Application of Fuzzy Control Algorithm in Urban Rail Transit Operation and Management

Recent rapid progress in rail transportation has helped alleviate urban traffic congestion and facilitate integrated economic and environmental development. However, the administration and operation of urban rail transit systems are complex and uncertain, posing a challenge to accurate mathematical model-based control approaches. This paper proposes a practical solution, the Urban Rail Transport operations and management, based on the Fuzzy Control Algorithm (URT-FCA), specifically the Mamdani and Sugeno fuzzy inference systems. The URT-FCA is a useful option for urban rail transit systems because it can handle ambiguous and inaccurate information by utilizing fuzzy logic and fuzzy set theory. Using triangular and trapezoidal membership functions, the algorithm transforms crisp input values into fuzzy sets. Next, it uses fuzzy rules and inference techniques like max–min composition and weighted average defuzzification to produce appropriate control outputs. Neuro-fuzzy systems and adaptive fuzzy clustering are utilized in case studies and simulations to assess the fuzzy control method in real-world scenarios. This study takes into account real-world variables and optimizes train timetables, passenger flows and disruption minimization. The algorithm improves urban rail transit systems’ efficiency and resilience by adapting to changing conditions and incorporating expert knowledge through fuzzy rules. This research advances urban rail transit intelligent control systems for practical applications such as timetable planning, energy optimization and incident management.