Search Results (429)

In recent years, the Internet of Things (IoT) has experienced considerable developments and has played an important role in various domains such as industry, agriculture, healthcare, transportation, and environment, especially for smart cities. Along with that, wireless sensor networks (WSNs) are considered to be important components of the IoT system (WSN-IoT) to create smart applications and automate processes. As the number of connected IoT devices increases, privacy and security issues become more complicated due to their external working environments and limited resources. Hence, solutions need to be updated to ensure that data and user privacy are protected from threats and attacks. To support the safety and reliability of such systems, in this paper, security issues in the WSN-IoT are addressed and classified as identifying security challenges and requirements for different kinds of attacks in either WSNs or IoT systems. In addition, security solutions corresponding to different types of attacks are provided, analyzed, and evaluated. We provide different comparisons and classifications based on specific goals and applications that hopefully can suggest suitable solutions for specific purposes in practical. We also suggest some research directions to support new security mechanisms. Full article

Articulated Tugs and Barges (ATBs) are increasingly recognised for their effectiveness in transporting chemicals, petroleum, bulk goods, and containers, primarily due to their exceptional flexibility and fuel efficiency. Recent projections indicate that the ATB market is on track for significant growth, which is expected to lead to an increase in the annual growth rate from 2025 to 2032. This study aims to analyse the current advancements in ATB technology and provide insights into the ATB fleet and the systems that connect tugboats and barges. Furthermore, it highlights the advantages of this transportation system, especially regarding its role in enhancing energy efficiency within the maritime transport sector. Currently, there is limited information available in the public domain about ATBs compared to other commercial vessels. The analysis reveals that much of the required information for modern ATB design is not accessible outside specialised design companies. The study also focuses on conceptual design aspects, which include the main dimensions, articulated connections, propulsion systems, and machinery, concluding with an evaluation of economic viability. Special emphasis is placed on defining the main dimensions, which is a critical part of the complex design process. In this context, the ratios of length to beam (L/B), beam to draft (B/D), beam to depth (B/T), draft to depth (T/D), and power to the number of tugs cubed (Pw/N³) are established as design control parameters in the conceptual design phase. This aspect underscores the novelty of the present study. Additionally, the economic viability is analysed in terms of both CAPEX (capital expenditures) and OPEX (operational expenditures). While CAPEX does not significantly differ between the methods used in different types of commercial ships, OPEX should account for the unique characteristics of ATB vessels. Full article

The barotropic sea level difference (SLD) across the Korea/Tsushima Strait (KTS) is considered an index of the volume transport into the East/Japan Sea. This study investigates the interannual variability of the barotropic SLD (the KTS inflow) from 1985 to 2017 and its relationship to upper-ocean (<300 m) current variability in the western North Pacific. An increase in the KTS inflow is associated with a weakening of the Kuroshio current through the Tokara Strait and upper-ocean cooling in the North Pacific Subtropical Gyre, characteristic of a La Niña-like state. Diagnostic analysis reveals that the KTS inflow variability is linked to at least two statistically distinct and concurrent modes of oceanic variability. The first mode is tied to the El Niño–Southern Oscillation through large-scale changes in the Kuroshio system. The second mode, which is linearly uncorrelated with the first, is associated with regional eddy kinetic energy variability in the western North Pacific. The identification of these parallel pathways suggests a complex regulatory system for the KTS inflow. This study provides a new framework for understanding the multi-faceted connection between the KTS and upstream oceanic processes, with implications for the predictability of the ocean environmental conditions in the East/Japan Sea. Full article

The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmented results and misidentification of overlapping roads as intersections. To address these limitations, we propose a forward and reverse tracking method for high-accuracy road interchange network generation. First, raw crowdsourced trajectory data is preprocessed by filtering out non-interchange trajectories and removing abnormal data based on both static and dynamic characteristics of the trajectories. Next, road subgraphs are extracted by identifying potential transition nodes, which are verified using directional and distribution information. Trajectory bifurcation is then performed at these nodes. Finally, a two-stage fusion process combines forward and reverse tracking results to produce a geometrically complete and topologically accurate road interchange network. Experiments using crowdsourced trajectory data from Shenzhen demonstrated highly accurate results, with 95.26% precision in geometric road network alignment and 90.06% accuracy in representing the connectivity of road interchange structures. Compared to existing methods, our approach enhanced accuracy in spatial alignment by 13.3% and improved the correctness of structural connections by 12.1%. The approach demonstrates strong performance across different types of interchanges, including cloverleaf, turbo, and trumpet interchanges. Full article

