This research paper investigates methodologies for forecasting the demand for railway tickets, a crucial aspect of optimizing resource allocation, enhancing service quality, and increasing revenue in railway transportation systems. The study explores various time series analysis techniques, including ARIMA, SARIMAX, and neural networks such as LSTM and CNN, to predict ticket demand. Railway ticket sales data spanning several years was utilized to train and validate the models. The performance of each model was evaluated based on key metrics like Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Demand is usually defined in Origin-Destination (OD) matrices, which represent movements from the origin station to the destination station for all OD pairs. The proposed CNN model predicts the OD matrix, whereas other models predict demand for each OD pair separately. Findings indicate that the CNN model gives more accurate predictions compared to ARIMA, SARIMAX, and LSTM models. Hence, interdependencies between various OD pairs allow for a more holistic and accurate prediction model.

Background: Employees working in hazardous industrial environments are required to wear personal protective equipment (PPE) and follow established safety procedures. Preventing incidents, minimizing risks to workers, and improving overall safety require continuous monitoring through computer vision techniques and automated alerts for hazardous conditions. These technologies help ensure compliance with safety standards and reduce the influence of human error. However, these systems are only as effective as the data they rely on. This underscores the importance of developing dedicated, high-quality annotated datasets. This work introduces a new dataset for segmenting PPE and tools in hazardous oilfield operations, including underground and major well workovers. The dataset was created based on real-world production environments. Aim: Creating and training a dataset to segment PPE and tools using computer vision methods, enabling the automatic detection of hazardous conditions and contributing to improved safety at industrial sites. Materials and methods: The dataset was constructed using video footage collected from a well workover crew at the Zhetybai oilfield. Annotation was carried out in CVAT, while segmentation was accelerated using the Segment Anything Model. The annotated data was then used to train a neural network based on the YOLOv8 architecture. Results: The resulting dataset consists of 16 classes. It includes personal protective equipment (helmet, glasses, jacket, gloves, trousers, and boots) as well as their absence, represented by negative classes. It also covers key production elements such as casing pipes, a hydraulic wrench, an elevator, and personnel. The dataset is used to train computer vision models. Models trained on this dataset have demonstrated stable performance under real-world industrial conditions. Conclusion: The dataset and model developed in this work mark a step toward building real-time safety monitoring systems for industrial settings. These systems can detect whether PPE is used properly, flag safety violations, and generate reports. The dataset can be adapted to other environments, extended with new classes, and integrated into larger safety management platforms.