| description abstract | Energy saving has become a key concern of metro systems to reduce operating costs and meet the requirements of sustainable development. Energy-efficient timetabling, an effective way to reduce traction energy consumption, has gotten much attention in recent decades. However, limited research about energy-efficient timetabling simultaneously considered realistic speed profiles and the influence of train loads, which change greatly in different sections and significantly affect energy consumption. Therefore, this paper proposes an energy-efficient timetabling approach to improve regenerative energy utilization based on varying train loads and realistic speed profiles for bidirectional metro lines. First, based on the operational and safety constraints, this paper constructs an integer energy-efficient timetabling model where dwell times and nonuniform headways are taken as decision variables. Besides, the turnaround process is also considered to ensure rolling stock circulation efficiency. Sectional passenger volumes are employed to determine the varying train loads. Energy consumption is calculated based on realistic speed profiles. Owing to the complexity of the model, an improved adaptive large neighborhood search (ALNS) algorithm is designed to solve this model according to its characteristics. Finally, numerical examples are conducted based on the practical data from Shanghai Metro Line 17. The results indicate that the proposed approach effectively solves the energy-efficient timetabling problem with varying train loads and realistic speed profiles for bi-directional metro lines. The approach is suitable for off-peak hours and can reduce energy consumption effectively, and the reduction is up to 11.9% for the presented cases. The improved ALNS algorithm can find good solutions within reasonable times in different cases. Moreover, compared with the widely used genetic algorithm in the literature, the improved ALNS algorithm is more efficient in solving the proposed energy-efficient timetabling problem in different cases. | |
