Resource Allocation Approach Research Articles

Integrating Kubernetes Autoscaling for Cost Efficiency in Cloud Services

Kubernetes Autoscaling Mechanism for Integration into Cloud Services to Achieve Cost Efficiency Organizations have turned towards containerized applications and microservices architecture. Optimizing and using resources appropriately as per the expected operational cost becomes the need of the hour. There are several autoscaling mechanisms within Kubernetes, that include Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler, working towards cost optimization. We study predictive scaling algorithms, multi-dimensional autoscaling strategies, and machine learning-based approaches for resource allocation. Among the new challenges of implementing the solution are the methodologies followed in evaluating the research, which also involves complex advanced optimization techniques: from integrating serverless, towards multicloud autoscaling. Our findings will give an understanding of the status quo of Kubernetes autoscaling towards cost efficiency and recommendations for future research and industrial implementation.

Resource Allocation Approach of Avionics System in SPO Mode Based on Proximal Policy Optimization

Single-Pilot Operations (SPO) mode is set to reshape the decision-making process between human-machine and air-ground operations. However, the limited on-board computing resources impose greater demands on the organization of performance parameters and the optimization of process efficiency in SPO mode. To address this challenge, this paper first investigates the flexible requirements of avionics systems arising from changes in SPO operational scenarios, then analyzes the architecture of Reconfigurable Integrated Modular Avionics (RIMA) and its resource allocation framework in the context of scarcity and configurability. A “mission-function-resource” mapping relationship is established between the reconfiguration service elements of SPO mode and avionics resources. Subsequently, the Proximal Policy Optimization (PPO) algorithm is introduced to simulate the resource allocation process of IMA reconfiguration in SPO mode. The objective optimization process is transformed into a sequential decision-making problem by considering constraints and optimization criteria such as load, latency, and power consumption within the feasible domain of avionics system resources. Finally, the resource allocation scheme for avionics system reconfiguration is determined by controlling the probability of action selection during the interaction between the agent and the environment. The experimental results show that the resource allocation scheme based on the PPO algorithm can effectively reduce power consumption and latency, and the DRL model has strong anti-interference and generalization. This enables avionics resources to respond dynamically to the capabilities required in SPO mode and enhances their ability to support the aircraft mission at all stages.

DYNAMIC RESOURCE ALLOCATION IN EDGE COMPUTING ENVIRONMENTS: A MACHINE LEARNING APPROACH

Edge computing is a promising paradigm that addresses the limitations of traditional cloud-centric architectures by placing computation near the data source. The main challenge is efficient resource allocation for various applications while optimizing resource utilization. This paper proposes a machine learning-based approach for dynamic resource allocation in edge computing environments. It uses machine learning algorithms to analyze real-time data traffic patterns, application demands, and edge node capabilities to dynamically allocate resources like compute, storage, and bandwidth. By continuously acquiring historical data and adjusting to varying workloads, the system improves overall system performance and user experience. Experimental evaluation and simulations show the effectiveness and efficiency of this approach in enhancing resource utilization, lowering latency, and improving scalability in edge computing environments.

Digital Sharing of Social Public Resources and Its Influencing Factors: Insight from China

Digital sharing of social public resources (SPRs) is an approach of resource-allocation that can effectively compensate for the limitations of time, space and communication modes in accessing SPRs and achieve the balanced allocation of them. The purpose of this paper is to study the development level of digital sharing of SPRs and its influencing factors. The panel data of 31 Chinese provinces from 2015 to 2020 are collected. A comprehensive evaluation index system for digital sharing of SPRs is constructed to evaluate its development level using the Global Entropy Weight Method (GEWM). And the influencing factors of the sharing level are systematically identified based on the stepwise regression model. The results show that: the development level of digital sharing of SPRs varies significantly among Chinese provinces and cities, but the overall development level is gradually increasing. The government fiscal expenditure capacity, industrial structure, regional economic development level, scientific and technological innovation capacity, and urbanization level can promote the digital sharing of SPRs. The degree of external openness and regional population size have negative effects on the digital sharing of SPRs.

