A route choice model was devised for personalized route guidance; it can adaptively change its routing function according to the demands of the user. A strategy for personalized route guidance is to incorporate an adaptation process into the route selection rules. In a study, regularities in route selection were discovered by using a rule-based approach, the C4.5 algorithm, which had advantages over other methods because of its comprehendible model structure. The route choice model is combined with a user interface, enabling the efficient collection of user feedback. To examine the adaptability of the model, experiments in which user preferences are predefined and later changed during the tests were carried out. The results of the experiments indicate the applicability of the model in personalized route guidance.

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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