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Search Results (239)

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Keywords = fleet management system

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17 pages, 5929 KiB  
Article
Optimization of Operations in Bus Company Service Workshops Using Queueing Theory
by Sergej Težak and Drago Sever
Vehicles 2025, 7(3), 82; https://doi.org/10.3390/vehicles7030082 - 6 Aug 2025
Abstract
Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization [...] Read more.
Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article
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17 pages, 1142 KiB  
Article
Logistical Challenges in Home Health Care: A Comparative Analysis Between Portugal and Brazil
by William Machado Emiliano, Thalyta Cristina Mansano Schlosser, Vitor Eduardo Molina Júnior, José Telhada and Yuri Alexandre Meyer
Logistics 2025, 9(3), 101; https://doi.org/10.3390/logistics9030101 - 31 Jul 2025
Viewed by 214
Abstract
Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured [...] Read more.
Background: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. Methods: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. Results: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. Conclusions: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies. Full article
(This article belongs to the Section Humanitarian and Healthcare Logistics)
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19 pages, 2289 KiB  
Article
Multicriteria Framework for Risk Assessment of Power Transformers
by João Marcondes Corrêa Guimarães, Ligia Cintra Pereira, Antonio Faria Neto, Agnelo Marotta Cassula and Talita Mariane Cristino
Energies 2025, 18(15), 4049; https://doi.org/10.3390/en18154049 - 30 Jul 2025
Viewed by 202
Abstract
Transformers are critical assets for power system reliability, as they connect different voltage levels across generation, transmission, and distribution. Their failure can lead to significant impacts on multiple aspects. Given the aging transformer fleet, supply chain challenges, and constrained investment capacity, the adoption [...] Read more.
Transformers are critical assets for power system reliability, as they connect different voltage levels across generation, transmission, and distribution. Their failure can lead to significant impacts on multiple aspects. Given the aging transformer fleet, supply chain challenges, and constrained investment capacity, the adoption of risk-based strategies is essential to support long-term maintenance planning and investment. This paper proposes a multicriteria framework to assess the probability and impact of transformer failure, enabling a more comprehensive and data-driven risk evaluation. The method was applied to a sample fleet, enabling the identification and prioritization of the most critical units through a risk plot. The framework enhances asset management by identifying critical units within a transformer fleet, promoting efficiency, reliability, and long-term planning based on objective risk indicators. Full article
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26 pages, 3405 KiB  
Article
Digital Twins for Intelligent Vehicle-to-Grid Systems: A Multi-Physics EV Model for AI-Based Energy Management
by Michela Costa and Gianluca Del Papa
Appl. Sci. 2025, 15(15), 8214; https://doi.org/10.3390/app15158214 - 23 Jul 2025
Viewed by 285
Abstract
This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including [...] Read more.
This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in the Novel Power System)
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37 pages, 863 KiB  
Systematic Review
Sustainable Water Resource Management to Achieve Net-Zero Carbon in the Water Industry: A Systematic Review of the Literature
by Jorge Alejandro Silva
Water 2025, 17(14), 2136; https://doi.org/10.3390/w17142136 - 17 Jul 2025
Viewed by 415
Abstract
With water scarcity becoming worse, and demand increasing, the urgency for the water industry to hit net-zero carbon is accelerating. Even as a multitude of utilities have pledged to reach net-zero by 2050, advancing beyond the energy–water nexus remains a heavy lift. This [...] Read more.
With water scarcity becoming worse, and demand increasing, the urgency for the water industry to hit net-zero carbon is accelerating. Even as a multitude of utilities have pledged to reach net-zero by 2050, advancing beyond the energy–water nexus remains a heavy lift. This paper, using a systematic literature review that complies with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA), aims to propose sustainable water resource management (SWRM) strategies that may assist water utilities in decarbonizing their value chains and achieving net-zero carbon. In total, 31 articles were included from SCOPUS, ResearchGate, ScienceDirect, and Springer. The findings show that water utilities are responsible for 3% of global greenhouse gas emissions and could reduce these emissions by more than 45% by employing a few strategies, including the electrification of transport fleets, the use of renewables, advanced oxidation processes (AOPs) and energy-efficient technologies. A broad-based case study from Scottish Water shows a 254,000-ton CO2 reduction in the period since 2007, indicative of the potential of these measures. The review concludes that net-zero carbon is feasible through a mix of decarbonization, wastewater reuse, smart systems and policy-led innovation, especially if customized to both large and small utilities. To facilitate a wider and a more scalable transition, research needs to focus on development of low-cost and flexible strategies for underserved utilities. Full article
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26 pages, 891 KiB  
Article
Modeling the Interactions Between Smart Urban Logistics and Urban Access Management: A System Dynamics Perspective
by Gaetana Rubino, Domenico Gattuso and Manfred Gronalt
Appl. Sci. 2025, 15(14), 7882; https://doi.org/10.3390/app15147882 - 15 Jul 2025
Viewed by 315
Abstract
In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach [...] Read more.
