Search Results (9,477)

Identifying critical infrastructure in transportation networks requires metrics that can capture both the topological structure and how demand is redistributed during disruptions. Conventional graph-theoretic approaches fail to jointly quantify these vulnerabilities. This study presents a computational framework for edge-criticality assessment based on the Fused Unbalanced Gromov–Wasserstein (FUGW) distance, incorporating both structural similarity and demand characteristics of network nodes in an optimal transport tool. The three hyperparameters that influence FUGW accuracy—fusion weight, entropic regularization, and marginal penalties—were tuned using Bayesian optimization. This ensures the rankings remain accurate, stable, and reproducible under temporal variability and demand shifts. We apply the framework to a benchmark transportation network evaluated across four diurnal periods, capturing dynamic congestion and shifting demand patterns. Systematic variation in the fusion parameter shows seven consistently critical edges whose rankings remain stable across analytical configurations. It can be concluded from the results that monotonic scaling with increasing feature emphasis, strong cross-hyperparameter correlation, and low temporal variability confirm the robustness of the inferred criticality hierarchy. These edges represent both structural bridges and demand concentration points, offering α indicators of network vulnerability. These findings demonstrate that FUGW provides a solid and scalable method of assessing transportation vulnerabilities. It helps support clear decisions on maintenance planning, redundancy, and resilience investments. Full article

Ensuring sustained driver attention is critical for intelligent transportation safety systems; however, the performance of data-driven driver distraction detection models is often limited by the high cost of large-scale manual annotation. To address this challenge, this paper proposes an adaptive semi-supervised driver distraction detection framework based on teacher–student learning and deformable pyramid feature fusion. The framework leverages a limited amount of labeled data together with abundant unlabeled samples to achieve robust and scalable distraction detection. An adaptive pseudo-label optimization strategy is introduced, incorporating category-aware pseudo-label thresholding, delayed pseudo-label scheduling, and a confidence-weighted pseudo-label loss to dynamically balance pseudo-label quality and training stability. To enhance fine-grained perception of subtle driver behaviors, a Deformable Pyramid Sparse Transformer (DPST) module is integrated into a lightweight YOLOv11 detector, enabling precise multi-scale feature alignment and efficient cross-scale semantic fusion. Furthermore, a teacher-guided feature consistency distillation mechanism is employed to promote semantic alignment between teacher and student models at the feature level, mitigating the adverse effects of noisy pseudo-labels. Extensive experiments conducted on the Roboflow Distracted Driving Dataset demonstrate that the proposed method outperforms representative fully supervised baselines in terms of mAP@0.5 and mAP@0.5:0.95 while maintaining a balanced trade-off between precision and recall. These results indicate that the proposed framework provides an effective and practical solution for real-world driver monitoring systems under limited annotation conditions. Full article

This study presents a robust framework for optimizing the site selection of Electric Vehicle Charging Stations (EVCS) in Qatar by integrating a Geographic Information System (GIS) with a Multi-Criteria Decision-Making (MCDM) model. The core innovation lies in the enhancement of the conventional Analytic Hierarchy Process (AHP) with a Removal Sensitivity Analysis (RSA). This unique integration moves beyond traditional, subjective expert-based weighting by introducing a transparent, data-driven methodology to quantify the influence of each criterion and generate objective weights. The Analytic Hierarchy Process (AHP) was used to evaluate fourteen criteria related to accessibility, economic and environmental factors that influence EVCS site suitability. To enhance robustness and minimize subjectivity, a Removal Sensitivity Analysis (RSA) was applied to quantify the influence of each criterion and generate objective, data-driven weights. The results reveal that accessibility factors, particularly proximity to road networks and parking areas exert the highest influence, while environmental variables such as slope, CO concentration, and green areas have moderate but spatially significant impacts. The integration of AHP and RSA produced a more balanced and environmentally credible suitability map, reducing overestimation of urban sites and promoting sustainable spatial planning. Environmentally, the proposed framework supports Qatar’s transition toward low-carbon mobility by encouraging the expansion of clean electric transport infrastructure, reducing greenhouse gas emissions, and improving urban air quality. The findings contribute to achieving the objectives of Qatar National Vision 2030 and align with global efforts to mitigate climate change through sustainable transportation development. Full article

