Search Results (5,534)

Fractional heat conduction models extend classical formulations by incorporating fractional differential operators that capture multiscale relaxation effects. In this work, we introduce an electrical analogy that represents the action of these operators via generalized longitudinal impedance and admittance elements, thereby clarifying their physical role in energy transfer: fractional derivatives account for the redistribution of heat accumulation and dissipation within micro-scale heterogeneous structures. This analogy unifies different classes of fractional models—diffusive, wave-like, and mixed—as well as distinct fractional operator types, including the Caputo and Atangana–Baleanu forms. It also provides a general computational methodology for solving heat conduction problems through the concept of thermal impedance, defined as the ratio of surface temperature variations (relative to ambient equilibrium) to the applied heat flux. The approach is illustrated for a semi-infinite sample, where different models and operators are shown to generate characteristic spectral patterns in thermal impedance. By linking these spectral signatures of microstructural relaxation to experimentally measurable quantities, the framework not only establishes a unified theoretical foundation but also offers a practical computational tool for identifying relaxation mechanisms through impedance analysis in microscale thermal transport. Full article

Port operations involving ship unloading have traditionally posed significant complexity and have proven difficult to solve optimally using exact methods. This study investigates the long continuous unloading of ships carrying liquid products, where transportation is carried out using full truckload deliveries. For the first time, this work integrates the problem of liquid-based ship unloading with full truckload vehicle routing and truck driver scheduling. The primary objective is to minimize the total transportation costs during the continuous unloading process, while satisfying extra constraints such as driver rest–break–drive regulations, time windows, a heterogeneous fleet structure, and port-specific constraints such as maintaining a minimum number of backup vehicles at the port during unloading. To address this complex problem, a route-based insertion heuristic is employed as an initial step in a column generation framework designed for exact optimization. The approach incorporates a nested label setting algorithm for column generation, enhanced with acceleration techniques involving multi-search strategies, and refined selection methods. Performance analysis, based on artificial datasets closely resembling real-world scenarios and consisting of 112 instances, demonstrates that optimality gaps below 1% can be achieved within computational times considered reasonable in the context of the existing literature, while the total number of customer nodes and the minimum number of required vehicles at the port are at most 100 and 5, respectively. Full article

All over the world, the problem of ensuring the safety of pedestrians, who are the most vulnerable road users, is becoming more acute due to urbanization and the growth of micromobility. In 2013, according to WHO data, more than 270 thousand pedestrians were dying each year worldwide (accounting for 22% of all traffic accidents). Currently, experts report that around 1.3 million people die every year globally from road crashes. The roads in developing countries are particularly hazardous, according to experts, because the increase in the number of vehicles far exceeds the development of road infrastructure and safety systems. Since the risk of hitting a pedestrian depends on many factors that can have different natures, and the severity of the consequences can be determined by a set of other factors, the risk of an accident can only be reduced by influencing all these factors in a comprehensive manner. The novelty of our approach is to create an intelligent system that will gradually accumulate all the best practices into a single complex aimed at reducing the risk of an accident with pedestrians and the severity of the consequences if an accident does occur. The distinction lies in offering an integrated system where each module addresses a particular task, so by mitigating risks at every stage, one achieves a synergistic outcome. From the analysis of existing and applied developments, it is known that many specialists mainly solve a narrowly focused problem aimed at ensuring the one subsystems sustainability in the “vehicle-infrastructure-driver-pedestrian” system. Some of these ideas are given as practical examples. The relevance of the designated problem increases with the emergence of autonomous vehicles and smart cities, the sustainability of which depends on the sustainable interaction between all road users. As experience shows, only the implementation of comprehensive solutions allows us to solve strategic problems, including improving road safety. Here, by complex solutions we mean solutions that combine technical issues, as well as environmental, social, and managerial aspects. To account for different kinds of effects, indicator systems are developed and composite indices are computed to choose the most rational solution. The novelty of our approach consists in combining within a unified DSS algorithms for assessing the efficiency of the proposed solution with respect to technological soundness, environmental sustainability, economic viability, social acceptability, as well as administrative rationality and computation of interrelated effects resulting from implementing any given project. In our opinion, the proposed system will lead to a synergistic effect due to the integrated application of various developments, which will ensure increased sustainability and safety of the transport system of smart cities. Our paper proposes a conceptual approach to addressing pedestrian safety, and the examples provided illustrate how the same model or algorithm can lead to positive changes from different perspectives. Full article

