Search Results (4,143)

The large-scale deployment of autonomous vehicles (AVs) in mixed-traffic environments raises an important question: how do human drivers evaluate safety when interacting with AVs under real-world uncertainty? This study aims to examine how drivers’ objective knowledge of AVs shapes their perceived safety when sharing the road with AVs in mixed-traffic environments. Using survey data from 905 licensed drivers in Wuhan, China, this study treats perceived road-sharing safety as an interaction-level evaluative outcome rather than merely a precursor of adoption intention. Latent class analysis was first used to identify knowledge-based driver segments, structural equation modeling was then applied to estimate Theory of Planned Behavior (TPB)-related psychological constructs, and ordered logit regression was finally employed to examine the determinants of perceived safety across segments. The results indicate that behavioral intention consistently shows a positive association with perceived safety; however, attitude toward AVs exhibits a significant negative association among high-knowledge drivers. This attitudinal reversal challenges the implicit homogeneity assumption embedded in conventional TPB applications and suggests that cognitive familiarity may recalibrate, rather than amplify, technological optimism. Overall, the findings show that knowledge-based heterogeneity changes the psychological mechanisms underlying safety appraisal in mixed traffic. These insights carry important implications for differentiated communication strategies and trust calibration in transitional automated mobility systems. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

To address the limitations of insufficient feature representation, large model size, and high deployment cost in network traffic classification, a lightweight classification framework based on multi-teacher knowledge distillation is proposed. The framework consists of two heterogeneous teacher networks and a compact student network to enable end-to-end traffic classification under constrained computational resources. The teacher networks incorporate complementary spatio-temporal modeling strategies, including a bidirectional temporal convolutional network (BiTCN) enhanced with attention mechanisms and convolutional neural network (CNN), and a parallel spatio-temporal fusion architecture integrating bidirectional long short-term memory (BiLSTM) and CNN. Knowledge from the teacher ensemble is distilled into a lightweight CNN-based student network through soft-target supervision, leading to improved generalization capability with significantly reduced model complexity. Experimental results demonstrate that effective knowledge transfer is achieved while reducing model parameters by more than 80%, and performance gains of about 1–3% are obtained compared with baseline methods. These results indicate strong potential for practical deployment in resource-constrained network environments. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article

On the basis of data from ten semi-structured interviews and selected secondary data from surveys conducted by the Office for National Statistics in Great Britain, this paper explores how young men and women articulate attitudes and experiences related to sustainable transport, using gender as an analytical lens. The study is exploratory and qualitative. Both traffic safety and personal safety appear to have a much more limiting influence on women’s travel mode choices than on men’s. Perceptions of safety, comfort, distance, convenience and accessibility are defined and shaped by the surrounding urban environment and transport infrastructure, and emerge as important considerations in the narratives of the study participants. The use of the car by men and women is somewhat linked to barriers to sustainable transport, such as infrequent and unreliable public transport, and, in the case of women, safety concerns. Concern for the environment is largely similar across male and female participants. Full article

With the rapid urbanization in China, the spatial interaction between newly constructed underground structures and existing transportation tunnels has become increasingly frequent and complex. However, studies on the dynamic response characteristics of underground pedestrian passages subjected to the combined effects of metro- and vehicle-induced vibrations remain relatively limited. This study takes the newly constructed underground pedestrian passage at Want Want Hospital in Hunan Province as the engineering background. The pedestrian passage features a unique structural configuration, in which it is jointly constructed with an overlying vehicular tunnel through a shared slab and simultaneously crosses above an existing metro tunnel. To explore the vibration research methods for this unique structure, a three-dimensional finite element model was developed using ABAQUS and validated through in situ vibration measurements. Based on the validated model, the dynamic response of the pedestrian passage was systematically investigated from two perspectives: traffic loading conditions and shared slab thickness. The results show that metro-induced loads dominate the vibration response of the pedestrian passage. Bidirectional (reversible) train operation produces significantly greater vibration levels than unidirectional operation, and the Z-direction vibration level increases with train speed, with local exceedances occurring at 80 km/h. Under vehicle loading, the vibration response of the passage exhibits a non-monotonic trend, first increasing and then decreasing within the speed range of 30–40 km/h. When metro and vehicle loads act simultaneously, the vibration level is further amplified and exceeds the allowable limit. In addition, a pronounced vibration energy concentration zone is identified on the pedestrian passage bottom slab directly beneath the tunnel sidewalls, highlighting the necessity for targeted vibration mitigation in this region. Parametric analysis demonstrates that appropriately increasing the thickness of the vehicular tunnel bottom slab does not effectively reduce the vibration response. The findings of this study provide a reliable numerical analysis framework and practical design guidance for vibration control of complex overlapping underground structures in urban environments. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

