Search Results (3,127)

The increasing adoption of electric vehicles (EVs) within intelligent transportation systems (ITSs) has elevated the importance of cybersecurity, especially with the rise in Vehicle-to-Everything (V2X) communications. Traditional intrusion detection systems (IDSs) struggle to address the evolving and complex nature of cyberattacks in such dynamic environments. To address these challenges, this study introduces a novel deep learning-based IDS designed specifically for EV communication networks. We present a hybrid model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) layers, and adaptive learning strategies. The model was trained and validated using the VeReMi dataset, which simulates a wide range of attack scenarios in V2X networks. Additionally, an ablation study was conducted to isolate the contribution of each of its modules. The model demonstrated strong performance with 98.73% accuracy, 97.88% precision, 98.91% sensitivity, and 98.55% specificity, as well as an F1-score of 98.39%, an MCC of 0.964, a false-positive rate of 1.45%, and a false-negative rate of 1.09%, with a detection latency of 28 ms and an AUC-ROC of 0.994. Specifically, this work fills a clear gap in the existing V2X intrusion detection literature—namely, the lack of scalable, adaptive, and low-latency IDS solutions for hardware-constrained EV platforms—by proposing a hybrid CNN–LSTM architecture coupled with an elastic weight consolidation (EWC)-based adaptive learning module that enables online updates without full retraining. The proposed model provides a real-time, adaptive, and high-precision IDS for EV networks, supporting safer and more resilient ITS infrastructures. Full article

Integrating advanced technologies, such as Connected Autonomous Vehicles (CAVs) and Connected Vehicles (CVs), represents new strategies and solutions in urban mobility, particularly during unexpected urban events. Vehicle connectivity facilitates real-time communication between vehicles and infrastructure, enhancing traffic management by enabling dynamic rerouting to minimize delays and prevent bottlenecks. This study employs the SUMO (Simulation of Urban Mobility) microsimulation to analyze the impact of dynamic rerouting strategies during urban disruptions within the IN2CCAM project’s Turin Living Lab. The Living Lab integrates simulation with real-world testing, including autonomous shuttle operations, to evaluate new mobility solutions. In the initial phase, offline simulations examine street, lane, and intersection closures along shuttle routes to assess how penetration levels of CVs and CAVs influence mobility. The results indicate that higher connectivity penetration improves traffic flow, with the greatest benefits observed at increased levels of autonomous vehicles. These findings highlight the potential of dynamic routing strategies, supported by vehicle connectivity and autonomous driving technologies, to enhance urban mobility and effectively respond to real-time traffic conditions. Additionally, this work demonstrates the capabilities and flexibility of SUMO for simulating complex urban traffic scenarios. Full article

Excessive energy consumption of communication protocols in IoT/IIoT systems constitutes one of the key constraints for the operational longevity of remote sensor nodes, where radio transmission often incurs higher energy costs than data acquisition or local computation. Previous studies have remained fragmented, typically focusing on selected technologies or specific layers of the communication stack, which has hindered the development of comparable quantitative metrics across protocols. The aim of this study is to design and validate a unified evaluation framework enabling consistent assessment of both wired and wireless protocols in terms of energy efficiency, reliability, and maintenance costs. The proposed approach employs three complementary research methods: laboratory measurements on physical hardware, profiling of SBC devices, and simulations conducted in the COOJA/Powertrace environment. A Unified Comparative Method was developed, incorporating bilinear interpolation and weighted normalization, with its robustness confirmed by a Spearman rank correlation coefficient exceeding 0.9. The analysis demonstrates that MQTT-SN and CoAP (non-confirmable mode) exhibit the highest energy efficiency, whereas HTTP/3 and AMQP incur the greatest energy overhead. Results are consolidated in the ICoPEP matrix, which links protocol characteristics to four representative RS-IoT scenarios: unmanned aerial vehicles (UAVs), ocean buoys, meteorological stations, and urban sensor networks. The framework provides well-grounded engineering guidelines that may extend node lifetime by up to 35% through the adoption of lightweight protocol stacks and optimized sampling intervals. The principal contribution of this work is the development of a reproducible, technology-agnostic tool for comparative assessment of IoT/IIoT communication protocols. The proposed framework addresses a significant research gap in the literature and establishes a foundation for further research into the design of highly energy-efficient and reliable IoT/IIoT infrastructures, supporting scalable and long-term deployments in diverse application environments. Full article