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms. Full article

Internet of Vehicles (IoV) serves as the data support for intelligent transportation systems, and the information security of the IoV is of paramount importance. In view of the problems of centralized processing, easy information leakage, and weak anti-interference ability in traditional vehicle networking systems, this paper proposes a blockchain architecture suitable for IoV forensics scenario. By leveraging the decentralized, distributed storage and tamper-proof capabilities of blockchain, it solves the privacy protection and data security issues of the system. Considering the threat of quantum computing to the encryption technology in traditional blockchain, this paper integrates lattice cryptography and ring signatures into digital signature technology, achieving privacy protection and traceability of the signer’s identity. To enhance the efficiency of lattice-based cryptographic algorithms, the DualRing technology is introduced, which reduces the computational time and storage consumption of ring signatures. Theoretical analysis has proved the correctness, anonymity, unlinkability, and traceability of the proposed scheme, which is applicable to the IoV forensics system. Simulation comparisons demonstrated that the proposed scheme significantly improves computational efficiency and reduces storage overhead. When the number of ring members is 256, the signature and verification times require only 65.76 ms and 21.46 ms, respectively. Full article

Against the backdrop of global emissions reduction and transportation electrification, electric vehicles are gradually replacing traditional fuel vehicles for delivery. However, issues such as limited range and charging times often conflict with time window service requirements. To balance economic and environmental performance, mixed fleets and multi-method charging strategies have emerged as viable approaches. This study addresses the problem by developing a mixed-integer programming model that incorporates multiple charging methods and carbon emission accounting. An Improved Adaptive Large Neighborhood Search (IALNS) algorithm is proposed, featuring multiple Removal and Insertion operators tailored for customers and charging stations, along with two local optimization operators. The algorithm’s superiority and applicability are validated through simulation and comparative analysis on benchmark instances and real-world data from an urban courier network. Sensitivity analysis further demonstrates that the proposed algorithm effectively coordinates vehicle type and charging mode selection, reducing total costs and carbon emissions while ensuring service quality. This approach provides practical reference value for operational decision-making in mixed fleet delivery. Full article

With the integration of the global economy and the rapid development of port logistics, the port dangerous goods transportation system faces complex risk-coupling problems, and the probability of accidents keeps climbing. However, the existing research on the system risk-coupling mechanism and dynamic control mechanism is still insufficient, and there is an urgent need to construct a scientific risk analysis and control model. This study takes the port dangerous goods transportation system as the object, based on the four-factor framework of “personnel-machine-environment-management,” uses the N-K model to quantify the degree of risk coupling, analyzes the dynamic evolution mechanism of risk under the action of a single factor, and uses Dufferin’s oscillation and bifurcation response equation to reveal the interaction between the system’s internal defenses and the external influences. It is found that the coupled risk value of personnel–machine factors is the highest, and the sudden change in system state is characterized by a sudden jump and lag. The system stability can be significantly improved by enhancing internal damping control and optimizing external excitation regulation. This study provides a quantitative tool for the risk assessment of dangerous goods transportation in ports and theoretical support for the development of the “damping-excitation” synergistic control strategy, which is of great practical significance for the improvement of the port safety management system. Full article