Abstract C044: Social vulnerability is an important predictor of cardiovascular disease risk after allogeneic hematopoietic cell transplantation

Abstract Introduction: Allogeneic hematopoietic cell transplantation (HCT) is a curative treatment for malignant and non-malignant hematologic diseases. However, long-term HCT survivors have a >4-fold risk of developing cardiovascular disease (CVD) compared to the general population, and CVD is a leading cause of excess mortality after HCT. Comorbidities that emerge after HCT such as hypertension (HTN), diabetes mellitus (DM), and dyslipidemia (DL) are important modifiers of CVD risk, but the impact of social determinants of health (SDOH) on the development of these risk factors and resultant CVD has not been well-characterized. Methods: This retrospective cohort study included 889 patients who underwent an allogenic HCT between 2013 and 2019 at City of Hope and survived ≥1y after HCT. Demographic, disease, treatment, comorbidity, and health outcomes data were abstracted from medical records. The social vulnerability index (SVI), obtained from the Center for Disease Control, represented overall social vulnerability and included 4 themes: socioeconomic status, household composition & disability, minority status & language, and housing & transportation. SVI scores were derived using patients’ addresses at time of HCT. SVI scores ranged from 0 (least vulnerable) to 100 (most vulnerable). Fine-Gray regression analysis evaluated the association between SVI and risk of HTN, DM, and DL, adjusted for age at HCT and treating death as competing risk. We also evaluated the association between having 0, 1, ≥2 cardiovascular risk factors on incidence of clinically significant CVD (heart failure, coronary artery disease, pericarditis, stroke, intracranial hemorrhage, arrhythmia, and/or valvular disease) after HCT. Results: Mean age at HCT was 44.2y (standard deviation 19.8y); 56.7% were male, 43.8% were non-Hispanic White, 35.4% were Hispanic, and 12.9% were Asian. The most common diagnosis was acute myeloid leukemia (39.6%); 48.6% received reduced intensity conditioning. Higher overall SVI was associated with increased risk of HTN (adjusted Hazard Ratio per 10 unit increase in SVI [aHR] 1.10, 95%CI 1.02-1.19) and DM (aHR 1.13, 95%CI 1.03-1.25). There was also an increased risk for DL for the highest SVI tertile for the housing & transportation theme (aHR 1.61, 95%CI 1.10-2.37). Accumulation of de novo risk factors after HCT was associated with an incremental 5y incidence of CVD (None: 6.1%, 95% CI 3.4%-9.9%; 1 risk factor: 15.7%, 95% CI 7.1%-27.3%; >2 risk factors: 31.6%, 95%CI 12.8%-52.4%; p<0.001). In the adjusted multivariable model, survivors with >2 risk factors had 7.0-fold (aHR 6.99, 95%CI 2.86-17.05) risk of CVD compared to those without (ref). Conclusion: Among allogeneic HCT recipients, higher SVI was associated with increased risk of HTN, DM, and DL after HCT, which in turn were associated with incrementally higher 5y incidence of CVD. These findings underscore the importance of SDOH on clinically important outcomes after allogeneic HCT, and may ultimately inform tailored approaches for risk-based screening and resource allocation in long-term survivors. Citation Format: Osariemen V Ogiamien, Sitong Chen, Lizel Atencio, Alysia Bosworth, Meagan Echevarria, Caitlyn Estrada, Mareen Kassabian, Lanie Lindenfeld, F. Lennie Wong, Saro H Armenian, Rusha Bhandari. Social vulnerability is an important predictor of cardiovascular disease risk after allogeneic hematopoietic cell transplantation [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C044.

Efficient resource allocation in cloud computing environments using AI-driven predictive analytics

This paper proposes an innovative AI-driven approach for efficient resource allocation in cloud computing environments using predictive analytics. The study addresses the critical challenge of optimizing resource utilization while maintaining high quality of service in dynamic cloud infrastructures. A hybrid predictive model combining XGBoost and LSTM networks is developed to forecast workload patterns across various time horizons. The model leverages historical data from a large-scale cloud environment, encompassing 1000 servers and over 52 million data points. A dynamic resource scaling algorithm is introduced, which integrates the predictive model outputs with real-time system state information to make proactive allocation decisions. The proposed framework incorporates advanced techniques such as workload consolidation, resource oversubscription, and elastic resource pools to maximize utilization efficiency. Experimental results demonstrate significant improvements in key performance indicators, including increasing resource utilization from 65% to 83%, reducing SLA violation rates from 2.5% to 0.8%, and enhancing energy efficiency, with PUE improving from 1.4 to 1.18. Comparative analysis shows that the proposed model outperforms existing prediction accuracy and resource allocation efficiency methods. The study contributes to the field by presenting a comprehensive, AI-driven solution that addresses the complexities of modern cloud environments and paves the way for more intelligent and autonomous cloud resource management systems.