NetworkGuard is a modular edge-based virtual network sensing framework designed for residential smart home security. The system interprets network telemetry—such as DNS queries, firewall events, VPN latency, and connection establishment delay—as structured sensing signals for gateway-level monitoring. Implemented on a Raspberry Pi 4 and managed via an Android interface, NetworkGuard integrates DNS filtering (Pi-hole), firewall enforcement (UFW), encrypted VPN tunneling (WireGuard), and an AI-assisted advisory layer for contextual log interpretation. During a six-week residential deployment, DNS blocking efficiency improved from 81.2% to 97.0% following blocklist refinement, while VPN connection establishment time decreased from approximately 3012 ms to 2410 ms after configuration tuning. ICMP-based measurements indicated a stable tunnel latency under moderate traffic conditions. Controlled validation scenarios—including DNS manipulation attempts, port scanning, and VPN interruption testing—confirmed consistent firewall enforcement and tunnel containment. The results demonstrate that layered security principles can be adapted into a lightweight, reproducible edge architecture suitable for small-scale residential IoT environments without a reliance on enterprise infrastructure. Full article

Urban transportation systems face increasing sustainability challenges due to the dominance of private-car use, particularly for short-distance trips. This study investigates the potential of micromobility to replace private-car travel on short-distance journeys and evaluates the resulting impacts on urban transportation networks and environmental sustainability. The analysis focuses on the Bornova district of Izmir and is based on a face-to-face survey conducted with 502 private-vehicle users. Survey data were analyzed using descriptive statistics, chi-square tests and a binary logit regression model to identify factors influencing the willingness to adopt micromobility. Within the surveyed sample of private-car users, modal-shift rates were estimated as 35% for trips up to 5 km and 33% for trips between 5 and 10 km. These rates were applied to the private-car demand and distance matrices developed for the year 2030 within the scope of the Izmir Transportation Master Plan, resulting in a revised private-car demand matrix and a separate demand matrix representing potential micromobility users. Network assignments were performed in the PTV VISUM modeling environment. Assignment results demonstrate notable network-level changes following micromobility integration. The total length of road segments with micromobility traffic volumes exceeding a threshold of 10 veh/h was calculated at 292.5 km. Environmental impacts were evaluated using a life-cycle assessment (LCA) framework, revealing an approximate 5.5% reduction in total life-cycle CO₂ emissions. Overall, the findings provide quantitative evidence supporting micromobility as an effective component of sustainable urban transport strategies and offer guidance for local governments in infrastructure planning and policy development. Full article

Wi-Fi networks used in smart grids are essential for enabling communication between smart meters and data aggregation units. A key challenge, however, is the ability to hide the existence and traffic patterns of these communications, so that sensitive information exchanges cannot be easily detected or intercepted. Unfortunately, most existing solutions do not provide support for traffic prioritization and steganographic channel encryption. In this paper, we propose a novel covert channel with Quality of Service (QoS) and encryption support for smart grid environments based on the IEEE 802.11 standard. We introduce an original steganographic approach that leverages the backoff mechanism, the Enhanced Distributed Channel Access (EDCA) function, frame aggregation, and the StegoPaddingCipher algorithm. This design ensures QoS-aware traffic handling while enhancing security through encryption of the transmitted covert data. The proposed protocol was implemented and evaluated using the ns-3 simulator, where it achieved excellent performance results. The system maintained high efficiency even under heavily saturated network conditions with additional background traffic generated by other nodes. The proposed covert channel offers an innovative and secure method for transmitting substantial volumes of QoS-related data within smart grid environments. Full article

The increasing heterogeneity of cloud, enterprise, and Internet of Things (IoT) environments raises concerns about the long-term reliability of machine-learning-based intrusion detection systems (IDSs). This study evaluates temporal robustness and cross-domain generalization using four publicly available datasets collected between 2017 and 2024. Five representative models—Random Forest, Gradient Boosting, Multi-Layer Perceptron, Autoencoder, and a lightweight 1D-CNN—are assessed under in-dataset, forward temporal, enterprise-to-IoT transfer, and dataset-agnostic evaluation protocols without retraining. In the dataset evaluation, models achieve Macro-F1 scores between 0.84 and 0.96. However, forward temporal testing reveals consistent degradation, with performance reductions reaching ΔF1 ≈ 0.20–0.27 when models trained on 2017 enterprise traffic are applied to IoT datasets from 2023 to 2024. Under cross-domain transfer, Macro-F1 decreases to 0.69–0.78, and benign false-positive rates increase up to 0.30, indicating substantial sensitivity to traffic distribution shifts. Tree-based ensemble models show comparatively lower degradation (≈6–23%) and reduced performance variance across datasets. Semantic feature analysis further indicates that flow intensity and temporal activity features exhibit higher cross-dataset stability than protocol-dependent indicators. These findings demonstrate that IDS robustness in evolving Internet environments depends strongly on evaluation methodology and feature stability, highlighting the need for generalization-oriented assessment strategies. Full article

