11-05-2012, 05:14 PM
CLOUD COMPUTING FOR AGENT-BASED URBAN TRANSPORTION SYSTEM
[attachment=21933]
INTRODUCTION
The mobile agent moves through the network to reach control devices and implements appropriate
strategies in either autonomous or passive modes.
In this way, traffic devices only need to provide an operating platform for mobile traffic agents working in dynamic environments, without having to contain every traffic strategies. This approach saves storage and computing capacity in physical control devices, which helps
reduce their update and replacement rates. Moreover, when faced with the different requirements of dynamic traffic scenes, a multiagent system taking advantage of mobile agents will perform better than any static
Traffic-strategy agent Module
The more typical traffic scenes used to test a traffic-strategy agent, the more detailed the learning about the advantages and disadvantages of different traffic strategy agents will be.
The initial agent-distribution map will be more accurate.
Testing a large amount of typical traffic scenes requires enormous computing resources
Intelligent Traffic Clouds Storage
With the support of cloud computing technologies, it will go far beyond other multi agent traffic management systems, addressing issues such as infinite system scalability, an appropriate agent management scheme, reducing the upfront investment and risk for users, and minimizing the total cost of ownership.
EXISTING SYSTEM
The large-scale use of mobile agents will lead to the emergence of a complex, powerful organization layer that requires enormous computing and power resources. To deal with this problem, we propose a prototype urban-traffic management system using intelligent traffic clouds.
[attachment=21933]
INTRODUCTION
The mobile agent moves through the network to reach control devices and implements appropriate
strategies in either autonomous or passive modes.
In this way, traffic devices only need to provide an operating platform for mobile traffic agents working in dynamic environments, without having to contain every traffic strategies. This approach saves storage and computing capacity in physical control devices, which helps
reduce their update and replacement rates. Moreover, when faced with the different requirements of dynamic traffic scenes, a multiagent system taking advantage of mobile agents will perform better than any static
Traffic-strategy agent Module
The more typical traffic scenes used to test a traffic-strategy agent, the more detailed the learning about the advantages and disadvantages of different traffic strategy agents will be.
The initial agent-distribution map will be more accurate.
Testing a large amount of typical traffic scenes requires enormous computing resources
Intelligent Traffic Clouds Storage
With the support of cloud computing technologies, it will go far beyond other multi agent traffic management systems, addressing issues such as infinite system scalability, an appropriate agent management scheme, reducing the upfront investment and risk for users, and minimizing the total cost of ownership.
EXISTING SYSTEM
The large-scale use of mobile agents will lead to the emergence of a complex, powerful organization layer that requires enormous computing and power resources. To deal with this problem, we propose a prototype urban-traffic management system using intelligent traffic clouds.