In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem. Full article
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15 pages, 1042 KiB  
Article
Balanced Truck Dispatching Strategy for Inter-Terminal Container Transportation with Demand Outsourcing
by Yucheng Zhao, Yuxiong Ji and Yujing Zheng
Mathematics 2025, 13(13), 2163; https://doi.org/10.3390/math13132163 - 2 Jul 2025
Viewed by 271
Abstract
This study proposes a balanced truck dispatching strategy for inter-terminal transportation (ITT) in large ports, incorporating proactive demand outsourcing to address stochastic and imbalanced ITT demand. A portion of ITT tasks are intentionally outsourced to third-party public trucks at a higher cost, so [...] Read more.
This study proposes a balanced truck dispatching strategy for inter-terminal transportation (ITT) in large ports, incorporating proactive demand outsourcing to address stochastic and imbalanced ITT demand. A portion of ITT tasks are intentionally outsourced to third-party public trucks at a higher cost, so that self-owned trucks can be reserved for more critical tasks. The ITT system is modeled as a closed Jackson network, in which self-owned trucks circulate among terminals and routes. An optimization model is developed to determine the optimal proactive outsourcing ratios for origin–destination terminal pairs and the appropriate fleet size of self-owned trucks, aiming to minimize total transportation costs. Reactive outsourcing is also included to handle occasional truck shortages. A mean value analysis method is used to evaluate system performance with given decisions, and a differential evolution algorithm is employed for optimization. The case study of Shanghai Yangshan Port demonstrates that the proposed strategy reduces total system cost by 9.8% compared to reactive outsourcing. The results also highlight the importance of jointly optimizing outsourcing decisions and fleet size. This study provides theoretical insights and practical guidance for ITT system management under demand uncertainty. Full article
(This article belongs to the Special Issue Queueing Systems Models and Their Applications)
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27 pages, 2290 KiB  
Article
Energy Management System for Renewable Energy and Electric Vehicle-Based Industries Using Digital Twins: A Waste Management Industry Case Study
by Andrés Bernabeu-Santisteban, Andres C. Henao-Muñoz, Gerard Borrego-Orpinell, Francisco Díaz-González, Daniel Heredero-Peris and Lluís Trilla
Appl. Sci. 2025, 15(13), 7351; https://doi.org/10.3390/app15137351 - 30 Jun 2025
Viewed by 375
Abstract
The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper [...] Read more.
The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article
(This article belongs to the Section Applied Industrial Technologies)
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24 pages, 4912 KiB  
Article
Integrated Fleet Management of Mobile Robots for Enhancing Industrial Efficiency: A Case Study on Interoperability in Multi-Brand Environments Within the Automotive Sector
by David Lopes, Tiago Pereira, André Gonçalves, Francisco Cunha, Fernando Lopes, João Antunes, Victor Santos, Fernanda Coutinho, Jorge Barreiros, João Durães, Patrícia Santos, Fernando Simões, Pedro Ferreira, Elisabete Dinora Caldas de Freitas, João Pedro F. Trovão, João P. Ferreira and Nuno Miguel Fonseca Ferreira
Appl. Sci. 2025, 15(13), 7235; https://doi.org/10.3390/app15137235 - 27 Jun 2025
Viewed by 508
Abstract
This paper presents the development of fleet management software for mobile robots, including AGV and AMR technologies, within the scope of a case study from the GreenAuto project. The system was designed to integrate position and status data from different robots, unifying this [...] Read more.
