Optimizing Railway Safety and Efficiency with NeuRaiSya: A Petri Net-Based Simulation and Modeling - Deplomo, Bhai Nhuraisha I. (Science and Technology: Developments and Applications Vol. 8, 2025)
For railway operations to be safe, effective, and dependable, monitoring systems are crucial. Using the NeuRaiSya framework and Petri Nets for dynamic modeling and simulation, this research introduces a unique method for railway monitoring, the NeuRaiSya (Neural Railway System Application), an innovative railway signaling system integrating deep learning for passenger analysis. The goal of this study is to use the GreatSPN tool, a graphical editor for Petri nets to simulate the NeuRaiSya and assess its efficacy. The Petri net (PN), conceived by Carl Adam Petri during the 1960s, serves as a valuable instrument for modeling and examining distributed systems. The Petri net has found applications in various scientific and technological domains, including computer science, automation technology, and mechanical design and manufacturing. Five models were designed and simulated using the Petri nets model, including the Dynamics of Train Departure model, Train Operations with Passenger Counting model, Timestamp Data Collection model, Train Speed and Location model, and Train Related-Issues model. Through simulations and modeling using Petri nets, the study demonstrates the feasibility of the proposed NeuRaiSya system. The results highlight its potential to enhance railway operations, ensure passenger safety, and maintain service quality amidst the evolving railway landscape in the Philippines. Future research should focus on implementing NeuRaiSya in real-world railway networks, enhancing sensor reliability, system interoperability, and regulatory barriers while addressing data privacy and ethical concerns in AI-driven railway operations.