Buscar en
Revista Iberoamericana de Automática e Informática Industrial RIAI
Toda la web
Inicio Revista Iberoamericana de Automática e Informática Industrial RIAI Algoritmo para el cálculo de la velocidad media óptima en una ruta (ASGA)
Información de la revista
Vol. 11. Núm. 4.
Páginas 435-443 (Octubre 2014)
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Vol. 11. Núm. 4.
Páginas 435-443 (Octubre 2014)
Open Access
Algoritmo para el cálculo de la velocidad media óptima en una ruta (ASGA)
ASGA: Algorithm to obtain the optimal average speed on a route
Visitas
4355
V. Corcoba Magañaa,
Autor para correspondencia
vcorcoba@it.uc3m.es

Autor para correspondencia. vcorcoba@it.uc3m.es
, M. Muñoz Organeroa
a Departamento de Ingeniería Telemática, Universidad Carlos III de Madrid, C/ Avenida de la Universidad, 30, 28911 Leganés, España
Este artículo ha recibido

Under a Creative Commons license
Información del artículo
Resumen
Texto completo
Bibliografía
Descargar PDF
Estadísticas

En este trabajo se propone un algoritmo para obtener la velocidad media óptima para ahorrar combustible y mejorar la seguridad. El algoritmo propuesto se basa en los algoritmos genéticos. El algoritmo emplea información sobre el entorno, la carretera y el vehículo para obtener la velocidad media que minimice el consumo de combustible sin incrementar drásticamente la duración del trayecto. Además, el algoritmo propuesto mejora la seguridad ya que adecua la velocidad a las condiciones de la vía. La información sobre el entorno se obtiene de servicios web y la información sobre el vehículo se obtiene a través del puerto OBD2. El algoritmo es validado en situaciones reales con incidentes de tráfico y sin ellos. Por otra parte, se analiza el impacto de la velocidad media y los incidentes de tráfico en las aceleraciones y su influencia en el consumo de combustible.

Palabras clave:
Conducción eficiente
Sistemas de ayuda a la conducción
Algoritmos Genéticos
Android
Sistemas Inteligentes de Transporte

This paper proposes an algorithm for obtaining the optimal average speed to save fuel and improve safety. The proposed algorithm is based on genetic algorithms. The algorithm uses information about the environment, the road and the vehicle for obtaining the optimal average speed which it minimizes fuel consumption without dramatically increasing the travel time. Moreover, the proposed algorithm improves safety adapting vehicle speed to road conditions. The environment information is obtained from web services and vehicle information is obtained through the OBD2 port. The algorithm is validated in situations with and without incidents. In addition, we analyze the impact of the average speed and acceleration incidents and their impact on fuel consumption.

Keywords:
Eco-driving
Advanced Driver Assistance Systems
Genetic Algorithms
Android
Intelligent Transport System
Bibliography
[1]
Long-term effects of training in economical driving: fuel consumption, accidents, driver acceleration behaviour and technical feedback. International Journal of Industrial Ergonomics. 2007; 37:333-43.
[2]
On-board vehicle diagnostics. SAE. 2004. 2004-21-0009
[3]
On-board system design to optimize energy management. Proceedings of the European Annual Conference on Human Decision-Making and Manual Control Control (EAM’06). France. 2006.
[4]
Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transportation Research Part D: Transport and Environment. 2009; 14.:400-10.
[5]
CoEco-Driving: Pilot Evaluation of Driving Behavior Changes among U.S. Drivers. University of California Riverside; 2010.
[6]
Efficient gear shifting strategies for green driving policies. American Control Conference (ACC). 2010; 4331-6.
[7]
Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment. 2001; 6.:325-45.
[8]
Rosengren. Proceedings from the 12th Symposium, Transport and Air Pollution Conference, Avignon. 2003.
[9]
The relationship between fuel economy and safety outcomes. Victoria. Australia: Monash University. 2001; 1-57.
[10]
Hegerty, B., Hung, C.C., Kasprak, K., 2009. A Comparative Study on Differential Evolution and Genetic Algorithms for Some Combinatorial Problems.URL: http://www.micai.org/2009/proceedings/../cd/ws../paper88. micai09. pdf.
[11]
Higgins, P., Williams, G., 2012. Vehicle Fuel Consumption Calculator. U.S. Patent No. 8,340,925. Washington, DC: U.S. Patent and Trademark Office.
[12]
Improving vehicle fuel economy and reducing emissions by driving technique. Proceedings of the15th ITS World Congress. New York. 2008.
[13]
Strategies for a road transport system based on renewable resources – The case of an import-independent Sweden in 2025. Applied Energy. 2010; 87.:1836-45.
[14]
Wide-area traffic signal control using predicted traffic based on real-time information. Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems. Beijing, China. October 12-15 2008.
[15]
O’Connor J.J., Robertson F. E.,“Student's t-test,” MacTutor History of Mathematics archive, University of St Andrews, http://www-history.mcs.st-andrews.ac.uk/Biographies/Gosset.html.
[16]
Subliminal vibro-tactile based notification of CO2 economy while driving. 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications AutomotiveUI 2010), November 11-12, 2010, Pittsburgh, Pennsylvania, USA. ACM. 2010; 92-101. 978-1-4503-0437-5
[17]
Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption. Applied Energy. 2010; 86.:1925-32.
[18]
Driving style and traffic measures-influence on vehicle emissions and fuel consumption. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 2004; 1.:35-40.
[19]
Modelo para la Conducción Eficiente y Sostenible basado en Lógica Borrosa. Revista Iberoamericana de Automática e Informática industrial. 2012; 9.:259-66.
Opciones de artículo
Herramientas