With the advancement of urbanization and the proposal of sustainable development goals, the complexity and vulnerability of urban transportation systems have become increasingly prominent, and their reliability is directly related to the sustainable operation of urban transportation. The reliability of urban road networks, characterized by their dynamic nature, multi-scale characteristics, and anti-interference capabilities, directly restricts the functional guarantee of urban traffic and the efficiency of emergency response. To address the limitations of existing road network connectivity reliability assessment methods in representing time dynamics and modeling failure correlation, this study proposes a road network reliability assessment method based on a Dynamic Bayesian Network (DBN) by constructing a probabilistic reasoning model that integrates cascading failure characteristics. First, the connectivity reliability of the road network under random and targeted attack strategies was evaluated using a Monte Carlo simulation, revealing the impact of different attack strategies on network reliability. Subsequently, the congestion delay index is used as the standard of road section failure, considering the failure distribution and mutual dependence of road sections over time, a cascade failure mechanism is introduced, and a time-varying reliability assessment model based on a DBN is constructed. The effectiveness of the proposed method was verified through a case study of a partial road network in Dalian. The results show that ignoring cascading effects can significantly overestimate the reliability of the road network, especially during peak traffic hours, where such deviations may mask the real paralysis risks of the network. In contrast, the method proposed in this study fully considers time dynamics and failure correlation and can better capture the reliability of the road network under various dynamic conditions, providing a scientific basis for the sustainable planning and emergency management of urban traffic systems. Full article

Understanding the microscopic characteristics and evolutionary patterns of pore structures during high-PV waterflooding is critical for improving the accuracy and efficiency of oil field development. While previous studies have primarily emphasized the geometric and morphological features of overall pore structures, they often overlook local pore-scale properties and their relationship with fluid transport capacity. This study proposes a novel classification method for microscopic pore structures that integrates both pore size and local flow conductivity, enabling a more physically grounded and quantitatively robust evaluation of pore systems across rocks with varying permeabilities. The classification scheme divides microscopic pores into six distinct types based on key parameters such as pore diameter and flow flux area. To validate this approach, high-PV waterflooding experiments were performed on six sandstone samples with different permeabilities. High-resolution micro-computed tomography (micro-CT) imaging was employed to capture the internal pore structures before and after flooding. The results reveal that while low-connectivity small pores dominate numerically across all samples, high-connectivity small pores account for the largest volumetric share in medium-permeability rocks. Although overall pore size distributions remain relatively stable during high-PV waterflooding, transitions between pore types occur, driven by localized structural changes. Notably, in medium-permeability rocks, the number of low-connectivity small pores increases, whereas high-connectivity small pores decline. These findings deepen our understanding of microscopic heterogeneity and provide a theoretical foundation for evaluating the occurrence of residual oil. Moreover, the proposed classification framework offers valuable guidance for optimizing enhanced oil recovery strategies in the late stages of ultra-high water cut development. Full article

With the advancement of autonomous driving technology, transportation systems are inevitably confronted with mixed traffic flows consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Current research has predominantly focused on implementing homogeneous control strategies for ramp merging vehicles in such scenarios, which, however, may result in the oversight of specific requirements in fine-grained traffic scenarios. Therefore, a classified cooperative merging strategy is proposed to address the challenges of microscopic decision-making in hybrid traffic environments where HDVs and CAVs coexist. The optimal cooperating vehicle on the mainline is first selected for the target ramp vehicle based on the principle of minimizing time differences. Three merging strategies—joint coordinated control, partial cooperation, and speed limit optimization—are then established according to the pairing type between the cooperating and ramp vehicles. Optimal deceleration and lane-changing decisions are implemented using the average speed change rate within the control area to achieve cooperative merging. Validation via a SUMO-based simulation platform demonstrates that the proposed strategy reduces fuel consumption by 6.32%, NO_x emissions by 9.42%, CO₂ emissions by 9.37%, and total delay by 32.15% compared to uncontrolled merging. These results confirm the effectiveness of the proposed strategy in mitigating energy consumption, emissions, and vehicle delays. Full article

Extended connectivity in graphs can be analyzed through k-path Laplacian matrices, which permit the capture of long-range interactions in various real-world networked systems such as social, transportation, and multi-agent networks. In this work, we present several alternative methods based on machine learning methods (LSTM, xLSTM, Transformer, XGBoost, and ConvLSTM) to predict the final consensus value based on directed networks (Erdös–Renyi, Watts–Strogatz, and Barabási–Albert) and on the initial state. We highlight how different k-hop interactions affect the performance of the tested methods. This framework opens new avenues for analyzing multi-scale diffusion processes in large-scale, complex networks. Full article