Many transportation structures collapse or sustain severe damage as a result of natural disasters such as earthquakes, floods, wars, and similar attacks. These collapsed or severely damaged structures must be rebuilt and returned to service as quickly as possible. Water is used in the mix for cement-bound concrete roads. It is known that drought problems are emerging due to climate change and that water resources are rapidly depleting. Significant amounts of water are used in concrete production, further depleting water resources. In order to contribute to the elimination of these two problems, the usability of polyurethane resin binder in road pavement construction was investigated. Polyurethane resin binder road pavement is a new type of pavement that does not contain cement or bitumen as binders and does not contain water in its mixture. This new type of road pavement can be opened to traffic within 5–15 min. After determining the aggregate and binder mixture ratios, four different curing methods were applied to the created samples. After the curing, the samples were subjected to compression test, flexural test, Bohme abrasion test, freeze–thaw test, bond strength by pull-off test, ultrasonic pulse velocity (UPV) test, SEM-EDX analysis, XRD analysis, and FT-IR analysis. The new type of road pavement created within the scope of this study exhibited a compression strength of 41.22 MPa, a flexural strength of 25.32 MPa, a Bohme abrasion value of 0.99 cm³/50 cm², a freeze–thaw test mass loss per unit area of 0.77 kg/m², and an average bond strength by pull-off value of 4.63 MPa. It was observed that these values ensured the road pavement specification limits. Full article

The Capacitated Vehicle Routing Problem (CVRP) is a central challenge in combinatorial optimization, with critical applications in logistics and transportation. Traditional methods struggle with large-scale instances, due to the computational demands, while learned construction models often suffer from degraded solution quality and constraint violations. This work proposes SPORL, a Scalable Partitioning and Optimization via Reinforcement Learning framework for large-scale CVRPs. SPORL decomposes the problem using a learned partitioning strategy, followed by parallel subproblem solving, and employs a greedy decoding scheme at inference to ensure scalability for instances with up to 1000 customers. A key innovation is a context-based attention mechanism that incorporates sub-route embeddings, enabling more informed and constraint-aware partitioning decisions. Extensive experiments on benchmark datasets with up to 1000 customers demonstrated that SPORL consistently outperformed state-of-the-art learning-based baselines (e.g., AM, POMO) and achieved competitive performance relative to strong heuristics such as LKH3, while reducing inference time from hours to seconds. Ablation studies confirmed the critical role of the proposed context embedding and decoding strategy in achieving high solution quality. Full article

One of the most common problems in modern urban environments is traffic congestion, which leads to unreliable bus arrival times and passenger delays. In this study, we apply various machine learning models to predict traffic density with the aim of improving the accuracy of bus arrival time estimations. A large dataset comprising over 100,000 instances containing attributes such as date and time, maximum, minimum, and average speed, longitude, latitude, and geohash is utilized to classify traffic density as either “1 (High)” or “0 (Low).” We implement and compare five machine learning models: Logistic Regression, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naïve Bayes. The results demonstrate the potential of machine learning in reducing unnecessary delays and enhancing the accuracy of bus arrival predictions. This research contributes to improving the efficiency of public transportation systems in the future. Full article

Today, on the island of Gran Canaria, conventional photovoltaic installations are being implemented on the ground, with the excuse that electricity production must be decarbonized. This is located on a highly populated island, with a shortage of flat land, and a high dependence on food, in a biodiversity hot spot on the planet. We would like to point out that agrivoltaics could provide a double solution and allow the carbon footprint of this human settlement to be further reduced. In addition, it provides greater resilience to climate change, and by reducing dependence on the outside, it would minimize the effects suffered by pandemics such as SARS-CoV-2. It would also help mitigate water stress in one area facing serious water shortage problems. The reduction of local CO₂ emissions would be achieved in four ways: production of clean electricity, reduction of the transport of fuel for electricity generation, reduction of the transport of food goods from abroad, and the absorption of CO₂ together with the emission of O₂ by the planted crops. It would also lead to greater job creation, a remedy against great soil desertification, stopping agricultural abandonment, and life in rural inland areas. This study analyzes two possible agrivoltaic installation configurations of equal power in a potato field: one with a vertical bifacial (VB) configuration and another with an optimum angle (OA). The monthly production is examined and, specifically, the economic income in the event of pouring all the production into the grid. All this takes into account the reality of the chosen place, the island of Gran Canaria (Spain). Full article

