Abstract
In this paper, soft computing and artificial intelligence techniques have been used to define a model for simulating users’ decisional process in a transportation system. Through this framework, the variables involved are expressed by approximate or linguistic values, like in the humans’ reasoning way, in order to forecast users’ mode choice behavior. The model has been specified and calibrated using a set of real life data. Results appear good in comparison with those obtained by a classical random utility based model calibrated with the same data, and the methodology seems promising also in case of different applications in the field of choice behavior simulation.
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
Preview
Unable to display preview. Download preview PDF.
References
Avineri, E.: Soft Computing Applications in Traffic and Transport Systems: A Review. In: Hoffmann, F., Köppen, M., Klawonn, F., Roy, R. (eds.) Soft Computing: Methodologies and Applications, pp. 17–25. Springer, Heidelberg (2005)
Teodorović, D., Vukadinović, K.: Traffic Control and Transport Planning: A Fuzzy Sets and Neural Networks Approach. Kluwer Academic Publishers, Boston (1998)
Teodorović, D.: Fuzzy Logic Systems for Transportation Engineering: The State of the Art. Transportation Research-Part A 33, 337–364 (1999)
Teodorović, D., Kikuchi, S.: Transportation route choice model using fuzzy inference technique. In: 1st International Symposium on Uncertainty Modeling and Analysis, pp. 140–145. IEEE Press, USA (1990)
Pappis, C., Mamdani, E.: A fuzzy logic controller to a traffic junction. IEEE Trans. on Systems, Man and Cybernet. 7(10), 707–717 (1977)
Akiyama, T., Kawahara, T.: Traffic assignment model with fuzzy travel time information. In: Proceedings of the 9th Mini-EURO Conference “Fuzzy sets in Traffic and Transport Systems”, Budva, Yugoslavia, September 17-19 (1997)
Lotan, T.: Modelling route choice behaviour in the presence of information using concepts of fuzzy sets theory and approximate reasoning. Thesis (PhD). MIT, Boston, MA (1992)
Lotan, T., Koutsopoulos, H.N.: Approximate reasoning models for route choice behavior in the presence of information. In: Daganzo, C.F. (ed.) Transportation and Traffic Theory, pp. 71–88. Elsevier, Amsterdam (1993)
Koutsopoulos, H.N., Lotan, T., Yang, Q.: A driving simulator and its application for modeling route choice in the presence of information. Transportation Research Part C: Emerging Technologies 2, 91–107 (1994)
Henn, V., Ottomanelli, M.: Handling uncertainty in route choice models: From probabilistic to possibilistic approaches. European J. of Operational Research 175, 1526–1538 (2006)
Ottomanelli, M., Dell’Orco, M., Sassanelli, D.: Modelling parking choice behaviour using Possibility Theory. Transp. Planning and Technology 34, 647–667 (2011)
Cascetta, E., Nuzzolo, A., Velardi, V.: A System of mathematical models for the evaluation of integrated traffic planning and control policies. In: Workshop on “Integration Problems in Urban Transportation Planning and Management Systems”, Ottobre, Capri, Italy, pp. 28–29 (1993)
Cascetta, E.: Transportation systems engineering: theory and methods. Kluwer Academic Publishers, Dordrecht (2001)
CSST: Un sistema di supporto alle decisioni per la gestione della mobilità e per la pianificazione dei trasporti urbani, Report no. 26 (1993) (in Italian)
Chiu, S.L.: A cluster estimation method with extension to fuzzy model identification. In: Proc. of the 3rd IEEE World Congress on Computational Intelligence, pp. 1240–1245. IEEE Press (1994)
Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transaction on System, Man and Cybernetics 23, 665–685 (1993)
Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems. Prentice Hall, Upple Saddle River (2001)
Zimmermann, H.J.: Fuzzy Set Theory and its Applications. Kluwer Academic Publishers (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dell’Orco, M., Ottomanelli, M. (2012). Simulation of Users Decision in Transport Mode Choice Using Neuro-Fuzzy Approach. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31075-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-31075-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31074-4
Online ISBN: 978-3-642-31075-1
eBook Packages: Computer ScienceComputer Science (R0)