP, with an emphasis on the detailed methodologies and a case study of the Manzanares prototype in Spain. The introduction delineates the historical context and physical modeling principles of solar chimneys, while highlighting their potential in industrial applications. The governing equations are meticulously discussed, covering assumptions in both 2D and 3D CFD modeling, the continuity and momentum equations, and the selection and accuracy of turbulence models, particularly the k-ε equations. The review also delves into heat transfer modeling, encompassing the energy equation and radiation modeling. Analytical evaluations of turbine pressure drop ratios and performance metrics for power generation efficiency are critically analyzed. The establishment of boundary conditions in solar chimney applications, including sky temperature assessments and distinctions between 2D and 3D boundary conditions, is extensively explored. Mesh generation techniques for both 2D and 3D CFD models are presented, supported by case studies. Parametric studies and experimental investigations are scrutinized to elucidate their impact on the performance of solar chimneys. The temperature–entropy diagram for an idealized Brayton cycle is introduced as a conceptual framework for efficiency analysis. Validation of the CFD codes, both 2D and 3D, against experimental data is performed to ensure model accuracy. The review further examines energy balance approaches in modeling solar chimneys, presenting state-of-the-art CFD results and discussing their implications in both 2D and 3D contexts. The synthesis of these findings culminates in a comprehensive conclusion, offering insights into the future directions and potential advancements in the CFD modeling of solar chimneys. This work aims to serve as a definitive reference for researchers and practitioners in the field, providing a robust foundation for the development and optimization of SCPP technology.

Iterative Development of a Clinical Decision Support Tool to Enhance Naloxone Co-Prescribing.

Background Opioid overdoses have contributed significantly to mortality in the United States. Despite long-standing recommendations from the Centers for Disease Control and Prevention to co-prescribe naloxone for patients receiving opioids who are at high risk of overdose, compliance with these guidelines has remained low. Objectives The objective of this study was to develop and evaluate a hospital-wide electronic health record (EHR)-based clinical decision support (CDS) tool designed to promote naloxone co-prescription for high-risk opioids. Methods We employed an iterative approach to develop a point-of-order, interruptive EHR alert as the primary intervention and assessed naloxone prescription rates, EHR efficiency metrics, and barriers to adoption. Data was obtained from our EHR's clinical data warehouse and analyzed using statistical process control and Chi-square analyses to assess statistically significant differences in prescribing rates during the intervention periods. Results The initial implementation phase of the intervention, spanning from April 2019 to May 2022, yielded a nearly 3-fold increase in the proportion of high-risk patients receiving naloxone, rising from 13.4% [95% CI, 12.9% - 13.8%] to 36.4% [95% CI, 35.2% - 37.5%; p = 1 x 10-38]. Enhancements to the CDS design and logic during the subsequent iteration's study period, June 2022 and December 2023, reduced the number of CDS triggers by more than 30-fold while simultaneously driving an additional increase in naloxone receipt to 42.7% [95% CI, 40.6% - 44.8%; p = 2 x 10-5]. The efficiency of the CDS demonstrated marked improvement, with prescribers accepting the naloxone co-prescription recommendation provided by the CDS in 41.1% of the encounters in version two, compared to 6.2% in version one (p = 6 x 10-9). Conclusion This study offers a sustainable and scalable model to address low rates of naloxone co-prescription and may also be used to target other opportunities for improving guideline-concordant prescribing practices.

Comparative Analysis of Holmium Laser Enucleation of the Prostate (HoLEP) and Robotic-Assisted Simple Prostatectomy (RASP) in BPH Management: A Systematic Review and Meta-Analysis.

