SIMULACIÓN BASADA EN AGENTES PARA EL CONTROL INTELIGENTE DE SEMÁFOROS MEDIANTE LÓGICA DIFUSA
Resumen
Resumen
Una de las grandes problemáticas a resolver en las grandes urbes es la relacionada con la sincronización de semáforos para agilizar y mejorar el tráfico vehicular. En este trabajo, se presenta un nuevo modelo cuya aportación es servir como un esquema de ajuste de tiempos en semáforos empleando un sistema de control inteligente basado en agentes autónomos, buscando balancear los tiempos de espera en luz roja y de siga en luz verde para agilizar el flujo sobre cruceros. Se emplea una topología Manhattan para representar dos cruceros viales en una red vial de 7 calles, y la lógica difusa es aplicada para el ajuste de los tiempos de los semáforos tomando la densidad o congestión de tráfico vehicular. Esta red fue modelada y simulada en la plataforma AnyLogic.
Palabras Claves: AnyLogic, control inteligente de tráfico, lógica difusa, semáforos, sistemas multiagente.
AGENT-BASED SIMULATION FOR THE INTELLIGENT CONTROL OF TRAFFIC LIGHTS USING FUZZY LOGIC
Abstract
One of the main problems to be solved in the big cities is related to traffic lights synchronization in order to speed up and improve vehicular traffic. In this paper, a new model is presented, which contributes to provide a scheme of time adjustment on traffic lights using an intelligent control system based on autonomous agents, seeking to balance waiting times in red light and follow times in green light, with the intention of speeding up the vehicular flow on vehicular cruises. A Manhattan topology is used to represent 2 road intersections in a road network of 7 streets, and fuzzy logic is applied to adjust times of traffic lights taking the vehicular traffic density or congestion. The road network was modeled and simulated on the AnyLogic platform.
Keywords: AnyLogic, fuzzy logic, intelligent traffic control, multiagent systems, traffic lights.
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Aguirre Quezada, J. P., Movilidad urbana en México, Dirección General de Análisis Legislativo, Instituto Belisario Domínguez, 2017.
Alkandari, A. A., & Alshammari, A. M., Theory of dynamic hybrid fuzzy logic control of traffic light network with accident detection and action system. Second International Conference on Computing Technology and Information Management (ICCTIM), 2016.
Borshchev, A., & Filippov, A., AnyLogic-multi-paradigm simulation for business, engineering and research. 6th IIE annual simulation solutions conference, 2004.
Brassil, J., Choudhury, A. K., & Maxemchuk, N. F., The Manhattan Street Network: a high performance, highly reliable metropolitan area network. Computer Networks and ISDN Systems, 26(6-8), 841-858, 1994.
El Hatri, C. & Boumhidi, J., Q-learning based intelligent multi-objective particle swarm optimization of light control for traffic urban congestion management. 4th IEEE International Colloquium on Information Science and Technology (CiSt), 2016.
Chen, G., & Pham, T. T., Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC press, 2000.
Chung, T. Y., & Agrawal, D. P., Design and analysis of multidimensional Manhattan Street Networks. IEEE transactions on communications, 41(2), 295-298, 1993.
Cingolani, P., & Alcala-Fdez, J., jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation. In Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on (pp. 1-8). IEEE, june 2012.
Fleck, J. L., Cassandras, C. G., & Geng, Y., Adaptive quasi-dynamic traffic light control. IEEE Transactions on Control Systems Technology, 2016.
Ghazal, B.,ElKhatib, K., Chahine, K., & Kherfan, M., Smart traffic light control system. Third International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA), 2016.
Grigoryev, I., AnyLogic 6 in three days: a quick course in simulation modeling. AnyLogic North America, 2012.
Kumaar, M. A., Kumar, G. A., & Shyni, S. M., Advanced traffic light control system using barrier gate and GSM. International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), 2016.
Grigoryev, I., AnyLogic 7 in three days. A quick course in simulation modeling, 2015.
International Electrotechnical Commission: http://www.iec.ch/dyn/www /f?p=103:91:0::::FSP_LANG_ID:25?q=Fuzzy Control Language, 2014.
Khasnabish, B., Topological properties of Manhattan street networks. Electronics Letters, 25(20), 1388-1389, 1989.
Kuzminvkh, I., Development of traffic light control algorithm in smart municipal network. 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2016.
Li, J., Zhang, Y., & Chen, Y., A Self-Adaptive Traffic Light Control System Based on Speed of Vehicles. IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), August 2016.
Moghaddam, M. J., Hosseini, M., & Safabakhsh, R., Traffic light control based on fuzzy Q-leaming. International Symposium on Artificial Intelligence and Signal Processing (AISP), 2015.
Qi, L., Zhou, M., & Luan, W., A Two-level Traffic Light Control Strategy for Preventing Incident-Based Urban Traffic Congestion. IEEE Transactions on Intelligent Transportation Systems, 2016.
Rashid, H. Ashrafi, M.J.F. Azizi, M. & Heydarinezhad, M.R., Intelligent traffic light control based on clustering using Vehicular Ad-hoc Networks. 7th Conference on Information and Knowledge Technology, 2016.
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