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Search Results (493)

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Keywords = transport multimodal

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15 pages, 968 KiB  
Article
A Vulnerability Index for Multimodal Transportation Networks: The Case of Korea
by Ki-Han Song, Ha-Jeong Lee and Wonho Suh
Appl. Sci. 2025, 15(15), 8201; https://doi.org/10.3390/app15158201 - 23 Jul 2025
Abstract
This study aimed to assess the vulnerability of transportation networks and identify critical nodes and regions within a multimodal transportation system. While previous research has predominantly focused on centrality measures to evaluate node importance from an accessibility perspective, this study emphasizes the need [...] Read more.
This study aimed to assess the vulnerability of transportation networks and identify critical nodes and regions within a multimodal transportation system. While previous research has predominantly focused on centrality measures to evaluate node importance from an accessibility perspective, this study emphasizes the need to evaluate network vulnerability comprehensively in response to rapid socioeconomic changes. We propose a vulnerability function that integrates network topology and connectivity. First, we defined the vulnerability of individual nodes and regional clusters. Second, we developed a methodology to evaluate the defined vulnerabilities systematically. Finally, we applied the framework to Korea’s multimodal transportation network, conducting a case study to validate the effectiveness and applicability of the proposed function. Conclusively, this study presents a comprehensive vulnerability assessment framework for multimodal transportation networks, offering valuable insights to support robust decision making for enhancing sustainable and efficient transportation systems. Full article
15 pages, 1306 KiB  
Article
Risk Perception in Complex Systems: A Comparative Analysis of Process Control and Autonomous Vehicle Failures
by He Wen, Zaman Sajid and Rajeevan Arunthavanathan
AI 2025, 6(8), 164; https://doi.org/10.3390/ai6080164 - 22 Jul 2025
Abstract
Background: As intelligent systems increasingly operate in high-risk environments, understanding how they perceive and respond to hazards is critical for ensuring safety. Methods: In this study, we conduct a comparative analysis of 60 real-world accident reports, 30 from process control systems (PCSs) and [...] Read more.
Background: As intelligent systems increasingly operate in high-risk environments, understanding how they perceive and respond to hazards is critical for ensuring safety. Methods: In this study, we conduct a comparative analysis of 60 real-world accident reports, 30 from process control systems (PCSs) and 30 from autonomous vehicles (AVs), to examine differences in risk triggers, perception paradigms, and interaction failures between humans and artificial intelligence (AI). Results: Our findings reveal that PCS risks are predominantly internal to the system and detectable through deterministic, rule-based mechanisms, whereas AVs’ risks are externally driven and managed via probabilistic, multi-modal sensor fusion. More importantly, despite these architectural differences, both domains exhibit recurring human–AI interaction failures, including over-reliance on automation, mode confusion, and delayed intervention. In the case of PCSs, these failures are historically tied to human–automation interaction; this article extrapolates these patterns to anticipate potential human–AI interaction challenges as AI adaptation increases. Conclusions: This study highlights the need for a hybrid risk perception framework and improved human-centered design to enhance situational awareness and responsiveness. While AI has not yet been implemented in PCS incident studies, this work interprets human–automation failures in these cases as indicative of potential challenges in human–AI interaction that may arise in future AI-integrated process systems. Implications extend to developing safer intelligent systems across industrial and transportation sectors. Full article
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16 pages, 3775 KiB  
Article
Optimizing Energy Efficiency in Last-Mile Delivery: A Collaborative Approach with Public Transportation System and Drones
by Pierre Romet, Charbel Hage, El-Hassane Aglzim, Tonino Sophy and Franck Gechter
Drones 2025, 9(8), 513; https://doi.org/10.3390/drones9080513 - 22 Jul 2025
Viewed by 58
Abstract
Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission [...] Read more.
Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems. Full article
(This article belongs to the Special Issue Urban Air Mobility Solutions: UAVs for Smarter Cities)
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21 pages, 730 KiB  
Article
A Multimodal Artificial Intelligence Framework for Intelligent Geospatial Data Validation and Correction
by Lars Skaug and Mehrdad Nojoumian
Inventions 2025, 10(4), 59; https://doi.org/10.3390/inventions10040059 - 22 Jul 2025
Viewed by 67
Abstract
Accurate geospatial data are essential for intelligent transportation systems and automated reporting applications, as location precision directly impacts safety analysis and decision-making. GPS devices are now routinely employed by law enforcement officers when filing vehicle crash reports, yet our investigation reveals that significant [...] Read more.
Accurate geospatial data are essential for intelligent transportation systems and automated reporting applications, as location precision directly impacts safety analysis and decision-making. GPS devices are now routinely employed by law enforcement officers when filing vehicle crash reports, yet our investigation reveals that significant data quality issues persist. The high apparent precision of GPS coordinates belies their actual accuracy as we find that approximately 20% of crash sites need correction—results consistent with existing research. To address this challenge, we present a novel credibility scoring and correction algorithm that leverages a state-of-the-art multimodal large language model (LLM) capable of integrated visual and textual reasoning. Our framework synthesizes information from structured coordinates, crash diagrams, and narrative text, employing advanced artificial intelligence techniques for comprehensive geospatial validation. In addition to the LLM, our system incorporates open geospatial data from Overture Maps, an emerging collaborative mapping initiative, to enhance the spatial accuracy and robustness of the validation process. This solution was developed as part of research leading to a patent for autonomous vehicle routing systems that require high-precision crash location data. Applied to a dataset of 5000 crash reports, our approach systematically identifies records with location discrepancies requiring correction. By uniting the latest developments in multimodal AI and open geospatial data, our solution establishes a foundation for intelligent data validation in electronic reporting systems, with broad implications for automated infrastructure management and autonomous vehicle applications. Full article
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29 pages, 5923 KiB  
Article
Activity Spaces in Multimodal Transportation Networks: A Nonlinear and Spatial Analysis Perspective
by Kuang Guo, Rui Tang, Haixiao Pan, Dongming Zhang, Yang Liu and Zhuangbin Shi
ISPRS Int. J. Geo-Inf. 2025, 14(8), 281; https://doi.org/10.3390/ijgi14080281 - 22 Jul 2025
Viewed by 156
Abstract
Activity space offers a valuable perspective for analyzing urban travel behavior and evaluating the performance of transportation systems in increasingly complex urban environments. However, the research on measuring activity spaces in multimodal transportation contexts remains limited. This study investigates multimodal transportation activity spaces [...] Read more.
Activity space offers a valuable perspective for analyzing urban travel behavior and evaluating the performance of transportation systems in increasingly complex urban environments. However, the research on measuring activity spaces in multimodal transportation contexts remains limited. This study investigates multimodal transportation activity spaces in Hangzhou using 2023 smart card data. Multimodal travel chains are extracted, and residents’ activity spaces are quantified using 95% confidence ellipses. By applying the XGBoost and GeoShapley models, this study reveals the nonlinear effects and geospatial heterogeneity in how built environment and socioeconomic factors influence activity spaces. The key findings show that the distance to the nearest metro station, commercial POIs, and GDP significantly shape activity spaces through nonlinear relationships. Moreover, the interaction between the distance to the nearest metro station and geographical location generates pronounced geospatial effects. The results highlight the importance of multimodal integration in urban transport planning and provide empirical insights for enhancing system efficiency and sustainability. Full article
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40 pages, 1540 KiB  
Review
A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
by Thi-Thu-Trang Do, Quyet-Thang Huynh, Kyungbaek Kim and Van-Quyet Nguyen
Appl. Sci. 2025, 15(14), 8089; https://doi.org/10.3390/app15148089 - 21 Jul 2025
Viewed by 227
Abstract
The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains [...] Read more.
