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

Published: 01 May 2016 Publication History

Abstract

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.

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  • (2024)An acceleration-based prediction strategy for dynamic multi-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09157-x28:2(1215-1228)Online publication date: 1-Jan-2024
  • (2022)Dynamic Scheduling, Operation Control and Their Integration in High-Speed Railways: A Review of Recent ResearchIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.313120223:9(13994-14010)Online publication date: 1-Sep-2022
  • (2022)High-Speed Train Platoon Dynamic Interval Optimization Based on Resilience Adjustment StrategyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304444223:5(4402-4414)Online publication date: 1-May-2022
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cover image IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems  Volume 17, Issue 5
May 2016
297 pages

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IEEE Press

Publication History

Published: 01 May 2016

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Cited By

View all
  • (2024)An acceleration-based prediction strategy for dynamic multi-objective optimizationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09157-x28:2(1215-1228)Online publication date: 1-Jan-2024
  • (2022)Dynamic Scheduling, Operation Control and Their Integration in High-Speed Railways: A Review of Recent ResearchIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.313120223:9(13994-14010)Online publication date: 1-Sep-2022
  • (2022)High-Speed Train Platoon Dynamic Interval Optimization Based on Resilience Adjustment StrategyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304444223:5(4402-4414)Online publication date: 1-May-2022
  • (2022)On-Line Train Speed Profile Generation of High-Speed Railway With Energy-Saving: A Model Predictive Control MethodIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304073023:5(4063-4074)Online publication date: 1-May-2022
  • (2022)Evolutionary Search With Multiview Prediction for Dynamic Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313502026:5(911-925)Online publication date: 1-Oct-2022
  • (2021)Optimal Train Speed Optimization under Several Safety Points by the PSO Algorithm2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504810(1333-1340)Online publication date: 28-Jun-2021
  • (2021)Dynamic Multi-objective Optimization via Sliding Time Window and Parallel ComputingAdvances in Swarm Intelligence10.1007/978-3-030-78811-7_5(45-57)Online publication date: 17-Jul-2021
  • (2020)A prediction strategy based on special points and multiregion knee points for evolutionary dynamic multiobjective optimizationApplied Intelligence10.1007/s10489-020-01772-750:12(4357-4377)Online publication date: 18-Jul-2020
  • (2019)An approach for accurate stopping of high-speed train by using model predictive control2019 IEEE Intelligent Transportation Systems Conference (ITSC)10.1109/ITSC.2019.8917237(846-851)Online publication date: 27-Oct-2019
  • (2017)A speed trajectory optimization model for rail vehicles using mixed integer linear programming2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2017.8317807(1-6)Online publication date: 16-Oct-2017

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