Aiming at the serious problems of missed detection, false detection and difficult deployment of existing target detection algorithms when applied to traffic scenes, a vehicle detection model YOLOv8-FDD with lower parameter count and higher accuracy is proposed in this paper. First, an integrated Feature Sharing Detection Head is proposed to compress the amount of redundant parameters in the model head while ensuring the accuracy of the algorithm. Second, to enhance the interaction between regression features and classification features in the detection head, an Feature Dynamic Interaction Detection Head is further designed based on the Feature Sharing Detection Head. Third, the Dilation-wise Residual (DWR) module to strengthen the multi-scale feature extraction capability of the backbone network. Finally, the dynamic upsampling module DySample is utilized to improve the nearest neighbor interpolation upsampling in YOLOv8. Validation on the UA-DETRAC dataset shows that the proposed YOLOv8-FDD model’s parameter count is only 72.89% of the original YOLOv8, with computational complexity slightly increasing from 8.1 GFLOPs to 8.5 GFLOPs. The mAP50 and mAP50-95 improved by 0.7% and 1.3% respectively, with FPS consistently above 300. Additionally, on a self-built traffic surveillance dataset, YOLOv8-FDD outperformed the original algorithm. The error type statistics indicate that YOLOv8-FDD significantly reduces both vehicle false negatives and false positives, making it a high-accuracy, well-generalized real-time vehicle detection method.

Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve its performance. The proposed algorithm is validated with a worm reducer example and compared with other state-of-the-art and recent metaheuristics. Finally, the results confirm the high solution quality and efficiency of the proposed improved whale algorithm.

This study is based on the concept of converting agricultural waste into green new energy, we combine the concept of green cycle development and the relevant theories in modern system engineering to optimize the study of agricultural production waste recycling network. In this paper, the optimization of the agricultural production waste recycling network is divided into two aspects—facility site selection and vehicle path planning—with the objectives of agricultural production waste green recycling and the minimization of system construction and operational costs. In this study, the site selection and path planning problems were unified and an optimization model for the agricultural production waste recycling network site-path (LRP) problem was constructed. The optimization results of agricultural production waste recycling network facility location and recycling vehicle path planning were obtained by using the simulation data in the optimization model and designing the genetic algorithm design with the relevant characteristics of agricultural production waste recycling. The feasibility and operability of the model were verified through experiments. The research related to the optimization of agricultural production waste recycling networks can be used to both reduce production costs in agricultural areas and progress the practical theory of reverse logistics in agricultural areas. Agricultural waste resource utilization provides important support for the development of an ecological agriculture cycle and helps protect the environment.

Due to the impact of scale variation of vehicle targets and changes in traffic environments in large-scale traffic monitoring systems, vehicle target detection methods often face challenges. To improve the adaptability of detection methods to these variations, we proposed an enhanced YOLOv7 for traffic systems (ETS-YOLOv7). To mitigate the effects of complex environments, we introduced the convolutional block attention module (CBAM) into the YOLOv7 framework, which filters important features in both channel and spatial dimensions, thereby enhancing the model’s capability to recognize traffic object features. To address the influence of aspect ratio variations in vehicle targets, we replaced the original complete intersection over union (CIoU) with wise intersection over union v3 (WIoUv3), eliminating the aspect ratio consistency loss and enhancing the model’s ability to generalize and its overall performance. Additionally, we employed the compact layer aggregation networks (CLAN) module to replace the efficient layer aggregation networks (ELAN) module, reducing redundant computations and improving computational efficiency without compromising model accuracy. The proposed method was validated on the large-scale traffic monitoring dataset UA-DETARC, achieving a mean average precision (mAP0.5–0.95) of 90.2%, which is a 3% improvement over the original YOLOv7. The frames per second (FPS) reached 149, demonstrating that the proposed model is highly competitive in terms of detection efficiency and vehicle detection accuracy compared to other advanced object detection methods.

Bike-sharing has emerged as an innovative green transportation mode, showing promising potential in addressing the ‘last-mile’ transportation challenge in an eco-friendly manner. However, shared bikes around metro stations often face supply–demand imbalance problems during peak hours, causing bike shortages or congestion that compromise user experience and bike utilization. Accurate prediction enables operators to develop rational dispatch strategies, improve bike turnover rate, and promote synergistic metro–bike integration. However, state-of-the-art research predominantly focuses on improving complex deep-learning models while overlooking their inherent drawbacks, such as overfitting and poor interpretability. This study proposes a model–data dual-driven approach that integrates the classical statistical regression model as a model-driven component and the advanced deep-learning model as a data-driven component. The model-driven component uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to extract periodic patterns and seasonal variations of historical data, while the data-driven component employs an Extended Long Short-Term Memory (xLSTM) neural network to process nonlinear relationships and unexpected variations. The fusion model achieved R-squared values of 0.9928 and 0.9770 for morning access and evening egress flows, respectively, and reached 0.9535 and 0.9560 for morning egress and evening access flows. The xLSTM model demonstrates an 8% improvement in R² compared to the conventional LSTM model in the morning egress flow scenario. For the morning egress and evening access flows, which exhibit relatively high variability, classical statistical models show limited effectiveness (SARIMA’s R² values are 0.8847 and 0.9333, respectively). Even in scenarios like morning access and evening egress, where classical statistical models perform well, our proposed fusion model still demonstrates enhanced performance. Therefore, the proposed data–model dual-driven architecture provides a reliable data foundation for shared bike rebalancing and shows potential for addressing the challenges of limited robustness in statistical regression models and the susceptibility of deep-learning models to overfitting, ultimately enhancing transportation ecosystem sustainability.

New concepts such as low-carbon economy, low-carbon production, low-carbon living and even low-carbon cities have become popular topics in environmental protection. The disassembly line part of reverse logistics is accompanied by high carbon emission, which is contrary to the original intention of sustainable development. In this paper, we design a systematic low-carbon layout for the disassembly line of the logistics processing center to address the problem of high carbon emissions caused by the unreasonable layout of the disassembly line. Taking the disassembly line in the logistics center of Company H as the research object, the process of the disassembly line is analyzed, and the SLP analysis method is applied to analyze the material flow and the material flow intensity level of the disassembly line layout, and three different optimization schemes are derived. Flexsim software was used to model and run the three initial layout schemes of the disassembly line, and the data related to the waiting time operation of each scheme were obtained. Finally, carbon emission and other disassembly-line-related indicators were introduced and weights were set, and the results were subjected to weighted gray correlation analysis to arrive at the optimal disassembly line layout optimization scheme. This study will provide reference for other reverse logistics processing center layout studies.