The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. Full article
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21 pages, 2699 KiB  
Article
Urban Sustainability of Quito Through Its Food System: Spatial and Social Interactions
by María Magdalena Benalcázar Jarrín, Diana Patricia Zuleta Mediavilla, Ramon Rispoli and Daniele Rocchio
Sustainability 2025, 17(14), 6613; https://doi.org/10.3390/su17146613 - 19 Jul 2025
Viewed by 256
Abstract
This study explores the spatial and social implications of urban food systems in Quito, Ecuador, focusing on how food access inequalities reflect and reinforce broader urban disparities. The research addresses a critical problem in contemporary urbanization: the disconnection between food provisioning and spatial [...] Read more.
This study explores the spatial and social implications of urban food systems in Quito, Ecuador, focusing on how food access inequalities reflect and reinforce broader urban disparities. The research addresses a critical problem in contemporary urbanization: the disconnection between food provisioning and spatial equity in rapidly growing cities. The objective is to assess and map disparities in food accessibility using a mixed-methods approach that includes field observation, participatory mapping, value chain analysis, and statistical modeling. Five traditional and emerging food markets were studied in diverse districts across the city. A synthetic accessibility function F(x) was constructed to model food access levels, integrating variables such as income, infrastructure, transport availability, and travel time. These variables were subjected to Principal Component Analysis (PCA) and hierarchical clustering to generate three typologies of territorial vulnerability. The results reveal that peripheral areas exhibit lower F(x) values and weaker integration with the formal food system, leading to higher consumer costs and limited fresh food options. In contrast, central districts benefit from multimodal infrastructure and greater diversity of supply. This study concludes that food systems should be treated as critical urban infrastructure. Integrating food equity into land use and mobility planning is essential to promote inclusive, sustainable, and resilient urban development. Full article
(This article belongs to the Section Sustainable Urban and Rural Development)
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23 pages, 2032 KiB  
Article
Factors Influencing Nighttime Tourists’ Satisfaction of Urban Lakes: A Case Study of the Daming Lake Scenic Area, China
by Huying Zhu and Mengru Li
Sustainability 2025, 17(14), 6596; https://doi.org/10.3390/su17146596 - 19 Jul 2025
Viewed by 287
Abstract
Tourist satisfaction of nighttime urban lakes as scenic areas, such as the Daming Lake, is influenced by multiple factors, which are crucial for tourists’ experiences and the sustainable development of these areas. This paper explores the factors impacting nighttime visitor satisfaction at the [...] Read more.
Tourist satisfaction of nighttime urban lakes as scenic areas, such as the Daming Lake, is influenced by multiple factors, which are crucial for tourists’ experiences and the sustainable development of these areas. This paper explores the factors impacting nighttime visitor satisfaction at the Daming Lake Scenic Area. Basing our studies on analysis of the literature and questionnaire surveys, the study constructs a visitor satisfaction evaluation index system based on the Expectancy-Disconfirmation Theory. Utilizing the revised importance-performance analysis method, the study identifies several significant influencing factors including the distinctive features of nighttime shopping products, the rich variety of nighttime tourscape and entertainment products, the aesthetically pleasing design of nighttime lighting products, the affordable price of nighttime dining products, and the diverse methods, reasonable pricing, and multimodal transit options of nighttime transportation. Furthermore, it finds the main factors that reduce tourists’ satisfaction in nighttime urban lakes include: premium pricing of nighttime shopping and dining products, transport infrastructure deficiencies, the cultural connotation of tourism products, and the safety of nighttime tourscape and entertainment products. This research provides insights to enhance satisfaction in urban lake scenic areas and expands the application of the tourist satisfaction theory. Full article
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24 pages, 1532 KiB  
Review
Polymeric Nanoparticle-Mediated Photodynamic Therapy: A Synergistic Approach for Glioblastoma Treatment
by Bandar Aldhubiab and Rashed M. Almuqbil
Pharmaceuticals 2025, 18(7), 1057; https://doi.org/10.3390/ph18071057 - 18 Jul 2025
Viewed by 294
Abstract
Glioblastoma is the most common and aggressive malignant primary brain tumour. Patients with glioblastoma have a median survival of only around 14.6 months after diagnosis, despite the availability of various conventional multimodal treatments including chemotherapy, radiation therapy, and surgery. Therefore, photodynamic therapy (PDT) [...] Read more.
