Classification of imbalanced travel mode choice to work data using adjustable svm model

The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorit...

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Main Authors: Qian, Y., Aghaabbasi, M., Ali, M., Alqurashi, M., Salah, B., Zainol, R., Moeinaddini, M., Hussein, E.E.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29606 /
Published: MDPI 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121446235&doi=10.3390%2fapp112411916&partnerID=40&md5=e3dcd54289547b7d81ece73df251bc85
http://eprints.utp.edu.my/29606/
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spelling utp-eprints.296062022-03-25T02:10:19Z Classification of imbalanced travel mode choice to work data using adjustable svm model Qian, Y. Aghaabbasi, M. Ali, M. Alqurashi, M. Salah, B. Zainol, R. Moeinaddini, M. Hussein, E.E. The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVMAK) based on a support vector machine model and the theory of adjusting kernel scaling. The kernel function�s choice was evaluated by applying the likelihood-ratio chi-square and weighting measures. The empirical assessment was performed on the 2017 National Household Travel Survey�California dataset. The performance of the SVMAK model was compared with several other models, including neural networks, XGBoost, Bayesian Network, standard support vector machine model, and some SVM-based models that were previously developed to handle the imbalanced datasets. The SVMAK model outperformed these models, and in some cases improved the accuracy of the minority class classification. For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. MDPI 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121446235&doi=10.3390%2fapp112411916&partnerID=40&md5=e3dcd54289547b7d81ece73df251bc85 Qian, Y. and Aghaabbasi, M. and Ali, M. and Alqurashi, M. and Salah, B. and Zainol, R. and Moeinaddini, M. and Hussein, E.E. (2021) Classification of imbalanced travel mode choice to work data using adjustable svm model. Applied Sciences (Switzerland), 11 (24). http://eprints.utp.edu.my/29606/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVMAK) based on a support vector machine model and the theory of adjusting kernel scaling. The kernel function�s choice was evaluated by applying the likelihood-ratio chi-square and weighting measures. The empirical assessment was performed on the 2017 National Household Travel Survey�California dataset. The performance of the SVMAK model was compared with several other models, including neural networks, XGBoost, Bayesian Network, standard support vector machine model, and some SVM-based models that were previously developed to handle the imbalanced datasets. The SVMAK model outperformed these models, and in some cases improved the accuracy of the minority class classification. For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Qian, Y.
Aghaabbasi, M.
Ali, M.
Alqurashi, M.
Salah, B.
Zainol, R.
Moeinaddini, M.
Hussein, E.E.
spellingShingle Qian, Y.
Aghaabbasi, M.
Ali, M.
Alqurashi, M.
Salah, B.
Zainol, R.
Moeinaddini, M.
Hussein, E.E.
Classification of imbalanced travel mode choice to work data using adjustable svm model
author_sort Qian, Y.
title Classification of imbalanced travel mode choice to work data using adjustable svm model
title_short Classification of imbalanced travel mode choice to work data using adjustable svm model
title_full Classification of imbalanced travel mode choice to work data using adjustable svm model
title_fullStr Classification of imbalanced travel mode choice to work data using adjustable svm model
title_full_unstemmed Classification of imbalanced travel mode choice to work data using adjustable svm model
title_sort classification of imbalanced travel mode choice to work data using adjustable svm model
publisher MDPI
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121446235&doi=10.3390%2fapp112411916&partnerID=40&md5=e3dcd54289547b7d81ece73df251bc85
http://eprints.utp.edu.my/29606/
_version_ 1741197270193274880
score 11.62408

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