Railroad construction sites are high-risk environments where monitoring personnel safety is critical for preventing accidents and enhancing construction efficiency. Traditional manual monitoring and image processing methods exhibit deficiencies in real-time performance and accuracy. This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. Multiscale features are then extracted with Inception v3 and combined with principal component analysis (PCA) for dimensionality reduction. Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. This method provides technical support for railroad construction safety monitoring and effectively addresses personnel detection tasks in complex construction environments.

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of “last-mile” transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060–3297 bikes/(h·m) and a pure bicycle capacity of 1538–2460 bikes/(h·m). The density–flow model calculates a pure EV capacity of 2349–2897 bikes/(h·m) and a pure bicycle capacity of 1753–2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion.

Railway tracks are prone to damage after long-term exposure to harsh environments, posing risks to transportation safety. Traditional manual inspection is inefficient and error-prone, while existing deep learning–based detection models, such as YOLO series, often struggle with small targets, complex backgrounds, and multi-scale damage. To address these challenges, this study proposes an improved track damage detection framework, termed SNBF-YOLO, which integrates the Star Net and BiFPN modules. Star Net adaptively enlarges the receptive field to enhance feature representation, while BiFPN optimizes bidirectional multi-scale feature fusion. Experimental results on a real-world dataset demonstrate that SNBF-YOLO improves precision, recall, and mAP by 19.4, 13.2, and 14.3%, respectively, compared to the baseline YOLOv10n model. The enhanced model achieves higher robustness and computational efficiency, enabling accurate real-time detection of track defects, including fine cracks and missing fasteners. Despite these advances, the study is limited by the scale and diversity of the dataset, which may affect generalization in more diverse conditions (e.g., rain, snow, or severe corrosion). Future work will focus on enlarging the dataset and exploring lightweight deployment strategies to further improve real-world adaptability.