Efficient coordination of heterogeneous mobile resources is essential for delivering large-scale urban services, such as sanitation, infrastructure inspection, or last-mile delivery. This study addresses the problem of scheduling aerial and ground service vehicles—unmanned aerial vehicles (UAVs) and mobile ground crews—to cover spatially distributed demand points under operational constraints. We formulate the task as a multi‑objective optimization problem that simultaneously maximizes service coverage, minimizes total completion time, and optimizes resource utilization while respecting safety, capacity, and time‑window restrictions. A hierarchical solution framework is proposed: global task allocation first assigns demand zones to vehicle types according to their capabilities, and local path planning then generates efficient routes for each agent. A dynamic re‑optimization mechanism adjusts schedules in real time when disturbances occur, such as resource depletion or environmental changes. The method is evaluated on scenarios of increasing scale (51, 113, and 212 demand points) that emulate urban public spaces. Results from ten repeated experiments show that the cooperative strategy achieves coverage rates (CRs) above 97% across all scales, reduces total operation time (TOT) by up to 33% compared with single‑mode operations, and improves resource efficiency by 21.10% and 47.40% Statistical analysis confirms the robustness of the improvements. The framework offers a scalable, resource‑aware solution for coordinating heterogeneous service fleets, with direct applicability to intelligent transportation systems, particularly in demand‑responsive urban services and multimodal fleet management.

Urban public transport systems are required to respond to pronounced temporal variations in passenger demand driven by calendar effects, weather conditions, and evolving mobility patterns. Reliable short-term demand forecasts have therefore become an important role in supporting operational planning and service management in large-scale transit systems. This study examines the daily ridership dynamics of the Transjakarta bus rapid transit system and evaluates the forecasting performance of three modeling approaches: seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), multilayer perceptron (MLP), and a dynamic moving-window model. The analysis is based on 851 daily observations from January 1, 2023 to April 30, 2025, with rainfall, working days, and national holidays included as exogenous variables. Each model is estimated using a training dataset and evaluated on a hold-out test set over a 30-day forecasting horizon. Forecast accuracy is assessed using the mean absolute percentage error (MAPE). The results indicate that the MLP model achieves the highest forecasting accuracy, with a MAPE of 8.547%, while SARIMAX and the dynamic model yield higher error levels of 33.345% and 37.754%, respectively. The findings suggest that non-linear modeling approaches are better suited to capturing the complex and irregular demand patterns observed in daily urban bus ridership data. The study provides empirical evidence that can support short-term planning and demand-aware operational decision-making in urban public transportation systems.

The rapid integration of technology, with increasing speeds, has transformed vehicles into cyber-physical systems by connecting them to each other Vehicle-to-Everything (V2X), significantly expanding the attack surface and leaving them vulnerable to network-based threats. Current cyber intrusion detection systems (CIDS) exhibit performance degradation due to significant class imbalance, limited resilience against adversarial attacks, and insufficient interpretability for security-critical environments. To overcome the identified issues in this study, we propose Hierarchical Classifier-Agnostic Boosted Stacking for Network Intrusion Detection (HCABS-NID), a hierarchical classifier-agnostic boosted stacking architecture for network intrusion detection in connected device ecosystems. The proposed framework adds the Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTENC)-based adaptive class balancing to increase minority attack detection and TreeSHAP to make it multi-level interpretable. As a hierarchical stacking strategy, a two-layer structure includes heterogeneous learners together with meta-learning, calibrated with LightGBM, XGBoost, CatBoost, and TabNet to take advantage of the complementary decision boundaries. Extensive experiments performed on the benchmark dataset from University of New South Wales Network-Based 15 (UNSW-NB15) should enhance generalization performance. HCABS-NID achieved 98.20% accuracy, 97.10% macro F1 score, and 0.989 macro Receiver Operating Characteristic Area Under the Curve (ROC-AUC), in contrast to the latest community-based methods found in the literature. The proposed model achieves 3.40 ms average inference latency, satisfying the real-time processing requirement of the V2X safety systems. Indeed, other analysis architectures show the same 96.8% accuracy at 5% corruption, which underscores their practicality. The results validate that hierarchical ensemble learning, with adaptive imbalance management artificial intelligence (AI) mechanisms, provides a sound, interpretable, and ready-to-use intelligent transportation security package.

