MODELO INTENCIONAL DE TRÁFICO COOPERATIVO

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1 MODELO INTENCIONAL DE TRÁFICO COOPERATIVO ALEJANDRO TRIANA CASTAÑEDA FACULTAD DE INGENIERÍA MAESTRÍA EN INGENIERÍA DE SISTEMAS Y COMPUTACIÓN PONTIFICIA UNIVERSIDAD JAVERIANA Octubre de 2012

2 AGENDA 1. PROBLEMA 2. OBJETIVOS PROYECTO 3. MARCO METODOLÓGICO 4. DESCRIPCIÓN DEL SISTEMA 5. MODELO COOPERATIVO

3 PROBLEMA DEL TRÁFICO 600 MILLONES DE AUTOMOVILES. CRECIMIENTO ANUAL 50 MILLONES MOVILIDAD AMBIENTE SEGURIDAD

4 PROBLEMA DEL TRÁFICO DOBLE INTERSECCIÓN CRUCE DOBLE CARRETERA L1 L2 I1 ZONA CONFLICTO I L5 L2 L3 L4 RECURSOS METAS

5 PROBLEMA INFORMÁTICO INTENCIÓN SMA RESOLUCIÓN CONFLICTOS METAS RECURSOS TOMA DE DECISIONES BENEVOLENTE INTENCIÓN COOPERACIÓN

6 OBJETIVO GENERAL Desarrollar un modelo de cooperación entre agentes de tráfico, basado en un enfoque intencional y utilizando técnicas de inteligencia computacional, orientado a mitigar problemas de congestión de tráfico en áreas urbanas.

7 OBJETIVOS ESPECIFICOS 1. Formular un modelo multiagente, utilizando un enfoque intencional explícito, que fomente la cooperación entre agentes tráfico. 2. Diseñar el sistema de toma de decisiones, para uno de los tipos de agentes de tráfico, utilizando técnicas de inteligencia computacional. 3. Validar el modelo de cooperación y el sistema de toma de decisiones a través de un proceso de simulación.

8 MARCO METODOLÓGICO MODELO COOPERATIVO TOMA DE DECISIONES INTELIGENTE IMPLEMENTACIÓN DEL MODELO VALIDACIÓN EXPERIMENTAL

9 UTS SISTEMAS TRÁFICO URBANO Red de Tráfico Control de Tráfico Urbano Drivers

10 RED DE TRÁFICO RED DE TRÁFICO SEGMENTOS CARRETERA Sistemas de Referencia Lineal (LRS) ANÁLISIS TOPOLÓGICO Sistemas de Representación basados en Carril (LBS) NODOS LINEAS LINEAS CENTRALES CALZADAS INTERSECCIONES

11 UTC CONTROL DE TRÁFICO URBANO Históricos Adaptativos Centralizados Distribuidos Sensores Parámetros de Control Toma de Decisiones Comunicación Local Medidas de Rendimiento

12 Driver Comportamiento DRIVERS Car Following Change Line Comunicación BROADCAST MULTICAST UNICAST

13 MODELO SMA ANÁLISIS DESCUBRIMIENTO AGENTES METAS, SENSORES, ACTUADORES INTERACCIONES COOPERACIÓN SÍNTESIS DESCOMPOSICIÓN METAS AOPOA NUEVAS VÍNCULOS INTERACCIONES

14 MODELO Traffic Intersection Agent (TIA) Light Traffic Sensor Agent TSA COMUNICACIÓN AMBIENTE Driver Control Agent DA Traffic Area Monitor Agent TAMA Traffic Sensor COMUNICACIÓN EXPLICITA Car Human Control Pedest. Traffic Supervisor Agent Traffic Signal

15 MODELO GENERAL

16 POR RESOLVER El modelo cooperativa será influenciado por el simulador? Quiénes y como serán los diálogos cooperativos? Cómo se resolverán los conflictos a través de la comunicación explicita de las intenciones? Quién/como se tomarán decisiones inteligentes? Cómo definir el concepto de localidad?

17 REFERENCIAS [ADLE2002] J. L. Adler and V. J. Blue. A cooperative multi-agent transportation management and route guidance system. Transportation Research Part C-emerging Technologies, 10(5-6): , October [ADLE2005] J. L. Adler, G. Satapathy, V. Manikonda, B. Bowles, and V. J. Blue. A multi-agent approach to cooperative traffic management and route guidance. Transportation Research Part B-methodological, 39(4): , May [AKIY2006] T. Akiyama and M. Okushima. Advanced fuzzy traffic controller for urban expressways. International Journal of Innovative Computing Information and Control, 2(2): , April [ALME2009] Dahal K. Hossain-A. Almejalli, K. An intelligent multi-agent approach for road traffic management systems. pages , cited By (since 1996) 1. [BALA2010] P. G. Balaji, X. German, and D. Srinivasan. Urban traffic signal control using reinforcement learning agents. Iet Intelligent Transport Systems, 4(3): , September [BAZZ2005] A. L. C. Bazzan, F. Klugl, and S. Ossowski. Agents in traffic and transportation: Exploring autonomy in logistics, management, simulation, and cooperative driving. Transportation Research Part C-emerging Technologies, 13(4): , August [BIEL1991] Ambrosino G.b Boero-M.b Mastretta-M.b Bielli, M.a. Artificial intelligence techniques for urban traffic control. Transportation Research Part A: General, 25(5): , Cited By (since 1996) 1. [BIEL1994] M. Bielli, G. Ambrosino, and M. Boero. Artificial intelligence applications to traffic engineering. Vsp, [BISH2000] R. Bishop. A survey of intelligent vehicle applications worldwide. In Intelligent Vehicles Symposium, IV Proceedings of the IEEE, pages 25 30, 2000.

