First-train timing synchronisation using multi-objective optimisation in urban transit networks

Missed transfers affect urban transportation by increasing the travel times and decreasing the travel possibility, especially in the case of longer headways. A synchronised timetable can improve the transport efficiency of urban mobility and become an important consideration in the operation of urban transit networks (UTN). A mixed integer programming model is proposed to generate an optimal train timetable and minimise the total connection time, which includes smooth synchronisations for rail first-trains and the seamless synchronisation from rail first-trains to the bus service. Meanwhile, to characterise the characteristics of first-trains, binary variables are used to denote key transfer directions. Subsequently, the Sub-network Connection Method in conjunction with Genetic Algorithm is designed to obtain near-optimal solutions in an efficient way. Finally, a real-world case study, 16 rail lines and 41 transfer stations, based on the Beijing metro network and travel demand is conducted to validate the proposed timetabling model. Preliminary numerical results show that our approach improves the synchronisation substantially compared with the currently operated timetable.