Glioblastoma is the most common and aggressive malignant primary brain tumour. Patients with glioblastoma have a median survival of only around 14.6 months after diagnosis, despite the availability of various conventional multimodal treatments including chemotherapy, radiation therapy, and surgery. Therefore, photodynamic therapy (PDT) has emerged as an advanced, selective and more controlled therapeutic approach, which has minimal systemic toxicity and fewer side effects. PDT is a less invasive therapy that targets all cells or tissues that possess the photosensitizer (PS) itself, without affecting the surrounding healthy tissues. Polymeric NPs (PNPs) as carriers can improve the targeting ability and stability of PSs and co-deliver various anticancer agents to achieve combined cancer therapy. Because of their versatile tuneable features, these PNPs have the capacity to open tight junctions of the blood–brain barrier (BBB), easily transport drugs across the BBB, protect against enzymatic degradation, prolong the systemic circulation, and sustainably release the drug. Conjugated polymer NPs, poly(lactic-co-glycolic acid)-based NPs, lipid–polymer hybrid NPs, and polyethylene-glycolated PNPs have demonstrated great potential in PDT owing to their unique biocompatibility and optical properties. Although the combination of PDT and PNPs has great potential and can provide several benefits over conventional cancer therapies, there are several limitations that are hindering its translation into clinical use. This review aims to summarize the recent advances in the combined use of PNPs and PDT in the case of glioblastoma treatment. By evaluating various types of PDT and PNPs, this review emphasizes how these innovative approaches can play an important role in overcoming glioblastoma-associated critical challenges, including BBB and tumour heterogeneity. Furthermore, this review also discusses the challenges and future directions for PNPs and PDT, which provides insight into the potential solutions to various problems that are hindering their clinical translation in glioblastoma treatment. Full article
(This article belongs to the Special Issue Tumor Therapy and Drug Delivery)
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26 pages, 2215 KiB  
Article
Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
by Manuel Felder, Matteo De Marchi, Patrick Dallasega and Erwin Rauch
Appl. Sci. 2025, 15(14), 8001; https://doi.org/10.3390/app15148001 - 18 Jul 2025
Viewed by 211
Abstract
Small and medium-sized enterprises (SMEs) face growing challenges in optimizing their sustainable supply chains because of fragmented logistics data and changing regulatory requirements. In particular, globally operating manufacturing SMEs often lack suitable tools, resulting in manual data collection and making reliable accounting and [...] Read more.
Small and medium-sized enterprises (SMEs) face growing challenges in optimizing their sustainable supply chains because of fragmented logistics data and changing regulatory requirements. In particular, globally operating manufacturing SMEs often lack suitable tools, resulting in manual data collection and making reliable accounting and benchmarking of transport emissions in lifecycle assessments (LCAs) time-consuming and difficult to scale. This paper introduces a novel hybrid AI-supported knowledge graph (KG) which combines large language models (LLMs) with graph-based optimization to automate industrial supply chain route enrichment, completion, and emissions analysis. The proposed solution automatically resolves transportation gaps through generative AI and programming interfaces to create optimal routes for cost, time, and emission determination. The application merges separate routes into a single multi-modal network which allows users to evaluate sustainability against operational performance. A case study shows the capabilities in simplifying data collection for emissions reporting, therefore reducing manual effort and empowering SMEs to align logistics decisions with Industry 5.0 sustainability goals. Full article
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16 pages, 1107 KiB  
Article
Pricing Strategy for High-Speed Rail Freight Services: Considering Perspectives of High-Speed Rail and Logistics Companies
by Guoyong Yue, Mingxuan Zhao, Su Zhao, Liwei Xie and Jia Feng
Sustainability 2025, 17(14), 6555; https://doi.org/10.3390/su17146555 - 18 Jul 2025
Viewed by 188
Abstract
It is well known that there is a significant conflict of interest between high-speed rail (HSR) operators and logistics companies. This study proposes an HSR freight pricing strategy based on a multi-objective optimization framework and a freight mode splitting model based on the [...] Read more.