Jordan Vision prioritizes the utilization of domestic resources, particularly renewable energy. The transportation sector, responsible for 49% of national energy consumption, remains central to this transition and accounts for around 28% of total greenhouse gas emissions. Electric vehicles (EVs) offer a promising solution to reduce waste and pollution, but they also pose challenges for grid stability and charging infrastructure development. This study addresses a critical gap in the planning of renewable-powered EV charging stations along Jordanian highways, where EV infrastructure is still limited and underdeveloped, by optimizing the design of a hybrid energy charging station using HOMER Grid (v1.9.2) Software. Region-specific constraints and multiple operational scenarios, including rooftop PV integration, are assessed to balance cost, performance, and reliability. This study also investigates suitable locations for charging stations along the Sahrawi Highway in Jordan. The proposed station, powered by a hybrid system of 53% wind and 29% solar energy, is projected to generate 1.466 million kWh annually at USD 0.0375/kWh, reducing CO₂ emissions by approximately 446 tonnes annually. The findings highlight the potential of hybrid systems to increase renewable energy penetration, support national sustainability targets, and offer viable investment opportunities for policymakers and the private sector in Jordan. Full article

The increasing demand for low-latency, computationally intensive vehicular applications, such as autonomous navigation and real-time perception, has led to the adoption of cloud–edge–vehicle infrastructures. These applications are often modeled as Directed Acyclic Graphs (DAGs) with interdependent subtasks, where precedence constraints enforce causal ordering while allowing concurrency. We propose a task offloading framework that decomposes applications into precedence-constrained subtasks and formulates the joint scheduling and offloading problem as a Markov Decision Process (MDP) to capture the latency–energy trade-off. The system state incorporates vehicle positions, wireless link quality, server load, and task-buffer status. To address the high dimensionality and sequential nature of scheduling, we introduce DepSchedPPO, a dependency-aware sequence-to-sequence policy that processes subtasks in topological order and generates placement decisions using action masking to ensure partial-order feasibility. This policy is trained using Proximal Policy Optimization (PPO) with clipped surrogates, ensuring stable and sample-efficient learning under dynamic task dependencies. Extensive simulations show that our approach consistently reduces task latency, energy consumption and QOS compared to conventional heuristic and DRL-based methods. The proposed solution demonstrates strong applicability to real-time vehicular scenarios such as autonomous navigation, cooperative sensing, and edge-based perception. Full article

Electric vehicle (EV) adoption is a critical step toward achieving sustainable transportation and reducing carbon emissions, especially in regions like Saudi Arabia that are undergoing rapid urban development and energy diversification. However, the widespread adoption of EVs is hindered by a variety of interrelated economic, infrastructural, and policy-related factors. This study aims to systematically identify and structure these influencing factors using Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. Based on a thorough literature review and expert consultation, 17 key factors affecting EV adoption in Saudi Arabia were identified. The ISM results reveal that purchase price, long-term savings, resale value, urban planning, and accessibility are among the most influential drivers of adoption. The MICMAC analysis complements these insights by categorizing the variables based on their driving and dependence power. The developed hierarchical model provides insights into the complex interdependencies among these factors and offers a strategic framework to support policymakers and stakeholders in accelerating EV uptake. The study contributes to a deeper understanding of the dynamics influencing EV adoption in emerging markets. Full article

Precise time synchronization is indispensable for enabling seamless coordination in Intelligent Transportation Systems (ITS) which rely on reliable vehicle communications. This work introduces lightweight online clock skew compensation algorithms based on Recursive Least Squares (RLS) and Recursive Weighted Least Squares (RWLS) techniques tailored for ITS time synchronization. Unlike traditional approaches relying on offline batch processing and large-scale data storage, the proposed algorithms continuously update clock skew estimates immediately upon receiving each timing sample, thereby significantly reducing memory requirements. These methods are applicable to Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Infrastructure-to-Infrastructure (I2I) communication scenarios, offering a cost-effective software solution to improve synchronization accuracy. Extensive simulations and experimental validations demonstrate that the developed estimators effectively minimize skew-related timing errors, thereby enhancing the robustness and precision of vehicular network timekeeping. Full article

In response to the rising demand for sustainable transportation, electric vehicles (EVs) are increasingly regarded as viable alternatives to conventional vehicles. This study investigates the intention of Malaysian consumers to choose EVs as their preferred mode of transportation. Consumption values were conceptualized as a multi-dimensional construct comprising functional value, symbolic value, emotional value, novelty value, and conditional value. This study examines the relationships between these consumption values, consumer attitudes, and intention to purchase EVs. In addition, this study also examines the mediating role of attitude and the moderating role of infrastructure readiness. Data were gathered using a proportionate stratified sampling method from 264 respondents in Klang Valley, Malaysia. Of the twelve (12) hypotheses tested, four (4) were supported. The analysis indicates positive relationship between attitude and emotional value with consumers’ intention to purchase EVs. Consumers’ attitudes mediate the relationship between functional value, emotional value, and intention to purchase EVs. Infrastructure readiness does not moderate the relationship between consumers’ attitudes towards EVs and their purchase intentions. This study enhances the existing knowledge of consumers’ multifaceted value views about EVs and offers practical guidance for marketers and serves as a reference for policymakers to improve the marketability of EVs. Full article

