Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm

Abstract

This study introduces a way to overcome the sensitivity of decision trees used for route choice behavior studies by using fuzzy logic while preserving the advantages of decision trees and the C4.5 algorithm, namely, comprehensibility and ease of application. Soft discretization of continuous values in fuzzy decision trees can provide a more robust classification. Also, the use of fuzzy logic makes it possible to accommodate qualitative attributes describing route characteristics. Apart from these features, fuzzy decision tree learning algorithms are also capable of assigning numeric values on decisions about the degree of certainty of each recommendation emanating from the fuzzy reasoning. This feature makes it possible to solve the multiple suggestion problem whereby the classic decision tree may suggest that more than one route is optimal. To investigate improvements resulting from the application of fuzzy decision tree learning algorithms, software for an adaptive route choice model using a fuzzy decision tree learning algorithm, fuzzy ID3, was developed and simulation experiments with the model were carried out. The comparison of results with the nonfuzzy adaptive route choice model indicates better predictive accuracy and more effective applicability for the fuzzy model in practice.

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References

Quinlan

C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Francisco, Calif., 1993.

Xie

and Parkany

. Work Travel Mode Choice Modeling Using Data Mining: Decision Trees and Neural Networks. In Transportation Research Record: Journal of the Transportation Research Board, No. 1854, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 50–61.

Park

Bell

Kaparias

and Bogenberger

. Learning User Preferences of Route Choice Behaviour for Adaptive Route Guidance. Intelligent Transport Systems, IET, Vol. 1, No. 2 2007, pp. 159–166.

Park

Bell

M. G. H.

Kaparias

and Bogenberger

. Adaptive Route Choice Model for Intelligent Route Guidance Using a Rule-Based Approach. In Transportation Research Record: Journal of the Transportation Research Board, No. 2000, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 88–97.

Yamamoto

Kitamura

and Fujii

. Drivers’ Route Choice Behavior: Analysis by Data Mining Algorithms. In Transportation Research Record: Journal of the Transportation Research Board, No. 1807, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 59–66.

Wets

Vanhoof

Arentze

and Timmermans

. Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms. In Transportation Research Record: Journal of the Transportation Research Board, No. 1718, Transportation Research Board of the National Academies, Washington, D.C., 2000, pp. 1–9.

Arentze

Borgers

Hofman

Fujii

Joh

Kikichi

Kitamura

Timmermans

H. J. P.

and Van Der Waerden

. Rule-Based Versus Utility Maximizing Models of Activity-Travel Patterns. Presented at 9th International Conference on Travel Behaviour Research, Goldcoast, Queensland, Australia, 2000.

Arentze

and Timmermans

H. J. P.

. Parametric Action Decision Trees: Incorporating Continuous Attribute Variables into Rule-Based Models of Activity-Travel Behavior. Presented at 84th Annual Meeting of the Transportation Research Board, Washington, D.C., 2005.

Zeidler

Schlosser

Ittner

and Posthoff

. Fuzzy Decision Trees and Numerical Attributes. Proc., 5th IEEE International Conference on Fuzzy Systems, New Orleans, La., 1996, pp. 985–990.

10.

Mitchell

Machine Learning. McGraw-Hill International Editions, Singapore, 1997.

11.

Janikow

C. Z.

Fuzzy Decision Trees: Issues and Methods. IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 28, No. 1 1998, pp. 1–14.

12.

Chang

R. L. P.

and Pavlidis

. Fuzzy Decision Tree Algorithms. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 7, No. 1 1977, pp. 28–35.

13.

Chiang

I. J.

and Hsu

J. Y.

. Fuzzy Classification Trees for Data Analysis. Fuzzy Sets and Systems, Vol. 130, No. 1 2002, pp. 87–99.

14.

Umano

Okamoto

Hatono

Tamura

Kawachi

Umedzu

and Kinoshita

. Fuzzy Decision Trees by Fuzzy ID3 Algorithm and Its Application to Diagnosis Systems. Proc., 3rd IEEE Conference on Fuzzy Systems, Vol. 3, 1994, pp. 2113–2118.

15.

Wang

Chen

Qian

and Ye

. On the Optimization of Fuzzy Decision Trees. Fuzzy Sets and Systems, Vol. 112, No. 1 2000, pp. 117–125.

16.

Klir

G. J.

and Bo

. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, Upper Saddle River, N.J., 1995.

17.

Kaparias

Bell

Chen

and Bogenberger

. ICNavS: A Tool for Reliable Dynamic Route Guidance. Intelligent Transport Systems, IET, Vol. 1, No. 4 2006, pp. 225–233.