Reference
Y. Wang, B. Ning, T. Tang,
T. J. J.
van den Boom, and B. De Schutter, "Efficient real-time train scheduling for
urban rail transit systems using iterative convex programming,"
IEEE Transactions on Intelligent Transportation Systems, vol.
16, no. 6, pp. 3337-3352, Dec. 2015.
Abstract
The real-time train scheduling problem for urban rail transit systems is
considered with the aim of minimizing the total travel time of passengers and
the energy consumption of the operation of trains. Based on the passenger
demand in the urban rail transit system, the optimal departure times, running
times, and dwell times are obtained by solving the scheduling problem. A new
iterative convex programming (ICP) approach is proposed to solve the train
scheduling problem. The performance of the ICP approach is compared with other
alternative 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 real-time train
scheduling problem with stop-skipping and shows how to solve it using an MINLP
approach and an MILP approach. The ICP approach is shown, via a case study, to
provide a better trade-off between performance and computational complexity for
the real-time train scheduling problem. Furthermore, for the train scheduling
problem with stop-skipping, the MINLP approach turns out to have a good
trade-off between the control performance and the computational efficiency.
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BibTeX
@article{WanNin:15-023,
author = {Wang, Yihui and Ning, Bin and Tang, Tao and van den Boom, Ton J.
J. and De Schutter, Bart},
title = {Efficient Real-Time Train Scheduling for Urban Rail Transit
Systems Using Iterative Convex Programming},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {16},
number = {6},
pages = {3337--3352},
month = dec,
year = {2015}
}