# Towards Intelligent, Programmable, and Open Railway Networks
## 4 Intelligent Railway Network Architecture
The TSICU executes the deployed ML models, converts the policy related to the temporary train speed into train commands, and sends train speed and track performance data to the TTICU for evaluation and optimization. The onboard train control system enforces the policy by following the commands sent by the TSICU. The trackside equipment provides measurement data, and the RMAO performs data collection and control as a termination point of the R3 interface. Figure 2 illustrates the procedure of the training and deployment of ML models. As preconditions, the collection and control functionalities in the RMAO have an established data collection and sharing process, and the TTICU has access to these data. The TTICU monitors track performance by collecting the relevant track performance events. The railway operator has set a trigger condition related to the monitored track parameters. 1. Upon detection of the triggered event, the TTICU accesses the track performance and train speed data from the RMAO and decides either to create a new ML model for track performance or to modify an existing one. 2. The TTICU performs ML model training, obtains track-performance-related models, and may internally deploy a train speed model. 3. The TTICU deploys or updates the ML model in the TSICU via the R1 interface. 4. The TSICU stores the received ML model. 5. The TTICU may configure, if required, a specific track performance measurement to collect the data required to assess the performance of the deployed ML model. Based on an evaluation of the ML model's performance and model retraining, the TTICU may update the ML model in the TSICU. 6. The procedure ends when a trigger condition specified by the railway operator is satisfied. As a post-condition, the TSICU stores the received track performance ML model and train speed model and executes the models for the dynamic optimization of the train speed control function. Figure 2: Training, deployment, and updating of the ML model for track performance. Figure 3 illustrates the procedure for rail policy generation, policy enforcement, and policy performance evaluation. As a precondition, track-performance-related models and train speed models have been deployed in the TTICU and TSICU. 1. The railway operator wants to generate a temporary train speed policy or to optimize the ML models. 2. The TTICU sends a train speed policy to the TSICU via the R1 interface. 3. The TSICU receives the train speed policy, infers about the track performance and train speed based on the ML models, and translates the policy into train speed control commands. 4. The TSICU sends the train speed control commands to the onboard train control system to optimize the train speed. 5. The onboard train control system enforces the commands received from the TSICU. 6. The TTICU may optionally receive policy feedback regarding the train speed from the TSICU, data from track performance measurements, and performance data of the train speed optimization function in the TSICU, and it may update the train speed policy. 7. The procedure ends when a trigger condition specified by the railway operator is satisfied. As a post-condition, the TTICU collects and monitors the relevant key performance indicators from the onboard train control system to observe the performance of the train speed optimization function in the TSICU. The research focus is on the R1 interface, so the next section provides more details on its description.