CLOUD COMPUTING FOR AGENT-BASED URBAN TRANSPORTATION SYSTEMS

1. CLOUD COMPUTING FOR AGENT-BASED URBAN TRANSPORTATION SYSTEMS VIVEK.R ROLL NO:18 DATE:17-02-12 S1 MCSE SLOT NO: 2

2. OVERVIEW • INTRODUCTION • HISTORY • AGENT-BASED TRAFFIC MANAGEMENT SYSTEMS • CHALLENGES • INTELLIGENT TRAFFIC CLOUDS • REFERENCES

4. INTRODUCTION • Agent-Based Traffic Management Systems • Cloud computing can help such systems to deal with large amounts of storage and computing resources • Development of Traffic control systems within evolving computing paradigm • Agent-Based Distributed and Adaptive Platforms for Transportation Systems (Adapts)

5. HISTORY • IBM 650 was first introduced to an urban traffic-management system in 1959 • Traffic control and management paradigm has five phases

6. In first phase, mainframes were shared by many terminals

7. At second stage a microcomputer could handle a single user’s requirements • Traffic Signal Controller (TSC) had enough capacity to control one intersection

8. In phase three, LANs appeared for resource sharing. • Traffic model became hierarchical

9. In the internet era, users could retreive data and from remote sites and process them locally • To reduce loss of bandwidth, mobile agents were introduced

10. Fifth computing paradigm- Cloud computing • Users do not need to know the infrastructure in the “clouds”

11. Parallel transportation Management System (PtMS) • Term ‘Parallel’ means the parallel interaction between an actual transportation system and their virtual counterparts • PtMS use Artificial Transportation Systems (ATS)

12. AGENT-BASED TRAFFIC MANAGEMENT SYSTEMS • Agent technology was used since 1992 • Multiagent systems came later • Mobile agents became popular in 2004 • Move through the network • Traffic device only need an operating platform

13. In 2005, ‘Adapts’ was proposed as a hierarchical urban traffic management system • It has three layers • Organization • Coordination • Execution • Currently Adapts is part of PtMS

14. Organization Layer • Functions • Agent-oriented task decomposition • Agent scheduling • Encapsulating traffic strategy • Agent management • Consists of • A management layer • Three databases • Artificial transportation system

15. Databases are • Control strategy • Typical traffic scenes • Traffic strategy agent • Code of new traffic strategy is saved in traffic strategy database • It is encapsulated into a traffic strategy agent and saved in it’s database

16. Traffic strategy agent tested with typical traffic scenes • Management agent embodies organization layer’s intelligence • Agent’s scheduling and agent-oriented task decomposition is based on MA knowledge base

17. When an unknown traffic scene is encountered • Urban management system sends a traffic task to organization layer • It is decomposed into a combination of traffic scenes • MA will return a combination of most appropriate agents and a map about their distribution

18. Testing System Performance • Set up an ATS to test performance of the urban-traffic management system • Computational experiments are faster than real world • If unsatisfactory, both systems will be modified

19. NEW CHALLENGES • Agent-distribution map and relevant agents need to be sent to ATS for experimental evaluation • A test was conducted to find the cost of this operation • If the time to complete evaluation exceed a threshold, results will become useless and meaningless

20. In the test, they used a 2.66-GHz PC with 1GB memory to run both ATS and Adapts • It took 3600s in real time • Number of intersections increased from 2 to 20

21. The time required to run ATS and Adapts experiments on one PC

22. When number of traffic control agents is 20, experiment takes 1,130 seconds • If time threshold is set to 600 seconds, maximum number of intersections in one experiment is only 12 • This is insufficient for major cities like Beijing • We will need several PCs or a high performance server

23. Future Systems • The future systems must have the following capabiliites • Computing Power • Testing a large amount of typical traffic scenes requires lot of computing resources • If a traffic strategy trains on actuator, it will damage the performance of the traffic AI agent • Better to train AI agent before moving it to the actuator

24. Storage • Vast amount of traffic data like configuration of traffic scenes, regulations and information about agents in ATS need vast amount of storage • Two solutions • Implement a super computer with all centers of urban-traffic management systems • Use cloud computing technologies. • For eg: Google’s Map-Reduce, IBM’s Blue Cloud and Amazon’s EC2

25. INTELLIGENT TRAFFIC CLOUDS Overview Of Urban-traffic management systems based on cloud computing

26. Prototype • Urban-traffic management using intelligent traffic clouds • It will go far beyond other multiagent traffic management systems • It has two roles • Service provider and • Customer

27. Service providers include ATS, traffic strategy database and traffic strategy agent database • They are all in system’s core: intelligent traffic clouds • Customers include urban-traffic management systems and traffic participants • They exist outside the cloud

28. Could provide traffic-strategy agents and agent-distribution maps to the traffic management systems • Numerous traffic management systems could connect and share cloud thereby saving resources • New strategies can be converted to mobile agents

29. Architecture • Intelligent traffic clouds have four architecture layers • Application • Platform • Unified source • Fabric

31. REFERENCES [1] D.C. Gazis, “Traffic Control: From Hand Signals to Computers,”Proc. IEEE, vol. 59, no. 7, 1971, pp. 1090–1099 [2] F.-Y. Wang, “Toward a Revolution in Transportation Operations: AI for Complex Systems,” IEEE Intelligent Systems, vol. 23, no.6, 2008,pp. 8–13 [3] F.-Y.Wang, “Parallel Control and Management for Intelligent Transportation Systems:Concepts,Architectures, and Applications,” IEEE Trans.Intelligent Transportation Systems, vol.11,no.2, 2010,pp.485-497

32. REFERENCES CONT’D…. [4] B. Chen and H. H. Cheng, “A Review of the Applications of Agent Technology in Traffc and Transportation Systems,” IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 2, 2010, pp. 485–497.

33.  THANK YOU 