Agent Based Urban Transportation Systems Computer Science Essay

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Agent-based traffic management systems can use the organization, mobility, and the act of changing of mobile agents to deal with dynamic traffic environments. Cloud computing can help such systems task with the large amounts of storage and computing resources required to use traffic strategy agents and mass transport data and produce in intended result . This article reviews the history of the development of traffic control and management systems within they gradually change and develop into different forms of 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 long term aim, and so on. With mobile agent technology, an urban-traffic management system based on Agent-Based Distributed and adjusted to new condition Platforms for Transportation Systems (Adapts) is both can be done and effective. However, the large-scale use of mobile agents will lead to the event of its coming into existence of a complex, powerful organization layer that requires large amount of computing and power resources. To deal with this problem, we propose a prototype urban-traffic management system using intelligent traffic clouds.


Whereas many existing works in cloud computing focus on the development of infrastructures and tools for pooling together computational resources, this work com-

plements and supplements existing works in cloud comput- ing by introducing "agent-based cloud computing"- applying agent-based approaches to managing cloud

computing infrastructures.


When an IBM 650 computer was first introduced to an urban traffic-management system in 1959, the traffic control and management paradigm closely aligned with the computing paradigm in IT science[1]. the research and application of parallel transportation management systems (PtMS), which consists of artificial systems, computational experiments, and parallel

execution, has become a hot spot in the traffic research field.[2],[3] the term parallel describes the parallel interaction between an actual transportation system and one or more of its corresponding artificial or virtual

counterparts.[4] Agent technology was used in traffic management systems as early as

1992, while multi agent traffic management

systems were presented later.[5] these systems focus on negotiation and collaboration between static agents for coordination and optimization.[6]-[8] The characteristics of mobile agents-autonomous, mobile, and adaptive-make them suitable to handling the uncertainties and inconstant states in a dynamic environment.[9] the Agent-Based Distributed and Adaptive Platforms for

Transportation Systems (Adapts) was

proposed as an hierarchical urban traffic-

management system.[10] we set up an ATS to test performance of the urban-traffic management system based on the map

showing the distribution of agents.

ATS is modeled from the bottom up,

and it mirrors the real urban transportation

environment.[11] the basic structure of

cloud computing,[12] an intelligent traffic clouds have four architecture layers: application, platform, unified source, and fabric. shows the relationship between

the layers and the function of each layer.

Proposed System:

Agent-based computing and mobile agents were plan to handle the difficult problem causes people a lot of trouble. Only requiring a runtime environment, mobile agents can run the use of computers near data to improve performance by reducing communication time and costs. This computing paradigm soon draw much attention in the transportation field. From multi agent systems and agent structure to ways of difficult route 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.


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 express clearly and in few words 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 gives a physical quality to the organization layer's intelligence.

2. Intelligent traffic Module

With the development of intelligent traffic clouds, many 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 quality performance, however, testing a large amount of typical traffic scenes requires very large 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 become wrose in some way. If the traffic AI agent is trained before moving it to be the motive for someone to do something, 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:









Software Requirement:

Operating System: Windows

Technology: JAVA, JFC (Swing), J2EE, JMX

Development IDE: Eclipse 3.x