It is well known that there is a significant conflict of interest between high-speed rail (HSR) operators and logistics companies. This study proposes an HSR freight pricing strategy based on a multi-objective optimization framework and a freight mode splitting model based on the Logit model. A utility function was constructed to quantify the comprehensive utility of different modes of transportation by integrating five key influencing factors: economy, speed, convenience, stability, and environmental sustainability. A bi-objective optimization model was developed to balance the cost of the logistics with the benefits of high-speed rail operators to achieve a win–win situation. The model is solved by the TOPSIS method, and its effectiveness is verified by the freight case of the Zhengzhou–Chongqing high-speed railway in China. The results of this study showed that (1) HSR has advantages in medium-distance freight transportation; (2) increasing government subsidies can help improve the market competitiveness of high-speed rail in freight transportation. This research provides theoretical foundations and methodological support for optimizing HSR freight pricing mechanisms and improving multimodal transport efficiency. Full article
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17 pages, 936 KiB  
Article
Improving the Freight Transportation System in the Context of the Country’s Economic Development
by Veslav Kuranovič, Leonas Ustinovichius, Maciej Nowak, Darius Bazaras and Edgar Sokolovskij
Sustainability 2025, 17(14), 6327; https://doi.org/10.3390/su17146327 - 10 Jul 2025
Viewed by 290
Abstract
Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A [...] Read more.
Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO2 emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO2 emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article
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22 pages, 1402 KiB  
Article
Fleet Coalitions: A Collaborative Planning Model Balancing Economic and Environmental Costs for Sustainable Multimodal Transport
by Anna Laura Pala and Giuseppe Stecca
Logistics 2025, 9(3), 91; https://doi.org/10.3390/logistics9030091 - 10 Jul 2025
Viewed by 209
Abstract
Background: Sustainability is a critical concern in transportation, notably in light of governmental initiatives such as cap-and-trade systems and eco-label regulations aimed at reducing emissions. In this context, collaborative approaches among carriers, which involve the exchange of shipment requests, are increasingly recognized as [...] Read more.
Background: Sustainability is a critical concern in transportation, notably in light of governmental initiatives such as cap-and-trade systems and eco-label regulations aimed at reducing emissions. In this context, collaborative approaches among carriers, which involve the exchange of shipment requests, are increasingly recognized as effective strategies to enhance efficiency and reduce environmental impact. Methods: This research proposes a novel collaborative planning model for multimodal transport designed to minimize the total costs associated with freight movements, including both transportation and CO2 emissions costs. Transshipments of freight between vehicles are modeled in the proposed formulation, promoting carrier coalitions. This study incorporated eco-labels, representing different emission ranges, to capture shipper sustainability preferences and integrated authority-imposed low-emission zones as constraints. A bi-objective approach was adopted, combining transportation and emission costs through a weighted sum method. Results: A case study on the Naples Bypass network (Italy) is presented, highlighting the model’s applicability in a real-world setting and demonstrating the effectiveness of collaborative transport planning. In addition, the model quantified the benefits of collaboration under low-emission zone (LEZ) constraints, showing notable reductions in both total costs and emissions. Conclusions: Overall, the proposed approach offers a valuable decision support tool for both carriers and policymakers, enabling sustainable freight transportation planning. Full article
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29 pages, 1138 KiB  
Article
Regularized Kaczmarz Solvers for Robust Inverse Laplace Transforms
by Marta González-Lázaro, Eduardo Viciana, Víctor Valdivieso, Ignacio Fernández and Francisco Manuel Arrabal-Campos
Mathematics 2025, 13(13), 2166; https://doi.org/10.3390/math13132166 - 2 Jul 2025
Viewed by 174
Abstract
Inverse Laplace transforms (ILTs) are fundamental to a wide range of scientific and engineering applications—from diffusion NMR spectroscopy to medical imaging—yet their numerical inversion remains severely ill-posed, particularly in the presence of noise or sparse data. The primary objective of this study is [...] Read more.
