Search Results (2,167)

In light of urbanization, climate change, and the escalation of extreme weather events, flood management is becoming more and more important. Improving community resilience and reducing flood risks require prompt decision-making and effective communication. This study investigates how flood management systems can incorporate Large Language Models (LLMs), especially those that use Retrieval-Augmented Generation (RAG) architectures. We suggest a multimodal framework that uses a Flood Knowledge Graph to aggregate data from various sources, such as social media, hydrological, and meteorological inputs. Although LLMs have the potential to be transformative, we also address important drawbacks like governance issues, hallucination risks, and a lack of physical modeling capabilities. When compared to text-only LLMs, the RAG system significantly improves the reliability of flood-related decision support by reducing factual inconsistency rates by more than 75%. Our suggested architecture includes expert validation and security layers to guarantee dependable, useful results, like flood-constrained evacuation route planning. In areas that are vulnerable to flooding, this strategy seeks to strengthen warning systems, enhance information sharing, and build resilient communities. Full article

Vehicle-mounted multi-UAV (VM-UAV) systems offer enhanced flexibility and rapid deployment for large-scale remote sensing tasks such as disaster response and land surveys. However, maximizing their operational efficiency remains challenging, as it requires the simultaneous resolution of task scheduling and coverage path planning—an NP-hard problem. This study presents a novel multi-objective genetic algorithm (GA) framework that jointly optimizes routing and scheduling for cost-constrained, load-balanced multi-UAV remote sensing missions. To improve convergence speed and solution quality, we introduce two innovative operators: a Multi-Region Edge Recombination Crossover (MRECX) to preserve superior path segments from parents and an Adaptive Hybrid Mutation (AHM) mechanism that dynamically adjusts mutation strategies to balance exploration and exploitation. The algorithm minimizes total flight distance while equalizing workload distribution among UAVs. Extensive simulations and experiments demonstrate that the proposed GA significantly outperforms conventional GA, particle swarm optimization (PSO), ant colony optimization (ACO), and clustering-based planning methods in both solution quality and robustness. The practical applicability of our framework is further validated through two real-world case studies. The results confirm that the proposed approach delivers an effective and scalable solution for vehicle-mounted multi-UAV scheduling and path planning, enhancing operational efficiency in time-critical remote sensing applications. Full article

Traffic calming zones (TCZs) are increasingly being implemented in urban areas to enhance road safety, reduce vehicle speeds, and support sustainable mobility. However, their impact on public transport (PT) operations, particularly bus services, remains underexplored. This study examines the impact of classifying streets into TCZs on bus transport performance in Poland’s ten largest cities. Geospatial analysis and a custom R algorithm delineated areas suitable for TCZs based on road class and administrative category. GTFS data were analysed for almost 1000 bus lines to evaluate the overlap of their routes with TCZs. The findings reveal that in several cities, a significant portion of bus operations would run through TCZs, with the average route segment affected notably by city and zone classification methods. Differences in TCZ size and shape across cities were also statistically significant. This study concludes that although TCZs contribute to safer and more liveable urban environments, their influence on bus speeds, which can lead to changes in fuel or energy consumption, and route design must be carefully managed. Strategic planning is essential to find a balance between the benefits of traffic calming and the operational efficiency of PT. These insights offer valuable guidance for integrating TCZs into sustainable urban transport policy without compromising PT performance. Full article

This paper addresses the simultaneous feeder routing and conductor sizing problem in unbalanced three-phase distribution systems, formulated as a nonconvex mixed-integer nonlinear program (MINLP) that minimizes the equivalent annualized expansion cost—combining investment and loss costs—under voltage, ampacity, and radiality constraints. The model captures nonconvex voltage–current–power couplings, $\Delta /Y$ load asymmetries, and discrete conductor selections, creating a large combinatorial design space that challenges heuristic methods. An exact MINLP formulation in complex variables is implemented in Julia/JuMP and solved with the Basic Open-source Nonlinear Mixed Integer programming (BONMIN) solver, which integrates branch-and-bound for discrete variables and interior-point methods for nonlinear subproblems. The main contributions are: (i) a rigorous, reproducible formulation that jointly optimizes routing and conductor sizing; (ii) a transparent, replicable implementation; and (iii) a benchmark against minimum spanning tree (MST)-based and metaheuristic approaches, clarifying the trade-off between computational time and global optimality. Tests on 10- and 30-node rural feeders show that, although metaheuristics converge faster, they often yield suboptimal solutions. The proposed MINLP achieves globally optimal, technically feasible results, reducing annualized cost by 14.6% versus MST and 2.1% versus metaheuristics in the 10-node system, and by 17.2% and 2.5%, respectively, in the 30-node system. These results highlight the advantages of exact optimization for rural network planning, providing reproducible and verifiable decisions in investment-intensive scenarios. Full article

