Adaptive Predictive Maintenance and Energy Optimization in Metro Systems Using Deep Reinforcement Learning

Mohammed Hatim Rziki, Atmane E. Hadbi, Mohamed Khalifa Boutahir and Mohammed Chaouki Abounaima ()
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Mohammed Hatim Rziki: Laboratory of AI, Faculty of Sciences, Moulay Ismail University of Meknes, Meknes 50050, Morocco
Atmane E. Hadbi: Laboratory of AI, Faculty of Sciences, Moulay Ismail University of Meknes, Meknes 50050, Morocco
Mohamed Khalifa Boutahir: IMIA Laboratory, IDMS Team, Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Meknes 50050, Morocco
Mohammed Chaouki Abounaima: Laboratory of Intelligent Systems, Application Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco

Sustainability, 2025, vol. 17, issue 11, 1-20

Abstract: The rapid growth of urban metro systems requires novel strategies to guarantee operational dependability and energy efficiency. This article describes a new way to use deep reinforcement learning (DRL) to help metro networks with predictive maintenance that adapts to changing conditions and energy optimization. We used real-world transit data from the General Transit Feed Specification (GTFS) to model the maintenance scheduling and energy management problem as a Markov Decision Process. This included important operational metrics like peak-hour demand, train arrival times, and station stop densities. A custom reinforcement learning environment mimics the changing conditions of metro operations. Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) sophisticated deep reinforcement learning techniques were used to identify the optimal policies for decreasing energy consumption and downtime. The PPO hyperparameters were additionally optimized using Bayesian optimization by implementing Optuna, which produces a far greater performance than baseline DQNs and basic PPO. Comparative tests showed that our improved DRL-based method improves the accuracy of predictive maintenance and the efficiency of energy use, which lowers operational costs and raises the dependability of the service. These results show that advanced learning and optimization techniques could be added to public transportation systems in cities. This could lead to more sustainable and smart transportation management in big cities.

Keywords: deep reinforcement learning; predictive maintenance; energy optimization; metro systems; Bayesian optimization; urban transit (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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