Vol: 56(70) No: 4 / December 2011 Multi-Agent System Architecture Used in Traffic Control Application Ioana Cobeanu Department of Automation, Transilvania University of Brasov, Faculty of Electrical Engineering and Computer Science, Mihai Viteazu Street, Number 5, 500039 Brasov – Romania, phone: +40268418836, e-mail: ioana.cobeanu@unitbv.ro Vasile Comnac Department of Automation, Transilvania University of Brasov, Faculty of Electrical Engineering and Computer Science , Mihai Viteazu Street, Number 5, 500039 Brasov – Romania, e-mail: comnac@unitbv.ro Keywords: Multi-agent system, architecture, application, traffic control, incident detection, route planning Abstract The system’s distributed environment is a dynamic one, being in a continuous change. The technology used to model such a system should be able to manage the unpredictable situations that appear in the system’s environment. The multi-agent technology is used with success to model the distributed systems. The proposed multi agent system used for modeling the application realizes the traffic control. Because transportation systems are dynamic systems, their control should be made in real-time. This way the system can respond immediately to the changes that appear in the system. The main goal of the application is to decrease the time spent by the cars in traffic and each driver to fulfill his daily plan. The proposed architecture is composed from mobile agents (the cars that are in the monitored area) and a specialized agent which makes the incidents detection. Using this architecture the system’s entities autonomy is maintained (the entities make their own decisions) without being necessary a central coordinator. In the same time it seeks to increase the system flexibility (to make easier adding or deleting agents). Each agent has access in real-time to the traffic status. Based on the traffic status the agents can make their own decisions regarding their route. The chosen route has to respect as much as possible the arriving times to clients and to destination. References [1] Moser. T., Merdan and M., Biff, S. “A Pattern-Based Coordination and Test Framework for Multi-Agent Simulation of Production Automation Systems”, Proc. 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), Krakow, pp. 526 – 533, 2010. [2] Liu, X.-M., Wang, F.-Y. “Study of City Area Traffic Coordination Control on The Basis of Agent”, Proc. IEEE 5th International Conference on Intelligent Transportation Systems, 2002 Proceedings, pp. 758 – 761, 2002. [3] Zhu, Y., Qian, D., Wang, J., Han, T. “Study on Intelligent Control Integrated System of China”, Proc. First International Conference on Innovative Computing, Information and Control, 2006, Beijing, China, pp. 591 – 594, 2006. [4] Guerrero-Ibanez, A., et. al. “A policy-based multi-agent management approach for intelligent traffic-light control”, Proc. 2010 IEEE Intelligent Vehicles Symposium (IV), San Diego, pp. 694 – 699, 2010. [5] Cobeanu, I., Comnac V., “Embedding of event processing into multi-agent system decision mechanism”, Proc. 6th IEEE International Symposium on Applied Computational Intelligence and Informatics - SACI 2011, Timisoara, Romania, pp. 105 – 109, 2011. [6] Zambonelli, F., Dyke Parunak H. Van, “Signs of a revolution in computer science and software engineering”, Proc. 3rd Int. Workshop Eng. Soc. Agents World, Lecture Notes in Computer Science, P. Petta, R. Tolksdorf, and F. Zambonelli, Eds., Vol. 2577, pp. 13 – 28, 2003. [7] Fox, M., Barbuceanu, M., Teigen, R., “Agent-Oriented Supply-Chain Management”, International Journal of Flexible Manufacturing Systems, Vol. V12, No. 2, pp. 165 – 188, 2000. [8] Wang, Y.-L., “Flexible and Responsive Multi-agent Based Logistics Coordination Management”, Proc. 2nd IEEE International Conference on Information Management and Engineering (ICIME), Chengdu, pp. 43 – 47, 2010. [9] Dia, H., “An agent-based approach to modelling driver route choise behaviour under the influence of real-time information”, Transportation Research Part C, Vol 10, No. 5, pp. 331 – 349, 2002. [10] Halle, S., Chaib-draa, B., “A collaborative driving system based on multiagent modelling and simulations”, Trasportation Research Part C, Vol 13, Issue 4, pp. 320 – 345, 2005. [11] Hernandez, J., et. al., “Multiagent architectures for intelligent traffic management systems”, Trasportation Research Part C, Vol. 10, Issues 5 – 6, pp. 473 – 506, 2002. [12] Katwijk, R.T., et. al., “Test bed for multiagent control systems in road traffic management”, Transportation Research Record, No. 1910, pp. 108 – 115, 2005. [13] Choy, M.C., et. al., “Real-time coordinated signal control using agents with online reinforcement learning”, Proc. 83rd Annual Meetings of the Transportation Research Board, Issue 1836, pp. 64 – 75, 2002. [14] Halle, S., et. al., “A decentralized approach to collaborative driving coordination”, Proc. 7th International IEEE Conference on Intelligent Transportation Systems, pp. 453 – 458, 2004. [15] Lei, J., Ozgumer, U., “Decentralized hybrid intersection control”, Proc. IEEE Decentralised Hybrid Intersection Control, Vol. 2, pp. 1237 – 1242, December 2001. [16] Kosonen, I., “Multi-agent fuzzy signal control based on real-time simulation”, Transportation Research Part C: Emerging Technologies, Vol. 11, Issue 5, pp. 389 – 403, 2003. [17] Zhang, H.S., et. al., “Spatial-Temporal Traffic Data Analysis Based on Global Data Management Using MAS”, IEEE Transsactions Intelligent Transportation Systems, Vol. 5, No. 4, pp. 267 – 275, 2004. [18] Shi, X., et. al., “A simulation study on agent-network based route guidance system”, Proc. 8th International IEEE Conference in Intelligent Transportation Systems, Vienna, pp. 248 – 253, 2005. [19] Lange, D.B., Oshima, M., “Seven good reasons for mobile agents”, Commun. ACM, Vol. 42, No. 3, pp. 88 – 89, 1999. [20] Halle, S., Chaib-draa, B., “A collaborative driving system based on multiagent modelling and simulations”, Transportation Research Part C, Vol. 13, Issue 4, pp. 320 – 345, 2005. [21] Logi, F., Ritchie, S., “A multi-agent architecture for cooperative inter-jurisdictional traffic congestion management”, Transportation Research Part C, Vol. 10, Issue 5, pp. 507 – 527, 2002. [22] Etzion, O., Niblet, P. “Event Processing in Action”, Manning Publications, USA, 2010, ISBN: 1935182218. [23] ***http://www.forrester.com/rb/Research/wave&trade%3B_complex_event_processing_cep_platforms,_q3/q/id/48084/t/2 The Forrester Wave™: Complex Event Processing (CEP) Platforms, Q3 2009, accessed in October 2010. [24] ***http://esper.codehaus.org/esper-4.0.0/doc/ reference/en/pdf/ esper_reference.pdf EsperTech: Esper Reference Documentation Version 4.0.0, accessed in December 2010. [25] Arasu, A., et,al, “Linear road: a stream data management benchmark”, Proc. Thirtieth International Conference on Very Large Data Bases (VLDB ’04), Vol. 30, pp. 480- -491, 2004. [26] Leone, N., et. al., ”The DLV System for Knowledge Representation and Reasoning”, ACM Transactions on Computational Logic (TOCL), Vol. 7, Issue 3, pp. 499 – 562, 2006. [27] *** http://en.wikipedia.org/wiki/Answer_set_programming, accessed June 2011. [28] ***Bihlmeyer, R., et. al. DLV – User Manual, http://www.dlv system.com/dlvsystem/html/DLV_ User _Manual .html, accessed June 2011. |