Practical pearls for management of cranial injury in the developing world.

Traumatic brain injury (TBI) remains a leading cause of morbidity and mortality, with approximately 69million individuals affected globally each year, particularly in low- and middle-income countries (LMICs) where neurosurgical resources are limited. The neurocognitive consequences of TBI range from life-threatening conditions to more subtle impairments such as cognitive deficits, impulsivity, and behavioral changes, significantly impacting patients' reintegration into society. LMICs bear about 70% of the global trauma burden, with causes of TBI differing from high-income countries (HICs). The lack of equitable neurosurgical care in LMICs exacerbates these challenges. Improving TBI care in LMICs requires targeted resource allocation, neurotrauma registries, increased education, and multidisciplinary approaches within trauma centers. Reports from successful neurotrauma initiatives in low-resource settings provide valuable insights into safe, adaptable strategies for managing TBI when "gold standard" protocols are unfeasible. This review discusses common TBI scenarios in LMICs, highlighting key epidemiological factors, diagnostic challenges, and surgical techniques applicable to resource-limited settings. Specific cases, including epidural hematoma, subdural hematoma, subarachnoid hemorrhage, and cerebrospinal fluid leaks, are explored to provide actionable insights for improving neurosurgical outcomes in LMICs.

Balancing allocative and dynamic efficiency with redundant R&D allocation: The role of organizational proximity and centralization

AbstractResearch SummaryResource‐based‐view scholars have mainly examined two resource allocation approaches for competitive advantage in multiunit firms: resource sharing and resource redeployment. These approaches emphasize allocative efficiency—the optimal allocation of resources to maximize their current value. In technology‐intensive industries, firm success also requires achieving dynamic efficiency to increase its future value‐creation. We propose that the redundant allocation of resources—the parallel deployment of non‐scale‐free resources towards the same objective—although allocatively inefficient, increases dynamic efficiency by stimulating inter‐unit competition. Firms' structural features moderate these effects. An analysis of large pharmaceutical firms reveals that redundant R&D increases innovations with high firm‐specific value but simultaneously increases project terminations to reduce wastage. Organizational proximity increases the former effect and decreases the latter. Firm's R&D centralization amplifies the effect of unit proximity.Managerial SummaryIn technology‐intensive industries, multiunit firms often employ redundant allocation of R&D resources, that is, the parallel deployment of scientists and equipment in different units towards realizing the same business objective. Although common, there is little managerial guidance on how this practice impacts firms' R&D outcomes, and how organizational characteristics influence this relationship. An analysis of large pharmaceutical firms reveals that redundant allocation of R&D resources across units increases wastage but also stimulates competing units to create innovations with high firm‐specific value. Organizationally proximate units are less likely to have their redundant projects terminated, while creating more high‐value‐innovations. Centralization of the firm's R&D amplifies the effect of unit proximity.

A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks

AbstractThe world of communications technology has recently undergone an extremely significant revolution. This revolution is an immediate consequence of the immersion that the fifth generation B5G and 6G have just brought. The latter responds to the growing need for connectivity and it improves the speeds and qualities of the mobile connection. To improve the energy and spectral efficiency of these types of networks, the non‐orthogonal multiple access (NOMA) technique is seen as the key solution that can accommodate more users and dramatically improve spectrum efficiency. The basic idea of NOMA is to achieve multiple access in the power sector and decode the required signal via continuous interference cancelation. A resource allocation approach is proposed for the B5G/6G‐NOMA network that aims to maximise system throughput, spectrum and energy efficiency and fairness among users while minimising latency. The proposed approach is based on reinforcement learning (RL) with the use of the Q‐Learning algorithm. First, the process of resource allocation as a problem of maximising rewards is formulated. Next, the Q‐Learning algorithm is used to design a resource allocation algorithm based on RL. The results of the simulation confirm that the proposed scheme is feasible and efficient.

