Generation of optimal schedules for metro lines using model predictive control

Passenger-demands-oriented train scheduling for an urban rail transit network

“…The headway between trains in the optimal schedules obtained by Cury et al (1980) varies with time instead of being a constant. Since the convergence rate of the hierarchical decomposition algorithm of Cury et al (1980) can be quite poor in some cases, Assis and Milani (2004) proposed a model predictive control algorithm based on linear programming to optimize the train schedule. The algorithm proposed by Assis and Milani (2004) can effectively generate train schedules for the whole day.…”

“…Since the convergence rate of the hierarchical decomposition algorithm of Cury et al (1980) can be quite poor in some cases, Assis and Milani (2004) proposed a model predictive control algorithm based on linear programming to optimize the train schedule. The algorithm proposed by Assis and Milani (2004) can effectively generate train schedules for the whole day. Furthermore, a demand-oriented timetable design has been proposed by Albrecht (2009), where the optimal train frequency and the capacity of trains are first determined and then the schedules of the trains are optimized.…”

Exploring time variants for short-term passenger flow

“…In the literature, demand forecasting models have commonly been constructed under the assumption of linear and stationary passenger flow time series, which may not be realistic. In addition, a train traffic model incorporated a dynamic equation based on the evolution of train headways and train passenger loads to estimate the variants of passenger flow on railways (Assis and Milani, 2004 Recently, Chen et al (2009) have studied the diurnal pattern of subway ridership in New York with the socio-demographics of population.…”

Modelling and optimization of average travel time for a metro line by simulation and response surface methodology

“…The objective is to minimize the total travel time and to increase service quality. Assis and Milani (2004) present a methodology for the computation of optimal train schedules in metro lines using a linear-programmingbased model predictive control formulation. The train traffic model with passenger demands varying in time comprises dynamic equations describing the evolution of train headways and train passenger loads along the metro line.…”