Cloud Computing for Agent-Based Urban Transportation Systems
Abstract:
Agent-based traffic management systems can use the autonomy, mobility, and adaptability of mobile agents to deal with dynamic traffic environments. Cloud computing can help such systems cope with the large amounts of storage and computing resources required to use traffic strategy agents and mass transport data effectively. This article reviews the history of the development of traffic control and management systems within the evolving computing paradigm and shows the state of traffic control and management systems based on mobile multi agent technology. 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.
Proposed System:
Agent-based computing and mobile agents were proposed to handle this vexing problem. Only requiring a runtime environment, mobile agents can run computations near data to improve performance by reducing communication time and costs. This computing paradigm soon drew much attention in the transportation field. From multi agent systems and agent structure to ways of negotiating between agents to control agent strategies, all these fields have had varying degrees of success.
Cloud computing provides on demand computing capacity to individuals and businesses in the form of heterogeneous and autonomous services. With cloud computing, users do not need to understand the details of the infrastructure in the “clouds;” they need only know what resources they need and how to obtain appropriate services, which shields the computational complexity of providing the required services.
System Diagram
Modules:
1. Agent-Based Traffic Management Systems
2. Intelligent traffic Module
3. Traffic-strategy agent Module
4. Intelligent Traffic Clouds Storage
Modules Description:
1. Agent-Based Traffic Management Systems
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. Also, the traffic strategy agent will be tested by the typical traffic scenes to review its performance. Typical traffic scenes, which are stored in a typical intersections database, can determine the performance of various agents. With the support of the three databases, the MA embodies the organization layer’s intelligence.
2. Intelligent traffic Module:
With the development of intelligent traffic clouds, numerous traffic management systems could connect and share the clouds’ infinite capability, thus saving resources. Moreover, new traffic strategies can be transformed into mobile agents so such systems can continuously improve with the development of transportation science.
3. 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. In this case, the initial agent-distribution map will be more accurate. To achieve this superior performance, however, testing a large amount of typical traffic scenes requires enormous computing resources. Researchers have developed many traffic strategies based on AI. Some of them such as neural networks consume a lot of computing resources for training in order to achieve satisfactory performance. However, if a traffic strategy trains on actuator, the actuator’s limited computing power and inconstant traffic scene will damage the performance of the traffic AI agent. As a result, the whole system’s performance will deteriorate. If the traffic AI agent is trained before moving it to the actuator, however, it can better serve the traffic management system.
4. Intelligent Traffic Clouds Storage
We propose 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.
System Requirements:
Hardware Requirement:
v PROCESSOR : PENTIUM IV 2.6 GHz
v RAM : 512 MB DD RAM
v MONITOR : 15” COLOR
v HARD DISK : 20 GB
v FLOPPY DRIVE : 1.44 MB
v CDDRIVE : LG 52X
v KEYBOARD : STANDARD 102 KEYS
v MOUSE : 3 BUTTONS
Software Requirement:
v Operating System : Windows 95/98/2000/NT4.0.
v Technology : JAVA, JFC(Swing),J2EE,JMX
v Development IDE : Eclipse 3.x
REFERENCE:
ZhenJiang Li and Cheng Chen, Kai Wang, “Cloud Computing for Agent-Based Urban Transportation Systems”, IEEE Journals on Intelligent Transportation systems, 2011.