Rheology plays an important role in the 3D concrete printing technology, because it directly governs the flowability and shape retention of the material, impacting both the printing process and the final quality of the obtained structure. Local raw materials such as Portland cement, washed sand, and tap water were used for the preparation of 3D-printed concrete mixtures. The solid-state polycarboxylate ether with an anti-foaming agent was used as superplasticizer. The Portland cement was partially replaced (by volume) with a natural zeolite additive in amounts ranging from 0% to 9% in 3D-printed concrete mixtures. A rotational rheometer with coaxial cylinders was used in this research for the determination of rheological characteristics of prepared 3D-printed concrete mixtures. The Herschel–Buckley model was used to approximate experimental flow curves and assess rheological parameters such as yield stress, plastic viscosity, and shear-thinning/thickening index. The additional experiments and calculations, such as water bleeding test and evaluation of the carbon footprint of 3D-printed concrete mixtures, were performed in this work. The replacement of Portland cement with natural zeolite additive positively influenced rheological and stability-related properties of 3D-printed concrete mixtures. Natural zeolite additive consistently reduced water bleeding, enhanced yield stress under increasing shear rates, and lowered plastic viscosity, thereby improving flowability and mixture transportation during the 3D printing process. As the shear-thinning/thickening index remained stable (indicating non-thixotropic behavior in most cases), higher amounts of natural zeolite additive introduced slight thixotropy (especially under decreased shear rates). These changes contributed to better shape retention, layer stability, and the ability to print taller and narrower structures without collapse, making natural zeolite additive suitable for use in the optimized processes of 3D concrete printing. A significant decrease in total carbon footprint (from 3% to 19%) was observed in 3D-printed concrete mixtures with an increase in the mentioned amounts of natural zeolite additive, compared to the mixture without this additive. Full article

Under the dual imperatives of carbon neutrality and marine energy transition, hydrogen has emerged as an emerging energy storage carrier, offering a new pathway for offshore wind power consumption. This study addresses the critical challenges of offshore wind power intermittency and hydrogen transport efficiency bottlenecks by proposing an innovative solution. A coordinated planning method for far-offshore wind–hydrogen systems considering nearshore relay points is developed, establishing a multi-stage optimization framework of “offshore hydrogen production—relay point storage and transportation—hierarchical vessel delivery”. By optimizing hydrogen transport routes through coordinated allocation of electrolyzers, storage tanks, and vessel transportation, and designing a hierarchical transportation model that differentiates between ocean-going and nearshore vessels, the simulation results of a coastal area in China demonstrate that, compared with traditional methods, the proposed approach reduces investment costs and operation costs by nearly 10% while decreasing the monthly wind curtailment rate by 10.53%. Full article

Conventional descriptive statistical approaches in per- and polyfluoroalkyl substance (PFAS) environmental forensics often fail under small-sample, ecosystem-level complexity, challenging the optimization of sampling, monitoring, and remediation strategies. This study presents an advance from passive description to adaptive decision-support for complex PFAS contamination. By integrating Bayesian optimization (BO) via Gaussian Processes (GP) with a Generalized Linear Mixed Model (GLMM), we developed a signal-extraction framework for both understanding and action from limited data (n = 18). The BO/GP model achieved strong predictive performance (GP leave-one-out R² = 0.807), while the GLMM confirmed significant overdispersion (1.62), indicating a patchy contamination distribution. The integrated analysis suggested a dominant spatiotemporal interaction: a transient, high-intensity perfluorooctane sulfonate (PFOS) plume that peaked at a precise location during early November (the autumn recharge period). Concurrently, the GLMM identified significant intra-sample variance (p = 0.0186), suggesting likely particulate-bound (colloid/sediment) transport, and detected n-ethyl perfluorooctane sulfonamidoacetic acid (NEtFOSAA) as a critical precursor (p < 0.0001), thus providing evidence consistent with the source as historic 3M aqueous film-forming foam. This coupled approach creates a dynamic, iterative decision-support system where signal-based diagnosis informs adaptive optimization, enabling mission-specific actions from targeted remediation to monitoring design. Full article