Inverse Laplace transforms (ILTs) are fundamental to a wide range of scientific and engineering applications—from diffusion NMR spectroscopy to medical imaging—yet their numerical inversion remains severely ill-posed, particularly in the presence of noise or sparse data. The primary objective of this study is to develop robust and efficient numerical methods that improve the stability and accuracy of ILT reconstructions under challenging conditions. In this work, we introduce a novel family of Kaczmarz-based ILT solvers that embed advanced regularization directly into the iterative projection framework. We propose three algorithmic variants—Tikhonov–Kaczmarz, total variation (TV)–Kaczmarz, and Wasserstein–Kaczmarz—each incorporating a distinct penalty to stabilize solutions and mitigate noise amplification. The Wasserstein–Kaczmarz method, in particular, leverages optimal transport theory to impose geometric priors, yielding enhanced robustness for multi-modal or highly overlapping distributions. We benchmark these methods against established ILT solvers—including CONTIN, maximum entropy (MaxEnt), TRAIn, ITAMeD, and PALMA—using synthetic single- and multi-modal diffusion distributions contaminated with 1% controlled noise. Quantitative evaluation via mean squared error (MSE), Wasserstein distance, total variation, peak signal-to-noise ratio (PSNR), and runtime demonstrates that Wasserstein–Kaczmarz attains an optimal balance of speed (0.53 s per inversion) and accuracy (MSE = 4.7×108), while TRAIn achieves the highest fidelity (MSE = 1.5×108) at a modest computational cost. These results elucidate the inherent trade-offs between computational efficiency and reconstruction precision and establish regularized Kaczmarz solvers as versatile, high-performance tools for ill-posed inverse problems. Full article
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30 pages, 4491 KiB  
Article
IoT-Enabled Adaptive Traffic Management: A Multiagent Framework for Urban Mobility Optimisation
by Ibrahim Mutambik
Sensors 2025, 25(13), 4126; https://doi.org/10.3390/s25134126 - 2 Jul 2025
Cited by 1 | Viewed by 506
Abstract
This study evaluates the potential of IoT-enabled adaptive traffic management systems for mitigating urban congestion, enhancing mobility, and reducing environmental impacts in densely populated cities. Using London as a case study, the research develops a multiagent simulation framework to assess the effectiveness of [...] Read more.
This study evaluates the potential of IoT-enabled adaptive traffic management systems for mitigating urban congestion, enhancing mobility, and reducing environmental impacts in densely populated cities. Using London as a case study, the research develops a multiagent simulation framework to assess the effectiveness of advanced traffic management strategies—including adaptive signal control and dynamic rerouting—under varied traffic scenarios. Unlike conventional models that rely on static or reactive approaches, this framework integrates real-time data from IoT-enabled sensors with predictive analytics to enable proactive adjustments to traffic flows. Distinctively, the study couples this integration with a multiagent simulation environment that models the traffic actors—private vehicles, buses, cyclists, and emergency services—as autonomous, behaviourally dynamic agents responding to real-time conditions. This enables a more nuanced, realistic, and scalable evaluation of urban mobility strategies. The simulation results indicate substantial performance gains, including a 30% reduction in average travel times, a 50% decrease in congestion at major intersections, and a 28% decline in CO2 emissions. These findings underscore the transformative potential of sensor-driven adaptive systems for advancing sustainable urban mobility. The study addresses critical gaps in the existing literature by focusing on scalability, equity, and multimodal inclusivity, particularly through the prioritisation of high-occupancy and essential traffic. Furthermore, it highlights the pivotal role of IoT sensor networks in real-time traffic monitoring, control, and optimisation. By demonstrating a novel and practical application of sensor technologies to traffic systems, the proposed framework makes a significant and timely contribution to the field and offers actionable insights for smart city planning and transportation policy. Full article
(This article belongs to the Special Issue Vehicular Sensing for Improved Urban Mobility: 2nd Edition)
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