This paper presents the development of fleet management software for mobile robots, including AGV and AMR technologies, within the scope of a case study from the GreenAuto project. The system was designed to integrate position and status data from different robots, unifying this information into a single map. To achieve this, a web-based platform was developed to allow the simultaneous, real-time visualization of all robots in operation. However, the main challenge of this research lies in the heterogeneity of the fleet, which comprises robots of different makes and models from various manufacturers, each using distinct data formats. The proposed approach addresses this by facilitating fleet monitoring and management, ensuring a greater efficiency and coordination in the robot movement. The results demonstrate that the platform improves the traceability and operational supervision, promoting the optimized management of mobile robots. It is concluded that the proposed solution contributes to industrial automation by providing an intuitive and centralized interface, enabling future expansions for new functionalities and the integration with other emerging technologies. The proposed system demonstrated efficiency in updating and supervising operations, with an average latency of 120 ms for task status updates and an interface refresh rate of less than 1 s, enabling near real-time supervision and facilitating operational decision-making. Full article
(This article belongs to the Section Robotics and Automation)
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26 pages, 2694 KiB  
Article
Informational Support for Agricultural Machinery Management in Field Crop Cultivation
by Chavdar Z. Vezirov, Atanas Z. Atanasov, Plamena D. Nikolova and Kalin H. Hristov
Agriculture 2025, 15(13), 1356; https://doi.org/10.3390/agriculture15131356 - 25 Jun 2025
Viewed by 295
Abstract
This study explores the potential of freely available tools for collecting, processing, and applying information in the management of mechanized fieldwork. A hierarchical approach was developed, integrating operational, logistical, and strategic levels of decision-making based on crop type, land conditions, machinery, labor, and [...] Read more.
This study explores the potential of freely available tools for collecting, processing, and applying information in the management of mechanized fieldwork. A hierarchical approach was developed, integrating operational, logistical, and strategic levels of decision-making based on crop type, land conditions, machinery, labor, and time constraints. Various technological and technical solutions were evaluated through simulations and manual data processing. The proposed methodology was applied to a real-world case in Kalipetrovo, Bulgaria. The results include a 3.5-fold reduction in required tractors and a 50% decrease in tractor driver needs, achieved through extended working hours and shift scheduling. Additional benefits were identified from replacing conventional tillage with deep tillage, resulting in higher fuel consumption but improved soil preparation. Detailed resource schedules were created for machinery, labor, and fuel, highlighting seasonal peaks and optimization opportunities. The approach relies on spreadsheets and free AI-assisted platforms, proving to be a low-cost, accessible solution for mid-sized farms lacking advanced digital infrastructure. The findings demonstrate that structured information integration can support the effective renewal and utilization of tractor and machinery fleets while offering a scalable basis for decision support systems in agricultural engineering. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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15 pages, 6013 KiB  
Article
Urban Air Mobility Vertiport’s Capacity Simulation and Analysis
by Antoni Kopyt and Sebastian Dylicki
Aerospace 2025, 12(6), 560; https://doi.org/10.3390/aerospace12060560 - 19 Jun 2025
Viewed by 651
Abstract
This study shows a comprehensive simulation to assess and enhance the throughput capacity of unmanned air system vertiports, one of the most essential elements of urban air mobility ecosystems. The framework integrates dynamic grid-based spatial management, probabilistic mission duration algorithms, and EASA-compliant operational [...] Read more.
This study shows a comprehensive simulation to assess and enhance the throughput capacity of unmanned air system vertiports, one of the most essential elements of urban air mobility ecosystems. The framework integrates dynamic grid-based spatial management, probabilistic mission duration algorithms, and EASA-compliant operational protocols to address the infrastructural and logistical demands of high-density UAS operations. It was focused on two use cases—high-frequency food delivery utilizing small UASs and extended-range package logistics with larger UASs—and the model incorporates adaptive vertiport zoning strategies, segregating operations into dedicated sectors for battery charging, swapping, and cargo handling to enable parallel processing and mitigate congestion. The simulation evaluates critical variables such as vertiport dimensions, UAS fleet composition, and mission duration ranges while emphasizing scalability, safety, and compliance with evolving regulatory standards. By examining the interplay between infrastructure design, operational workflows, and resource allocation, the research provides a versatile tool for urban planners and policymakers to optimize vertiport layouts and traffic management protocols. Its modular architecture supports future extensions. This work underscores the necessity of adaptive, data-driven planning to harmonize vertiport functionality with the dynamic demands of urban air mobility, ensuring interoperability, safety, and long-term scalability. Full article
(This article belongs to the Special Issue Operational Requirements for Urban Air Traffic Management)
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20 pages, 1140 KiB  
Article
Optimization of Autonomous Vehicle Safe Passage at Intersections Based on Crossing Risk Degree
by Jiajun Shen, Yu Wang, Haoyu Wang and Chunxiao Li
Symmetry 2025, 17(6), 893; https://doi.org/10.3390/sym17060893 - 6 Jun 2025
Viewed by 676
Abstract
In the context of autonomous driving, ensuring safe passage at intersections is of significant importance. An effective method is necessary to optimize the passage rights of autonomous vehicles at intersections to enhance traffic safety and operational efficiency. This paper proposes an analytical model [...] Read more.
