Xiaonian Shan

Quantitative visual assessment of bolt loosening in railway hump retarders is challenging because subtle rotations are obscured by low illumination, water droplets, specular reflections, and viewpoint variations. This study proposes a top-down monocular vision framework for non-contact measurement of brake-rail fastening-bolt rotation. The framework localizes bolt instances and uses HexTopo-RTMPose to recover six ordered corners of each hexagonal bolt head by modeling cyclic adjacency and geometric consistency. Point-wise reliability derived from SimCC coordinate distributions is integrated into a hybrid orientation solver in a 60° periodic space. Multi-estimator disagreement and geometric residuals quantify uncertainty and support rejection of low-confidence outputs. A field dataset of 1,322 unique images, 5,205 geometrically valid bolt instances, and 32 scenes was established under daytime, nighttime, water-droplet, and nighttime–water-droplet conditions. Scene-exclusive partitioning evaluated generalization to unseen scenes. On the test set, the Faster R-CNN R50-FPN detector achieved an instance recall of 98.90 ± 0.41%. Under the ground-truth bounding-box protocol, HexTopo-RTMPose attained PCK@0.05 and PCK@0.10 of 54.85 ± 1.75% and 72.62 ± 2.48%. Paired field validation covered 128 bolts. The MAE was 1.29° for 108 zero-rotation controls. For 20 loosened bolts with reference angles of 3.24°–31.86°, the pipeline achieved an MAE of 3.63°, an RMSE of 4.80°, and a Pearson correlation coefficient of 0.892; 80% and 95% of absolute errors were within 5° and 10°, respectively. The framework enables quantitative assessment of connection-state changes from challenging railway-site imagery.

To scientifically explore the vehicle capacity characteristics of circular earthwork transportation routes in water conservancy projects, this paper takes the second-phase project of the Huaihe River Sea Entrance Channel as the research background. Key influencing factors such as road conditions, vehicle performance parameters, safe car-following distance, and earthwork loading–unloading duration are comprehensively considered, and a cellular automaton simulation model is constructed. Horizontal comparative verification is carried out with the Intelligent Driver Model, System Dynamics model, and field measured data to verify model accuracy. The results reveal that the cellular automaton (CA) model yields a total vehicle transport trip count of 606, with a MAPE of 0.66% when compared against the field-measured average of 602 trips. The simulated average travel speed reaches 16.71 km/h, corresponding to a MAPE of 2.89% relative to the field measurement of 16.24 km/h. The error metrics of these two indicators are markedly lower than those derived from alternative models. Due to differences in modeling paradigms and applicable mechanisms, the three models exhibit distinct characteristics in simulation performance. Among them, the cellular automaton model is more suitable for the circular earthwork transportation scenario of this study, which can accurately reflect the coupling characteristics of microscopic traffic behaviors such as multi-route confluence and node queuing, and has high consistency with actual engineering operation. Sensitivity analysis indicates that improving earth loading efficiency and reasonably arranging excavator quantity can significantly enhance the overall transportation efficiency. The modeling ideas and simulation analysis method adopted in this paper are not only applicable to the specific engineering scenario, but also can be extended to similar water conservancy earthwork transportation and large-scale engineering logistics transportation fields. It can provide theoretical basis and engineering reference for earthwork scheduling optimization and quantitative calculation of traffic capacity in water conservancy projects.

Mixed traffic flow, consisting of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs), is expected to dominate future roadways. This study develops a heterogeneous traffic flow model in an intelligent connected environment to analyze macroscopic characteristics, including traffic capacity, critical density, and fundamental diagrams, under varying CAV penetration rates. Three car-following modes are considered: HDVs following HDVs, HDVs following CAVs, and CAVs following CAVs, modeled respectively by the Intelligent Driver Model (IDM), Adaptive Cruise Control (ACC), and Cooperative Adaptive Cruise Control (CACC). Model parameters are calibrated using genetic algorithms based on publicly available autonomous driving datasets. Calibration results demonstrate high predictive accuracy, with mean absolute percentage errors of 0.009% (distance) and 2.37% (speed) for IDM, 0.27% and 3.67% for ACC, and near-zero errors for CACC. Analysis of fundamental diagrams shows that increasing CAV penetration significantly enhances traffic flow efficiency, improves stability, and mitigates congestion. The findings provide theoretical guidance for optimizing mixed traffic operations and support the development of intelligent and connected transportation systems.