To reduce the pollutant emissions of water ecological POI logistics, the water ecological POI logistics route-planning method based on the improved water network space AGNES clustering model and the symmetrical simulated Huffman spatial searching tree (SHSST) algorithm is innovatively established. The improved AGNES algorithm is established for water ecological POI clustering, and then the logistics distribution center location model based on water ecological POI clustering is constructed. On the basis of an optimal distribution center, combining the symmetrical feature of vehicle moving paths and distances in logistics sub-intervals and logistics intervals, the sub-interval optimal route-searching algorithm based on the symmetrical SHSST is constructed to determine the optimal path for each logistics sub-interval, and then the global logistics route-planning algorithm based on undirected complete graph spatial search is constructed to search for the global optimal logistics route. Experiments prove that the proposed algorithm can accurately cluster water ecological POIs and output the logistics route with the lowest costs and pollutant emissions. Compared to the traditional AGNES and other clustering algorithms, the improved AGNES algorithm has lower time complexity. Compared to the traditional logistics route algorithms, SHSST has lower algorithm complexity, route costs, and pollutant emissions, and strong stability. The minimum and maximum optimization rates for the same route are 10.06% and 17.58%, while the minimum and maximum optimization rates for the optimal route are 11.41% and 14.29%; it could effectively reduce the negative impact of pollutants on the water ecological environment and POIs. Full article

Electrical outfitting is sometimes overlooked despite its significant impact on build efficiency and vessel performance. It typically occurs towards the end of a ship’s construction. An organized and traceable method for organizing, carrying out, and verifying electrical installation operations is presented in this paper as the Generalized Electrical Outfitting Traceability Management (GEOTM) model. Data on labor utilization, cable routing methods, and cold insulation records were meticulously analyzed when the model was applied to a real-world setting—a 10,000 DWT chemical tanker project. Using organized from-to routing sheets, thoroughly documenting all connections and tests, and integrating electrical components early on during block assembly were all given special attention. This led to a 7.9% reduction in cable waste, less rework, and better timeline compliance, all of which were supported by GEOTM. The early and planned integration of electrical work, which made up a smaller fraction of the total labor, greatly improved build quality and schedule consistency. Beyond the scope of this particular case study, the results indicate that shipyards could benefit from adopting more sustainable, lean, and predictable building techniques by utilizing a digitally backed, traceable model such as GEOTM. Full article

The transition to sustainable transport in the logistics sector requires innovative strategies, yet companies still face uncertainty regarding the operational, economic, and environmental feasibility of replacing diesel trucks with electric ones. Electric trucks represent a sustainable alternative, contributing to the reduction in pollutant gas emissions, noise reduction in traffic, and lower operational costs, in addition to building sustainable logistics through recharges from renewable energy sources. Although electric trucks offer sustainability benefits, existing research often lacks analyses based on real-world delivery conditions. In this context, the objective of this paper is to analyze the logistical impact of introducing electric trucks for beverage transportation. This study includes assessments of planned route profiles, economic evaluation during operation, emission mitigation costs, and charging analyses under different pricing models in consumer units. These elements were selected to reflect the actual challenges companies face. The results demonstrate that electric trucks can reduce fuel costs by 83.90% and significantly lower carbon emissions, confirming their viability for last-mile freight transport operations. Therefore, the results demonstrate that the process of replacing diesel trucks with electric ones is a viable alternative for companies due to the savings generated during operation and the reduction in pollutant emissions. Full article

This study provides a comprehensive review of advancements in the field of train–track–subway system interaction dynamics and suggests future directions for research and development. Mathematical modeling of train–track–subway interaction system is addressed, including wheel–track contact mechanics and wear, train multibody dynamics, train–track system coupling dynamics, track slab subsystem dynamics, subway tunnel–ground interaction models, building vibration excited by ground-borne seismic waves, and noise. Advanced computing and simulation techniques used for numerical studies of the dynamics of train–track–subway system interaction in the past two decades are also addressed, including high-performance computing with efficient algorithms, multi-physics and multi-scale simulation, real-time hardware-in-the-loop simulation, and laboratory and field validation. The study extends the applications of train–track–subway interaction dynamics to subway route planning, structural and material design, subway maintenance, operations safety and reliability, and passenger comfort. Emerging technologies and future perspectives are also reviewed and discussed, including artificial intelligence, smart sensing and real-time monitoring, digital twin technology, and sustainable design integration. Full article