Developing classical three-dimensional consolidation theories considers the small-strain assumption. This small-strain assumption is inappropriate when studying the consolidation process of soft or very soft clay layers. Instead, this study derives a novel generalized mathematical model describing a three-dimensional finite-strain consolidation process and applies the deep Galerkin method to deduce its novel numerical solution. Developing this mathematical model uses the Reynolds transport theorem to describe mass and momentum balances for clay grain and pore water phases. The governing equation is the sum of the resulting mass and momentum balance equations. Next, the deep Galerkin method is applied to train a deep neural network to minimize the loss function defined by the governing equation and available initial and boundary conditions. The unknowns are the average velocity, effective stress, and pore water pressure. Predicting consolidation settlements is implemented by updating the problem domain using the resulting average velocity. Beneficial from the deep Galerkin method, two real-world examples demonstrate that the current mathematical model provides accurate predictions of consolidation settlements caused by the self-weight of two very soft clay layers. The deep Galerkin method helps resolve ill-posed problems by fitting a family of fields constrained by sampling/regularization rather than physics if the physics is under-determined. Full article

The scientific site selection for pallet pooling systems is pivotal to enhancing logistics efficiency and environmental performance. However, previous studies mainly adopt single-objective optimization approaches, which fail to simultaneously account for economic, environmental, and operational performance factors. The contribution of this paper lies in proposing an integrated decision-making method based on BWM-GIS-DEA to address the site selection problem for pallet pooling service centers. First, the Best-Worst Method (BWM) determines the weights of 13 criteria across 5 dimensions: economic, transportation, geographical location, technological, and service coverage. These criteria include factors such as the distribution density of pallet manufacturers and potential customers. Then, suitability maps are generated using Geographic Information System (GIS) spatial overlay technology to identify 6 alternative cities. Finally, a two-layer Data Envelopment Analysis (DEA) model is applied to measure the efficiency of the alternative sites. This method is applied in Inner Mongolia, China, and Ejin Horo Banner is identified as the optimal site with an efficiency score of 1.156, demonstrating superior resource allocation characterized by lower land costs and higher pallet turnover rates. The proposed framework not only fills a methodological gap in sustainable facility location research but also provides a replicable and policy-ready tool to guide practical decision-making. Full article

Inland Waterway Transport (IWT) is widely recognised as an energy-efficient freight mode, yet its decarbonisation is increasingly constrained not by propulsion technology, but by the absence of infrastructure capable of delivering clean energy where and when it is needed. This paper presents a structured review of over a decade of academic, policy and technical literature, identifying systemic gaps in current decarbonisation strategies. The analysis shows that most pilot projects are vessel-specific, and poorly scalable, with infrastructure planning rarely based on vessel-level energy demand data, leaving energy provision as an afterthought. Current approaches overemphasise technology readiness while neglecting the complexity of aligning supply chains, operational diversity, and infrastructure deployment. This review reframes IWT decarbonisation as a problem of provision, not propulsion. It calls for demand-led, demand driven, fuel agnostic infrastructure models and proposes a roadmap that integrates technical, operational, and policy considerations. Without rethinking energy access as a core design challenge—on par with vessel systems and regulatory standards—the sector risks investing in stranded assets and missing climate and modal shift targets. Aligning vessel operations with dynamic, scalable energy delivery systems is essential to achieve a commercially viable, fully decarbonised IWT sector. Full article

At the international level, new measures, policies, and technologies are being developed to reduce greenhouse gas emissions and, more broadly, air pollutants. Road transportation is one of the main contributors to such emissions, as vehicles are extensively used in logistics operations, and many fleet owners of fossil-fueled trucks are adopting new technologies such as electric, hybrid, and hydrogen-based vehicles. This paper addresses the Hybrid Fleet Capacitated Vehicle Routing Problem with Time Windows (HF-CVRPTW), with the objectives of minimizing costs and mitigating environmental impacts. A mixed-integer linear programming model is developed, incorporating split deliveries, scheduled arrival times at stores, and a carbon cap-and-trade mechanism. The model is tested on a real case study provided by Decathlon, evaluating the performance of internal combustion engine (ICE), electric (EV), and hydrogen fuel cell (HV) vehicles. Results show that when considering economic and emission trading costs, the optimal fleet deployment priority is to use ICE vehicles first, followed by EVs and then HVs, but considering only total emissions, the result is the reverse. Further analysis explores the conditions under which alternative fuel, electricity, or hydrogen prices can achieve competitiveness, and a further analysis investigates the impact of different electricity generation and hydrogen production pathways on overall indirect emissions. Full article