This paper deals with the issue concerning the optimization of energy consumption saving in passenger railway traffic. The background is mainly related to the decision to modernize existing trains or purchase new units, which is a key dilemma for rail transport managers. Concerning the methods used for the determination of the proper results, there is a very wide range of possibilities. This issue is complex, encompassing technical, economic, environmental, and social aspects; therefore, artificial intelligence methods were used for analysis. The obtained results have shown that the choice is not clear-cut, as each option offers both benefits and limitations. The investigations are based on real measurement values obtained from a Polish regional railway. In conclusion, it can be found that the final decision should take into account the long-term goals and the specific characteristics of the given rail system. Several factors influencing the energy consumption were taken into account. So, the aim of this paper was achieved, and the main factors were determined, which have influenced energy consumption and its impact, as well as the possibility of energy consumption reduction. Full article

Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions from machine learning, neural networks, and computer vision. We synthesize current research that leverages these sophisticated AI methodologies to mitigate risks associated with railroad accidents and optimize railroad tracks management. The scope of this review encompasses diverse applications, including real-time monitoring of track conditions, predictive maintenance for infrastructure components, automated defect detection, and intelligent systems for obstacle and intrusion detection. Furthermore, it delves into the use of AI in assessing human factors, improving signaling systems, and analyzing accident/incident reports for proactive risk management. By examining the integration of advanced analytical techniques into various facets of railway operations, this paper highlights how AI is transforming traditional safety paradigms, paving the way for more resilient, efficient, and secure railway networks worldwide. Full article

To investigate the complex interaction in multi-structure systems, this study establishes a refined 3D numerical model based on a transportation hub project in Tianjin to analyze the asymmetric coupling deformation mechanism of a deep excavation adjacent to a shared-wall metro station and elevated bridge piles. This study highlights the transition from soil-mediated interaction mechanisms to those dominated by structures under shared-wall constraints. Results show that the existing station acts as a high-stiffness boundary, effectively suppressing lateral-wall deflection and basal heave on the proximal side. A critical finding is the reversal of the station’s deformation mode: while stations with a soil buffer typically tilt toward the excavation, the shared-wall station exhibits a clockwise rotation away from the excavation; this phenomenon is driven by excavation-induced basal rebound directly transferred through the common diaphragm wall. Furthermore, the station exerts a significant “shielding effect” on adjacent bridge piles, shifting their maximum lateral displacement from the pile head to the toe and reducing overall deformation. Parametric analyses reveal that optimizing shared-wall thickness is more effective for controlling lateral deformation, whereas increasing wall depth primarily mediates vertical heave. This study concludes that, for shared-wall systems, design priorities must shift from settlement control to anti-heave measures, and pile monitoring should extend to the deeper critical zones identified in this study. Full article

This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the uncertainty of both emergency materials’ demand and the network capacity by LR triangular fuzzy numbers, and thus explores a reliable routing problem for transporting emergency materials that is further formulated by a fuzzy linear programming model. Considering the decision makers’ cautious attitude on the transportation of emergency materials to avoid transportation interruption, this study adopts chance-constrained programming based on necessity measure to build a solvable reformulation of the proposed model. A numerical case study is carried out to reveal the conflicting relationship between improving the reliability and reducing the time of transporting emergency materials. The decision-makers of the emergency materials transportation organization should select a reasonable confidence level based on the actual decision-making scenario to plan the reliable intermodal route for emergency materials. By comparing with deterministic modeling, this study verifies the feasibility of the modeling the uncertainty of both demand and capacity in avoiding unreliable transportation and enhancing the flexibility of the intermodal routing for emergency materials. By comparing with chance-constrained programming using possibility measure, this study demonstrates the feasibility of the necessity measure in planning the reliable intermodal route. This study further analyzes how the capacity level of the intermodal network, demand level of the emergency materials and stability of the LR triangular fuzzy parameters influence the optimization results. Accordingly, this study emphasizes the importance of objectively evaluating the uncertain demand for emergency materials, and reveals that the enhancement of the capacity level of the intermodal network and stability of LR triangular fuzzy parameters is able to reduce the transportation time of emergency materials and meanwhile maintain a high reliability. Full article