Assessing vehicle interdiction strategies on a complex transportation network: A simulation-based study

The escape interdiction problem within the context of attacker activities on a transportation network is addressed in this study. In the absence of traffic within the network, the attacker attempts to flee the city by choosing one of the shortest paths from the crime scene to a randomly selected exit point. However, in the presence of traffic, the attacker strategically selects the optimal route that minimizes his time to reach a randomly selected exit point. On the other side, defenders try to interdict the attacker on his escape route. Defenders face the daunting challenge of interdicting the attacker’s escape route while operating under limited resources. Dealing with a real city road network further adds complexity to the scenario. A simulation-based model is proposed for the optimal allocation of resources to tackle this issue. The focus then shifts to the development of an advanced search strategy that involves routing with optimal resource allocation. This paper presents the first comparative study for escape interdiction problems within a simulation environment, explicitly focusing on solution methodologies. An optimal resource allocation approach is proposed in the presence of traffic, constituting a novel contribution that has not been previously implemented in escape interdiction problems. In addition, the paper introduces a Genetic Algorithm (GA)-based meta-heuristic approach within a simulation environment. This approach generates optimal paths for defenders, wherein each node is associated with a fixed time window, representing the defender’s waiting time. In this proposed methodology, defenders undertake a tour of the network rather than remaining stationary at a single location. This approach expands the network search capabilities, thereby requiring optimization to ascertain the optimal routes and schedules for the defender vehicles. A case study is conducted using the map of IIT Kharagpur, India, to evaluate the effectiveness of this approach. By employing this approach and conducting in-depth analyses, the aim is to provide valuable insights into the efficiency and practicality of the developed methods on real-world transportation networks.

A Survey of Fog Computing-Based Resource Allocation Approaches: Overview, Classification, and Opportunity for Research

Recently, cloud computing has affected a large part of the computer industry, including software companies and internet service providers. It has proven efficient in managing tasks for applications. Despite its popularity, cloud computing does not meet the requirements of applications because it faces many limitations, such as high latency and bandwidth bottlenecks. These limitations will significantly affect applications sensitive to delays. To meet this challenge, fog computing is introduced as an extension to cloud computing. It improves quality of service (QoS) for applications that suffer from latency by keeping resources and services close to the end-user. How to efficiently and fairly allocate the available resources, e.g., CPU, bandwidth, and memory, between different requested tasks is a complex challenge. The main goal of this paper is to study the concepts of fog computing, architecture, environment, and metrics that affect resource allocation in fog computing. It also summarizes the classification of modern resource allocation approaches based on QoS metrics (2017–2023). On the other hand, highlighting the pros and cons of these studies as well as future research directions to develop different approaches.

A multi-objective optimization approach for resource allocation and transportation planning in institutional quarantine centres

A multi-objective optimization approach for resource allocation and transportation planning in institutional quarantine centres

A Novel Approach for Efficient Resource Allocation in 6G V2V Networks using Neighbor-Aware Greedy Algorithm and Sweep Line Model

A Novel Approach for Efficient Resource Allocation in 6G V2V Networks using Neighbor-Aware Greedy Algorithm and Sweep Line Model

Enhanced Wireless Communication Optimization with Neural Networks, Proximal Policy Optimization and Edge Computing for Latency and Energy Efficiency

This research proposes a novel approach for efficient resource allocation in wireless communication systems. It combines dynamic neural networks, Proximal Policy Optimization (PPO), and Edge Computing Orchestrator (ECO) for latency-aware and energy-efficient resource allocation. The proposed system integrates multiple components, including a dynamic neural network, PPO, ECO, and a Mobile Edge Computing (MEC) server. The experimental methodology involves utilizing the NS-3 simulation platform to assess latency and energy efficiency in resource allocation within a wireless communication network, incorporating an ECO, MEC server, and dynamic task scheduling algorithms. It demonstrates a holistic and adaptable approach to resource allocation in dynamic environments, showcasing a notable reduction in latency for devices and tasks. Latency values range from 5 to 20 milliseconds, with corresponding resource utilization percentages varying between 80% and 95%. Additionally, energy-efficient resource allocation demonstrates a commendable reduction in energy consumption, with measured values ranging from 10 to 30 watts, coupled with efficient resource usage percentages ranging from 70% to 85%. These outcomes validate the efficacy of achieving both latency-aware and energy-efficient resource allocation for enhanced wireless communication systems. The proposed system has broad applications in healthcare, smart cities, IoT, real-time analytics, autonomous vehicles, and augmented reality, offering a valuable solution to optimize energy consumption, reduce latency, and enhance system efficiency in these industries.

