Learning-based model predictive control for passenger-oriented train rescheduling with flexible train composition

TL;DR

This paper introduces a learning-based model predictive control method for real-time train rescheduling that adapts to passenger demand and flexible train composition, improving efficiency and constraint satisfaction.

Contribution

It develops a novel integration of LSTM-based predictions with nonlinear optimization for real-time train rescheduling considering flexible train composition.

Findings

Effective in real-world Beijing urban rail data

Reduces integer decision variables with presolve techniques

Balances efficiency and constraint satisfaction

Abstract

This paper focuses on passenger-oriented real-time train rescheduling, considering flexible train composition and rolling stock circulation, by integrating learning-based and optimization-based approaches. A learning-based model predictive control (MPC) approach is developed for real-time train rescheduling with flexible train composition and rolling stock circulation to address time-varying passenger demands. In the proposed approach, the values of the integer variables are obtained by pre-trained long short-term memory (LSTM) networks, while the continuous variables are determined through nonlinear constrained optimization. The learning-based MPC approach enables us to jointly consider efficiency and constraint satisfaction by combining learning-based and optimization-based approaches. In order to reduce the number of integer variables, four presolve techniques are developed to prune a subset of integer decision variables. Numerical simulations based on real-life data from the Beijing urban rail transit system are conducted to illustrate the effectiveness of the developed learning-based MPC approach.