Jie Zhang

As electric buses become increasingly integrated into urban public transport systems, research on their role in transfer networks remains limited. Passengers often encounter extended waiting times during transfers, raising equity concerns. This study formulates a multi-objective mixed-integer quadratic programming (MIQP) model to optimise electric-bus transfer-station planning, considering station-level transfer equity, the surrounding built environment, multimodal transfers involving metro services and taxis, and the operational characteristics of electric buses. The model aims to (1) minimise passenger transfer-time burden, (2) reduce system operating costs, and (3) improve station-level horizontal transfer equity. Sensitivity analyses are conducted for time cost and preference parameters. The Harbin case study shows that, compared with the original scheme, the optimised scheme reduces total transfer time by 11.25%, decreases the variance-based equity indicator by 25.52%, reduces the maximum transfer utility by 14.99%, and lowers annual operating costs by 206,700 CNY. This study provides crucial insights for optimising electric-bus deployment in the broader context of public transport electrification.

Urban bus networks are exposed to cascading disruptions whose propagation depends not only on network topology but also on temporally varying traffic states and passenger-flow distributions. Existing vulnerability analyses often rely on static topology or hypothetical flows, limiting their ability to characterize traffic-dependent failure dynamics. This study develops a data-driven vulnerability framework for urban bus networks by integrating a coupled road–bus network, user-equilibrium assignment and a weighted Coupled Map Lattice (CML) model. Smart-card data are used to estimate passenger origin–destination demand, while road traffic states at four representative periods are mapped onto bus links to characterize travel-speed-dependent capacity reductions. The resulting weighted networks are evaluated using degree, strength, conventional betweenness, and traffic-flow betweenness. The improved CML model jointly incorporates topological connectivity, passenger-flow characteristics, external perturbations, and flow redistribution to simulate cascading failures under targeted and random attacks. The Harbin trunk bus network is used as an empirical case study. Results show that cascading-failure patterns are strongly traffic-state dependent: under more congested conditions, failures reach their peak propagation rate earlier. Across the tested parameter settings, perturbation intensity and coupling strength influence the timing and extent of cascade propagation, with responses varying by attack strategy and traffic condition. These findings demonstrate that incorporating time-varying traffic states and actual passenger flows can reveal vulnerability patterns that static topology-based analyses may overlook, providing a basis for time-sensitive disruption management and resilience-oriented bus operations.

Remote sensing images often exhibit characteristics such as significant scale variation, dense distribution, and complex background textures, which lead to the obscuring of small target features and result in missed and false detections in occlusion scenarios. To address this, we propose a lightweight remote sensing dense small target detection network, HSRNet. This method introduces a Sparse Adaptive Attention Enhancement Module (SASR Transformer) in the deep features to strengthen effective context and suppress background interference. An Asymmetric Feature Aggregation Module (AFAM) is designed at the neck stage to improve multi-scale feature aggregation quality. Additionally, the detection head incorporates hypergraph relation modeling to construct higher-order semantic topology and better distinguish adjacent dense targets through gated distribution. Experimental results on the DOTA1.0, DIOR, and NWPU VHR-10 datasets demonstrate that HSRNet achieves $AP_{50}$ scores of 0.712, 0.864, and 0.936, and $AP_{50:95}$ scores of 0.435, 0.662, and 0.611. Notably, in terms of small object detection ($AP_{S}$), compared with other state-of-the-art models, our method achieves significant improvements of $3.5\%--14.7\%$, $2.3\%--14.2\%$, and $6.6\%--27.0\%$ on these datasets, respectively. In terms of efficiency, the model has approximately 3.5M parameters, a computational cost of about 9.0G FLOPs, and achieves real-time inference at 58.4 FPS on the Jetson Orin Nano, demonstrating its deployment potential in resource-constrained scenarios.

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.

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.

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.