With the increasing size and range of urban networks, the metro, as an important component of public transportation (PT), makes a significant contribution to the daily service in many cities. Finally, numerical experiments based on the Batong line of the Beijing Metro were carried out, the results of which verify the effectiveness and efficiency of our method. By explicitly taking the passenger activity information into account, a mixed integer nonlinear programming (MINLP) model was developed, and a genetic algorithm (GA) is proposed to solve the model. The main objective pursued by dispatchers in the model is the enhancement of punctuality while minimizing train delays by adjusting the buffer time. This paper presents a robust timetabling model (RTM) for a metro line with passenger activity information, which takes into account congestion and buffer time adjustments. When the activity reality diverges from the real-time or historical information, it is imperative that dispatchers present a good solution during the planning stage in order to minimize the nuisance for passengers and reduce the crowding risk. General oversaturation events, rather than accidents or track damage, still have a significant impact on metro systems, so that trains are canceled and delayed. In terms of the delays caused by passengers’ boarding and alighting behaviors during rush hours, the planned timetable for a metro line with high-frequency service tends to be difficult to implement. Timetable design is crucial to the reliability of a metro service.

Metro schedules