PROJECT TITLE :

Efficient Real-Time Train Scheduling for Urban Rail Transit Systems Using Iterative Convex Programming

ABSTRACT:

The important-time train scheduling problem for urban rail transit systems is taken into account with the aim of minimizing the total travel time of passengers and also the energy consumption of the operation of trains. Primarily based on the passenger demand within the urban rail transit system, the optimal departure times, running times, and dwell times are obtained by solving the scheduling problem. A replacement iterative convex programming (ICP) approach is proposed to solve the train scheduling downside. The performance of the ICP approach is compared with different various approaches, i.e., nonlinear programming approaches, a mixed-integer nonlinear programming (MINLP) approach, and a mixed-integer linear programming (MILP) approach. In addition, this paper formulates the $64000-time train scheduling drawback with stop-skipping and shows how to unravel it using an MINLP approach and an MILP approach. The ICP approach is shown, via a case study, to provide a better tradeoff between performance and computational complexity for the real-time train scheduling downside. Furthermore, for the train scheduling downside with stop-skipping, the MINLP approach seems to have a sensible tradeoff between the management performance and also the computational potency.