This study examines the energy efficiency of battery electric vehicles (BEVs) from a system-level and lifecycle perspective. The analysis highlights that the high mass of automotive battery packs, often an order of magnitude greater than conventional fuel tanks for equivalent energy storage, contributes to increased vehicle weight and may necessitate higher installed power to maintain performance. Consequently, larger battery capacities and associated increases in vehicle dimensions are commonly required, which in turn influences subsystems such as tires, resulting in higher rolling resistance. Operational advantages of BEVs are primarily observed in low-speed acceleration, while overall efficiency can be limited by frictional losses and auxiliary energy demands. Battery production is particularly energy-intensive, accounting for a substantial portion of the embodied energy in BEVs. Charge and discharge efficiencies vary with current rates and usage conditions. Slow charging, which takes approximately 10–12 hours, can reach an efficiency of around 95%, while fast charging from 25 to 75% of capacity over one hour typically achieves an efficiency of 85–90%. Discharge efficiency decreases from near 95% at low rates to roughly 70% at high C-rates (10–15 C). Despite the high efficiency of modern electric motors, including permanent magnet machines, system-level efficiency is further impacted by battery losses, power electronics, and auxiliary components. The effective energy delivery from the grid depends on the generation and distribution infrastructure; in contexts dominated by thermal power and limited renewable penetration, overall electricity efficiency may be around 40%, comparable to modern internal combustion engine vehicles, which operate at 40–50% thermal efficiency. Finally, current battery recycling technologies recover only 40–50% of materials and require additional energy input, highlighting limitations in end-of-life management. These findings suggest that BEVs may not always offer a clear energy efficiency advantage over conventional vehicles when evaluated on a comprehensive, life-cycle basis.

This study proposed a novel pin-level dynamic compensation strategy to combat the critical challenge of springback in the three-dimensional numerically controlled bending of ship hull plates. A collaborative prediction model combining convolutional and bidirectional recurrent networks (CNN-BiLSTM) was optimized using an improved metaheuristic algorithm, the Modified Sparrow Search Algorithm (MCSSA), to achieve millimeter-level precision in springback compensation. Based on the 225-pin independent control architecture, the system enabled real-time compensation with millisecond-level response ($\leq$ 50 ms) on standard industrial computing hardware, to overcome the limitations of conventionally fixed compensation methods. The optimized algorithm enhanced global search capability, population diversity, and convergence efficiency, hence yielding a prediction accuracy of RMSE = 4.41 $\times$ $10^{-5}$ mm. The integrated spatiotemporal learning framework effectively combined feature extraction, sequential modeling, and critical region emphasis, to achieve a test-set $R^2$ of 0.969. Industrial validation of the SKWB-1600 system demonstrated significant improvements in traditional stepwise approximation methods: (i) Post-compensation forming errors were reduced to 0.13–0.26 mm with a 47–62% improvement; and (ii) Curvature errors in high-stress zones were maintained within $\pm$ 0.02 mm, thus forming iterations decreased by 42% and energy consumption reduced by 35%. This certified pin-level dynamic compensation solution provides a new methodology for forming precision of complex curved ship hull plates under industrial conditions and establishes a technical paradigm for manufacturing related components requiring high precision and efficiency.

Road surface cracks are a major contributor to vehicular accidents, particularly in high-speed and high-traffic environments. Conventional crack detection techniques that rely on grayscale imaging often fail to maintain accuracy under varying lighting conditions and in the presence of noise. To address these challenges, a robust detection methodology is proposed, based on a Gradient-based Crack Enhancement, Color Consistency, and Smoothness Regularization Model (GCSM). This model leverages Gaussian smoothing to reduce noise, gradient-based enhancement to accentuate crack features, and color consistency to effectively differentiate cracks from surrounding textures. Smoothness regularization ensures the continuity of crack patterns and minimizes false positives, enhancing the accuracy of detection. The resulting crack maps form the foundation for advanced risk analysis, directly linking crack detection to safety evaluation. The integration of crack detection with accident prediction is achieved by a hybrid model that estimates the likelihood of accidents induced by road surface deterioration. This hybrid model combines logistic regression to assess variables such as crack density, width, traffic volume, vehicle speed, and pavement condition, with a fuzzy inference system (FIS) to handle the imprecision inherent in road condition assessments. The final accident risk score is computed as a weighted combination of these components, offering enhanced prediction accuracy. Experimental results on datasets from Peshawar, Khyber Pakhtunkhwa, demonstrate that GCSM outperforms existing methods in terms of Intersection over Union (IoU), Precision, Recall, and Structural Similarity Index Measure (SSIM), with statistical significance (p < 0.01) confirmed via ANOVA. The hybrid prediction model achieves an accuracy of 88.23% and a mean squared error (MSE) of 0.042, highlighting its efficiency and robustness. This framework facilitates automated crack visualization and accident risk classification, providing valuable insights for engineers and urban planners. Future work will focus on real-time deployment and system adaptability to various road conditions, supporting intelligent transportation systems and proactive road safety management.

