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...
Ausführliche Beschreibung
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: |
---|
Übergeordnetes Werk: |
In: Applied Sciences - MDPI AG, 2012, 11(2021), 24, p 11916 |
---|---|
Übergeordnetes Werk: |
volume:11 ; year:2021 ; number:24, p 11916 |
Links: |
---|
DOI / URN: |
10.3390/app112411916 |
---|
Katalog-ID: |
DOAJ016622804 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ016622804 | ||
003 | DE-627 | ||
005 | 20240412095556.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230226s2021 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.3390/app112411916 |2 doi | |
035 | |a (DE-627)DOAJ016622804 | ||
035 | |a (DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a TA1-2040 | |
050 | 0 | |a QH301-705.5 | |
050 | 0 | |a QC1-999 | |
050 | 0 | |a QD1-999 | |
100 | 0 | |a Yufeng Qian |e verfasserin |4 aut | |
245 | 1 | 0 | |a Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model |
264 | 1 | |c 2021 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a 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. | ||
650 | 4 | |a imbalanced data | |
650 | 4 | |a travel mode choice data | |
650 | 4 | |a hybrid support vector machine-based model | |
653 | 0 | |a Technology | |
653 | 0 | |a T | |
653 | 0 | |a Engineering (General). Civil engineering (General) | |
653 | 0 | |a Biology (General) | |
653 | 0 | |a Physics | |
653 | 0 | |a Chemistry | |
700 | 0 | |a Mahdi Aghaabbasi |e verfasserin |4 aut | |
700 | 0 | |a Mujahid Ali |e verfasserin |4 aut | |
700 | 0 | |a Muwaffaq Alqurashi |e verfasserin |4 aut | |
700 | 0 | |a Bashir Salah |e verfasserin |4 aut | |
700 | 0 | |a Rosilawati Zainol |e verfasserin |4 aut | |
700 | 0 | |a Mehdi Moeinaddini |e verfasserin |4 aut | |
700 | 0 | |a Enas E. Hussein |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Applied Sciences |d MDPI AG, 2012 |g 11(2021), 24, p 11916 |w (DE-627)737287640 |w (DE-600)2704225-X |x 20763417 |7 nnns |
773 | 1 | 8 | |g volume:11 |g year:2021 |g number:24, p 11916 |
856 | 4 | 0 | |u https://doi.org/10.3390/app112411916 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb |z kostenfrei |
856 | 4 | 0 | |u https://www.mdpi.com/2076-3417/11/24/11916 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2076-3417 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_171 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 11 |j 2021 |e 24, p 11916 |
author_variant |
y q yq m a ma m a ma m a ma b s bs r z rz m m mm e e h eeh |
---|---|
matchkey_str |
article:20763417:2021----::lsiiainfmaacdrvloehieookaas |
hierarchy_sort_str |
2021 |
callnumber-subject-code |
TA |
publishDate |
2021 |
allfields |
10.3390/app112411916 doi (DE-627)DOAJ016622804 (DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Yufeng Qian verfasserin aut Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. imbalanced data travel mode choice data hybrid support vector machine-based model Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Mahdi Aghaabbasi verfasserin aut Mujahid Ali verfasserin aut Muwaffaq Alqurashi verfasserin aut Bashir Salah verfasserin aut Rosilawati Zainol verfasserin aut Mehdi Moeinaddini verfasserin aut Enas E. Hussein verfasserin aut In Applied Sciences MDPI AG, 2012 11(2021), 24, p 11916 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:11 year:2021 number:24, p 11916 https://doi.org/10.3390/app112411916 kostenfrei https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb kostenfrei https://www.mdpi.com/2076-3417/11/24/11916 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 24, p 11916 |
spelling |
10.3390/app112411916 doi (DE-627)DOAJ016622804 (DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Yufeng Qian verfasserin aut Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. imbalanced data travel mode choice data hybrid support vector machine-based model Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Mahdi Aghaabbasi verfasserin aut Mujahid Ali verfasserin aut Muwaffaq Alqurashi verfasserin aut Bashir Salah verfasserin aut Rosilawati Zainol verfasserin aut Mehdi Moeinaddini verfasserin aut Enas E. Hussein verfasserin aut In Applied Sciences MDPI AG, 2012 11(2021), 24, p 11916 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:11 year:2021 number:24, p 11916 https://doi.org/10.3390/app112411916 kostenfrei https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb kostenfrei https://www.mdpi.com/2076-3417/11/24/11916 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 24, p 11916 |
allfields_unstemmed |
10.3390/app112411916 doi (DE-627)DOAJ016622804 (DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Yufeng Qian verfasserin aut Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. imbalanced data travel mode choice data hybrid support vector machine-based model Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Mahdi Aghaabbasi verfasserin aut Mujahid Ali verfasserin aut Muwaffaq Alqurashi verfasserin aut Bashir Salah verfasserin aut Rosilawati Zainol verfasserin aut Mehdi Moeinaddini verfasserin aut Enas E. