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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Estimating motorized travel mode choice using classifiers: An application for high-dimensional multicollinear data

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Author(s):
Lindner, Anabele ; Pitombo, Cira Souza ; Cunha, Andre Luiz
Total Authors: 3
Document type: Journal article
Source: TRAVEL BEHAVIOUR AND SOCIETY; v. 6, p. 100-109, JAN 2017.
Web of Science Citations: 4
Abstract

Studies in the field of discrete choice analysis are crucial for transportation planning. Generally, travel demand models are based on the maximization of the random utility and straightforward mathematical functions, such as logit models. These assumptions lead to a continuous model that presents constraints concerning fitting the data. Artificial Neural Networks (ANN) and Classification Trees (CT) are classification techniques that can be applied to discrete choice models. These techniques can overcome some disadvantages of traditional modeling, especially the drawback of not being able to model high-dimensional multicollinear data. This research paper compares the performance of estimating motorized travel mode choice through ANN and CT with a binary logit in a multicollinear study case (aggregated and disaggregated covariates). The dataset refers to an Origin-Destination Survey carried out in Sao Paulo Metropolitan Area, Brazil in 2007. Classification techniques have shown a good ability to forecast (approximately 80% match rate), as well as to recognize travel behavior patterns. Furthermore, by using the classifier application, the most important covariates within all the datasets can be selected. These covariates can be related to households, as well as to Traffic Analysis Zones. (C) 2016 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 13/25035-1 - Incorporation of kriging in modal choice models
Grantee:Cira Souza Pitombo
Support type: Regular Research Grants