Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Abstract

How to reduce the energy consumption of metro trains by optimizing both the timetable and control strategy is a major focus. Due to the complexity and difficulty of the combinatorial operation problem, the commonly-used method to optimize the train operation problem is based on an unchanged dwelling time for all trains at a specific station. Here, we develop a simulation-based method to design an energy-efficient train control strategy under the optimized timetable constraints, which assign the dwelling time margin to the running time. This time margin is caused by dynamically uncertain passenger demands in off-peak hours. Firstly, we formulate a dwelling time calculation model to minimize the passenger boarding and alighting time. Secondly, we design an optimal train control strategy with fixed time and develop a time-based model to describe mass-belt train movement. Finally, based on this simulation module, we present numerical examples based on the real-world operation data from the Beijing metro Line 2, in which the energy consumption of one train can be reduced by 21.9%. These results support the usefulness of the proposed approach.

Sustainable Mobility: A Review of Possible Actions and Policies

“…In recent years, research has been very focused on ecodriving applied to rail transport [78][79][80][81][82][83][84][85][86][87][88][89]. Although the rail transport system is more environmentally friendly, energy consumption and greenhouse gas emissions, where the energy consumed is not produced from renewable sources, are still high.…”

Energy Efficient Operation of Light Rail Transit (LRT) System for Kelana Jaya Line via Train Headway Time in Malaysia

“…A recent research in [12] shows that having a reasonable adjustment of the dwell time can also improve the possibilities of traction and braking conditions to improve the utilization of regenerative energy where dwell time refers to time before the doors open, period of time during passenger exchange and the time prior to departure after the doors have closed. Generally, the dwell time for the doors to open and closed is fixed.…”

A Passenger-Oriented Optimization Model for Implementing Energy-Saving Strategies in Railway Contexts

“…Furthermore, Sicre et al [44] proposed a simulation-based optimization procedure in which the simulation model provides the most energy-efficient driving profile, by computing energy consumption Pareto curves for each stretch, and the optimization tool allocates the total running reserve time available in the most efficient way among the different stretches. Finally, Feng et al [45] enriched the common optimization framework, which combined energy control strategies with a suitable design of operational times, by performing the estimation of dwell times at stations as flow-dependent factors. It is worth noting that both manual [46][47][48] and automatic [49][50][51] driving systems have been considered.…”