Simulation of Users Decision in Transport Mode Choice Using Neuro-Fuzzy Approach
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.
KeywordsMembership Function Fuzzy Logic Fuzzy Inference System Mode Choice Route Choice
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- 4.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)Google Scholar
- 6.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)Google Scholar
- 7.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)Google Scholar
- 8.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)Google Scholar
- 12.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)Google Scholar
- 14.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)Google Scholar
- 15.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)Google Scholar
- 18.Zimmermann, H.J.: Fuzzy Set Theory and its Applications. Kluwer Academic Publishers (1991)Google Scholar