Passenger and energy-saving oriented train timetable and stop plan synchronization optimization model

The stability of train timetables plays an important role in ensuring the safety and punctuality of train operations. Meanwhile, a reasonable stop plan has a significant effect on passenger and energy consumption. Existing research has focused exclusively on adjusting timetables after delays occur, or on adjusting them without considering stop plans. By contrast, in this study, we developed a train timetable and stop plan synchronization optimization model that is passenger and energy-saving oriented. The following objectives were considered for minimization: probability of train delays, energy consumption, and travel time of the trains. A parallelism selection genetic algorithm was designed to solve the model. The section of high-speed railway from Nanjing South to Shanghai Hongqiao was utilized as a case study to evaluate the proposed model. The results showed that, while meeting passenger demand, the proposed optimization approach increased the punctuality rate by 12.55% and decreased the energy consumption by 8.16%.

Automated summary

The study developed a train timetable and stop plan synchronization optimization model oriented towards saving passengers and energy, solved by a parallel selection genetic algorithm. In a real case study, the optimization approach not only met passenger demand, but also significantly improved train punctuality and reduced energy consumption.