Search Results (10,593)

Accurate traffic flow prediction remains challenging due to the complex spatio-temporal dependencies inherent in road networks. Existing graph-based prediction models typically construct the graph structure from geographical adjacency. This limits spatial feature extraction to physically neighboring nodes. Consequently, potential correlations among non-adjacent nodes within the same region are overlooked. Moreover, these models generally treat the influence of neighboring nodes uniformly, whereas, in practice, such influences are inherently heterogeneous. On the temporal side, many approaches rely on recurrent units such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRUs) to capture short-term dependencies, yet these architectures are prone to gradient vanishing or explosion, leading to training instability. To address these issues, this paper proposes Seq2-ResGCRN, an encoder–decoder framework based on residual graph convolutional recurrent networks with an attention mechanism. The model reconstructs the adjacency matrix via graph diffusion convolution to learn adaptive edge weights, thereby capturing heterogeneous spatial influences. Seq2-ResGCRN further integrates graph convolution into the GRU architecture and introduces residual connections to alleviate gradient degradation during training. This study conducts experiments on two open-source real-world datasets (i.e., PEMS04 and PEMS08) to evaluate the Seq2-ResGCRN. The raw data, originally collected at a sampling frequency of 30 s, are aggregated into 5 min time intervals. Each record comprises three features: traffic flow, traffic speed, and road occupancy. The experimental results demonstrate that our Seq2-ResGCRN outperforms state-of-the-art methods, achieving 1.6–2.3% and 2.1–5.5% relative improvements in MAE and RMSE, respectively. Seq2-ResGCRN effectively captures the spatio-temporal correlations of short-term traffic flow and achieves superior predictive performance. Full article

Smart electric vehicle (EV) charging infrastructures are evolving into large-scale cyber-physical Internet of Things (IoT) systems that depend on distributed communication, real-time sensing, and spatially coordinated charging operations. However, their interconnected communication architecture exposes charging stations, EV communication links, and network gateways to coordinated distributed denial-of-service (DDoS) attacks. Existing intrusion detection approaches primarily rely on localized or static traffic analysis and therefore have limited capability to capture spatially distributed and temporally evolving attack behavior. This study proposes the Spatio-Temporal Attack Graph Guard Network (STAG-GuardNet), an unmanned aerial vehicle (UAV)-assisted spatio-temporal attack graph learning framework for DDoS detection and security monitoring in smart EV charging networks. The framework integrates spatiotemporal signal conditioning, telemetry-adaptive graph aggregation, temporal dependency learning, attack-memory encoding, and adaptive risk-aware attention to model coordinated cyber-physical attack behavior. UAV-assisted telemetry provides complementary spatial and wireless information on communication instability, signal variation, neighboring congestion, and distributed attack-related behavior. A Hybrid Hawk–Manta Adaptive Optimizer (HHMAO) is employed to improve hyperparameter selection and convergence stability under imbalanced, heterogeneous, and nonstationary traffic conditions. The framework is evaluated using a smart-city EV charging cybersecurity dataset and three benchmark IoT intrusion detection datasets, namely TON_IoT, Edge-IIoTset, and X-IIoTID. Experimental results show that STAG-GuardNet achieves 97.7% accuracy, a 97.7% weighted F1-score, and a 98.4% area under the receiver operating characteristic curve (AUC) on the primary dataset. The framework also maintains stable performance under noisy telemetry, missing observations, heterogeneous traffic distributions, and charging-node outages. These findings demonstrate the potential of STAG-GuardNet for resilient and spatially informed security monitoring in UAV-assisted IoT-enabled EV charging infrastructures. Full article

The Time-Dependent Vehicle Routing Problem with Time Windows (TDVRPTW) extends the classical vehicle routing problem by incorporating time-varying travel conditions and customer service time windows, making it better suited to realistic logistics operations. This study aims to systematically review TDVRPTW research published between 2015 and 2026 and examine modeling characteristics, solution approaches, practical applications, sustainability integration, evaluation metrics, and research gaps. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA 2020 guidelines, 6818 records were identified from six academic databases, and 61 journal articles were included in the final review. The results show that metaheuristic methods are the most frequently used solution category, appearing in 26 studies (42.62%), followed by heuristic methods in 21 (34.43%), and exact methods in 18 (29.51%). Economic sustainability was the most frequently incorporated sustainability dimension (90.16%), while only 9 studies (14.75%) simultaneously optimized environmental, economic, and service and operational dimensions. The review also identifies gaps in computational scalability, real-world and operational realism, dynamic traffic and uncertainty, and sustainability and multi-objective integration. Full article