In the context of autonomous driving, ensuring safe passage at intersections is of significant importance. An effective method is necessary to optimize the passage rights of autonomous vehicles at intersections to enhance traffic safety and operational efficiency. This paper proposes an analytical model for assigning the right-of-way to autonomous vehicles approaching intersections from different directions. Assuming that fully autonomous vehicles equipped with advanced Vehicle-to-Everything (V2X) communication and real-time data processing can utilize gaps to proceed at unsignalized intersections in the future, the Crossing Risk Degree (CRD) indicator is introduced for safety assessment. A higher CRD value indicates a higher crossing risk. CRD is defined as the product of the kinetic energy loss from collisions between vehicles in the priority and conflicting fleets, and the probability of conflict between these two fleets. By comparing CRD values, the passage priority of vehicles at intersection entrances can be determined, ensuring efficient passage and reduced conflict risks. SUMO microsimulation modeling is employed to compare the proposed traffic optimization method with fixed signal control strategies. The simulation results indicate that under a traffic demand of 1200 vehicles per hour, the proposed method reduces the average delay per entry approach by approximately 20 s and decreases fuel consumption by about 50% compared to fixed-time signal control strategies. In addition, carbon emissions are significantly reduced. The findings provide critical insights for developing intersection safety management policies, including the establishment of CRD-based priority systems and real-time traffic monitoring frameworks to enhance urban traffic safety, symmetry, and efficiency. Full article
(This article belongs to the Section Mathematics)
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27 pages, 297 KiB  
Article
A Practical Performance Benchmark of Post-Quantum Cryptography Across Heterogeneous Computing Environments
by Maryam Abbasi, Filipe Cardoso, Paulo Váz, José Silva and Pedro Martins
Cryptography 2025, 9(2), 32; https://doi.org/10.3390/cryptography9020032 - 21 May 2025
Viewed by 3206
Abstract
The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by [...] Read more.
The emergence of large-scale quantum computing presents an imminent threat to contemporary public-key cryptosystems, with quantum algorithms such as Shor’s algorithm capable of efficiently breaking RSA and elliptic curve cryptography (ECC). This vulnerability has catalyzed accelerated standardization efforts for post-quantum cryptography (PQC) by the U.S. National Institute of Standards and Technology (NIST) and global security stakeholders. While theoretical security analysis of these quantum-resistant algorithms has advanced considerably, comprehensive real-world performance benchmarks spanning diverse computing environments—from high-performance cloud infrastructure to severely resource-constrained IoT devices—remain insufficient for informed deployment planning. This paper presents the most extensive cross-platform empirical evaluation to date of NIST-selected PQC algorithms, including CRYSTALS-Kyber and NTRU for key encapsulation mechanisms (KEMs), alongside BIKE as a code-based alternative, and CRYSTALS-Dilithium and Falcon for digital signatures. Our systematic benchmarking framework measures computational latency, memory utilization, key sizes, and protocol overhead across multiple security levels (NIST Levels 1, 3, and 5) in three distinct hardware environments and various network conditions. Results demonstrate that contemporary server architectures can implement these algorithms with negligible performance impact (<5% additional latency), making immediate adoption feasible for cloud services. In contrast, resource-constrained devices experience more significant overhead, with computational demands varying by up to 12× between algorithms at equivalent security levels, highlighting the importance of algorithm selection for edge deployments. Beyond standalone algorithm performance, we analyze integration challenges within existing security protocols, revealing that naive implementation of PQC in TLS 1.3 can increase handshake size by up to 7× compared to classical approaches. To address this, we propose and evaluate three optimization strategies that reduce bandwidth requirements by 40–60% without compromising security guarantees. Our investigation further encompasses memory-constrained implementation techniques, side-channel resistance measures, and hybrid classical-quantum approaches for transitional deployments. Based on these comprehensive findings, we present a risk-based