Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network

Abstract

This paper falls under the problems of the monitoring of a Discrete Event System (DES) with time constraints. Among the various techniques used for online and distributed monitoring, we are interested in the chronicle recognition. Chronicles are temporal patterns that represent the system’s possible evolutions. The proposed models are based on P-time Petri nets that are suitable to represent with accuracy and modularity the Tunisian railway network. These models are scalable and may be used to represent a large variety of railway networks. Then, monitoring is based on the generation of chronicles that are suitable to detect and isolate traffic incidents in a distributed setting. Consequently, the proposed approach is tractable for large networks. Finally, to demonstrate the effectiveness and accuracy of the approach, an application to the case study of the Tunisian railway network is outlined.

Robust control strategies facing disturbances in Railway Transport Networks

“…In this part, we sum up the different parts of the identification approach discussed in (14) . This approach is based on experimental measurements observed during travel operation in the the studied Railway Network.…”

A Monitoring Approach Based on Fuzzy Stochastic P-Timed Petri Nets of a Railway Transport Network

“…is system must respond to different requirements, minimizing the waiting times in the stations, respecting schedules, and ensuring the safety of passengers and equipment. In our previous works [2], synchronized Petri nets (P-TPNs) are used to model railway transport systems. In this way, the modeling process is greatly simplified, and the complexity of the model is widely reduced.…”

“…In this way, the modeling process is greatly simplified, and the complexity of the model is widely reduced. In [2], an identification of the P-TPN model of the Sahel railway network in Tunisia was made. e objective was to identify the temporal parameters of the model from a set of real measurements collected by the SCADA system of TNRC.…”