Abstract
In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and 30 min. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexandre, E., Cuadra, L., Salcedo-Sanz, S., Pastor-Snchez, A., Casanova-Mateo, C.: Hybridizing extreme learning machines and genetic algorithms to select acoustic features in vehicle classification applications. Neurocomputing 152, 58–68 (2015)
European Commission: Special Eurobarometer 422a, Quality of Transport (2014)
del Jesus, M.J., Hoffmann, F., Junco, L., Sánchez, L.: Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Trans. Fuzzy Syst. 12(3), 296–308 (2004)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
Gray, J.B., Fan, G.: Classification tree analysis using target. Comput. Stat. Data Anal. 52(3), 1362–1372 (2008)
Han, Y., Xing, B., Yao, J., Liu, J.: Optimal model of regional traffic signal control under mixed traffic flow condition. Jiaotong Yunshu Gongcheng Xuebao/J. Traffic Transp. Eng. 15(1), 119–126 (2015)
Hernández, S.A., Leguizamón, G., Mezura-Montes, E.: Hybridization of differential evolution using hill climbing to solve constrained optimization problems. Revista Iberoamericana de Inteligencia Artificial 16(52), 3–15 (2013)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor (1975)
Kanarachos, S., Kanarachos, A.: Intelligent road adaptive suspension system design using an experts’ based hybrid genetic algorithm. Expert Syst. Appl. 42(21), 8232–8242 (2015)
Karakatic, S., Podgorelec, V.: A survey of genetic algorithms for solving multi depot vehicle routing problem. Appl. Soft Comput. 27, 519–532 (2015)
Lim, T.Y.: Structured population genetic algorithms: a literature survey. Artif. Intel. Rev. 41(3), 385–399 (2014)
Liu, Y., Qin, Z., Shi, Z., Chen, J.: Rule discovery with particle swarm optimization. In: Chi, C.-H., Lam, K.-Y. (eds.) AWCC 2004. LNCS, vol. 3309, pp. 291–296. Springer, Heidelberg (2004)
Lopez-Garcia, P., Onieva, E., Osaba, E., Masegosa, A.D., Perallos, A.: A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans. Intell. Trans. Syst. 17(2), 557–569 (2016)
Luaces, O.: Inflating examples to obtain rules. Int. J. Intel. Syst. 18, 1113–1143 (2003)
Makridakis, S., Hibon, M.: The M3-competition: results, conclusions and implications. Int. J. Forecast. 16(4), 451–476 (2000)
Olivares-Mendez, M.A., Fu, C., Kannan, S., Voos, H., Campoy, P.: Using the cross-entropy method for control optimization: a case study of see-and-avoid on unmanned aerial vehicles, pp. 1183–1189 (2014)
Onieva, E., Milanes, V., Villagra, J., Perez, J., Godoy, J.: Genetic optimization of a vehicle fuzzy decision system for intersections. Expert Syst. Appl. 39(18), 13148–13157 (2012)
Osaba, E., Diaz, F., Onieva, E.: Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl. Intell. 41(1), 145–166 (2014)
Qiu, Y.: Vehicle routing problem with weight coefficients and stochastic demands based on the cross-entropy method, pp. 159–162 (2009)
Rubinstein, R.Y.: Optimization of computer simulation models with rare events. Eur. J. Oper. Res. 99(1), 89–112 (1997)
Sánchez, L., Couso, I., Corrales, J.A.: Combining GP operators with SA search to evolve fuzzy rule based classifiers. Inf. Sci. 136(1–4), 175–192 (2001)
Vlahogianni, E., Karlaftis, M.: Testing and comparing neural network and statistical approaches for predicting transportation time series. Transp. Res. Rec. J. Transp. Res. Board 2399, 9–22 (2013)
Zhou, M., Bi, S., Dong, C., He, C.: Regenerative braking system for electric vehicles based on genetic algorithm fuzzy logic control. ICIC Express Lett. Part B: Appl. 5(3), 689–695 (2014)
Acknowledgments
Authors work was supported by TIMON Project. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 636220.
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
Lopez-Garcia, P., Osaba, E., Onieva, E., Masegosa, A.D., Perallos, A. (2016). Short-Term Traffic Congestion Forecasting Using Hybrid Metaheuristics and Rule-Based Methods: A Comparative Study. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-44636-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44635-6
Online ISBN: 978-3-319-44636-3
eBook Packages: Computer ScienceComputer Science (R0)