Search Results (37)

Background: Understanding public acceptance of autonomous vehicles (AVs) is essential for cities transitioning toward smart mobility systems. Dubai aims to transform 25% of trips to autonomous mode by year 2030, yet little is known about residents’ readiness. Methods: An online survey (N = 302; 2024/2025) measured awareness, perceived benefits/risks, trust, cybersecurity concerns, and behavioral intention (BI). Constructs were analyzed using descriptive statistics and regression. Results: Cybersecurity concern was the strongest negative predictor of BI, while perceived usefulness (accident reduction) showed a weak, marginal positive effect. Gender, age, and cost effects were not statistically significant. Conclusions: Public acceptance is shaped more by trust, safety perception, and perceived system reliability than by demographics or cost. Policy actions should focus on transparent regulation, cybersecurity audits, and public AV pilots. Full article

The low-altitude economy (LAE), encompassing urban air mobility, drone logistics and sub 3000 m aerial surveillance, demands secure, intelligent infrastructures to manage increasingly complex, multi-stakeholder operations. This survey evaluates the integration of Internet of Things (IoT) networks, artificial intelligence (AI) decision-making and blockchain trust mechanisms as foundational enablers for next-generation LAE ecosystems. IoT sensor arrays deployed at ground stations, unmanned aerial vehicles (UAVs) and vertiports form a real-time data fabric that records variables from air traffic density to environmental parameters. These continuous data streams empower AI models ranging from predictive analytics and computer vision (CV) to multi-agent reinforcement learning (MARL) and large language model (LLM) reasoning to optimize flight paths, identify anomalies and coordinate swarm behaviors autonomously. In parallel, blockchain architectures furnish immutable audit trails for regulatory compliance, support secure device authentication via decentralized identifiers (DIDs) and automate contractual exchanges for services such as airspace leasing or payload delivery. By examining current research and practical deployments, this review demonstrates how the synergistic application of IoT, AI and blockchain can bolster operational efficiency, resilience and trustworthiness across the LAE landscape. Full article

In recent years, Positive Energy Districts (PEDs) have been interpreted in many—and often conflicting—ways. We recast PEDs as a vehicle for verifiable climate neutrality and present a declaration-ready assessment that integrates (i) a cumulative, science-based GHG budget per m² gross floor area (GFA), (ii) full life-cycle accounting, and (iii) time-resolved conversion factors that include everyday motorized individual mobility and quantify flexibility. Two KPIs anchor the framework: the cumulative GHG LCA balance (2025–2075) against a maximum compliant budget of 320 kg_CO2e·m⁻²_GFA and the annual primary energy balance used to declare PED status with or without mobility. We follow EN 15978 and apply time-resolved emission factors that decline to zero by 2050. Its applicability is demonstrated on six Austrian districts spanning new builds and renovations, diverse energy systems, densities, and mobility contexts. The baseline scenarios show heterogeneous outcomes—only two out of six meet both the cumulative GHG budget and the positive primary energy balance—but design iterations indicate that all six districts can reach the targets with realistic, ambitious packages (e.g., high energy efficiency and flexibility, local renewables, ecological building materials, BESS/V2G, and mobility electrification). Hourly emission factors and flexibility signals can lower import-weighted emission intensity versus monthly or annual factors by up to 15% and reveal seasonal import–export asymmetries. Built on transparent, auditable rules and open tooling, this framework both diagnoses performance gaps and maps credible pathways to compliance—steering PED design away from project-specific targets toward verifiable climate neutrality. It now serves as the basis for the national labeling/declaration scheme klimaaktiv “Climate-Neutral Positive Energy Districts”. Full article

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

As intelligent transportation systems (ITSs) evolve rapidly, the increasing computational demands of connected vehicles call for efficient task offloading. Centralized approaches face challenges in scalability, security, and adaptability to dynamic network conditions. To address these issues, we propose a blockchain-based decentralized task offloading framework with network-aware resource allocation and tokenized economic incentives. In our model, vehicles generate computational tasks that are dynamically mapped to available computing nodes—including vehicle-to-vehicle (V2V) resources, roadside edge servers (RSUs), and cloud data centers—based on a multi-factor score considering computational power, bandwidth, latency, and probabilistic packet loss. A blockchain transaction layer ensures auditable and secure task assignment, while a proof-of-stake (PoS) consensus and smart-contract-driven dynamic pricing jointly incentivize participation and balance workloads to minimize delay. In extensive simulations reflecting realistic ITS dynamics, our approach reduces total completion time by 12.5–24.3%, achieves a task success rate of 84.2–88.5%, improves average resource utilization to 88.9–92.7%, and sustains >480 transactions per second (TPS) with a 10 s block interval, outperforming centralized/cloud-based baselines. These results indicate that integrating blockchain incentives with network-aware offloading yields secure, scalable, and efficient management of computational resources for future ITSs. Full article

