12-06-2012, 05:52 PM
CLOUD COMPUTING FOR AGENT-BASED URBAN TRANSPORTATION SYSTEMS
[attachment=24264]
ABSTRACT
Intelligent transportation clouds could provide services such as decision support, a standard development environment for traffic management strategies, and so on. With mobile agent technology, an urban-traffic management system based on Agent-Based Distributed and Adaptive Platforms for Transportation Systems (Adapts) is both feasible and effective. However, 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.
EXISTING SYSTEM
The function of the agents’ scheduling and agent-oriented task decomposition is based on the MA’s knowledge base, which consists of the performances of different agents in various traffic scenes. If the urban management system cannot deal with a transportation scene with its existing agents, it will send a traffic task to the organization layer for help. The traffic task contains the information about the state of urban transportation, so a traffic task can be decomposed into a combination of several typical traffic scenes. With knowledge about the most appropriate traffic strategy agent to deal with any typical traffic scene, when the organization layer receives the traffic task, the MA will return a combination of agents and a map about the distribution of agents to solve it.
PROPOSED SYSTEM
Urban-traffic management systems using intelligent traffic clouds to overcome the issues we’ve described so far. 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. The three layers in Adapts are organization, coordination, and execution, respectively. Mobile agents play a role as the carrier of the control strategies in the system. The organization layer consists of a management agent (MA), three databases (control strategy, typical traffic scenes, and traffic strategy agent), and an artificial transportation system. As one traffic strategy has been proposed, the strategy code is saved in the traffic strategy database. Then, according to the agent’s prototype, the traffic strategy will be encapsulated into a traffic strategy agent that is saved in the traffic strategy agent database.
[attachment=24264]
ABSTRACT
Intelligent transportation clouds could provide services such as decision support, a standard development environment for traffic management strategies, and so on. With mobile agent technology, an urban-traffic management system based on Agent-Based Distributed and Adaptive Platforms for Transportation Systems (Adapts) is both feasible and effective. However, 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.
EXISTING SYSTEM
The function of the agents’ scheduling and agent-oriented task decomposition is based on the MA’s knowledge base, which consists of the performances of different agents in various traffic scenes. If the urban management system cannot deal with a transportation scene with its existing agents, it will send a traffic task to the organization layer for help. The traffic task contains the information about the state of urban transportation, so a traffic task can be decomposed into a combination of several typical traffic scenes. With knowledge about the most appropriate traffic strategy agent to deal with any typical traffic scene, when the organization layer receives the traffic task, the MA will return a combination of agents and a map about the distribution of agents to solve it.
PROPOSED SYSTEM
Urban-traffic management systems using intelligent traffic clouds to overcome the issues we’ve described so far. 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. The three layers in Adapts are organization, coordination, and execution, respectively. Mobile agents play a role as the carrier of the control strategies in the system. The organization layer consists of a management agent (MA), three databases (control strategy, typical traffic scenes, and traffic strategy agent), and an artificial transportation system. As one traffic strategy has been proposed, the strategy code is saved in the traffic strategy database. Then, according to the agent’s prototype, the traffic strategy will be encapsulated into a traffic strategy agent that is saved in the traffic strategy agent database.