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Solution of traffic equilibrium status with neural network

Solution of traffic equilibrium status with neural network
Solution of traffic equilibrium status with neural network
It is aimed to construct neural network model to assign flow links under user equilibrium of transportation network. Previous analytical method of traffic assignment under traffic equilibrium status are reviewed, and a method using neural network to simulate traffic equilibrium status is put forward. So the new methods can substitute for traditional method, and quickly finish traffic assignment. It is defined the concept of traffic equilibriums function and traffic equilibriums status, and strict mathematical proof indicates that we can recognize the equilibrium status of traffic flows by neutral network. The principle and method on how to construct, train and evaluate a neural network are also presented. At last, with an example the author explains how to use the algorithm, and to evaluate accuracy and CPU time consuming of the algorithm
1004-731X
Dong, Jing-xin
d5d34c80-0845-4121-8d10-834351438a1f
Wu, Jian-ping
5a0119e5-a760-4ff5-90b9-ec69926ce501
Dong, Jing-xin
d5d34c80-0845-4121-8d10-834351438a1f
Wu, Jian-ping
5a0119e5-a760-4ff5-90b9-ec69926ce501

Dong, Jing-xin and Wu, Jian-ping (2005) Solution of traffic equilibrium status with neural network. Journal of System Simulation,, 17 (6).

Record type: Article

Abstract

It is aimed to construct neural network model to assign flow links under user equilibrium of transportation network. Previous analytical method of traffic assignment under traffic equilibrium status are reviewed, and a method using neural network to simulate traffic equilibrium status is put forward. So the new methods can substitute for traditional method, and quickly finish traffic assignment. It is defined the concept of traffic equilibriums function and traffic equilibriums status, and strict mathematical proof indicates that we can recognize the equilibrium status of traffic flows by neutral network. The principle and method on how to construct, train and evaluate a neural network are also presented. At last, with an example the author explains how to use the algorithm, and to evaluate accuracy and CPU time consuming of the algorithm

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Published date: June 2005

Identifiers

Local EPrints ID: 53301
URI: https://eprints.soton.ac.uk/id/eprint/53301
ISSN: 1004-731X
PURE UUID: 05542f25-ea54-418d-9d34-d451b0ead4b0

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Date deposited: 04 Aug 2008
Last modified: 13 Mar 2019 20:41

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