Multi-objective Optimization Method For The
ATO System Using Cellular Automata
Author(s)
J. Xun, B. Ning & K. P. Li
Abstract
Automatic Train Operation (ATO) is one of the most important functions for an
advanced train control system in high-speed railway systems. Research on
optimization methods for ATO has been done before it is implemented in a train
control system. From a theoretical point of view, it can be formulated as one of
the functions of multi-objective Optimal Control Theory. This paper presents a
new multi-objective optimization method for an ATO system using Cellular
Automata (CA). A CA model for an ATO system is applied to simulate train
operation. An optimal method for ATO is proposed. Compared with actual train
operation results, the control algorithm can reduce energy consumption and
ensure train operation safety such as higher accuracy of train stop. Therefore, it
can improve the efficiency and safety of the train operation.
Keywords: Automatic Train Operation (ATO), Cellular Automata (CA), optimal
method.
1 Introduction
ATO system has been known as one of the most effective methods for saving
energy and improving the transportation efficiency. Along with the rapid
improvement of technologies, especially with the development of CBTC
(Communication Based Train Control) system [1], more and more ATO systems
have been put in operation. The control algorithm is the core of ATO system.
Many scholars did a lot of researches on ATO control algorithm. C.S.Chang
proposed a novel approach of differential evolution (DE) by incorporating the
Pareto-optimal set which is presented for optimizing train movement through
tuning fuzzy membership functions in mass transit systems [2]. Satoshi Sekine
Keywords
Automatic Train Operation (ATO), Cellular Automata (CA), optimal
method.
Related Book
Other papers in this volume
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