IEICE TRANSACTIONS on Fundamentals
Model Predictive Control of Traffic Flow Based on Hybrid System Modeling
Summary :
This paper presents a new framework for traffic flow control based on an integrated model description by means of Hybrid Dynamical System (HDS). The geometrical information on the traffic network is characterized by Hybrid Petri Net (HPN). Then, the algebraic behavior of traffic flow is transformed into Mixed Logical Dynamical Systems (MLDS) form in order to introduce an optimization technique. These expressions involve both continuous evolution of traffic flow and event driven behavior of traffic signal. HPN allows us to easily formulate the problem for complicated and large-scale traffic network due to its graphical understanding. MLDS enables us to optimize the control policy for traffic signal by means of its algebraic manipulability and use of model predictive control framework. Since the behavior represented by HPN can be directly transformed into corresponding MLDS form, the seamless incorporation of two different modeling schemes provide a systematic design scenario for traffic flow control.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.2 pp.549-560
- Publication Date
- 2005/02/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietfec/e88-a.2.549
- Type of Manuscript
- PAPER
- Category
- Systems and Control
Authors
Keyword
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Tatsuya KATO, YoungWoo KIM, Tatsuya SUZUKI, Shigeru OKUMA, "Model Predictive Control of Traffic Flow Based on Hybrid System Modeling" in IEICE TRANSACTIONS on Fundamentals,
vol. E88-A, no. 2, pp. 549-560, February 2005, doi: 10.1093/ietfec/e88-a.2.549.
Abstract: This paper presents a new framework for traffic flow control based on an integrated model description by means of Hybrid Dynamical System (HDS). The geometrical information on the traffic network is characterized by Hybrid Petri Net (HPN). Then, the algebraic behavior of traffic flow is transformed into Mixed Logical Dynamical Systems (MLDS) form in order to introduce an optimization technique. These expressions involve both continuous evolution of traffic flow and event driven behavior of traffic signal. HPN allows us to easily formulate the problem for complicated and large-scale traffic network due to its graphical understanding. MLDS enables us to optimize the control policy for traffic signal by means of its algebraic manipulability and use of model predictive control framework. Since the behavior represented by HPN can be directly transformed into corresponding MLDS form, the seamless incorporation of two different modeling schemes provide a systematic design scenario for traffic flow control.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.2.549/_p
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@ARTICLE{e88-a_2_549,
author={Tatsuya KATO, YoungWoo KIM, Tatsuya SUZUKI, Shigeru OKUMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Model Predictive Control of Traffic Flow Based on Hybrid System Modeling},
year={2005},
volume={E88-A},
number={2},
pages={549-560},
abstract={This paper presents a new framework for traffic flow control based on an integrated model description by means of Hybrid Dynamical System (HDS). The geometrical information on the traffic network is characterized by Hybrid Petri Net (HPN). Then, the algebraic behavior of traffic flow is transformed into Mixed Logical Dynamical Systems (MLDS) form in order to introduce an optimization technique. These expressions involve both continuous evolution of traffic flow and event driven behavior of traffic signal. HPN allows us to easily formulate the problem for complicated and large-scale traffic network due to its graphical understanding. MLDS enables us to optimize the control policy for traffic signal by means of its algebraic manipulability and use of model predictive control framework. Since the behavior represented by HPN can be directly transformed into corresponding MLDS form, the seamless incorporation of two different modeling schemes provide a systematic design scenario for traffic flow control.},
keywords={},
doi={10.1093/ietfec/e88-a.2.549},
ISSN={},
month={February},}
Copy
TY - JOUR
TI - Model Predictive Control of Traffic Flow Based on Hybrid System Modeling
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 549
EP - 560
AU - Tatsuya KATO
AU - YoungWoo KIM
AU - Tatsuya SUZUKI
AU - Shigeru OKUMA
PY - 2005
DO - 10.1093/ietfec/e88-a.2.549
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2005
AB - This paper presents a new framework for traffic flow control based on an integrated model description by means of Hybrid Dynamical System (HDS). The geometrical information on the traffic network is characterized by Hybrid Petri Net (HPN). Then, the algebraic behavior of traffic flow is transformed into Mixed Logical Dynamical Systems (MLDS) form in order to introduce an optimization technique. These expressions involve both continuous evolution of traffic flow and event driven behavior of traffic signal. HPN allows us to easily formulate the problem for complicated and large-scale traffic network due to its graphical understanding. MLDS enables us to optimize the control policy for traffic signal by means of its algebraic manipulability and use of model predictive control framework. Since the behavior represented by HPN can be directly transformed into corresponding MLDS form, the seamless incorporation of two different modeling schemes provide a systematic design scenario for traffic flow control.
ER -
English