Maritime ferries increasingly operate under non-stationary hydro–meteorological conditions that complicate cost planning. This study investigates how short-term weather variability affects expenditures for a ferry on the Gdynia–Karlskrona route. We combine a state-based operational framework (18 discrete states) with a subsystem-level cost model covering navigation, propulsion/steering, loading/unloading, stability control, and mooring/anchoring. Direct and indirect costs are linked to subsystem activity and state duration, while weather is incorporated through hazard categories that scale hourly costs. Expert-elicited rates and observed monthly state durations provide the basis for baseline estimates and hazard scenario simulations. Results reveal a disproportionate cost structure: two open-sea states constitute over 97% of the baseline monthly cost (19,490.19 PLN). Weather hazards further amplify costs, with moderate (1st-degree) and severe (2nd-degree) scenarios producing increases of ~8% and ~20%, respectively, compared to normal conditions. By embedding weather as an endogenous factor in a probabilistic cost model based on a semi-Markov process, the approach enhances predictive fidelity and supports decision-making for climate-resilient planning. These findings suggest that adaptive routing, speed management, and targeted maintenance of the propulsion and steering subsystems during open-sea navigation offer the highest potential for cost resilience. The study provides operators and policymakers with a transparent framework for climate-resilient planning and investment in semi-enclosed maritime corridors. Full article

The basic requirement for ship voyage planning is to determine a safe route while meeting certain safety and economic criteria. ECDIS are a commonly used tool for this purpose. Route planning is normally accomplished by setting route cross track limit-XTE. The XTE value can be adjusted on individual sections of the planned route. As a complementary criterion, the own ship domain is proposed, understood as the area around the ship which is to remain free of other objects. The concept of a hydrographic object domain, analogous to the vessel domain, is proposed. The proposed domain complements existing safety criteria, particularly the criterion of a safe passing distance, and can also be used to define the safe cross-track error (XTE) limit. Different types of these objects are considered, and their classification is proposed. A methodology for determining such domains is presented, consisting of a vessel track analysis method (based on AIS data) and specific methods for determining domains for different types of hydrographic objects. Based on actual recorded Automatic Identification System (AIS) data for conventional ships, domains of fixed objects and navigational hazards have been determined. The domains of hydrographic objects may be applied to the delineation of ‘NoGo areas’ around them in nautical charts. Full article

This paper investigates the joint scheduling problem of unmanned aerial vehicles (UAVs) and trucks for community logistics, where UAVs act as service providers for last-mile delivery and trucks serve as mobile storage platforms for drone deployment. To address the complexity of decision variables, this paper proposes a three-stage solution scheme that divides the problem into the following: (1) UAV mission set generation via clustering, (2) truck-drone route planning, and (3) collaborative scheduling via a Mixed-Integer Linear Programming (MILP) model. The MILP model, solved exactly using Gurobi, optimizes truck movements and drone operations to minimize total delivery time, representing the core contribution. In the experimental section, to verify the correctness and effectiveness of the proposed Mixed-Integer Linear Programming (MILP) model, comparative experiments were conducted against a heuristic algorithm based on empirical intuitive decision-making. The solution results of experiments with different scales indicate that the joint scheduling model outperforms the scheduling strategies based on empirical experience across various scenario sizes. Additionally, multiple experiments conducted under different parameter settings within the same scenario successfully demonstrated that the model can stably be solved without deteriorating results when parameters change. Furthermore, this study observed that the relationship between the increase in the number of drones and the decrease in the total consumed time is not a simple linear relationship. This phenomenon is speculated to be due to the periodic patterns exhibited by the drone scheduling sequence, which align with the average duration of individual tasks. Full article

Accurately predicting the trip-level energy consumption of battery electric vehicles (BEVs) can alleviate range anxiety of drivers and improve intelligent route planning. However, although data-driven methods excel in predicting with multi-feature inputs, each vehicle often requires a dedicated model due to potential inconsistencies in feature spaces of collected data. Consequently, the necessity of sufficient trip data challenges newly registered vehicles. To address the challenges, this study proposed a transformer-based pre-trained model for BEV energy consumption prediction adapting to inconsistent feature spaces, referred to as IFS-Former. By innovatively introducing trainable missing-feature embeddings and placeholder masks, the IFS-Former can tolerate new or missing features of downstream tasks after pre-training. The IFS-Former was pre-trained on a dataset comprising 837 vehicles from 8 different cities, containing 492 thousand trips, and validated on 13 vehicles with inconsistent feature spaces. After applying transfer learning to the 13 vehicles, the pre-trained IFS-Former attains high prediction accuracy (R² = 0.97, mean absolute error (MAE) = 1.19). Even under extremely inconsistent feature spaces, the IFS-Former maintains robust performance (R² = 0.96, MAE = 1.31) leveraging its pre-trained knowledge. Furthermore, the IFS-Former is well-suited for on-board deployment with a size of only 32 MB. This study facilitates on-board artificial intelligence for accurate and practical energy consumption prediction. Full article

