Smart Positioning strategies for High-Speed Train Using Machine Learning and Grey Wolf Optimization

Abstract

In order to improve the precision and effectiveness of high-speed train positioning systems, this study proposes an Intelligent Positioning Approach (IPA). Grey Wolf Optimization (GWO) and machine learning (ML) techniques are combined in the proposed method to produce a robust and adaptable model that can manage the complex nature of high-speed train operations. By utilizing the advantages of both data-driven prediction and bio-inspired optimization, the IPA's main goal is to optimize train routing and positioning. By mimicking the pheromone-based path finding behavior of ant colonies, we use GWO to find and continuously improve the best routing routes [1] [2]. With the help of this method, the system can dynamically identify the most effective routes in response to real-time constraints. By examining a variety of data inputs, such as historical train movements, track geometry, and external environmental factors like weather and visibility, machine learning algorithms are used concurrently to forecast positional adjustments. The system can react efficiently to real-time inputs, including track conditions, speed limits, and operational disruptions, thanks to the combination of GWO and ML. The IPA continuously learns from operational data and modifies decision parameters to improve safety through accurate train tracking, guarantee real-time adaptability, and lower latency in response to changes. Tests and simulation results demonstrate that the suggested IPA provides a versatile solution for upcoming high-speed rail networks and significantly improves positioning accuracy when compared to older systems. By making high-speed train operations safer, more dependable, and more effective, this strategy advances intelligent transportation systems.