Adaptive Control for Optimizing a Traffic Light Intersection Based on an Embedded System
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Abstract
In order to optimize the traffic flow on a road intersection, an adaptive control algorithm and a data base were designed; both components were hosted on a Raspberry Pi B+ embedded system. The data base helps to debug the performance of the controller. The efficiency of the algorithm was assessed using a virtual instrument, which emulated a traffic light intersection in the city of Cucuta, i. e., the magnetorresistive sensors, the activation process of the traffic lights and the traffic flow. By processing and updating the times assigned to the traffic lights, the traffic flow was increased up to 5.5 % and the maximum time a vehicle has to wait before passing through the traffic light was decreased up to 28 seconds. Aditionally the length of line was diminished up to 18 %. Based on this case study, it can be inferred that is possible to integrate the adaptive control and the embedded systems as software and hardware tools to improve the operation of traffic control systems.
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CELIS-PEÑARANDA, Jose M et al.
Adaptive Control for Optimizing a Traffic Light Intersection Based on an Embedded System.
Ingeniería y Ciencia | ing.cienc., [S.l.], v. 12, n. 24, p. 169-193, nov. 2016.
ISSN 2256-4314.
Available at: <http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/3458>. Date accessed: 20 sep. 2017.
doi: https://doi.org/10.17230/ingciencia.12.24.8.
Keywords
Data base; adaptive control; virtual instrumentation; traffic light; embedded system
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[3] J. Morales Pérez and J. Fernández Gómez, “Análisis discriminante de algunas variables que influyen en la contaminación acústica debida al tráfico urbano en una gran ciudad,” Revista Ingenierías Universidad de Medellín, vol. 11, no. 21, pp. 13–22, Jun. 2012. 171
[4] H. González Fernández, M. Zamora Martí, and A. Duharte González, “Análisis de la circulación en la intersección carretera central-Avenida delas Américas,” Ciencia en su PC, no. 2, pp. 94–103, 2008. 171
[5] Invias; and Inco. (2011) Análisis gráfico del tráfico vehicular por categoría del vehículo. Bogotá. [Online]. Available: https://goo.gl/2rYZqw 171
[6] L. F. Pedraza, C. A. Hernández, and D. A. López, “Control de tráfico vehicular usando ANFIS,” Revista chilena de Ingeniería, vol. 20, no. 1, pp. 79–88, 2012. [Online]. Available: http://dx.doi.org/10.4067/S0718-33052012000100008 171
[7] L. F. PedrazaMartínez, C. A. Hernández Suárez, and D. A. López Sarmiento, “Sistema de comunicación TCP/IP para el control de una intersección de tráfico vehicular,” Ingeniería, Investigación y Tecnología, vol. XIV, no. 4, pp. 583–594, 2013. 171
[8] F. A. Espinosa Valcárcel, C. A. Gordillo Chaves, R. Jiménez Moreno, and O. F. Avilés Sanchez, “Machine vision algorithms applied to dynamic traffic light control,” Dyna, vol. 80, no. 178, pp. 132–140, 2013. 171
[9] M. Mesa Mazo, J. Valencia Calvo, and G. Olivar Tost, “Modelo para la dinámica de un vehículo a través de una secuencia de semáforos,” Dyna, vol. 81, no. 186, pp. 138–145, 2014. [Online]. Available: http://dx.doi.org/10.15446/dyna.v81n186.39767 172
[10] M. Jiménez and I. Sarmiento, “Sistema adaptativo de control y optimización del tráfico de un corredor vial semaforizado. Aplicación a la ciudad de Medellín,” Dyna, vol. 78, no. 169, pp. 71–78, 2011. 172, 182
[11] T. Juntunen, V. Kostakos, P. Mikko, and D. Ferreira, “Web tool for traffic engineers: direct manipulation and visualization of vehicular traffic using Google maps,” in MindTrek ’12 Proceeding of the 16th International Academic MindTrek Conference, 2012, pp. 209–210. [Online]. Available: https://doi.org/10.1145/2393132.2393173 172
[12] S. Hu, L. Su, H. Liu, H. Wang, and T. F. Abdelzaher, “SmartRoad: Smartphone-Based Crowd Sensing for Traffic Regulator Detection and Identification,” ACM Transactions on Sensor Networks, vol. 11, no. 4, 2015. [Online]. Available: http://dx.doi.org/10.1145/2770876 172
[13] M. Fogue, P. Garrido, F. J.Martínez, J.-C. Cano, C. T. Calafate, and P. Manzoni, “A Realistic Simulation Framework for Vehicular Networks,” in SIMUTOOLS ’12 Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques, Mar. 2012, pp. 37–46. 172
[14] G. Chen and T. T. Pham, Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. Boca Raton London New York Washington, D.C.: CRC Press, 2001. 173
[15] Lendher, Sensor Magnetorresistivo para Detección de Vehículos FVD-L, lendher, 2012. [Online]. Available: https://goo.gl/G22z93 175 [16] J. R. Lajara Viscaíno and J. Pelegri Sebastia, LabVIEW Entorno gráfico de programación. Barcelona, México D.F.: Alfaomega and Marcombo, 2007. 176
[17] C. Kang-Ching, Y. Li, and S. Romesh, “Validation of Stochastic Traffic Flow Model with Microscopic Traffic Simulation,” Conference on Automation Science and Engineering (CASE), 2011 IEEE, 2011. 182
[18] J. C. Correa, “Estimación por intervalo del parámetro de la distribución de Poisson con una sola observación,” Revista Colombiana de Estadística, vol. 30, no. 1, pp. 69–75, Jun. 2007. 183