To achieve a scientific and efficient emergency response, a dynamic route-planning strategy for emergency vehicles based on government–enterprise collaboration was studied. Firstly, a hybrid evaluation approach was developed, integrating the Analytic Hierarchy Process, Entropy Weight Method, and Gray Relation Analysis-TOPSIS to quantitatively assess the urgency of demands at disaster sites. Secondly, a government–enterprise coordinated route-planning strategy was designed, leveraging the government’s strong mobilizing capabilities and enterprises’ flexible operational mechanisms. Thirdly, to optimize scheduling efficiency, a dynamic route-planning model was proposed, incorporating multiple distribution conditions to minimize scheduling time, delay penalties, and unmet demand rates. A two-stage cellular genetic algorithm was employed to address realistic constraints such as demand splitting, soft time windows, open scheduling, and differentiated services. Numerical simulations of potential flooding in Hunan Province revealed that the collaborative strategy significantly improved performance: the demand satisfaction rate rose from 70.1% (government-led) to 92.3%, while the material transportation time per unit decreased by 23.6% (from 1.61 to 1.23 min/unit). Vehicle path characteristics varied under different operational behaviors, aligning with theoretical expectations. Even under sudden road disruptions, the model maintained a 98% demand satisfaction rate with only a negligible 0.076% increase in system loss. This research fills the gaps in previous studies by comprehensively addressing multiple factors in emergency vehicle route planning, offering a practical and efficient solution for post-disaster emergency response. Full article

The transportation industry and transportation infrastructure are undergoing a profound transformation due to advances in the development of artificial intelligence (AI) algorithms that are not just a concept of the future but a reality. Advanced algorithms, predictive systems, and intelligent automation contribute to optimizing logistics, reducing costs, increasing safety, and reducing traffic congestion. AI is also used to optimize routes by analyzing multiple variables, such as distance, traffic, time constraints, and user preferences, to generate optimal routes between departure and destination points. Route planning systems can be integrated with real-time data on traffic, planned or unforeseen events, and other conditions that may affect the trip. AI algorithms can use this data to adapt routes and estimated arrival times based on changes in traffic or other conditions. The purpose of this article is to develop a model for predicting traffic flows at intersections based on historical and real-time data. The focus is on the genetic algorithm used to optimize a Long Short-Term Memory (LSTM) encoder–decoder. Specifically, the research aims to determine how well the proposed model performs when the data is optimized using the genetic algorithm. The results obtained for the proposed GA-LSTM show an average TTS reduction of −18.7%, a maximum improvement of −27.3%, an RMSE of 0.003587, and an MSE of 0.00348 compared to traditional models used in real time for traffic management. Finally, the performance of GA-LSTM was compared with the results reported in the literature to demonstrate the usefulness of the proposed algorithm. Full article

Last-mile logistics is a critical operational and environmental challenge in urban areas. This paper introduces an intelligent path planning system using the Resistive Grid Path Planning Methodology (RGPPM) to optimize distribution based on energy and environmental metrics. The foundational innovation is the integration of electrical-circuit analogies, modeling the distribution network as a resistive grid where optimal routes emerge naturally as current flows, offering a paradigm shift from conventional algorithms. Using a multi-connected grid with georeferenced resistances, RGPPM estimates minimum and maximum paths for various starting points and multi-agent scenarios. We introduce five key performance indicators (KPIs)—Percentage of Distance Savings (PDS), Coefficient of Savings (CS), Coefficient of Global Savings (CGS), Percentage of Load Imbalance (PLI), and Percentage of Deviation with Multi-Agent (PDM)—to evaluate system performance. Simulations for textbook delivery to 129 schools in the Veracruz–Boca del Río area show that RGPPM significantly reduces travel distances. This leads to substantial savings in energy consumption, CO₂ emissions, and operating costs, particularly with electric vehicles. Finally, the results validate RGPPM as a flexible and scalable strategy for sustainable urban logistics. Full article