This paper proposes a monitoring approach based on stochastic fuzzy Petri nets (SFPNs) for railway transport networks. In railway transport, the time factor is a critical parameter as it includes constraints to avoid overlaps, delays, and collisions between trains. The temporal uncertainties and constraints that may arise on the railway network may degrade the planned schedules and consequently affect the availability of the transportation system. This leads to many problems in the decision and optimization of the railway transport systems. In this context, we propose a new fuzzy stochastic Petri nets for monitoring (SFPNM). The main goal of the proposed supervision approach is to allow an early detection of traffic disturbance to avoid catastrophic scenarios and preserve stability and security of the studied railway networks. Finally, to demonstrate the effectiveness and accuracy of the approach, an application to the case study of the Tunisian railway network is outlined.

中文翻译：

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基于模糊随机P-定时Petri网的铁路运输网监测方法

提出了一种基于随机模糊Petri网（SFPN）的铁路运输网络监测方法。在铁路运输中，时间因素是一个关键参数，因为它包括避免火车之间重叠，延误和碰撞的约束条件。铁路网络上可能出现的时间不确定性和约束条件可能会降低计划的时间表，并因此影响运输系统的可用性。这在铁路运输系统的决策和优化中导致许多问题。在这种情况下，我们提出了一种新的用于监控的模糊随机Petri网（SFPNM）。提出的监管方法的主要目标是允许及早发现交通干扰，以避免发生灾难性情况，并保持所研究铁路网络的稳定性和安全性。最后，