Cargando…
Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata
In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by em...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421676/ https://www.ncbi.nlm.nih.gov/pubmed/28353664 http://dx.doi.org/10.3390/s17040716 |
_version_ | 1783234620574138368 |
---|---|
author | Chen, Yangzhou Guo, Yuqi Wang, Ying |
author_facet | Chen, Yangzhou Guo, Yuqi Wang, Ying |
author_sort | Chen, Yangzhou |
collection | PubMed |
description | In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. |
format | Online Article Text |
id | pubmed-5421676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54216762017-05-12 Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata Chen, Yangzhou Guo, Yuqi Wang, Ying Sensors (Basel) Article In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. MDPI 2017-03-29 /pmc/articles/PMC5421676/ /pubmed/28353664 http://dx.doi.org/10.3390/s17040716 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Yangzhou Guo, Yuqi Wang, Ying Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata |
title | Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata |
title_full | Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata |
title_fullStr | Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata |
title_full_unstemmed | Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata |
title_short | Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata |
title_sort | modeling and density estimation of an urban freeway network based on dynamic graph hybrid automata |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421676/ https://www.ncbi.nlm.nih.gov/pubmed/28353664 http://dx.doi.org/10.3390/s17040716 |
work_keys_str_mv | AT chenyangzhou modelinganddensityestimationofanurbanfreewaynetworkbasedondynamicgraphhybridautomata AT guoyuqi modelinganddensityestimationofanurbanfreewaynetworkbasedondynamicgraphhybridautomata AT wangying modelinganddensityestimationofanurbanfreewaynetworkbasedondynamicgraphhybridautomata |