Modeling and operation optimization of train operation plan for intercity railway frequent timetabling

In recent years, China's regional economy develops rapidly, and city clusters flourish. Meanwhile, travel demand for cross-city commuting, business, study, etc., within city clusters increases significantly, urgently requiring transportation modes characterized by large capacity, speed, efficiency, and flexibility. The frequent timetabling operation mode of intercity railways, utilizing Electric Multiple Units (EMUs) to achieve short train headways, short travel times, fixed stops, and convenient boarding/alighting, meets the transportation needs within and between city clusters effectively. To optimize the train operation plan design for intercity railway frequent timetabling, this team focuses on optimizing train headways based on passenger flow distribution along the intercity railway line, aiming to reduce capacity redundancy, maximize resource utilization, and meet flexible transportation demands. This project establishes a Mixed Integer Programming (MIP) model and a dynamic Origin-Destination (OD) model considering actual operational constraints. Building upon existing algorithms, the study employs a Genetic Algorithm (GA) for solution. The operation plan adjusts flexibly according to passenger flow changes in different time periods to test the optimized train operation plan's practical response capability under different passenger flow distributions. Simultaneously, sensitivity analysis evaluates the effectiveness and accuracy of different algorithms in optimizing train headways. Through scientific modeling and intelligent algorithms, optimizing train headways balances passenger demand and transport supply, achieving flexible and convenient ride services. This approach enhances the attractiveness of cities along the line and promotes regional economic development.