18 REFERENCIAS [BURG2009] [CHEN2005] [CHEN2010] [CHOY2003] [DIA2002] J. C. Burguillo-Rial, P. S. Rodriguez-Hernandez, E. Costa-Montenegro, and F. Gil-Castineira. History-based self-organizing traffic lights. Computing and Informatics, 28(2): , R. S. Chen, D. K. Chen, and S. Y. Lin. Actam: Cooperative multi-agent system architecture for urban traffic signal control. Ieice Transactions On Information and Systems, E88D(1): , January B. Chen and H. H. Cheng. A review of the applications of agent technology in traffic and transportation systems. Ieee Transactions On Intelligent Transportation Systems, 11(2): , June M. C. Choy, D. Srinivasan, and R. L. Cheu. Cooperative, hybrid agent architecture for real-time traffic signal control. Ieee Transactions On Systems Man and Cybernetics Part A-systems and Humans, 33(5): , September H. Dia. An agent-based approach to modelling driver route choice behavior under the influence of real-time information rid b Transportation Research Part C-emerging Technologies, 10(5-6): , October [DONG002] Y. Dong, J. Hallam, and M. S. Li. On constructing a cooperative paradigm. Applied Artificial Intelligence, 16(3): , March [DOTO2006] M. Dotoli, M. P. Fanti, and C. Meloni. A signal timing plan formulation for urban traffic control. Control Engineering Practice, 14(11): , November [FERB1999] Jacques Ferber. Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, [FERN2002] R. Fernandes. A BDI-based approach for assessment of drivers decision-making in commuter. PhD thesis, Universidade Federal Do Rio Grande Do Sul, nov [HALL2005] S. Halle and B. Chaib-Draa. A collaborative driving system based on multiagent modelling and simulations. Transportation Research Part C- emerging Technologies, 13(4): , August [HERN2002] J. Z. Hernandez, S. Ossowski, and A. Garcia-Serrano. Multiagent architectures for intelligent traffic management systems. Transportation Research Part C-emerging Technologies, 10(5-6): , October [HILLE2006] [HUAN2009] [MA2002] J. Hillenbrand, A. M. Spieker, and K. Kroschel. A multilevel collision mitigation approach - its situation assessment, decision making, and performance tradeoffs. Ieee Transactions On Intelligent Transportation Systems, 7(4): , December Chung T.-H.b c Huang, Y.-S.a. Modeling and analysis of urban traffic light control systems. Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch eng Hsuch K an, 32(1):85 95, cited By (since 1996) 2. T. Ma and B. Abdulhai. Genetic algorithm-based optimization approach and generic tool for calibrating traffic microscopic simulation parameters. Intelligent Transportation Systems and Vehicle-highway Automation 2002: Highway Operations, Capacity, and Traffic Control, (1800):6 15, 2002.

19 REFERENCIAS [OU2000] [PREV2012] [TRC2007] [TAN1996] [VERM2009] [WAZE2012] [WEIS1999] [WOOL2001] [ZHAN2000] [ZHAO2011] Zhang W.-D. Zhang-W.-Y. Xu X.-M. Ou, H.-T. Urban intelligent traffic control system based on multi-agent technology. Tien Tzu Hsueh Pao/Acta Electronica Sinica, 28(12):52 55, cited By (since 1996) 2. «Fondo de Prevención Vial». [Online]. Available: [Accessed: 22-abr-2012]. Artifical Intelligence and Advanced Computing Applications Committee. Artificial intelligence in transportation, January Khalid M. Yusof R. Tan, K.K. Intelligent traffic lights control by fuzzy logic. volume 9, pages 29 35, cited By (since 1996) 19. Honcrio L.D.M. De Jesus E.O.-Freire-M. Barbosa D.A. Vermaas, L.L.G. Intelligent vehicle survey and applications. Frontiers inartificial Intelligence and Applications, 186(1): , cited By (since 1996) 0. «Free GPS Navigation with Turn by Turn - Waze». [Online]. Available: [Accessed: 22-abr- 2012]. Gerhard Weiss. Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, MA, USA, Michael Woolridge and Michael J. Wooldridge. Introduction to Multiagent Systems. John Wiley & Sons, Inc., New York, NY, USA, Zhang W.-D. Zhang-W.-Y. Xu X.-M. Ou, H.-T. Urban intelligent traffic control system based on multi-agent technology. Tien Tzu Hsueh Pao/Acta Electronica Sinica, 28(12):52 55, cited By (since 1996) 2. D. Zhao, Y. Dai, and Z. Zhang. Computational intelligence in urban traffic signal control: A survey. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, PP(99):1 10, 2011.

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MODELO INTENCIONAL DE TRAFICO COOPERATIVO

MODELO INTENCIONAL DE TRAFICO COOPERATIVO ALEJANDRO TRIANA CASTAÑEDA FACULTAD DE INGENIERÍA MAESTRÍA EN INGENIERÍA DE SISTEMAS Y COMPUTACIÓN PONTIFICIA UNIVERSIDAD JAVERIANA Marzo de 2012 AGENDA 1. PROBLEMA