交通流系统建模与动态在线控制

IF 1.8 4区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2020-11-01 DOI:10.1080/13873954.2020.1810076

Xiao-qiong Huang, Yun-xiang Han

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引用次数: 1

摘要

摘要地下交通系统在公共交通中起着至关重要的作用，表现出复杂的动力学特征。本文提出了一种适用于地下列车调度的模型预测控制方法。系统建模技术在空间上聚合列车，以在互联控制单元网络中生成交通流模型。研究了地铁线路地下列车运行的状态空间模型。仿真结果证明了优化控制模型的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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System model building and dynamic online control of traffic flow

ABSTRACT The Underground transportation system plays a vital role in public transportation and exhibits complicated dynamics. The model predictive control approach for underground train dispatching is proposed in this paper. The system modelling technique spatially aggregates trains to generate the traffic flow model in a network of interconnected control units. The state-space model for the underground train traffic of a metro line is investigated. Simulation results are reported to demonstrate the effectiveness of the optimization control model.

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来源期刊

Mathematical and Computer Modelling of Dynamical Systems 数学-计算机：跨学科应用

CiteScore

3.80

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.