Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm
Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm
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.
20-28
Park, Kyounga
e57acb58-82f1-4a4c-a9a3-01a92a9597fd
Bell, Michael
a762d32a-e6f9-452a-845d-6b139e0275e6
Kaparias, Ioannis
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Belzner, Heidrun
63a4cc0b-73e1-4aea-a657-cc7c34dee48b
1 December 2008
Park, Kyounga
e57acb58-82f1-4a4c-a9a3-01a92a9597fd
Bell, Michael
a762d32a-e6f9-452a-845d-6b139e0275e6
Kaparias, Ioannis
e7767c57-7ac8-48f2-a4c6-6e3cb546a0b7
Belzner, Heidrun
63a4cc0b-73e1-4aea-a657-cc7c34dee48b
Park, Kyounga, Bell, Michael, Kaparias, Ioannis and Belzner, Heidrun
(2008)
Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm.
Transportation Research Record, 2076, .
(doi:10.3141/2076-03).
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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Published date: 1 December 2008
Organisations:
Transportation Group
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Local EPrints ID: 411360
URI: http://eprints.soton.ac.uk/id/eprint/411360
ISSN: 0361-1981
PURE UUID: 3865c55b-70c2-40c8-b922-b98097e531b7
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Date deposited: 19 Jun 2017 16:31
Last modified: 16 Mar 2024 04:28
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Author:
Kyounga Park
Author:
Michael Bell
Author:
Heidrun Belzner
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