The widespread deployment of low-altitude drones in Internet of Things (IoT) applications, such as smart transportation and urban security, demands efficient, real-time visual perception on resource-constrained edge devices. A critical challenge in this domain is the precise detection and localization of small objects against complex backgrounds. Low-altitude UAV imagery is particularly challenging due to blurred small-target edges, weak anti-interference for medium and large targets, and fragmented multi-scale features. Existing convolutional and general-purpose Transformer-based detectors exhibit significant limitations in localization accuracy and robustness under these conditions. To address these issues, we propose E²-Former, a novel object detection model based on the Detection Transformer (DETR) framework, designed for real-time performance on computational-edge devices. Our model integrates three core components: an Edge-Enhanced Backbone that augments sensitivity to contours while preserving deep semantics; a Polarized Dynamic Multi-Feature Fusion (PDMF) Transformer that leverages dual-path polarized attention and frequency-domain modulation to enhance local-global feature modeling and suppress background noise; and an Edge-aware Path Aggregation Network (E-PAN) that uses bidirectional gating and multi-level context interaction to resolve feature fragmentation and promote fine-grained, cross-scale integration. On three challenging low-altitude datasets—VisDrone2019, UAVDT, and CODrone—E²- Former achieves leading AP₅₀ scores of 48.9%, 41.2%, and 33.0%, respectively, outperforming existing mainstream methods in both detection accuracy and robustness. Practical deployment and scene detection experiments further validate its impressive performance in real-world scenarios, establishing a new paradigm for building efficient and reliable low-altitude drone perception systems.

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning.

Small object detection in unmanned aerial vehicle (UAV) remote sensing images remains challenging due to large-scale variations, dense object distributions, and complex background interference. Although Transformer-based detectors have improved global context modeling for remote sensing object detection, many existing designs still rely on spatial geometric relationships and conventional cross-level fusion, which may limit the aggregation of spatially scattered small-object features and introduce background interference. To address these issues, this paper proposes a Multi-granularity Prompt and Intensity Guidance Detection Transformer (MPI-DETR), an efficient end-to-end Transformer-based detector for small-object detection in UAV remote sensing images. MPI-DETR consists of three key components: a Dual-Stream Ranked Self-Attention (DRSA) module for intensity-ordered global feature aggregation, a Bilateral Tanh Gating and Cosine Attention Feature Alignment Module (BTC-FAM) for noise-resistant cross-level alignment, and a Prompt-driven Multi-granularity Fusion (PMGF) module for enhancing weak small-object details. Experiments on AI-TOD, DIOR, and NWPU VHR-10 demonstrate that MPI-DETR achieves $A{P}_{50}$ scores of $43.8\%$, $87.5\%$, and $92.5\%$, respectively. Compared with the RT-DETR-R18 baseline, MPI-DETR improves $A{P}_{50}$ by 3.0, 1.0, and 3.9 percentage points on the three datasets, respectively, and increases $A{P}_{\mathrm{S}}$ by 2.9, 2.9, and 11.6 percentage points. It also surpasses the strongest compared models by 1.7, 1.0, and 1.3 percentage points in $A{P}_{50}$ on AI-TOD, DIOR, and NWPU VHR-10, respectively. These results indicate that MPI-DETR provides a robust and efficient solution for small-object perception in complex UAV remote sensing applications, especially in scenes with dense targets, background interference, and limited computational resources.

With the rapid growth of the civil aviation industry, airport gate resources—especially those equipped with jet bridges (more convenient than shuttles)—have become increasingly scarce, posing new challenges to the sustainable management of airport operations. In a real-world application of the airport transport optimization study field, the airport gate assignment problem (AGAP) has emerged as a critical scheduling task in airport operations with the rapid growth of passenger demand. In this study, a mixed-integer linear programming model is developed for AGAP, aiming to minimize baggage transfer vehicle usage, maximize airline satisfaction, reduce passenger boarding time, and enhance the overall sustainability of airport operations. To efficiently address the computational complexity of this integrated modeling framework, a customized multi-objective particle swarm optimization (MOPSO) algorithm is proposed, augmented by a tabu search (TS) strategy. The TS algorithm provides high-quality initial solutions for MOPSO and performs local intensification on elite particles, thereby enhancing both convergence speed and solution quality. Extensive numerical experiments demonstrate that the proposed hybrid approach significantly outperforms the standalone MOPSO algorithm, achieving a 26.37% improvement over the original gate assignment scheme and a further 1.25% improvement compared to the standalone MOPSO, confirming the effectiveness and practicality of the proposed method.