As the prevalence of benign prostatic hyperplasia (BPH) increases, the demand for surgical interventions that optimize patient outcomes while minimizing complications grows. This systematic review compares the efficacy, efficiency, and safety of holmium laser enucleation of the prostate (HoLEP) with robotic-assisted simple prostatectomy (RASP), providing insights for evidence-based surgical decision-making in BPH treatment. Adhering to PRISMA guidelines, the study protocol was registered with Prospero [CRD42024509627]. Searches were conducted in Medline, Embase, Web of Science, Scopus, and CINAHL up to February 1, 2024, to include studies that compare HoLEP and RASP in patients with BPH. Risk of bias was evaluated using the Newcastle Ottawa Scale. HoLEP and RASP demonstrated equivalent effectiveness in treating BPH, as shown by similar functional outcomes such as maximum urinary flow rate and postvoid residual volume. However, HoLEP outperformed RASP in several operational efficiency metrics, reducing operative time by 49.48 minutes, hospitalization duration by 1.5 days, and catheterization period by 3.8 days. HoLEP also significantly reduced the risk of blood transfusions by 75%. Patients undergoing RASP were 1.87 times more at risk for grade 2 complications and 3.41 times more at risk for developing grade 3 or above complications. HoLEP and RASP are effective for managing BPH. HoLEP shows advantages in recovery metrics and lower blood transfusion rates, while RASP benefits from ease of implementation in robotic-equipped facilities. Optimizing surgical outcomes will depend on reducing disparities in technique adoption, improving surgical training, and aligning with evidence-based guidelines.

Evaluating the Technological Impact of Unmanned Aerial Vehicles on Efficiency Metrics in Precision Agriculture: A Secondary Data Analysis

Evaluating the Technological Impact of Unmanned Aerial Vehicles on Efficiency Metrics in Precision Agriculture: A Secondary Data Analysis

Structure Design on Thermoplastic Composites Considering Forming Effects.

Carbon fiber reinforced polypropylene (CF/PP) thermoplastics integrate the superior formability of fabrics with the recoverable characteristics of polypropylene, making them a pivotal solution for achieving lightweight designs in new energy vehicles. However, the prevailing methodologies for designing the structural performance of CF/PP vehicular components often omit the constraints imposed by the manufacturing process, thereby compromising product quality and reliability. This research presents a novel approach for developing a stamping-bending coupled finite element model (FEM) utilizing ABAQUS/Explicit. Initially, the hot stamping simulation is implemented, followed by the transmission of stamping information, including fiber yarn orientation and fiber yarn angle, to the follow-up step for updating the material properties of the cured specimen. Then, the structural performance analysis is conducted, accounting for the stamping effects. Furthermore, the parametric study reveals that the shape and length of the blank holding ring exerted minimal influence on the maximum fiber angle characteristic. However, it is noted that the energy absorption and crushing force efficiency metrics of the CF/PP specimens can be enhanced by increasing the length of the blank holding ring. Finally, a discrete optimization design is implemented to enhance the bending performance of the CF/PP specimen, accounting for the constraint of the maximum shear angle resulting from the stamping process. The optimized design resulted in a mass reduction of 14.3% and an improvement in specific energy absorption (SEA) by 17.5% compared to the baseline sample.

Impact of advanced endoscopy training on colonoscopy quality and efficiency.

Few reports have detailed improvements in the quality of colonoscopies with continuous training post-fellowship completion. We examined the changes in colonoscopy performance among trainees during our advanced endoscopy training program. Screening or surveillance colonoscopies performed by 11 trainees who participated in our 3-year advanced endoscopy training program between April 2015 and March 2020 were retrospectively analyzed. Quality and efficiency metrics of colonoscopies were evaluated annually. Altogether, 297, 385, and 438 colonoscopies were enrolled in the first, second, and third training years, respectively. The mean insertion times were 8.6, 7.6, and 6.9 min in the first, second, and third training years, respectively, with significant improvement from the first to second year (p = 0.03) and from the first to third year (p < 0.01). The adenoma detection rate, proximal adenoma detection rate, and mean number of adenomas per patient exhibited a tendency to improve annually; however, the difference was not significant. Polypectomy efficiency was 10.5%, 11.2%, and 13.0%, with significant improvements from the first to third year (p < 0.01) and from the second to third year (p = 0.02). Insertion time and polypectomy efficiency showed significant improvements, especially among trainees experienced with <500 colonoscopies. Through our advanced endoscopy training program, there has been an improvement in the quality and efficiency of colonoscopy for trainees who have completed their fellowships, particularly those with <500 colonoscopies.