While autonomous driving systems and intelligent transportation infrastructures become increasingly software-defined and network-connected, ensuring their cybersecurity has become a critical component of traffic safety. Large language models (LLMs) have recently shown promise in automating aspects of penetration testing, yet most existing approaches remain limited to simple, single-step exploits. They struggle to handle complex, multi-stage vulnerabilities that demand precise coordination, contextual reasoning, and knowledge reuse. This is particularly problematic in safety-critical domains, such as autonomous vehicles, where subtle software flaws can cascade across interdependent subsystems. In this work, we present CurriculumPT, a novel LLM-based penetration testing framework specifically designed for the security of intelligent systems. CurriculumPT combines curriculum learning and a multi-agent system to enable LLM agents to progressively acquire and apply exploitation skills across common vulnerabilities and exposures-based tasks. Through a structured progression from simple to complex vulnerabilities, agents build and refine an experience knowledge base that supports generalization to new attack surfaces without requiring model fine-tuning. We evaluate CurriculumPT on 15 real-world vulnerabilities scenarios and demonstrate that it outperforms three state-of-the-art baselines by up to 18 percentage points in exploit success rate, while achieving superior efficiency in execution time and resource usage. Our results confirm that CurriculumPT is capable of autonomous, scalable penetration testing and knowledge transfer, laying the groundwork for intelligent security auditing of modern autonomous driving systems and other cyberphysical transportation platforms. Full article

Overtaking relies heavily on the driver’s attention and cognitive state, and illegal overtaking can lead to accidents, severe injuries, or fatalities. To enhance highway safety, we propose a method for accurately detecting illegal overtaking on continuous road lanes. We used dashboard-mounted smartphone cameras and geolocation data to filter the analysis areas. We used the state-of-the-art deep learning model You Only Look Once version 8 (YOLOv8) to detect yellow road lanes. When these lanes suggest potential illegal overtaking, we apply the YOLO for Panoptic driving Perception version 2 (YOLOPv2) model, followed by post-processing. We confirm overtaking events by checking for overlaps between detections from both models. We store confirmed instances and evaluate the information temporally rather than just from individual frames. We then analyze the entire video to identify violations and extract the moments of occurrence. We tested the algorithm on real-world traffic data under various weather and lighting conditions. Our method demonstrates reliability and consistency in identifying illegal overtaking. We achieved 16 TP and only 1 FP over 56 videos totaling 41 h, 9 min, and 24 s, with precision, recall, and F1-score values of 1.000, 0.941, and 0.970, respectively. Consequently, our innovative and practical solution, utilizing simple cameras and advanced computer vision models, can significantly enhance highway safety and support vehicle auditing systems. Full article

The city of Valparaíso, Chile, faces significant mobility challenges due to its steep slopes, complex urban infrastructure, and socioeconomic conditions. In this direction, this study explores the potential promotion of E-bike uses by identifying the optimal routes that connect metro stations to strategic hilltop streets in the city. A hybrid methodology combining a multicriteria GIS-based analysis and an experimental study was used to evaluate potential routes and the possibility of increasing the power limitations for non-motorized mobility in Chile. Fifteen routes were assessed based on criteria including the slope, traffic safety, directionality, intersections, and travel distance. The results indicate that routes such as Cumming from Puerto and Bellavista stand out as the most viable for e-bike use given their favorable characteristics. The experimental study revealed that higher-powered E-bikes (500 W and 750 W) would be more able to overcome the steep slopes of Valparaíso, with an average speed of 5.36 km/h and 9.52 km/h on routes with a 10.88% average slope. These findings challenge the current regulatory limit of 250 W for non-motorized vehicles in Chile, highlighting the potential benefits of increasing their power limits to enhance sustainable mobility in the hilly urban contexts of this country. This study highlights the need to adapt urban mobility policies to the unique topographical conditions of each city. Future research should build upon more experimental studies, develop specific street-scale analyses using audit methods, incorporate climate-related variables, and evaluate the economic viability of e-bike infrastructure. Addressing these aspects could position Valparaíso as a leading example of sustainable urban mobility for cities facing comparable challenges. Full article

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder–noise–decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks. Full article

Emerging applications such as blockchain, autonomous vehicles, healthcare, federated learning, self-consistent large language models (LLMs), and multi-agent LLMs increasingly rely on the reliable acquisition and provision of data from external sources. Multi-component networks, which supply data to the applications, are defined as data provision networks (DPNs) and prioritize accuracy and reliability over delivery efficiency. However, the effectiveness of the security mechanisms of DPNs, such as self-correction, is limited without a fine-grained log of node activities. This paper presents DPTracer: a novel logging system designed for DPNs that uses tamper-evident logging to address the challenges of maintaining a reliable log in an untrusted environment of DPNs. By integrating logging and validation into the data provisioning process, DPTracer ensures comprehensive logs and continuous auditing. Our system uses Process Tree as a data structure to store log records and generate proofs. This structure permits validating node activities and reconstructing historical data provision processes, which are crucial for self-correction and verifying data sufficiency before results are finalized. We evaluate the overheads introduced by DPTracer regarding computation, memory, storage, and communication. The results demonstrate that DPTracer incurs reasonable overheads, making it practical for real-world applications. Despite these overheads, DPTracer enhances security by protecting DPNs from post-process and in-process tampering. Full article

