Metro train delay-recovery strategy considering passenger waiting time and energy consumption: a real-world case study

Based on actual delay-recovery strategies (ADRS) on site only considers reducing the delays and ignores the operation cost of the enterprise. This study proposes optimized delay-recovery strategies (ODRS) to reduce the impact of delay on passengers and increase energy efficiency simultaneously. A bi-objective optimization model is used to deal with different initial delay scenarios and minimize the additional waiting time of passengers (AWTP) and total energy consumption (TEC). In the solving process, the non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve the models, which can get the effective Pareto frontier solutions. Finally, Chengdu Metro is taken as the numerical experiments to verify the performance of the models. The results show that the ODRS can effectively reduce the AWTP and TEC than the ADRS. Furthermore, with the increase of initial delay time, the optimization effect of ODRS will be more obvious.

Automated summary

This study proposes optimized delay-recovery strategies (ODRS) to reduce the impact of delay on passengers and increase energy efficiency simultaneously. A bi-objective optimization model and the non-dominated sorting genetic algorithm-II (NSGA-II) are used to minimize the additional waiting time of passengers and total energy consumption, and the performance of the models is verified using Chengdu Metro as a case study.