Cloud Computing for Agent-Based Urban Transportation Systems

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Cloud Computing for Agent-Based Urban Transportation Systems

Posted by hari Saturday, 31 March 2012 2 comments

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 system 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.

Existing System:

In the first phase, computers were huge and costly, so mainframes were usually shared by many terminals. In the 1960s, a whole traffic management system always shared the resources of one computer in a centralized model. Thanks to large-scale integrated (LSI) circuits and the miniaturization of computer technology, the IT industry welcomed the second transformation in computing paradigm. At this point, a microcomputer was powerful enough to handle a single user’s computing requirements. At that time, the same technology led to the appearance of the traffic signal controller (TSC).

Each TSC had enough independent computing and storage capacity to control one intersection. During this period, researchers optimized the control modes and parameters of TSC offline to improve control. Traffic management systems in this phase, such as TRANSYT, consisted of numerous single control points. In phase three, local area networks (LANs) appeared to enable resource sharing and handle the increasingly complex requirements

Disadvantage:

· Computers were huge and costly, so mainframes were usually shared by many terminals.

· A whole traffic management system always shared the resources of one computer in a centralized model

Proposed System:

The IT industry has ushered in the fifth computing paradigm: cloud computing. Based on the Internet, 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.

Advantage:

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

Such systems can take advantage of cloud computing to organize computing experiments, test the performance of different traffic strategies.

Hardware Requirements

· System : Pentium IV 2.4 GHz.

· Hard Disk : 40 GB.

· Floppy Drive : 1.44 Mb.

· Monitor : 15 VGA Color.

· Mouse : Logitech.

· Ram : 512 MB.

Software Requirements

· Operating System : Windows xp , Linux

· Language : Java1.4 or more

· Technology : Swing, AWT

· Back End : Oracle 10g

· IDE : MyEclipse 8.6

Module Description

Modules:

Network Module
System Model
Scheduled Task
Query Processing

Network Module:

A network channel lets two subtasks exchange data via a TCP connection. Network channels allow pipelined processing, so the records emitted by the producing subtask are immediately transported to the consuming subtask. As a result, two subtasks connected via a network channel may be executed on different instances. However, since they must be executed at the same time, they are required to run in the same Execution Stage

Scheduled Task:

Such complex systems make it difficult or even impossible to build accurate models and perform experiments, so PtMSs use artificial transportation systems (ATS) to compensate for this defect. Moreover, ATSs also help optimize and evaluate large amounts of

Traffic control strategies. Cloud computing caters to the idea of “local simple, remote complex” in parallel traffic systems. Such systems can take advantage of cloud computing to organize computing experiments, test the performance of different traffic strategies, and so on. Thus, only the optimum traffic strategies will be used in urban-traffic control and management systems

In our test, we used a 2.66-GHz PC with a 1-Gbyte memory to run both ATS and Adapts. The experiment took 3,600 seconds in real time. The number of intersections we tested increased from two to 20, and Figure 3 shows the time cost of each experiment. When the number of traffic-control agents is 20, the experiment takes 1,130 seconds. If we set the time threshold to 600 seconds, the maximum number of intersections in one experiment is only 12. This is insufficient to handle model major urban areas such as Beijing, where the central area within the Second Ring Road intersection contains up to 119 intersections. We would need several PCs or a high performance server to handle the experimental scale of several hundreds of intersections.

System Model:

Query Processing

During the runtime of Adapts, we need to send the agent-distribution map and the relevant agents to ATS for experimental evaluation, so we tested the cost of this operation. In our test, traffic-control agents must communicate with ATS to get traffic detection data and send back lamp control data. Both running load and communication volumes increase with the number of intersections. If the time to complete the experimental evaluation exceeds a certain threshold, the experimental results become meaningless and useless. As a result, the carry capacity for experimental evaluation of one PC is limite

System Architecture