Potholes threaten public safety and automated vehicles (AVs) safe navigation by increasing accident risks and maintenance costs. Traditional pavement inspection methods, which rely on human assessment, are inefficient for rapid pothole detection and reporting due to potholes’ random and sudden occurring. Advancements in Artificial Intelligence (AI) now enable automated pothole detection using image-based object recognition, providing innovative solutions to enhance road safety and assist agencies in prioritizing maintenance. This paper proposes a novel approach that evaluates the integration of 3 state-of-the-art AI models (YOLOv8n, YOLOv11n, and YOLOv12n) with an ADAS-like camera, GNSS receiver, and Robot Operating System (ROS) to detect potholes in uncontrolled real-life scenarios, including different weather/lighting conditions and different route types, and generate ready-to-use data in a real-time manner. Tested on real-world road data, the algorithm achieved an average precision of 84% and 84% in recall, demonstrating its effectiveness, stable, and high performance for real-life applications. The results highlight its potential to improve road safety, allow vehicles to detect potholes through ADAS, support infrastructure maintenance, and optimize resource allocation. Full article

Climate change has prompted the adoption of sustainable measures to reduce greenhouse gas (GHG) emissions, particularly in urban transportation. The integration of renewable energy sources, such as solar energy, offers a promising strategy to enhance sustainability in urban transit systems. This study assessed solar irradiation along the tram route in Cuenca—an Andean city characterized by distinctive topographic and climatic conditions—with the aim of evaluating the technical feasibility of integrating solar energy into the tram infrastructure. A descriptive, applicative, and longitudinal approach was adopted. Solar irradiation was monitored using a system composed of a fixed station and a mobile station, the latter installed on a tram vehicle. Readings carried out over fourteen months facilitated the analysis of seasonal and spatial variability of the available solar resource. The fixed station recorded average irradiation values ranging from 3.80 to 4.61 kWh/m²·day, while the mobile station reported values between 2.60 and 3.41 kWh/m²·day, revealing losses due to urban shading, with reductions ranging from 14.7% to 18.8% compared to fixed-site values. It was estimated that a fixed photovoltaic system of up to 1.068 MWp could be installed at the tram maintenance depot using 580 Wp panels, with the capacity to supply approximately 81% of the annual electricity demand of the tram system. Complementary solar installations at tram stops, stations, and other related infrastructure are also proposed. The results demonstrate the technical feasibility of integrating solar energy—through fixed and mobile systems—into the tram infrastructure of Cuenca. This approach provides a scalable model for energy planning in urban transport systems in Andean contexts or other regions with similar characteristics. Full article

As of mid-2025, California maintains a target (and legal agreement with truck OEMs) to achieve 100% zero-emission medium- and heavy-duty (M/HD) truck sales by 2036. While the US federal government has relaxed its targets, fuel economy standards continue to incentivize electrification. To meet these ambitions, the adequate rollout of charging infrastructure at scale is needed. This paper reviews existing studies on M/HD charging and investment needs in California and the U.S. This paper introduces a novel matrix that delineates charging needs by charging power, truck type (Class 2b-8), charger-to-vehicle ratios, and charger investment costs. Results indicate that California may require 151,000 to 156,000 depot and public chargers on the road by 2030, growing to 434,000 to 460,000 chargers on the road by 2035. Corresponding investment—including new installation and replacement—could reach USD 7.1 to USD 7.4 billion by 2030 and USD 16.4 to USD 17.8 billion by 2035. Meeting this scale of infrastructure deployment represents not only a technical challenge but also a sustainability imperative, demanding unprecedented coordination among policymakers, utilities, and fleet operators to overcome barriers like financing and permitting and to ensure infrastructure growth aligns with climate commitments and equitable access. Full article

As crucial transportation hubs and economic nodes, the underwater security and infrastructure maintenance of harbors are of paramount importance. Harbors are characterized by high vessel traffic and complex underwater environments, where traditional underwater inspection methods, such as diver operations, face challenges of low efficiency, high risk, and limited operational range. This paper introduces a collaborative survey and disposal system that integrates a deformable unmanned surface vehicle (USV) with a lightweight remotely operated vehicle (ROV). The USV is equipped with a side-scan sonar (SSS) and a multibeam echo sounder (MBES), enabling rapid, large-area searches and seabed topographic mapping. The ROV, equipped with an optical camera system, forward-looking sonar (FLS), and a manipulator, is tasked with conducting close-range, detailed observations to confirm and dispose of abnormal objects identified by the USV. Field trials were conducted at an island harbor in the South China Sea, where simulated underwater objects, including an iron drum, a plastic drum, and a rubber tire, were deployed. The results demonstrate that the USV-ROV collaborative system effectively meets the demands for underwater environmental measurement, object localization, identification, and disposal in complex harbor environments. The USV acquired high-resolution (0.5 m × 0.5 m) three-dimensional topographic data of the harbor, effectively revealing its topographical features. The SSS accurately localized and preliminarily identified all deployed simulated objects, revealing their acoustic characteristics. Repeated surveys revealed a maximum positioning deviation of 2.2 m. The lightweight ROV confirmed the status and location of the simulated objects using an optical camera and an underwater positioning system, with a maximum deviation of 3.2 m when compared to the SSS locations. The study highlights the limitations of using either vehicle alone. The USV survey could not precisely confirm the attributes of the objects, whereas a full-area search of 0.36 km² by the ROV alone would take approximately 20 h. In contrast, the USV-ROV collaborative model reduced the total time to detect all objects to 9 h, improving efficiency by 55%. This research offers an efficient, reliable, and economical practical solution for applications such as underwater security, topographic mapping, infrastructure inspection, and channel dredging in harbor environments. Full article

