# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 1 Introduction  
In today's urbanization process, tunnels have become an essential component of urban infrastructure due to the increasingly constrained urban space. Tunnels play a critical role in various domains, including transportation, water supply, and energy transmission, significantly improving the efficiency of urban operations [1]. Specifically, in the transportation field, the rapid development of metro systems, which are built upon tunnel engineering, has effectively mitigated the mounting traffic congestion in densely populated areas. Moreover, metro systems effectively meet the growing transportation needs of densely populated areas.  
However, along with this development comes a set of challenges, with train delays being particularly prominent. As metro lines and infrastructure age, the occurrence of train delays tends to increase, especially during peak hours. These delays have far-reaching consequences, impacting the functioning of the city and the daily routines of its residents. Train delays are not merely operational issues, they have a significant impact on passenger services and the overall efficiency of the metro system. Delays disrupt passenger travel plans, cause frustration, and reduce the overall capacity of the trains, leading to dissatisfaction and complaints from passengers. Moreover, delays pose a safety risk, especially during crowded peak hours, as sudden incidents can result in stampcles or other accidents.  
Train delays are a frequent and significant problem in metro operations, occurring when trains deviate from their scheduled arrival or departure times. Various factors contribute to these delays, including equipment malfunctions (such as issues with the catenary, signal system failures, or vehicle breakdowns), human factors (such as passengermismocanduct or obstructions), adverse weather conditions (such as heavy rain, snowstorms, or thunderstorms), and planned maintenance activities [2]. To effectively manage train delays, they are often categorized based on their duration. Minor delays are typically brief, lasting only a few minutes, and are often caused by minor issues like temporary signal failures or slow passenger boarding and alighting. Moderate delays encompass a range of several minutes to half an hour and involve more complex challenges such as equipment malfunctions, adverse weather conditions, or traffic accidents. Severe delays are the most significant, lasting for more than half an hour and potentially extending to several hours. They are often the result of major equipment failures, severe weather events, or significant traffic accidents, requiring an extended period to restore normal operations. In cases where a train experiences prolonged delays due to external disruptions, train dispatchers can address the recovery process by selectively canceling certain trains and modifying train routes [3].  
Currently, the handling of metro train delays heavily relies on the experience of dispatchers and manual interventions [4]. However, as metro systems continue to expand, the effectiveness and accuracy of manual delay management face challenges. Therefore, there is an urgent need to explore intelligent and efficient methods to respond to delays. Technological support is crucial in addressing metro train delay issues, as it can enhance the operational stability of metro systems and ensure passenger safety and satisfaction. Through employing advanced technologies and intelligent systems, metro operators can better manage and respond to train delays, leading to an improved operational efficiency, enhanced safety measures, and a better overall travel experience for passengers. This paper proposes a new method using an improved Q-learning algorithm to assist metro staff to deal with train delays.