Modern railway signalling systems are critical for ensuring the safe and efficient operation of railway networks. Educational institutions play a vital role in training students to design and implement these systems, replicating practical scenarios to bridge the gap between theory and application. Advanced electronic interlocking systems, such as SIMIS W, are widely used to manage complex signalling requirements in real-world railway environments. Existing model railway systems lack the capability to simulate advanced interlocking systems with real-time performance. This paper presents a model railway signalling system based on the SIMIS W interlocking system. The setup integrates ILTIS-N dispatcher workstations with a SIMATIC S7-1200 PLC, controlling signals, switches, and track circuits. A REST API ensures seamless data exchange between ILTIS-N and the PLC, achieving efficient system responsiveness. The system effectively replicates real-world signalling conditions with compatibility with existing protocols. Performance testing demonstrated a response time of approximately 1.2 s, achieving the design objective of a maximum 1.5 s. This ensures rapid and accurate signalling under real-time constraints. The developed system provides a practical educational platform for students to gain experience with advanced interlocking systems and serves as a basis for further research into scalable railway signalling solutions.

In this study is proposed and evaluated an adaptive traffic signal control system designed to synchronize the road and railway signaling, minimize collision risks, and improve traffic flow. Results show that adaptive control reduced average queue length for over 46%, cut vehicle delay for 34%, lowered stop frequency for 74%, and decreased CO2 emissions for nearly 46% compared to static control. Further analysis examined the effect of varying the distance between intersections and railway crossings, revealing significant safety and efficiency gains when the separation exceeded 80 m. The findings demonstrate that intelligent, sensor driven traffic management can substantially enhance both safety and throughput in these complex transport nodes, while delivering environmental benefits.