Task delegation in multi-agent systems (MASs) is crucial for ensuring efficient collaboration among agents with different capabilities and skills. Traditional delegation models rely on social mechanisms such as trust and reputation to evaluate potential partners. While these approaches are effective in selecting competent agents, they often lack transparency, making it difficult for users to understand and trust the decision-making process. To address this limitation, we propose a novel task delegation model that integrates explainability through argumentation-based reasoning. Our approach employs the quantitative argumentation with votes framework (QuAD-V), a voting-based argumentation system that enables agents to justify their partner selection. We evaluate our model in a scenario involving the distribution of petroleum products via pipelines, where agents represent bases capable of temporarily storing a quantity of product. The connections between agents represent transportation routes, allowing the product to be sent from an origin to a destination base. The results demonstrate the effectiveness of our model in optimizing delegation decisions while maintaining clear, understandable explanations for agents’ decisions. Full article

The global demand for transportation systems is growing due to the rise in passenger and cargo traffic, predicted to reach twice the current level by 2050. Although this growth signifies social and economic progress, its impact on energy consumption and greenhouse gas emissions cannot be overlooked. Developments in the transportation industry must align with advancements in emerging energy production systems. In this regards, UNSDG 7 advocates for “affordable and clean energy”, leading to a global shift towards the electrification of transport systems, sourcing energy from a mix of renewable and non-renewable resources. This paper proposes an integrated hybrid renewable energy system with grid connectivity to meet the electrical and thermal loads of a tourist complex, including an electric vehicle charging station. The analysis was carried on in nine locations with different weather conditions, with various components such as wind turbines, photovoltaic systems, diesel generators, boilers, converters, thermal load controllers, and battery energy storage systems. The proposed model also considers the effects of seasonal variations on electricity generation and charging connectivity. Sensitivity analysis has been carried on investigating the impact of variables on the techno-economic parameters of the hybrid system. The obtained results led to interesting conclusions. Full article

As the importance of safety, efficiency, and sustainability in urban transportation becomes more apparent, intelligent transportation systems are changing and growing. Smart intersections play a crucial role in different parts of this context. Technologies such as Vehicle-to-Everything (V2X) communication, artificial intelligence, multi-sensor data fusion, and more are incorporated into these intersections to improve capacity and safety and reduce damage to the environment. This literature review aims to merge various recent works on advancing smart intersection technologies, their thematic application, methodological approach, and regional implementations. Highlighting adaptive traffic signal control, real-time data processing, and connected autonomous vehicle (CAV) integrations sheds light on the way the effectiveness of transportation in cities can be improved. At the same time, this study tackles questions of cybersecurity and standardization. This review provides insights for researchers, policymakers, and practitioners who aim to improve transportation systems’ sustainability, fairness, and operability. Full article

River nodes play a crucial role in regulating water and sediment transport within river networks. The SiXianJiao (SXJ) node serves as a key exchange point between the West River (WR) and North River (NR) in the Pearl River Delta (PRD), South China. Understanding the differences in flood diversion dynamics between X-shaped and H-shaped configurations under varying geomorphic conditions is essential for flood management. This study employs the Delft3D-Flow model to investigate the flood diversion mechanisms of these composite river nodes. Results revealed distinct hydrodynamic behaviors: the X-shaped node facilitates greater water exchange due to a shared channel segment, whereas the H-shaped node experiences restricted exchange due to flow resistance in the connecting branch. Both configurations exhibit self-regulating flood diversion processes that significantly reduce flood risks. A critical flow fraction of approximately 75.9% [WR/(WR + NR)] is identified, at which water levels (WLs) at both ends of the SXJ node almost equalize. When the WR flow fraction exceeds this threshold, floodwaters are diverted toward the NR. Below it, the diversion direction reverses. Additionally, flood diversion synchronizes asynchronous flood waves, stabilizing the discharge fraction at Makou (Sanshui), which fluctuates around 75.8% (24.2%) for the X-shaped node and 76.6% (23.4%) for the H-shaped node. These findings enhance our understanding of flood diversion dynamics and provide valuable insights for optimizing flood mitigation strategies and hydraulic infrastructure planning in the PRD and comparable river systems worldwide. Full article

