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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Autor*in:	Yufeng Qian [verfasserIn] Mahdi Aghaabbasi [verfasserIn] Mujahid Ali [verfasserIn] Muwaffaq Alqurashi [verfasserIn] Bashir Salah [verfasserIn] Rosilawati Zainol [verfasserIn] Mehdi Moeinaddini [verfasserIn] Enas E. Hussein [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	imbalanced data travel mode choice data hybrid support vector machine-based model

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 11(2021), 24, p 11916
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:24, p 11916

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app112411916

Katalog-ID:	DOAJ016622804

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callnumber-first	T - Technology
author	Yufeng Qian
spellingShingle	Yufeng Qian misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc imbalanced data misc travel mode choice data misc hybrid support vector machine-based model misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model
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topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc imbalanced data misc travel mode choice data misc hybrid support vector machine-based model misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model
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title_sort	classification of imbalanced travel mode choice to work data using adjustable svm model
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title_auth	Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model
abstract	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 (SVM<sub<AK</sub<) 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 SVM<sub<AK</sub< 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 SVM<sub<AK</sub< 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.
abstractGer	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 (SVM<sub<AK</sub<) 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 SVM<sub<AK</sub< 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 SVM<sub<AK</sub< 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.
abstract_unstemmed	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 (SVM<sub<AK</sub<) 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 SVM<sub<AK</sub< 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 SVM<sub<AK</sub< 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.
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title_short	Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model
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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 (SVM<sub<AK</sub<) 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 SVM<sub<AK</sub< 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 SVM<sub<AK</sub< 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. 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Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model

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