Detecting denial of service traffic remains challenging when malicious sessions exhibit flow characteristics that closely resemble benign network behavior, particularly in low-rate attack settings. This study examines whether autoencoder-based feature compression can improve flow-based intrusion detection while maintaining a deployment-oriented design. We develop a lightweight pipeline that learns a low-dimensional latent representation of tabular flow features using an autoencoder and performs classification using Random Forest, LightGBM, and a radial basis function neural network. Using the CICIDS 2017 dataset, the best performing configurations achieve 99.43 percent accuracy with autoencoder plus Random Forest and 99.39 percent with autoencoder plus LightGBM, while autoencoder plus radial basis function neural network achieves 98.27 percent, with consistently strong precision, recall, and F1-score. The findings support practice by showing that high detection performance can be achieved using compact learned features that reduce input complexity for downstream models, which is beneficial for operational monitoring environments. The study advances knowledge by providing a reproducible evaluation of representation learning as a feature compression step for tabular intrusion detection, and by linking model performance to measurable computational considerations relevant to real-world deployment. Full article

Adverse weather conditions significantly degrade mobility and safety at rural signalized intersections, where high approach speeds and limited driver expectancy amplify operational and crash risks. While autonomous vehicles (AVs) have the potential to improve traffic performance, it takes a significant duration to penetrate. During this period, mixed traffic with human drivers and AVs will dominate. In this mixed traffic, the impacts of AVs at low penetration levels on adverse weather remain insufficiently understood, particularly in rural contexts. This study presents a simulation-based assessment of the effects of low AV penetration on mobility and safety at a rural signalized intersection under varying weather conditions. A calibrated microsimulation model was developed using PTV VISSIM to represent clear, rain, and snow scenarios with autonomous vehicles introduced at low penetration rates within conventional traffic. Mobility performance was evaluated using delay, travel time, and average speed, while safety impacts were assessed through surrogate safety measures extracted using the Surrogate Safety Assessment Model (SSAM), including time-to-collision and post-encroachment time. Results indicate that low levels of AV penetration of 10% can improve overall mobility performance compared with conventional traffic, particularly under adverse weather conditions. Safety outcomes show a reduction in conflict frequency and severity under low AV penetration, with more pronounced benefits observed during degraded weather scenarios. Further AV penetration from 10% to 25% may not significantly improve in a rural environment. The findings suggest that early-stage AV deployment may offer measurable mobility and safety benefits at rural signalized intersections, even before widespread adoption. This study provides practical insights for transportation agencies and policymakers regarding the potential role of low-penetration AV integration in enhancing rural traffic operations and safety under adverse weather conditions. Full article

To develop a refined traffic diversion scheme for expressway reconstruction and expansion, this study establishes generalized link impedance functions for cars and trucks, considering their differences in road travel time, time value, and toll costs. Subsequently, a traffic diversion model is constructed based on user equilibrium theory, taking the heterogeneity between cars and trucks into consideration. A path-based solution algorithm using the method of successive averages is designed to solve the model. To evaluate the environmental impact of the traffic diversion, a vehicle exhaust emission (including CO₂, CO, HC, and NO_x) estimation method based on the COPERT model is proposed. The results of a case study show that the optimized traffic diversion scheme significantly reduces the average V/C ratio while increasing the average velocity of both cars and trucks on the reconstructed links, without substantially compromising the traffic efficiency of other links. Additionally, the diversion scheme reduces the exhaust pollutant emissions, but increases the CO₂ emissions within the network. The findings justify the effectiveness of the traffic diversion approach on alleviating the traffic congestion on the reconstructed expressway and its mixed impacts on the environment. Full article

Cognitive or visual distraction caused by drivers using mobile phones, operating the central console, or conversing with passengers while driving is a significant contributing factor to road traffic accidents. Aiming to solve the problem that existing driving behavior monitoring systems exhibit insufficient recognition accuracy and low real-time detection performance in complex driving environments, this study proposes a dual-flow driver distraction detection model based on Sobel edge detection (DFSED-Model). The model is designed with a collaborative learning framework: the first flow adopts a lightweight pre-trained backbone network to achieve efficient semantic feature extraction. The second flow utilizes Sobel edge detection to extract the driver’s driving contours and enhances the model’s spatial sensitivity to driving movements and hand movements. Through the feature learning process of the first-flow-guided auxiliary branch, collaborative optimization of knowledge transfer and attention focusing is realized, thereby improving the model’s convergence speed and discriminative performance. The proposed model is evaluated on three widely used public datasets: the State Farm Distracted Driver Detection (SFD) dataset, the 100-Driver dataset, and the American University in Cairo Distracted Driver Dataset (AUCDD-V1). Under the premise of maintaining low computational overhead, the accuracy of the DFSED-Model reaches 99.87%, 99.86%, and 95.71%, respectively, which is significantly superior to that of many mainstream models. The results demonstrate that the proposed method achieves a favorable balance between accuracy, parameter count, and efficiency, and possesses strong practical value and deployment potential. Full article