To accurately investigate the loss characteristics of fuel cell vehicles (FCVs) under actual operating conditions and enhance their power performance and economic efficiency, this study establishes a numerical model of the proton exchange membrane fuel cell (PEMFC) stack based on real-world data from the second-generation Mirai. The stack model incorporates leakage current losses and imposes a limit on maximum current density. Besides, this study analyzes the effects of operating parameters (PEM water content, hydrogen partial pressure, current density, oxygen partial pressure, and operating temperature) on stack power output, efficiency, and eco-performance coefficient (ECOP). Furthermore, Non-Dominated Sequential Genetic Algorithm (NSGA-II) is employed to optimize the PEMFC stack performance, yielding the optimal operating parameter set for FCV operation. Further simulations are conducted on dynamic performance characteristics of the second-generation Mirai under two typical driving cycles, evaluating the power performance and economy of the FCV before and after optimization. Results demonstrate that the established PEMFC stack model accurately analyzes the output performance of an actual FCV when compared with real-world performance test data from the second-generation Mirai. Through optimization, output power increases by 7.4%, efficiency improves by 1.95%, and ECOP rises by 3.84%, providing guidance for enhancing vehicle power performance and improving overall vehicle economy. This study provides a practical framework for enhancing the power performance and overall energy sustainability of fuel cell vehicles, contributing to the advancement of sustainable transportation. Full article

Optimizing efficient electrode materials that combine high energy density, rapid charge transport, and excellent cycling stability remains a challenge for advanced supercapacitors. Here, we report the synthesis of an innovative copper(I)-based metal–organic framework (MOF), Cu₃(NDI)₃, prepared via a simple solvothermal method using N,N’-bis(3,5-dimethylpyrazol-4-yl)-naphthalene diimide (H₂NDI-H) as a linker. Structural analyses (XRD, FTIR, SEM, EDX, and BET) confirmed the formation of a highly crystalline, porous MOF. Integration of this MOF into laser-induced graphene (LIG) matrices yielded hybrid electrodes with enhanced structural characteristics and electrochemical activity, compared to its only-LIG counterpart. Electrochemical studies (CV, CD, EIS) revealed that the LIG–MOF electrode exhibited the highest performance, delivering a specific capacitance of 4.6 mF cm⁻² at 0.05 mA cm⁻², and an areal energy density of 60.03 μWh cm⁻² at a power density of 1292.17 μW cm⁻², outperforming both LIG and MOF–LIG configurations. This enhancement arises from the synergetic interaction between the conductive LIG network and the redox-active Cu₃(NDI)₃ framework, highlighting the potential of LIG–MOF hybrids as next-generation materials for high-performance supercapacitors. Full article

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article

This study, for the first time, investigates and simulates ballistic-transport-induced avalanche behavior in two-dimensional materials. Using a technology computer-aided design simulation platform, a device model for ballistic avalanche transport is systematically established. By accurately calibrating the material parameters of two-dimensional materials and selecting appropriate physical models, the key features of the ballistic avalanche effect are successfully reproduced, including low threshold voltage and high gain. The simulation results show good agreement with experimental data. Furthermore, mechanism-based analysis is performed to clarify the influence of critical design parameters on the avalanche threshold and multiplication gain. Finally, based on the same physical models and mechanistic understanding, the operational paradigm and performance of ballistic-transport avalanche photodetectors based on two-dimensional materials are predicted. This work provides a reliable theoretical foundation and a robust simulation framework for the optimized design of high-performance and low-power avalanche photon devices. Full article

This study examines whether twisted tape inserts in a pipe system can reduce pipe erosion under a liquid–solid flow regime. Three different twisted tape configurations were designed using 3D printing technology: tapes with one twist, four twists, and four twists with perforations. Experiments were performed using a PVC pipe with a carbon steel plate as the material under investigation. Slurries of water and silica sand were prepared with varying sand concentrations—1%, 3%, and 5%—to induce different erosion rates. The experimental results were backed by Computational Fluid Dynamics (CFD) using the discrete phase model (DPM) to predict particle flow and erosion attributes. Erosion trends were also tested through mass loss and paint loss tests. The analysis outcomes demonstrated that the one-twist, four-twist, and perforated four-twist tapes reduced the erosion rate by 18%, 39%, and 45%, respectively. Among the different configurations, the four-twist tape with holes reduced erosion the most. These results suggest that twisted tape inserts can control erosion, thereby increasing the service life of pipes that handle abrasive flows. Full article