## 1 Introduction  
Over the past two decades, Intelligent Transport Systems (ITSs) have emerged as a critical technology for enhancing the efficiency and performance of transportation systems. High-speed railway (HSR) has garnered significant attention as a fast, eco-friendly, and sustainable means of transport, driving the innovation of modern transportation systems [1, 2]. HSR offers distinct advantages, including substantially lower greenhouse gas emissions compared to air and road transportation, while ensuring comfort, safety, and high punctuality for passengers. These attributes have positioned HSR as an efficient and reliable transportation option worldwide, with China's HSR network expected to expand to 18,000 km by 2020 [3]. Despite these advantages, HSR systems present unique challenges due to their operational characteristics. High speeds of approximately 100 m/s lead to frequent handovers and interference issues. Additionally, channel characteristics are difficult to model accurately as they are influenced by diverse environmental factors. High-speed trains (HSTs) operating on HSR lines also face challenges in optimizing the quality of service (QoS) for various applications, including passenger services [4]. High-Performance Computing (HPC) technologies are emerging as promising solutions to address these challenges. By supporting stable and efficient communication, HPC can be applied in areas such as fault diagnosis, management systems, and simulations [5]. When combined with artificial intelligence (AI) techniques like machine learning and reinforcement learning, HPC can significantly enhance the performance of HSR systems. HPC techniques, such as parallel processing, distributed computing, and mobile cloud computing, enable advanced capabilities. In the HSR environment, characterized by high mobility, existing mobile cloud computing (MCC) approaches [6] face limitations in minimizing task execution delays and network load. Mobile Edge Computing (MEC) technology has, thus, emerged as a viable alternative by placing servers closer to the network edge to perform computational tasks [7]. MEC servers (MECSs) offer significant advantages, including reduced task execution delays, real-time services, and improved user experience. They also support mobility and rapid response times, enabling stable and efficient data processing in HSR environments. However, the limited computational resources of MECSs, such as CPU, memory, and bandwidth, necessitate efficient task distribution and processing strategies. Advances in technology have also heightened passenger demands for high-spec services such as real-time streaming, online gaming, and remote work. These services impose stringent processing constraints, including large-scale data handling and low-latency requirements. Task offloading and partitioning have become essential mechanisms for meeting these demands. Task offloading involves transferring tasks from user devices to nearby MECSs, thereby reducing computational burdens and minimizing execution delays. Task partitioning, on the other hand, involves dividing a task into subtasks for parallel processing across multiple MECSs, achieving load balancing and enhancing system performance [8]. Existing research has explored various techniques for optimizing MEC systems, including reducing delay and improving energy efficiency [9]. For instance, ref. [10] utilized binary particle swarm optimization (BPSO) to dynamically allocate tasks in vehicular networks, though their approach is limited to static network states. Similarly, ref. [11] employed support vector machines (SVMs) for task offloading decisions based on network state and resource availability, but their method struggles to adapt to real-time changes. Ref. [12] proposed a game-theoretic approach to allocate MEC resources efficiently, managing delays and network loads. While these techniques leverage metaheuristics, machine learning, and game theory, they fall short of processing large volumes of tasks in highly dynamic, real-time environments.