Correct packet delivery does not guarantee correct semantic interpretation when endpoint meanings for the same learned codeword diverge. CoSMA DAI is proposed for mismatch detection, protected confirmation, task preservation and semantic repair. Channel-conditioned global evidence, semantic class local evidence, temporal dynamics, channel context and protected probe evidence are fused by a causal machine-learned mismatch belief. A transmitter-derived task belief preserves the downstream decision while repair is pending and BDIx agents select guarded intentions for probing, fallback and resynchronisation. Evaluation uses 30 held-out drift seeds, 300 matched null streams and 300 degrading channel controls. Six referenced sequential monitors receive the same conditioned payload score. CoSMA DAI obtains 100.00 percent balanced accuracy, precision, recall, F1 score and Matthews correlation coefficient with zero observed matched null false alarms. Its aggregate delay is 5.62 slots, compared with 11.58 slots for the other zero false alarm method. The task-preservation belief maintains 94.73 percent task accuracy through every divergence scenario, above the quantised accuracy ceiling of 0.919 of the semantic path, because it is derived from the unquantised transmitter latent. A task-label-only control confirms that this accuracy is secured by the preservation belief alone, independently of the detector, so task preservation and mismatch detection are decoupled by design and detectors are compared on residual functional semantic outage, outage duration and semantic reconstruction fidelity, which measure the restoration of the semantic representation itself. Without repair, the residual semantic outage is 73.69 percent at 15.97 dB reconstruction fidelity, whereas CoSMA DAI reduces it to 0.73 percent over 6.62 slots at 21.74 dB. Under five declared parity tiers, in which multivariate and supervised baselines receive the identical features, training seeds, protected probe and candidate budget, the protected confirmation stage reduces false repair for every detector to which it is attached. Zero-shot evaluation over 7 unseen mismatch families and 5 unseen link models retains full detection with zero observed false repair in 6 of the 7 families and on every link and identifies receiver-side decoder drift as a condition the present observation model cannot detect. The learned belief is validated at slot level with an area under the receiver operating characteristic curve of 0.99997 and a class overlap of 0.00039, leave-one-mechanism-out and cross-channel retraining are reported, behaviour is characterised down to the practical detection boundary and scaling to 64-dimensional representations with 2048-entry codebooks is demonstrated. The task-belief mechanism is shown to be economical only for small closed-set output spaces and the channel-conditioning tables are shown to reduce to 6 cells without loss. Every comparator is additionally retuned on the same development budget, paired bootstrap intervals and signed-rank tests are reported over the shared streams, auxiliary traffic and radio energy are normalised per correct decision, authentication of the task belief is specified and charged and transfer to MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 is demonstrated without retraining, including on a convolutional VQ-VAE representation with a jointly learned 512-entry codebook, where foreground segmentation and localisation are restored to within the quantisation limit while a class decision cannot serve either task. The control traffic share is 23.59 percent, which is 12.62 percent lower than the monitor value. The additional semantic side information increases radio energy to 0.393 mJ per stream and reduces control-adjusted resource efficiency to 6.203 source-equivalent bits per channel use. The results therefore establish reliable detection and semantic repair within the principal comparison, with comparator-specific delay advantages and without claiming task-accuracy, semantic-rate or energy superiority. Full article

Artificial intelligence (AI) and machine learning (ML) are increasingly applied to road traffic congestion prediction, but heterogeneous outcomes, models, horizons, and evaluation practices limit comparability. The objective of this study was to synthesize methods, applications, validation, explainability, and reproducibility in AI/ML-based road traffic congestion prediction and forecasting. Following PRISMA 2020, Scopus, Web of Science Core Collection, and IEEE Xplore were searched through 5 July 2026 for English-language journal articles and full conference papers published from 2000 to 2026. Two external reviewers independently screened 734 unique records and assessed the retrieved full texts, while the author resolved disagreements against the predefined eligibility criteria. Study characteristics, prediction tasks, congestion indicators, model families, metrics, explainability, validation, and data/code availability were synthesized descriptively and narratively. Of 1131 records identified, 397 duplicates were removed and 734 records were screened. Full-text retrieval was sought for 339 reports; 195 could not be retrieved, 144 were assessed for eligibility, and 129 were included. Congestion level was the main prediction task, while traffic flow and speed were the most frequent indicators. Heterogeneity and the absence of verified numerical performance values precluded meta-analysis or model ranking. Explainability was limited, and external validation, transferability, and reproducibility were insufficiently documented. Progress requires standardized outcomes, transparent validation, reproducible workflows, explainable models, and independent testing across networks and cities. Full article