Stochastic optimization of a mass-in-mass cell with piecewise hybrid nonlinear–linear restoring force

This article investigates the optimization under uncertainty of a mass-in-mass meta-cell for its potential use within a metamaterial. The specificity of the proposed mass-in-mass system stems from the hybrid nonlinear–linear stiffness at the inner level. It is well known that these systems are highly sensitivity to small perturbations in loading conditions or design parameters. In fact, the sensitivity is such that the system can exhibit discontinuous behaviors. Therefore the proposed optimization approach not only accounts for sources of uncertainties but also can handle discontinuous responses. The objective of the stochastic optimization is to find the stiffness properties of the mass-in-mass system which minimize the expected value of a specific efficiency metric. In order to better understand the system’s dynamic behavior and the origins of the discontinuities, slow invariant manifolds and frequency response curves are provided. The efficiency of the optimized system with hybrid stiffness is compared with that of a similar optimized system featuring pure cubic nonlinearity.

Prediction of Overall Equipment Effectiveness in Assembly Processes Using Machine Learning

Abstract Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

Service priority classification using machine learning

This article details a procedure for classifying service cases with various priority levels based on machine learning (ML). It accurately defines the priority level of each service case. The presence of imbalanced datasets in service cases poses a challenge for achieving reliable classification accuracy. To address this, the use of the synthetic minority over-sampling technique (SMOTE) was proposed as the method for balancing the datasets prior to applying the ML method. From these experimental results, an improvement in the precision of the learning process was observed, which led to better outcomes in the test sets. This improvement was measured using the efficiency metrics from the confusion matrix. The experiment involved 6,182 service cases, categorized into four levels: critical, serious, moderate, and low. These were based on test comparisons with other ML methods. The accuracy achieved in the test data was 94.37%. By employing a hybrid technique to address the imbalance in SMOTE and the support vector machine model, it was found to be more effective than the comparative term frequency-inverse document frequency model that was used in conjunction with cosine similarity, which achieved an evaluation score of 70.14%.

Association of genomically enhanced residual feed intake with performance, feed efficiency, feeding behavior, gas flux, and nutrient digestibility in growing Holstein heifers.

Residual feed intake (RFI), a metric of feed efficiency, is moderately heritable and independent of body size and productivity, making it an ideal trait for investigation as a selection criterion to improve feed efficiency of growing cattle. The objective of this study was to examine the differences in performance, feed efficiency, feeding behavior, gas flux, and nutrient digestibility in Holstein heifers with divergent genomically enhanced breeding values for RFI (RFIg). Holstein heifers (n = 55; BW = 352 ± 64kg) with low (n = 29) or high (n = 26) RFIg were selected from a contemporary group of 453 commercial Holstein heifers. Heifers were rotated between 1 of 2 pens, each equipped with four electronic feed bunks and one pen with a GreenFeed gaseous exchange monitoring (GEM) system. Individual dry matter intake (DMI) and feeding behavior data were collected for 84-d. Body weight (BW) was measured weekly and spot fecal samples collected at weighing. Phenotypic RFI (RFIp) was calculated as the residual from regression of DMI on average daily gain (ADG) and mid-test metabolic BW (BW0.75). A mixed model including the fixed effect of RFIg classification and random effect of group was used to evaluate the effect of RFIg classification on response variables. There were no differences (P > 0.05) in BW and ADG for heifers with divergent RFIg; however, low RFIg heifers consumed 7.5% less (P < 0.05) feed per day. Consequently, low RFIg heifers exhibited a more favorable (P < 0.05) RFIp (-0.196 vs 0.222kg/d, respectively). Low RFIg heifers had 8.7% fewer (P < 0.05) bunk visit (BV) events per day and tended to have a 11.2% slower (P < 0.10) eating rate. Low RFIg heifers had 7.7% lower (P < 0.05) methane (CH4) emissions (g/d), 6.1% lower (P ≤ 0.05) carbon dioxide (CO2) production (g/d), and 5.6% lower (P ≤ 0.05) heat production (Mcal/d) than high RFIg heifers. However, CH4 yield and CO2 yield (g/kg DMI), and heat production per unit DMI (Mcal/kg DMI) did not differ (P > 0.05) between heifers with divergent RFIg. Dry matter and nutrient digestibility did not differ (P > 0.05) between heifers with divergent RFIg. Overall, heifers selected to be more feed efficient exhibited more favorable energy efficiencies and feed efficiency phenotypes. Results suggest that selection based on RFIg provides opportunities to select cattle with favorable feed efficiency phenotypes to increase the economic and environmental sustainability of the cattle industry.

