Abstract
This work integrates a multi-objective evolutionary algorithm with the multi-agent transport simulator MATSim and the comprehensive modal emission model simulator CMEM to analyze the evolutionary optimization of traffic signals minimizing travel time and fuel consumption on a real-world large scenario. We simulate the movement of 20.000 vehicles on the transport network of a 5\(\times \)8 Km\(^2\) area of Quito including 70 signal lights. Our aim is to clarify the nature and the extent of the conflict between these objectives. We also compare with a single-objective optimization algorithm where only travel time is optimized and evaluate the impact of the signals settings on gas emissions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
United Nations, Economic Commission for Europe. Intelligent Transport Systems (ITS) for Sustainable Mobility (2012)
US Environmental Protection Agency, EPA. Air trends (2010). http://www.epa.gov/air/airtrends/2010/. Accessed August 2014
Kim, J.H., Bae, Y.K., Chung, J.H.: Multi objective optimization for sustainable road network design problem. In: Proceedings of International Conference on Transport, Environment and Civil Engineering (ICTECE), pp. 104–108 (2012)
Stolfi, D.H., Alba, E.: Eco-friendly reduction of travel times in european smart cities. In: Proceedings of Conference on Genetic and Evolutionary Computation (GECCO). ACM, pp. 1207–1214 (2014)
Multi agent transport simulation (MATSim). http://matsim.org. Accessed January 2014
Comprehensive modal emission model (CMEM). http://www.cert.ucr.edu/cmem/index.html. Accessed January 2014
Wardrop, J.G.: Some theoretical aspects of road traffic research. ICE Proc. Eng. Divisions 1(3), 325–362 (1952)
Grether, D., Neuman, A.: Traffic light control in multi-agent transport simulations. Technical report, Transport Systems Planning and Transport Telematics, Technical University Berlin (2011)
Scora, G., Barth, M.: Comprehensive Modal Emission Model (CMEM), User’s Guide version 3.01. University of California Riverside Center for Environmental Research and Technology (2006)
Aguirre, H., Oyama, A., Tanaka, K.: Adaptive \(\varepsilon \)-sampling and \(\varepsilon \)-hood for evolutionary many-objective optimization. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 322–336. Springer, Heidelberg (2013)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multiobjective optimization. Evol. Comput. 10(3), 263–282 (2002)
Aguirre, H., Yazawa, Y., Oyama, A., Tanaka, K.: Extending A\(\varepsilon \)S\(\varepsilon \)H from many-objective to multi-objective optimization. In: Dick, G., Browne, W.N., Whigham, P., Zhang, M., Bui, L.T., Ishibuchi, H., Jin, Y., Li, X., Shi, Y., Singh, P., Tan, K.C., Tang, K. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 239–250. Springer, Heidelberg (2014)
Aguirre, H., Liefooghe, A., Verel, S., Tanaka, K.: An analysis on selection for high-resolution approximations in many-objective optimization. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 487–497. Springer, Heidelberg (2014)
Spiegelman, C., Sug-Park, E., Rilett, L.: Transportation Statistics and Microsimulation. CRC Press Taylor and Francis Group, Abingdon (2011)
Teklu, F., Sumalee, A., Watling, D.: A genetic algorithm approach for optimizing traffic control signals considering routing. Comput. Aided Civil Infrastruct. Eng. 22(1), 31–43 (2007)
Frederik, R., Topf, J., Karch, C.: Geofabrik (2007). http://www.geofabrik.de. Accessed January 2014
Souris, M.: Institut de Reserche pour le Developpement (IRD) (2014). http://www.savgis.org/ecuador.htm#DEM30. Accessed October 2014
Demoraes, F.: Movilidad, elementos esenciales y riesgos en el Distrito Metropolitano de Quito. PhD thesis, Universidad de Savoie - Francia (2005)
National Institute of Statistics of Ecuador (INEC) (2010). http://www.ecuadorencifras.gob.ec/. Accessed October 2014
Armas, R., Aguirre, H., Tanaka, K.: Effects of mutation and crossover operators in the optimization of traffic signal parameters. In: Dick, G., Browne, W.N., Whigham, P., Zhang, M., Bui, L.T., Ishibuchi, H., Jin, Y., Li, X., Shi, Y., Singh, P., Tan, K.C., Tang, K. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 167–179. Springer, Heidelberg (2014)
Acknowledgements
The first author gratefully acknowledges the support of National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Armas, R., Aguirre, H., Zapotecas-Martínez, S., Tanaka, K. (2016). Traffic Signal Optimization: Minimizing Travel Time and Fuel Consumption. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2015. Lecture Notes in Computer Science(), vol 9554. Springer, Cham. https://doi.org/10.1007/978-3-319-31471-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-31471-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31470-9
Online ISBN: 978-3-319-31471-6
eBook Packages: Computer ScienceComputer Science (R0)