The advancement of intelligent road transport represents a promising direction in the evolution of transportation systems, aimed at improving road safety and reducing traffic accidents. The integration of artificial intelligence, sensors, and machine vision systems enables autonomous vehicles (AVs) to rapidly adapt to changes in the road environment, minimizing human error and significantly reducing collision risks. These technologies provide continuous and highly precise control, including adaptive acceleration, braking, and maneuvering, thereby enhancing overall road safety. Connected vehicles utilizing C-V2X (Cellular Vehicle-to-Everything) communication primarily feature real-time operation, safety, and stability. However, communication flaws, such as signal fading, time delays, packet loss, and malicious network attacks, can affect vehicle-to-vehicle interactions in cooperative intelligent transport systems (C-ITSs). This study explores how C-V2X technology, compared to traditional DSRC, improves communication latency and enhances vehicle communication efficiency. Using SUMO simulations, various traffic scenarios were modeled with different autonomous vehicle penetration rates and communication technologies, focusing on traffic conflict rates, travel time, and communication performance. The results demonstrated that C-V2X reduced latency by over 99% compared to DSRC, facilitating faster communication between vehicles and contributing to a 38% reduction in traffic conflicts at 60% AV penetration. Traffic flow and safety improved with increased AV penetration, particularly in congested conditions. While C-V2X offers substantial benefits, challenges such as data packet loss, communication delays, and security vulnerabilities must be addressed to fully realize its potential. Future advancements in 5G and subsequent wireless communication technologies are expected to further reduce latency and enhance the effectiveness of C-ITSs. This study underscores the potential of C-V2X to enhance collision avoidance, alleviate congestion, and improve traffic management, while also contributing to the development of more reliable and efficient transportation systems. The continued refinement of simulation models and collaboration among stakeholders will be crucial to addressing the challenges in CAV integration and realizing the full benefits of connected transportation systems in smart cities. Full article

This study examines the impact of monitoring, reporting, and verification (MRV) system indicators on the costs associated with the emissions trading system (ETS) of the maritime sector in the European Union. Since maritime transport has recently been incorporated into the ETS, it becomes essential to understand how different operational and environmental factors affect the economic burden of shipping companies and port competitiveness. To this end, a model based on Bayesian networks is used to analyse the interdependencies between key variables, facilitating the identification of the most influential factors in the determination of the costs of the ETS. The results show that fuel efficiency and CO₂ emissions in port are decisive in the configuration of costs. In particular, it was identified that emissions during the stay in port have a greater weight than expected, which suggests that strategies such as the use of electrical connections in port (cold ironing) may be key to mitigating costs. Likewise, navigation patterns and traffic regionalisation show a strong correlation with ETS exposure, which could lead to adjustments in maritime routes. This probabilistic model offers a valuable tool for strategic decision-making in the maritime sector, benefiting shipping companies, port operators, and policymakers. However, future research could integrate new technologies and regulatory scenarios to improve the accuracy of the analysis and anticipate changes in the ETS cost structure. Full article

In the context of big data and intelligent connectivity, optimizing scheduled bus dispatch can enhance urban transit efficiency and passenger experience, which is vital for the sustainable development of urban transportation. This paper, based on existing fixed bus stops, integrates traditional demand-responsive transit and travel booking models, considering the spatiotemporal variations in scheduled travel demands and passenger flows and addressing the combined scheduling issues of fixed-capacity bus models and skip-stop strategies. By leveraging intelligent connected technologies, it introduces a dynamic grouping method, proposes an intelligent connected bus dispatching model, and optimizes bus timetables and dispatch control strategies. Firstly, the inherent travel characteristics of potential reservation users are analyzed based on actual transit data, subsequently extracting demand data from reserved passengers. Secondly, a two-stage optimization program is proposed, detailing passenger boarding and alighting at each stop and section passenger flow conditions. The first stage introduces a precise bus–traveler matching dispatch model within a spatial–temporal–state framework, incorporating ride matching to minimize parking frequency in scheduled travel scenarios. The second stage addresses spatiotemporal variations in passenger demand and station congestion by employing a skip-stop and bus operation control strategy. This strategy enables the creation of an adaptable bus operation optimization model for temporal dynamics and station capacity. Finally, a dual-layer optimization model using an adaptive parameter grid particle swarm optimization algorithm is proposed. Based on Beijing’s Route 300 operational data, the simulation-driven study implements contrasting scenarios of different bus service patterns. The results demonstrate that this networked dispatching system with dynamic vehicle grouping reduces operational costs by 29.7% and decreases passenger waiting time by 44.15% compared to baseline scenarios. Full article