Lei Xing

Public transportation is the core of easing urban traffic congestion, reducing pollution and advancing smart city transportation intellectualization. Its refined operation relies heavily on accurate, real-time passenger origin–destination (OD) data. However, traditional manual surveys are costly with low sampling rates, while smart card big data lacks alighting information and has deviations, failing to reflect real travel behaviors and becoming a bottleneck for intelligent public transportation development. To address this, this paper proposes a bus passenger boarding/alighting detection and recognition study based on video images and the YOLO algorithm. Aiming at traditional YOLO’s shortcomings in on-vehicle scenarios (insufficient feature extraction, inefficient feature fusion, slow convergence), the baseline YOLOv8n is improved for bus scenarios’ high-density, high-occlusion and variable-target scales: (1) DAC2f structure (deformable attention + C2f) captures occluded passengers’ core features and suppresses background interference; (2) SWD-PAN enables bidirectional cross-scale feature interaction to adapt to scale differences; and (3) WIoUv3 balances sample weights for small targets and non-standard posture passengers. Experiments show that precision, recall and mAP increase by 3.68%, 5.12% and 6.26%, respectively, meeting real-time requirements. The improved YOLOv8 is deeply integrated with DeepSORT to enhance tracking stability. Tests show that MOTA reaches 31.24% (2.6% higher than YOLOv8n, 16.4% higher than YOLO-X) and MOTP reaches 88.06%, solving trajectory breakage and ID switching. This addresses traditional OD data collection pain points, providing technical support for intelligent public transportation refined management and smart city transportation optimization.

To address the inherent limitations of single-modality sensors constrained by physical properties and data modalities, we propose DTC-YOLO (Depth-Texture Coupling Mechanism YOLO), a depth-texture coupled multimodal detection framework. The main contributions are as follows: RGB-LiDAR (RGB-Light Detection and Ranging) Fusion: We propose a depth-color mapping and weighted fusion strategy to effectively integrate depth and texture features. ADF³-Net (Adaptive Dimension-aware Focused Fusion Network): A feature fusion network with hierarchical perception, channel decoupling, and spatial adaptation. A dynamic gated fusion mechanism enables adaptive weighting across multidimensional features, thereby enhancing depth-texture representation. Adown Module: A dual-path adaptive downsampling module that separates high-frequency details from low-frequency semantics, reducing GFLOPs (Giga Floating-point Operations Per Second) by 10.53% while maintaining detection performance. DTC-YOLO achieves substantial improvements over the baseline: +3.50% mAP50, +3.40% mAP50-95, and +3.46% precision. Moreover, it maintains moderate improvements for medium-scale objects while significantly enhancing detection of extremely large and small objects, effectively mitigating the scale-related accuracy discrepancies of vision-only models in complex traffic environments.

The accurate prediction of the behavior of surrounding agents is crucial for the safe operation of autonomous vehicles. Currently, the dominant approach involves manually defining rules, which often fail to cover all potential scenarios. To address the rigidity and challenges of generalizing these rules to real-world driving contexts, we introduce a novel attention mechanism called “Out Way Attention”. This mechanism improves the model’s capacity to dynamically adapt to various driving situations by incorporating attention exits (out way) into the attention framework. Additionally, we present a new trajectory prediction framework that includes a learnable proposal matrix and permutation-invariant positional encoding. This matrix aids in forecasting future multimodal trajectories for multiple interacting agents in dynamic settings. The permutation-invariant positional encoding ensures that the processing sequence of agents at the same time does not influence the prediction outcomes. By integrating the proposed methods into the Transformer architecture, our approach reduces human intervention and significantly enhances the model’s adaptability, as well as improving training and inference efficiency. We have validated the effectiveness of our model on three public datasets, Argoverse, Trajnet++, and ETH/UCY. The results confirm that our method sustains robust performance in complex, highly dynamic environments with multiple interacting agents.

With the rapid advancement of urbanization in our country, the increase in urban population and motor vehicles has caused problems such as traffic congestion, environmental pollution and traffic accidents.To this end, the country has begun to vigorously develop urban rail transit, thereby optimizing the spatial layout and enhancing urban functions.In urban rail transit scheduling, passenger flow prediction is used as a key decision-making basis, and its results are essential to the smooth operation of urban rail transit.This paper takes the inbound passenger flow of the Shanghai subway as the research object, and comprehensively considers multi-source data such as AFC credit card data, external environmental data, air quality data, and the nature of the land around the subway station.By fully processing and analyzing these data, the potential laws between them and passenger flow are deeply explored. On this basis, a short-term passenger flow prediction model of urban rail traffic based on ResNet-Bi-At-LSTM is constructed.In the end, through the example analysis of Shanghai Urban rail transit, it is verified that the model proposed in this paper can more accurately and comprehensively predict short-term passenger flow.

With the development of e-commerce logistics in China, online shopping has become increasingly popular among consumers, and the fresh e-commerce industry has shifted to a pre-positioning warehouse distribution model closer to consumers. This study starts from consumer demands and considers the spatial distribution characteristics of consumers. Through cluster analysis, demand points with high consumer density are identified, and potential locations are determined through two rounds of clustering. Drawing insights from the warehousing and distribution models of Freshippo and MissFresh in Wuhan and Nanjing, four classification algorithms are compared to determine the optimal pre-positioning warehouse distribution model for the enterprise in this study. A multi-objective optimization model is established, aiming to minimize enterprise costs and maximize customer satisfaction, and the optimal site selection plan is obtained. By determining the number and locations of pre-positioning warehouses through the optimal solution, the enterprise can achieve maximum investment returns in the spatial layout of these warehouses.