eprintid: 14151
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/41/51
datestamp: 2023-11-10 03:28:43
lastmod: 2023-11-10 03:28:43
status_changed: 2023-11-10 01:56:09
type: article
metadata_visibility: show
creators_name: Qian, Y.
creators_name: Aghaabbasi, M.
creators_name: Ali, M.
creators_name: Alqurashi, M.
creators_name: Salah, B.
creators_name: Zainol, R.
creators_name: Moeinaddini, M.
creators_name: Hussein, E.E.
title: Classification of imbalanced travel mode choice to work data using adjustable svm model
ispublished: pub
note: cited By 14
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 (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.
date: 2021
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121446235&doi=10.3390%2fapp112411916&partnerID=40&md5=e3dcd54289547b7d81ece73df251bc85
id_number: 10.3390/app112411916
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 11
number: 24
refereed: TRUE
issn: 20763417
citation:   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).   ISSN 20763417