This study analyzes the safety risk transmission mechanism in urban logistics drone last-mile delivery within the policy-driven low-altitude economy. To address the limitations of traditional risk identification methods, which rely heavily on accident data, this research integrates the Fuzzy Decision Analysis Laboratory Method (Fuzzy-DEMATEL),Interpretive Structural Modeling (ISM), and the Matrix of Cross-Impact Multiplication (MICMAC) to construct a three-dimensional analytical framework based on causal relationships, structural hierarchy, and attribute classification.First, Fuzzy-DEMATEL is employed to quantify the strength of causal relationships among risk factors. Next, ISM is used to deconstruct the multi-level hierarchical network and identify fundamental causes within the risk system. Finally, MICMAC is applied to calculate the dependencies and driving forces of each influencing factor, helping prioritize risk governance measures. The research findings indicate that: (1) The safety risk system of urban logistics drones for last-mile delivery exhibits a “dual-core driven – multi-loop coupled” characteristic. Equipment failures act as the physical carriers of systemic failures, while the root-cause risks stem from institutional factors such as inadequate pre-service training and violations of laws and regulations. (2) The risk hierarchy follows a pyramid-shaped transmission path, with risks propagating from the root layer to the surface in successive layers. Open airspace serves as an accelerator, transforming environmental disturbances into institutional defects, which in turn lead to technical failures. (3) The dependency attributes of each factor indicate the priority order for risk prevention and control: management leverage points serve as the strategic control core, the environment-technology interaction network is central to joint prevention, standardized processes solidify basic operations, and systemic risk levels are reduced.

Vehicles comprise several critical systems, including the braking, steering, transmission, and suspension systems, which operate in concert to ensure safe and efficient movement. Research has established that vehicle malfunctions, particularly in the braking system, contribute significantly to road accidents, with technical failures accounting for approximately 15% of crashes and brake system failures responsible for 17.4% of these incidents. In light of this, an investigation was conducted to identify the factors that influence the braking coefficient and the variability of braking force in vehicle service brakes. A total of 1,018 vehicles were involved in the study, with results indicating that variables such as vehicle production year, category, place of registration, engine power and displacement, gross and curb weight, and payload significantly affect the braking coefficient. Furthermore, the analysis revealed that factors such as vehicle production year, category, registration location, gross and curb weight, and payload are prominent in determining the braking force variability. Neural network analysis was employed to further assess these influential factors, highlighting that the year of manufacture, place of registration, and vehicle payload are particularly influential in predicting both compliance with minimum braking coefficient requirements and variations in braking force. The findings underscore the importance of these factors in the development of more precise models for vehicle brake performance, with potential implications for safety standards and regulatory frameworks.

The integration of edge computing for real-time data processing in autonomous systems has been identified as a promising solution to mitigate the performance bottlenecks and latency associated with traditional cloud-based models. Autonomous systems, including vehicles, drones, and robotics, rely heavily on quick data analysis to make timely decisions. However, cloud computing, with its inherent data transmission delays, hinders the responsiveness and efficiency of these systems. To address these challenges, edge computing is proposed as a means to process data locally, at the point of use, thus enabling faster decision-making processes and reducing data transfer overhead. This approach leverages distributed machine learning for decision-making and dynamic resource allocation to balance computational tasks between edge and cloud resources. Through extensive experimentation, it has been demonstrated that the edge computing paradigm can reduce latency by up to 65%, offering substantial improvements in both energy efficiency and data processing speed when compared to traditional cloud-based methods. Furthermore, the proposed system outperforms existing alternatives in terms of computational speed, reliability, and energy consumption. The introduction of an Edge Computing Decision Model (ECDM) and a Dynamic Resource Allocation Algorithm (DRAA) is shown to optimize system performance by balancing computational demands between local edge nodes and remote cloud servers. These innovations enable autonomous systems to function more effectively and efficiently, even in resource-constrained environments. This study highlights the importance of integrating edge computing into autonomous system architectures to meet the growing demand for low-latency, high-performance applications. The potential of edge computing to significantly enhance the reliability and operational capacity of autonomous systems has been established, paving the way for more reliable and scalable solutions in real-time environments.