Moving Horizon Optimization of Dynamic Trajectory Planning for High-Speed Train Operation

Trajectory planning plays a crucial role in train operation by providing with the authorized speed at each position. The traditional static train trajectory planning methods are always designed offline according to a preplanned timetable, and they ignored the uncertainties of parameters, resulted by...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2016-05, Vol.17 (5), p.1258-1270
Main Authors: Yan, Xi-Hui, Cai, Bai-Gen, Ning, Bin, ShangGuan, Wei
Format: Article
Language:English
Subjects:
Coefficients
differential evolution
Dynamical systems
Dynamics
Energy consumption
High-speed train
Mathematical models
moving horizon optimization
optimal planning
Optimization
Planning
Rail transportation
Real time
Real-time systems
Resistance
Traffic
train operation
Trains
Trajectory
Trajectory planning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Trajectory planning plays a crucial role in train operation by providing with the authorized speed at each position. The traditional static train trajectory planning methods are always designed offline according to a preplanned timetable, and they ignored the uncertainties of parameters, resulted by line condition, resistance coefficient, and delay. These uncertain disturbances have not been considered adequately in previous studies. This paper deals with the dynamic optimal train trajectory planning problem with uncertainties. First, in order to identify uncertain resistance coefficients and calculate the dynamic limited speed, we present the optimization framework using onboard equipment such as a global navigation satellite system (GNSS) terminal, a power supply system, and a communication device to sample the real-time traffic information. Then, by taking the energy consumption and punctuality as objectives, we propose a moving horizon train trajectory planning optimization model with an adaptive weight allocation mechanism based on trip time error. The innovation of this paper lies not only in the establishment of a novel dynamic optimization model for train trajectory planning but also the strategy that combines real-time traffic information with the trajectory planning procedure. By contrast with most existing solutions, the proposed approach fully takes advantage of the real-time information and thus avoids the difficulties for modeling the uncertain coefficients for train trajectory planning. The efficiency of the proposed approach is illustrated by showing some numerical results of simulations with the infrastructure data from Beijing-Shanghai High-speed Railway of China.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2015.2499254