Reliable visual perception is essential for autonomous urban mobility, where dense traffic, severe occlusion, low illumination, and heterogeneous object distributions challenge pedestrian and animal detection. This study proposes an optimization-oriented hybrid visual perception architecture that formulates visual perception as a unified optimization problem by jointly improving object localization, adaptive suppression, confidence calibration, feature refinement, and safety-oriented risk assessment. The proposed architecture integrates YOLOv11 for real-time object detection, Dynamic Non-Maximum Suppression (DNMS) for adaptive overlap filtering, EfficientNetB7 for hierarchical feature refinement, Bayesian confidence fusion for probabilistic confidence recalibration, and a mathematical risk assessment model for real-time decision support. The architecture was evaluated on COCO2017, Open Images Dataset V7, BDD100K, and a harmonized hybrid dataset and compared with representative CNN-based and YOLO-based baselines, including YOLOv5, YOLOv8, MobileNet, ResNet50, DenseNet121, and EfficientNetB7. The experimental results demonstrate that the proposed architecture consistently achieves superior performance, reaching 96% accuracy, 96% F1-score, 81% mAP@0.5:0.95, a 4% Miss Rate, a 3% False Positive Rate, and real-time inference at 25 Frames Per Second (FPS). Furthermore, the mathematical risk assessment model classifies detected objects into Safe, Warning, and Danger categories, enabling interpretable safety-aware decision support. The findings indicate that the proposed architecture offers an effective optimization-based solution for reliable and real-time perception in autonomous urban mobility. Full article

Closed-set evaluation is the most common method in Internet of Things (IoT) sensor network botnet attack detection research. In this setting, classifiers are tested only on attack families encountered during training. As a result, they often fail to detect genuinely new (zero-day) malware in real-world environments. This study evaluates a calibrated two-tier detection pipeline combining an XGBoost gradient-boosted tree classifier for known-class prediction with a Monte Carlo (MC) dropout multilayer perceptron (MLP) as an independent uncertainty estimator, under conditions closer to real deployment than most prior evaluations. The uncertainty model is used to flag unfamiliar traffic that may represent zero-day traffic. The classifier is trained and calibrated on the Bot-IoT dataset and evaluated for zero-day generalization on N-BaIoT, which contains the previously unseen Mirai and BASHLITE malware families. During data preparation, a severe train/test duplication problem was identified. Around 97% of a naive split was found to be duplicates and was removed before splitting. The base classifier achieved 99.4% accuracy in known-class detection. However, the confidence scores were negatively correlated with zero-day traffic, producing an area under the receiver operating characteristic curve (AUROC) of 0.32. This indicates the overconfident misclassification of unseen attacks. The proposed uncertainty gate improved zero-day discrimination to an AUROC of 0.66, with catch rates reaching 13.75% for Mirai. An explainable AI analysis using Shapley Additive Explanations (SHAP) linked this gap to packet size statistics. To evaluate practical feasibility, the complete framework was deployed on an embedded Raspberry Pi Compute Module 5 (CM5) edge device. The full uncertainty-gated pipeline achieved mean inference latency of 15.03 ms per sample. Overall, these findings demonstrate that uncertainty-aware gating can improve zero-day robustness over conventional closed-set classification in resource-constrained IoT sensing environments. Full article

The Internet of Vehicles (IoV) connects vehicles, roadside infrastructure, and cloud or edge platforms to support safer and more efficient transportation. This connectivity also exposes vehicular networks to message spoofing, denial-of-service, and man-in-the-middle attacks, and to threats that target the machine-learning defenses themselves, such as model poisoning, gradient inversion, and adversarial examples. Machine learning and deep learning are now the dominant defensive tools, and the resulting literature is large and fragmented. This survey reviews that literature using a documented database search and stated inclusion criteria. The retained studies are organized into three categories, each defined by the dominant security-design problem it addresses. The categories cover classical detection and prevention, decentralized security based on federated learning and blockchain, and the assurance directions of Large Language Model (LLM)-driven detection, adversarial robustness, and explainable artificial intelligence. Rather than pooling reported scores, the survey records the evaluation setting of each study. This shows that de-duplication, class balancing, and metric aggregation account for much of the apparent variation in reported performance. A study-level assessment finds that no reviewed study demonstrates more than one of the three assurance capabilities, and no detector evaluated on vehicular traffic has been tested against adversarial perturbation. The survey concludes with a cross-category comparison, an EU AI Act alignment assessment, and recommendations for future research. Full article