An attention mechanism and residual network based knowledge graph-enhanced recommender system

Recommender systems enhanced by a knowledge graph (KG) have attained widespread popularity and attention in recent years. However, traditional KG-based recommender systems encounter the challenge of gradient explosion as the network depth increases. Additionally, the abundance of unreliable paths in a KG has a detrimental impact on feature representation learning. In this article, we propose a KG-enhanced recommender system based on residual network and attention mechanism, which can capture high-order connectivity and long-range dependencies of the KG. Specifically, a resource allocation approach is employed to calculate the resource amount, which is subsequently utilized to evaluate the path reliability of the KG. After completing path extraction, we employ an attention mechanism to capture semantic correlations and structural information. To leverage the KG for enhancing recommender systems, we design a deep residual network with shortcut connections, effectively amalgamating advanced and abstract features using deep neural networks. The introduction of shortcut connections not only facilitates the fitting of residual mappings but also mitigates potential issues such as gradient explosion and convergence difficulties due to excessive network depth. Extensive experiments conducted on three standard datasets over baseline methods have demonstrated the superiority of our proposed recommender system.

A DRL-based resource allocation for IRS-enhanced semantic spectrum sharing networks

Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE).

A complex network-based approach for resilient and flexible design resource allocation in industry 5.0

The development of Industry 5.0 focuses on customization, personalization in production, and the innovative thinking of employees, elevating the value of human contribution. Design, being an innovation-driven domain, demands greater flexibility in resource allocation. Consequently, rapidly and effectively allocating cloud service resources for personalized design tasks becomes crucial. With the emergence of the Industrial Metaverse, which blurs the boundaries between real and virtual design and manufacturing, it is gaining increasing attention. To embrace the advent of Industry 5.0 and the Industrial Metaverse, swift collaborative cloud services for design and manufacturing resources are essential. In this context, this article introduces a novel approach combining complex networks with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), aimed at rapidly optimizing the dynamic allocation of distributed design resources (DRs). Initially, a multipartite graph is created from raw data and mapped to multiple bipartite graphs to identify key nodes in the network through intersection. Subsequently, these key nodes are used as reference points in the NSGA-III algorithm to achieve high-quality cloud service combinations, meeting the needs of design tasks with multiple subtasks, and related multi-objective optimization, including time, cost, reliability, maintainability, and reputation associated with the design. Finally, the Pareto service combinations obtained are used to construct a new complex network and employ the Girvan-Newman algorithm based on edge betweenness to identify community structures. In case of anomalies in the best service combination, alternative options can be swiftly searched from the identified communities, thereby enhancing the resilience of the cloud service process. Experimental results demonstrate the method's advantages in recovery and robustness, contributing significantly to the optimization of rapid cloud service allocation for DRs in the context of Industry 5.0.

Multi-armed bandit approach for mean field game-based resource allocation in NOMA networks

Facing the exponential demand for massive connectivity and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets. Particularly, the operators have to cope with the continuous prosperity of the Internet of things (IoT) along with the ever-increasing deployment of machine-type devices (MTDs). In this regard, due to its compelling benefits, non-orthogonal multiple access (NOMA) has sparked a significant interest as a sophisticated technology to address the above-mentioned challenges. In this paper, we consider a hybrid NOMA scenario, wherein the MTDs are divided into different groups, each of which is allocated an orthogonal resource block (RB) so that the members of each group share a given RB to simultaneously transmit their signals. Firstly, we model the densely deployed network using a mean field game (MFG) framework while taking into consideration the effect of the collective behavior of devices. Then, in order to reduce the complexity of the proposed technique, we apply the multi-armed bandit (MAB) framework to jointly address the resource allocation and the power control problem. Thereafter, we derive two distributed decision-making algorithms that enable the users to autonomously regulate their transmit power levels and self-organize into coalitions based on brief feedback received from the base station (BS). Simulation results are given to underline the equilibrium properties of the proposed resource allocation algorithms and to reveal the robustness of the proposed learning process.

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

Comparing distance-based and stress-based centralities to rank priority locations for cycling infrastructure investments in small-sized cities

The lack of technical guidelines to define investment priority locations is one of the barriers to cycling in emerging countries, limiting the preparation of urban mobility plans even when legally required. The objective of this paper is to propose and compare two approaches, with and without considering the cyclists’ perception of stress (assessed with the LTS, or Level of Traffic Stress), to determine the relative importance of road segments in the network and to rank priority locations for investments in cycling infrastructure. A case study was conducted in the city of Bariri (Brazil), for which the overall contribution of each network link to the identified cycling routes was mapped and ranked according to both criteria. The spatial distribution of differences between homologous ranks (i.e., ranks of the same network link according to different criteria) was also mapped, and the spatial autocorrelation between these differences was assessed by the Local Moran’s Index, allowing the identification of road segments of greater similarity and dissimilarity between the proposed approaches for resource allocation.