In urban arterials, buses face dual constraints from signal-controlled intersections and bus stop dwell demands, and frequent start–stop cycles result in reduced operational efficiency and elevated energy consumption. To address this critical challenge, a sustainable eco-driving strategy integrating offline and online Reinforcement Learning (RL) is proposed in this study. Leveraging real-world trajectory data from a 15.47 km route with 31 stops, the energy consumption characteristics of electric buses under the combined effects of stops and intersections are systematically analyzed, and high energy consumption scenarios are precisely identified. An initial energy saving strategy is first trained using offline RL, and subsequently subjected to online optimization in a vehicle–infrastructure cooperative simulation environment that incorporates three typical stop configurations. The soft actor-critic algorithm is employed to reconcile the dual goals of energy efficiency and ride comfort. Simulation results reveal a significant improvement with the proposed strategy, achieving an 11.2% reduction in energy consumption and a 37.7% decrease in travel time compared to the Krauss benchmark model. This study confirms the effectiveness of RL in boosting the operational sustainability of public transport systems, offering a scalable technical framework to promote the development of green urban mobility. The research findings provide theoretical support and practical references for the large-scale promotion and engineering application of energy saving autonomous driving technology for electric buses.

The occurrence of flight risks and accidents is closely related to pilot workload. Effective detection of pilot workload has been a key research area in the aviation industry. However, traditional methods for detecting pilot workload have several shortcomings: firstly, the collection of metrics via contact-based devices can interfere with pilots; secondly, real-time detection of pilot workload is challenging, making it difficult to capture sudden increases in workload; thirdly, the detection accuracy of these models is limited; fourthly, the models lack cross-pilot generalization. To address these challenges, this study proposes a large language model, WorkloadGPT, which utilizes low-interference indicators: eye movement and seat pressure. Specifically, features are extracted in 10 s time windows and input into WorkloadGPT for classification into low, medium, and high workload categories. Additionally, this article presents the design of an appropriate text template to serialize the tabular feature dataset into natural language, incorporating individual difference prompts during instance construction to enhance cross-pilot generalization. Finally, the LoRA algorithm was used to fine-tune the pre-trained large language model ChatGLM3-6B, resulting in WorkloadGPT. During the training process of WorkloadGPT, the GAN-Ensemble algorithm was employed to augment the experimental raw data, constructing a realistic and robust extended dataset for model training. The results show that WorkloadGPT achieved a classification accuracy of 87.3%, with a cross-pilot standard deviation of only 2.1% and a response time of just 1.76 s, overall outperforming existing studies in terms of accuracy, real-time performance, and cross-pilot generalization capability, thereby providing a solid foundation for enhancing flight safety.

Exclusive bus lane strategy is widely adopted in many cities to improve bus operation efficiency and reliability. With the development of connected vehicle technologies, the dynamic bus lane (DBL) strategy was proposed, with allowing general vehicles to share use of the bus lane to improve traffic efficiency in general purpose lanes (GPLs). Previous studies have rarely considered the eco-driving strategy of connected and automated vehicles/buses (CAVs/CABs) in GPLs under the mixed traffic conditions, and how to ensure bus priority with DBL control. In this study, a novel DBL control strategy was developed under the partially connected vehicle environment. A trajectory planning method while considering the joint effects of bus stop and signal phase for CAB was adopted, an eco-driving strategy for CAVs in GPL was proposed using a trigonometry trajectory planning method. And a novel DBL control method was established by integrated trajectory planning for both the CAVs and CABs to ensure bus operation priority. Numerical experiments were conducted to evaluate performance of the proposed novel DBL control in terms of travel time and energy consumption of general vehicles at the different levels of CAV market penetration rates (MPRs). Results indicated that about 16%-42% energy savings can be achieved with MPR varying from 20% to 100%, and the travel time can be improved by about 4%-10%. Meanwhile, sensitivity analysis was conducted to quantify the impacts of key parameters, including vehicle target speeds, heterogeneous traffic flow, random arrival interval of cars, position of bus stop, traffic volume in GPLs and buffer length at the stop line of signalized intersections.

With the rapid development of the cooperative vehicle infrastructure system, eco‐approach and departure has been attracting increasing attention for mitigating the impact of signalized intersections, which helps improve the energy efficiency of equipped vehicles. However, existing studies have rarely considered the balance of electric bus energy consumption and traveling time. Meanwhile, the joint effect of bus stops and signalized intersections, and their interactions were not discussed widely with connected and automated electric buses (CAEBs). In this study, an integrated CAEB trajectory planning methodology with the balance of traveling time and energy consumption is proposed. A bi‐level programming model is developed to obtain the optimal trajectories of CAEBs. Numerical experiments are conducted to analyse and compare the model with two baseline models. The results demonstrate the optimal performance of the derived scheme in terms of bus energy consumption, with approximately 30–50% energy savings. Furthermore, model sensitivity analysis is conducted to quantify the impacts of the related system parameters. The findings also reveal that the entry time, conversion coefficient, and final speed have significant impacts on electric bus energy consumption along signalized arterials with bus stops.