migration framework and algorithm selection guidelines tailored to specific use cases, including financial transactions, secure firmware updates, vehicle-to-infrastructure communications, and IoT fleet management. This practical roadmap enables organizations to strategically prioritize systems for quantum-resistant upgrades based on data sensitivity, resource constraints, and technical feasibility. Our results conclusively demonstrate that PQC is deployment-ready for most applications, provided that implementations are carefully optimized for the specific performance characteristics and security requirements of target environments. We also identify several remaining research challenges for the community, including further optimization for ultra-constrained devices, standardization of hybrid schemes, and hardware acceleration opportunities. Full article
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14 pages, 321 KiB  
Article
Enhancing Efficiency in Transportation Data Storage for Electric Vehicles: The Synergy of Graph and Time-Series Databases
by Marko Šidlovský and Filip Ravas
World Electr. Veh. J. 2025, 16(5), 269; https://doi.org/10.3390/wevj16050269 - 14 May 2025
Viewed by 501
Abstract
This article introduces a novel hybrid database architecture that combines graph and time-series databases to enhance the storage and management of transportation data, particularly for electric vehicles (EVs). This model addresses a critical challenge in modern mobility: handling large-scale, high-velocity, and highly interconnected [...] Read more.
This article introduces a novel hybrid database architecture that combines graph and time-series databases to enhance the storage and management of transportation data, particularly for electric vehicles (EVs). This model addresses a critical challenge in modern mobility: handling large-scale, high-velocity, and highly interconnected datasets while maintaining query efficiency and scalability. By comparing a naive graph-only approach with our hybrid solution, we demonstrate a significant reduction in query response times for large data contexts-up to 64% faster in the XL scenario. The scientific contribution of this research lies in its practical implementation of a dual-layer storage framework that aligns with FAIR data principles and real-time mobility needs. Moreover, the hybrid model supports complex analytics, such as EV battery health monitoring, dynamic route optimization, and charging behavior analysis. These capabilities offer a multiplier effect, enabling broader applications across urban mobility systems, fleet management platforms, and energy-aware transport planning. By explicitly considering the interconnected nature of transport and energy data, this work contributes to both carbon emission reduction and smart city efficiency on a global scale. Full article
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18 pages, 1759 KiB  
Article
DHDRDS: A Deep Reinforcement Learning-Based Ride-Hailing Dispatch System for Integrated Passenger–Parcel Transport
by Huanwen Ge, Xiangwang Hu and Ming Cheng
Sustainability 2025, 17(9), 4012; https://doi.org/10.3390/su17094012 - 29 Apr 2025
Viewed by 1016
Abstract
Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting [...] Read more.
Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting packages. This limitation causes two issues: (1) wasted vehicle capacity in cities, and (2) extra carbon emissions from cars waiting idle. Our solution combines passenger rides with package delivery in real time. This dual-mode strategy achieves four benefits: (1) better matching of supply and demand, (2) 38% less empty driving, (3) higher vehicle usage rates, and (4) increased earnings for drivers in changing conditions. We built a Dynamic Heterogeneous Demand-aware Ride-hailing Dispatch System (DHDRDS) using deep reinforcement learning. It works by (a) managing both passenger and package requests on one platform and (b) allocating vehicles efficiently to reduce the environmental impact. An empirical validation confirms the developed framework’s superiority over conventional approaches across three critical dimensions: service efficiency, carbon footprint reduction, and driver profits. Specifically, DHDRDS achieves at least a 5.1% increase in driver profits and an 11.2% reduction in vehicle idle time compared to the baselines, while ensuring that the majority of customer waiting times are within the system threshold of 8 min. By minimizing redundant vehicle trips and optimizing fleet utilization, this research provides a novel solution for advancing sustainable urban mobility systems aligned with global carbon neutrality goals. Full article
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