Energy efficiency and management studies in rail transport worldwide have focused on optimizing reducing traction energy consumption, which accounts for a significant portion of operating costs. The development of energy-efficient driving techniques and the recovery of braking energy offer significant potential for reducing traction energy consumption and costs in rail systems. This study introduces a novel Energy-Efficient Dynamic Driving Technique (EEDDT) model, designed to optimize speed profiles and travel dynamics of rail vehicles by minimizing traction energy consumption (MTEC), maximizing regenerative braking energy production (MRBEP), and minimizing total travel time (MTT). The proposed model incorporates a multi-objective optimization framework and leverages two advanced metaheuristic algorithms—the Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO)—to determine optimal acceleration, cruising, coasting, and braking regimes. In contrast to existing approaches, the model integrates real-world operational factors, including gradient effects, speed-dependent resistance, horizontal curvature constraints, and passenger load variability, enhancing simulation accuracy and applicability. The model is validated through empirical data collected from a 4.6 km segment of the Samsun urban tramway system consisting of seven stations. Simulation results demonstrate that the EEDDT model achieves up to a 48.95% reduction in total energy consumption, a 137.60% increase in regenerative energy recovery, and 99.62% travel time adherence compared to conventional driving strategies. The findings confirm the practical viability of the EEDDT framework in enhancing energy efficiency in urban rail networks, with FPA demonstrating superior performance in constraint optimization. Overall, this study contributes a scalable, data-driven, and optimization-based approach to sustainable urban rail operations.
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