Optimizing Railway Safety and Efficiency with NeuRaiSya: A Petri Net-Based Simulation and Modeling

Описание: Optimizing Railway Safety and Efficiency with NeuRaiSya: A Petri Net-Based Simulation and Modeling

Abstract:
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.

Layman Abstract:
Railway systems must be safe, efficient, and reliable—and that’s where smart monitoring technology comes in. This study introduces NeuRaiSya, a modern railway signaling system that uses artificial intelligence (AI) and deep learning to help monitor train operations and analyze passenger data. To test how well this system works, researchers used Petri Nets—a visual tool for simulating how different parts of a complex system interact. Using software called GreatSPN, they created five models to simulate various train activities such as departures, passenger counting, speed tracking, and issue reporting. The simulations showed that NeuRaiSya could greatly improve train operations and passenger safety, especially in busy railway systems like those in the Philippines. The study suggests that with further real-world testing and improvements—such as better sensors and stronger privacy protections—NeuRaiSya could be a key part of the future of rail transport.

#SmartRailways #AIinTransportation #PetriNets #NeuRaiSya #RailwayInnovation

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Disclaimer: This scientific-creative video was produced using AI voice-over and royalty-free stock images and clips. All content is sourced exclusively from peer-reviewed journal articles and book chapters, with full citations provided below. The peer-review process for these sources is also detailed here.

Source / Reference: https://doi.org/10.9734/bpi/stda/v8/4876

Peer Review History: https://peerreviewarchive.com/review-...