Public electric vehicle (EV) charging infrastructure is crucial for accelerating EV adoption and reducing transportation emissions; however, disparities in infrastructure access have raised significant equity concerns. This review synthesizes existing knowledge and identifies gaps regarding equity in EV public charging research. Following structured review protocols, 91 peer-reviewed studies from Scopus and Google Scholar were analyzed, focusing explicitly on equity considerations. The findings indicate that current research on EV public charging equity mainly adopts geographic information systems (GIS), network optimization, behavioral modeling, and hybrid analytical frameworks, yet lacks consistent normative frameworks for assessing equity outcomes. Equity assessments highlight four key dimensions: spatial accessibility, cost burdens, reliability and usability, and user awareness and trust. Socio-economic disparities, particularly income, housing tenure, and ethnicity, frequently exacerbate inequitable access, disproportionately disadvantaging low-income, renter, and minority populations. Additionally, infrastructure-specific choices, including charger reliability, strategic location, and pricing strategies, significantly influence adoption patterns and equity outcomes. However, the existing literature primarily reflects the contexts of North America, Europe, and China, revealing substantial geographical and methodological limitations. This review suggests the need for more robust normative evaluations of equity, comprehensive demographic data integration, and advanced methodological frameworks, thereby guiding targeted, inclusive, and context-sensitive infrastructure planning and policy interventions. Full article

In this paper, an energy efficiency (EE)–spectral efficiency (SE) trade-off scheme is investigated for the distributed reconfigurable intelligent surface (RIS)-assisted backscatter vehicle-to-infrastructure (V2I) communication system. Firstly, a multi-objective optimization framework balancing EE and SE is established using the linear weighting method, and the quadratic transformation is utilized to recast the optimization problem as a strictly convex problem. Secondly, an alternating optimization (AO) approach is applied to partition the original problem into two independent subproblems of the BS and RIS beamforming, which are, respectively, designed by the weighted minimization mean-square error (WMMSE) and the Riemannian conjugate gradient (RCG) algorithms. Finally, according to the trade-off factor, the power reflection coefficients of backscatter devices (BDs) are dynamically optimized with the BS beamforming vectors and RIS phase shift matrices, considering their activation requirements and the vehicle minimum quality of service (QoS). The simulation results verify the effectiveness of the proposed algorithm in simultaneously improving SE and the EE in practical V2I applications through rational optimization of the BD power reflection coefficient. Full article

With the rapid expansion of the electric vehicle (EV) market, optimizing the distribution of charging stations has attracted increasing attention. Unlike internal combustion engine vehicles, EVs are typically charged at the end of a trip rather than during transit. Therefore, analyzing EV users’ charging preferences based on their activity–travel patterns is essential. This study seeks to improve the operational efficiency and accessibility of EV charging stations in Lanzhou City by optimizing their spatial distribution. To achieve this, a novel multi-objective optimization model integrating NSGA-III and TOPSIS is proposed. The methodology consists of two key steps. First, the NSGA-III algorithm is applied to optimize three objective functions: minimizing construction costs, maximizing user satisfaction, and maximizing user convenience, thereby identifying charging station locations that address diverse needs. Second, the TOPSIS method is employed to rank and evaluate various location solutions, ultimately determining the final sitting strategy. The results show that the 232 locations obtained by the optimization model are reasonably distributed, with good operational efficiency and convenience. Most of them are distributed in urban centers and commercial areas, which is consistent with the usage scenarios of EV users. In addition, this study demonstrates the superiority in determining the distribution of charging station locations of the proposed method. In summary, this study determined the optimal distribution of 232 EV charging stations in Lanzhou City using multi-objective optimization and ranking methods. The results are of great significance for improving the operational efficiency and convenience of charging station location optimization and offer valuable insights for other cities in northwestern China in planning their charging infrastructure. Full article