This paper proposes a Priority–Urgency Index (PUI) mechanism to address the multi-objective conflict problem in electric vehicle charging scheduling at public fast-charging stations. The mechanism dynamically quantifies the charging urgency of each vehicle based on remaining dwell time, current state of charge (SoC), and target SoC, enabling differentiated service prioritization under resource scarcity. The PUI is systematically integrated into four multi-agent reinforcement learning (MARL) algorithms. To handle the time-varying number of vehicle entities caused by random arrivals and departures, the chargers are modeled as fixed agents; a partially observable Markov decision process (POMDP) is formulated, and a centralized training with decentralized execution (CTDE) architecture is adopted. On this basis, a state-aware dynamic threshold mechanism is introduced to distinguish urgency levels of charging tasks, and an adaptive reward function is designed to accommodate complex operating conditions. Empirical comparisons show that PUI-MAPPO (multi-agent proximal policy optimization) achieves the best performance among all PUI-enhanced variants. Under extreme supply–demand conditions—such as resource-scarce and heavy-traffic scenarios—PUI-MAPPO improves the target-SoC fulfillment rate and net revenue by up to 42.7% and 23.2%, respectively, and reduces the cumulative grid-limit exceedance by 22.8% to 47.3%, relative to the first-come, first-served (FCFS) baseline. Ablation studies further validate the individual effectiveness of the PUI urgency mechanism, the dynamic threshold framework, and the adaptive reward function. Full article

The paper presents a Bayesian Hierarchical Generalized Ordered Probit model quantifying how the dyadic age difference between delivery riders (DR) and Non-Delivery e-bike Riders (NDR) determines traffic collision injury severity. Analysis of 741 adjudicated civil verdicts from China’s OpenLaw platform demonstrated a strict negative association between this relational age gap and physical trauma outcomes. The dyadic age difference yielded a posterior coefficient of −0.13. Shifting the DR from the 5th to the 95th relative age percentile increased the NDR “Not disabled” probability by 22.25 percentage points. This identical interval concurrently decreased severe Grade 8+ injury probabilities by 10.29 percentage points. Conversely, NDR absolute age positively escalated injury severity across the clinical spectrum. These statistical estimates suggest that older DR are associated with reduced physiological vulnerability in aging commuter populations. Current automated dispatch algorithms optimize exclusively for delivery speed and completely atomize the gig workforce. Platform architects can reconfigure these digital systems to proactively pair experienced older riders with younger peers during overlapping dispatch windows. This algorithmic peer-mentoring strategy bypasses ineffective asynchronous training modules. Re-engineering dispatch logic directly operationalizes the relational age gradient to mitigate the systemic trauma burden threatening modern mobility. Full article

The increasing density and complexity of airport surface operations have intensified the risk of crossing conflicts between ground service vehicles and taxiing aircraft. Such interactions are characterized by strong spatiotemporal coupling, asymmetric right-of-way relationships, and stringent safety requirements, making conventional human-driven conflict avoidance highly dependent on drivers’ perception and judgment. To address this problem, this study proposes a spatiotemporal prediction-driven model predictive control (MPC) framework for autonomous ground vehicles on airport surfaces. First, the spatial interaction between the aircraft safety boundary and the vehicle service road is modeled to define the vehicle–aircraft conflict zone. Aircraft motion information is then used to predict the temporal occupancy of the conflict zone, based on which a dynamic time-window constraint is constructed to characterize the time-varying safe passage conditions for autonomous vehicles. The predicted spatiotemporal constraints are embedded into a rolling MPC framework that continuously optimizes vehicle motion while jointly considering safety, traffic efficiency, and energy consumption. Simulation results show that, compared with human-driven vehicles, the proposed method reduces average energy consumption from 1200.16 kJ to 866.67 kJ and shortens average arrival time from 43.63 s to 41.27 s. In addition, the method demonstrates effective disturbance compensation under aircraft-state uncertainty and adaptability to sequential multi-aircraft crossing scenarios. Full article

Supplier concentration and dependence on Hormuz-associated origins describe different dimensions of China’s crude-oil import security. Using 195 monthly observations for January 2010–March 2026, we estimate separate vector error-correction models (VECMs) driven by a geopolitical news-risk proxy combining Israel, Saudi Arabia and Türkiye. In the six-variable reference, the Herfindahl–Hirschman index (HHI) for 35 reported supplier categories excluding Iran rises by 2.383% after twelve months (95% interval, 0.728% to 3.677%), while the adjusted five-country origin share declines by 0.453 percentage points after one month (−0.916 to −0.069). The initial exposure decline remains supported in models with improved residual diagnostics. Persistent HHI growth is not established across dynamic specifications or under the original adjusted-HHI scenario, and adding Saudi Arabia limits the generalization of the exposure result. These estimates document a baseline contrast between two import-security indicators rather than an identified procurement-substitution mechanism. Connectedness, local projections and spatial statistics provide supporting evidence; post-2024 associations remain exploratory. The results support monitoring supplier balance and source-group dependence separately, with country-origin shares interpreted as proxies rather than observed strait traffic. Full article