Measuring, interpreting and monitoring economic efficiency in South Australia’s Spencer Gulf and West Coast Prawn Fisheries

Fishers and fishing vessels are not homogeneous, with their level of catch affected by the choice of physical inputs such as engine size, boat size and the type of fishing technology employed, as well as less tangible factors such as skipper skill and experience. Economic output also vitally depends upon the value of the product in the market and the cost of inputs. These differences in the ability of individual fishers to catch fish, and create economic returns, can be assessed through the application of efficiency analysis. This paper provides an empirical examination of efficiency indicators in the South Australian Spencer Gulf and West Coast Prawn Fisheries. Applying data envelopment analysis (DEA) to a unique vessel-level dataset that includes both quantity and value information, we estimate measures of efficiency in these input-controlled fisheries where effort decisions are made in real-time using a participative management approach. We find that quantity and value-based efficiency measures are relatively high but that the variability of value-based measures is increasing. This increasing heterogeneity in value, despite the homogeneity in quantities, has potential implications for pressures on cooperative management going forwards as fishers pursue different business strategies. Comparing the measures of efficiency to direct measures of individual profit, we find a divergence: technical and cost measures of efficiency are more strongly positively related to short-run economic performance while revenue-efficiency is more strongly positively correlated with long-run economic performance, suggesting that different efficiency metrics may be required for short- and long-run monitoring.

Experiment on Lung Disease Classification using YOLOv8

Lung diseases are a leading cause of illness and mortality worldwide. Accurate and timely diagnosis is crucial for improving patient outcomes, but manual interpretation of chest X-rays by radiologists may consume a significant amount of time and is susceptible to errors. This study leverages the state-of-the-art YOLOv8 deep learning model to develop an automated system for classifying lung diseases from chest X-ray images. The proposed approach utilizes a large, diverse dataset of 21,165 chest X-ray images categorized into four classes: COVID-19, viral pneumonia, lung opacity, and normal. The YOLOv8-cls model is fine-tuned using transfer learning and advanced data augmentation techniques. The system achieves a high accuracy of approximately 95% across all disease classes while maintaining real-time performance. Confusion matrix analysis demonstrates strong performance in correctly identifying each condition, with COVID-19, normal, viral pneumonia and lung opacity cases correctly identified 97%, 97%, 99% and 93% of the time respectively. The outcomes validate the adaptability and reliability of deep learning, specifically YOLOv8, for automated lung disease detection, offering the potential to improve clinical workflows and patient care by providing efficient screening tools for practitioners. The study addresses limitations of previous works by utilizing a large consolidated dataset, reporting comprehensive computational efficiency metrics, and extending classification complexity without substantially impacting accuracy or speed. Future research should focus on further optimizations and extending the curated image repositories to include under-represented patient groups to enhance the model's inclusiveness and robustness.

Demand-Aware Distributed Pathfinding for Repositioning Vehicles in Shared-use Autonomous Mobility Services

Shared-use autonomous mobility services (SAMS) have the potential to provide accessible and demand-responsive mobility to passengers, while benefitting from autonomous vehicle (AV) technology and bypassing challenges related to supply-side incentives or individual driver goals. SAMS operators typically aim to achieve efficiency and improved service quality in their fleet operations, both of which are further enabled by the use of AVs. Specifically, fleet repositioning decisions in anticipation of future demand can improve service quality, but existing approaches in the literature seldom consider the problem of routing repositioning vehicles in a way that further improves SAMS objectives. This paper presents an approach for demand-aware distributed routing pathfinding for repositioning vehicles, which can supplement existing vehicle repositioning approaches. The problem is formulated with a multi-criteria objective that minimizes the vehicles’ total travel time and maximizes their total demand-serving potential, while distributing that potential equitably among the ride-seeking passengers across the transportation network. We evaluate the proposed approach via numerical experiments using an agent-based simulation of SAMS operations in the network of Manhattan in New York City. The proposed approach is compared to a baseline simple shortest path approach for routing the repositioning vehicles. The results demonstrate that mean passenger waiting times for pick-up can be reduced, while also reducing the total vehicle miles and the empty miles travelled due to repositioning. Thus, the proposed approach can help improve the overall system performance in terms of both service quality and efficiency metrics, relative to the baseline approach.