Adaptive Adjustment Strategy of Train Attendant’s Control Guidance System Based on Machine Learning

The purpose of this paper is to study the self-adaptive adjustment strategy of railway locomotive crew operation guidance system based on machine learning, so as to improve the efficiency and safety of train operation. In view of the complexity and diversity of flight attendants’ manipulation behavior in the field of railway transportation, the traditional fixed weight model is often difficult to meet the needs of different operation situations. Therefore, this study proposes an adaptive adjustment strategy based on ensemble learning, which can dynamically adjust the model weight according to real-time data and environmental changes, so as to achieve adaptive adaptability to different operating conditions. In this paper, a series of experiments are designed to verify the effectiveness of this strategy and compare it with traditional methods. The experimental results show that the operation guidance system with adaptive adjustment strategy has significantly improved the prediction accuracy and recall rate, which can guide the flight attendants’ operation behavior more accurately and improve the train operation efficiency and safety. In addition, the application value of the system in the actual scene is analyzed through an application case. To sum up, the results of this study show that the adaptive adjustment strategy of railway locomotive crew control guidance system based on machine learning has broad application prospects and important practical significance, which provides new ideas and methods for improving the intelligence and safety of railway transportation system.