Mixed-autonomy traffic with autonomous vehicles (AVs) and human-driven vehicles (HDVs) presents forecasting challenges because agent interactions and event-affected spatial dependencies vary over time. We propose Heterogeneous Adaptive Dynamic Spatiotemporal forecasting (HADS), a graph-attention framework that combines event-driven local topology reconfiguration, penetration-aware dynamic time warping (DTW) attention, and task-level temporal fusion for multi-horizon traffic-flow prediction. The forecasting target is traffic flow. The evaluation uses a semi-synthetic, penetration-controlled benchmark built from field-observed traffic-flow targets and 912 labeled anomalous events on a Beijing pilot-zone network (534 nodes and 3180 directed edges; April–July 2023), paired with SUMO-generated AV features at 20%, 40%, and 60% penetration. Under the reported single-seed runs, HADS obtains lower 15 min MAPE than AGCRN at 60% penetration, decreasing the point estimate from 2.92% to 2.61% under regular conditions and from 3.32% to 2.98% under anomalous conditions. Results across the reported 15 and 30 min settings indicate that event-conditioned topology and lag-aware heterogeneous attention can improve traffic-flow forecasting on this hybrid real–simulation benchmark. Results are from single-seed runs and should be read as preliminary point-estimate evidence rather than statistically demonstrated improvements; the semi-synthetic evaluation shows potential for pilot-zone applications rather than confirming real-world deployment performance. Full article

Amid global urban transition from sprawling expansion to stock-oriented regeneration, exploring the spatiotemporal heterogeneity and mechanisms of urban spatial vitality in old urban areas is crucial for advancing sustainable urban renewal and enhancing human well-being. This study builds a built environment index system, taking the old urban areas of Wuwei City, China, as a case study, and employs the MGWR model to analyze spatial vitality and its drivers. Results show: (1) Diverse functional elements exhibit significant spatiotemporal variations, with basic living facilities having stable impacts, while cultural and catering facilities show morning local agglomeration and nighttime region-wide driving effects, respectively. (2) Regarding accessibility, the road network is a key spatial factor. It shows a positive statistical association with regional vitality during the day, but may correlate with traffic and environmental stress at night. This indicates a potential trade-off between commercial activity and residential comfort across different time periods. (3) Spatial quality elements show strong spatial stability. Specifically, floor area ratio and open space ratio exhibit positive statistical associations with vitality, while building density and height tend to show negative correlations. This may reflect structural bottlenecks imposed by high-density development on sustainable living spaces. (4) Strategies like micro-functional layouts, day-night differentiated traffic networks, and spatial chassis optimization are proposed to provide quantitative evidence for sustainable stock-oriented regeneration, balancing heritage conservation with modern urban vitality. Full article

Internet of Things (IoT) traffic anomaly detection is essential for limiting device compromise and large-scale attacks. Existing detectors may miss attack families absent from model development, while heterogeneous benign traffic makes it difficult to maintain a low false-positive rate (FPR). To address these two practical limitations, we propose the Mode-Calibrated Dual-Evidence Detector (MCDE). Its supervised branch estimates the probability that a sample is malicious from labeled benign and known-attack traffic, while its benign-deviation branch measures distance from multiple learned benign traffic modes, providing a complementary route for unseen attacks. MCDE maps the heterogeneous probability and distance scores to comparable empirical benign-tail evidence, normalizes each branch by its allocated share of the target FPR, and fuses them into an anomaly score. A disjoint held-out benign set determines the decision threshold. Equivalently, the fusion compares budget-adjusted benign-tail surprisal, linking the decision rule to empirical self-information. We further establish the conditions under which the budgeted fusion controls the nominal overall FPR. Family-hold-out experiments on IoT-23 and N-BaIoT validate MCDE. At a 1% target benign FPR, MCDE improves IoT-23 unseen recall over histogram-based gradient boosting from 85.77% to 90.85% and harmonic known–unseen recall from 91.82% to 95.07%, while maintaining a 0.96% benign-test FPR. It also achieves 99.87% unseen recall on N-BaIoT. Full article