Proceedings of the
European Safety and Reliability Conference (ESREL2026)
14 – 19 June 2026, Braga, Portugal
Maritime Traffic Risk Monitoring Using Time-Extended Stochastic Petri Nets: A Case Study on Collision Prevention for LNG-Fueled Vessels
Merchant Marine College, Shanghai Maritime University, China.
Merchant Marine College, Shanghai Maritime University, China.
College of Transport & Communications, Shanghai Maritime University, China.
School of Navigation, Wuhan University of Technology, China.
Vocational College, Shanghai Jian Qiao University, China.
ABSTRACT
Modern maritime transportation systems face complex, dynamic risks. Traditional static models often fail to capture the path-dependent nature of risk evolution. This paper proposes a Path-Dependent Stochastic Petri Net (PD-SPN) framework. We integrate Systems Theoretic Process Analysis (STPA) with a Time-extended Stochastic Petri Net (TSPN). A key innovation is the path dependence coefficient (α), which dynamically adjusts transition rates based on operational history. This allows the model to overcome the memoryless assumption of Markov chains. A case study on LNG-fueled vessel collision risk demonstrates a 97.2 % prognostic accuracy. Importantly, it reveals that high-risk vessels evolve toward critical states 1.8 times faster than low-risk ones. The PD-SPN model provides a proactive, data-driven tool for dynamic safety assessment in complex maritime systems.
Keywords: Path-Dependent Risk Prognostics; Stochastic Petri Net (SPN); Systems Theoretic Process Analysis (STPA); Maritime Transportation Safety; Dynamic Risk Assessment; Path Dependence; Complex System Safety; Continuous-Time Markov Chain.
