# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 4 Multi-Agent System for Train Rescheduling Problems
### Structure of MAS for Train Rescheduling Problem  
To reduce computational complexity, a large-scale railway network is decomposed into single junctions. Each junction is represented by a junction agent, and these agents are overseen by a collaborative agent in the system's upper layer. The collaborative agent supervises the decision-making process of each junction agent, ensuring that local decisions align with the global network goals. Below are the detailed definitions of these agents, with Figure 2 illustrating the overall system structure and data flow. **Definition 1**. : _A junction agent is an intelligent entity that gathers train movement information from the signaling and interlocking systems of the network. It makes decisions regarding the future actions of the local junction based on its local knowledge. In the optimization process of the train rescheduling problem, the junction agent will only consider the operational constraints in the local area._  
**Definition 2**. : _A collaborative agent monitors the junction agents within its control area, ensuring that the decisions made by each junction agent are feasible from a global perspective. The collaborative agent is responsible for maintaining a balance between local and global optimality while ensuring solution feasibility throughout the optimization process._  
Figure 1: An example of the decomposition of the railway network. In the proposed system, the collaborative agent and junction agents are organized in a hierarchical structure to address the train dispatching problem in a decentralized manner. This hierarchical multi-agent system (MAS) establishes a closed-loop framework for dynamic railway traffic management, ensuring both local responsiveness and global coordination. At the local level, junction agents continuously monitor their designated segments of the rail network, employing real-time optimization algorithms to generate efficient train rescheduling solutions in response to disruptions, such as delays or track blockages. These agents operate autonomously, leveraging localized data--including train positions, speeds, and track occupancy--to make time-critical decisions that minimize immediate congestion and maintain operational fluidity within their respective areas. Once local solutions are formulated, they are transmitted to the collaborative agent, which serves as the system's global decision-making hub. The collaborative agent aggregates and evaluates these proposals using advanced conflict-detection algorithms and multi-objective optimization techniques, weighing factors such as network-wide throughput, energy efficiency, and adherence to timetable priorities. By synthesizing these inputs, the collaborative agent resolves interdependencies and conflicts between junction-level solutions, producing a harmonized, globally optimal dispatching plan that balances competing demands across the entire rail network. The finalized dispatching decisions are then disseminated back to the junction agents, enabling them to adjust their local operations in alignment with the global strategy. This feedback loop ensures continuous adaptation and coordination, fostering system-wide resilience and efficiency. Furthermore, the hierarchical architecture of the MAS is deliberately designed to interface seamlessly with existing railway traffic management systems, which are inherently decentralized and hierarchical. By mirroring the organizational and operational structure of conventional systems, the MAS enhances interoperability, simplifies deployment, and facilitates incremental upgrades without requiring extensive overhauls of legacy infrastructure. Currently, real-time dispatching decisions are centralized in a traffic control center that disseminates instructions to local areas for  
Figure 2: The structure of MAS for large-scale train rescheduling problem. signaling and interlocking. By adopting the MAS, we can potentially enhance this system, addressing the growing complexity as traffic volume increases.