The objective of the article was to describe the importance of a good technically and economically planned process for purchasing a rail vehicle. Compliance with this process with safety standards and energy efficiency is crucial regarding effectiveness in the long-life cycle cost of a rail vehicle. Methods that were used focused on audit and document analysis. In the result based on a specific audit of a railway company, some non-compliances were found, and it was methodically proven that they are significant risk factors in terms of performing such processes in the future. Major conclusions regarded the importance of fulfilling legal requirements of SMS for purchase purposes, involvement of a safety engineer post in this process, and usage of technical feedback regarding previous operation of railway vehicles and operational findings from the past. The transport policy of the EU and other developed countries sets ambitious goals for reducing energy consumption in transportation activities, which is linked to the aim of reducing environmental burdens. Full article

The integration of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) has opened up new possibilities in various industries. However, with the increasing number of Internet of Drones (IoD) networks, the risk of network attacks is also rising, making it increasingly difficult to identify malicious attacks on IoD systems. To improve the accuracy of intrusion detection for IoD and reduce the probability of false positives and false negatives, this paper proposes a Q-learning-based two-layer cooperative intrusion detection algorithm (Q-TCID). Specifically, Q-TCID employs an intelligent dynamic voting algorithm that optimizes multi-node collaborative intrusion detection strategies at the host level, effectively reducing the probability of false positives and false negatives in intrusion detection. Additionally, to further reduce energy consumption, an intelligent auditing algorithm is proposed to carry out system-level auditing of the host-level detections. Both algorithms employ Q-learning optimization strategies and interact with the external environment in their respective Markov decision processes, leading to close-to-optimal intrusion detection strategies. Simulation results demonstrate that the proposed Q-TCID algorithm optimizes the defense strategies of the IoD system, effectively prolongs the mean time to failure (MTTF) of the system, and significantly reduces the energy consumption of intrusion detection. Full article

The intricate topography and numerous hazards of highland roads contribute to a significantly higher incidence of traffic accidents on these roads compared to those on the plains. Although precise road data can enhance the safety evaluation and management of these road segments, the cost of data acquisition in highland areas is prohibitively high. To tackle this issue, our paper proposes a system of assessment indices and extraction methods specifically designed for plateau regions, supplementing existing road safety audit techniques. We are pioneers in integrating a high-precision 3D point cloud model into the safety risk assessment of low-traffic plateau roads, utilizing unmanned aerial vehicle (UAV) LiDAR technology. This innovative approach enhances both the efficiency and accuracy of road mapping. Building on this, we amalgamated three categories of indices—road 3D alignment, geographical environment, and natural disasters—to formulate a comprehensive safety risk assessment model. Applying this model to seventeen representative road segments on the Tibetan Plateau, we found that road alignment significantly influences road safety risk. The segments with the highest risk ratings are predominantly those located in the southwestern part of the Tibetan region, such as Zanda and Gar. Road safety management should prioritize road alignment, particularly the role of the curve radius, without overlooking the impact of environmental factors and natural disasters. Full article

The safety culture and safety climate of transport companies have a significant impact on fleet safety outcomes. Ample research shows that transport companies with a strong safety culture also show lower crash statistics. In spite of modern technologies that help with having a safer fleet, it is difficult to achieve a safer fleet without a proactive safety culture and climate. In Ethiopia, it is assumed that most transport companies have failed to create a distinguishable safety climate in their fleet safety administration and that their heavy vehicle drivers have a poor safety culture. These could be important factors contributing to a higher rate of road traffic crashes involving heavy vehicles. This study aims to assess the existing safety culture among a sample of transport companies in Ethiopia and identify suitable intervention methods to improve the safety culture. Moreover, the study sought to identify the readiness of the transport companies to apply modern technology in their fleets by examining their safety culture and safety climate. In total, 10 fleet managers and 174 heavy vehicle drivers participated in the fleet safety audit survey. A descriptive analysis and a detailed fleet safety audit score were calculated. Based on the scale scoring, ten companies score below best practices, one scores well below best practices, and only one company meets the criteria to be considered achieving best practices. The results from this study show that the safety culture and safety climate in most transport companies are quite limited. In addition, most transport companies implement similar safety measures, including inconsistent driver training and annual maintenance. Full article

Thermography for building audits is commonly carried out by means of terrestrial recording processes with static cameras. The implementation of drones to automatically acquire images from various perspectives can speed up and facilitate the procedure but requires higher recording distances, utilizes changing recording angles and has to contend with the effects of movement during image capture. This study investigates the influence of different drone settings on the quality of thermographic images for building audits in comparison to ground-based acquisition. To this end, several buildings are photographically captured via unmanned aerial vehicle and classical terrestrial means to generate a dataset of 968 images in total. These are analyzed and compared according to five quality criteria that are explicitly chosen for this study to establish best-practice rules for thermal image acquisition. We discover that flight speeds of up to 5 m/s have no visible effects on the image quality. The combination of smaller distances (22 m above a building) and a 45° camera angle are found to allow for both the qualitative and quantitative analysis of rooftops as well as a qualitative screening of building façades. Greater distances of 42 m between camera and building may expedite the acquisition procedure for larger-scaled district coverage but cannot be relied upon for thermal analyses beyond qualitative studies. Full article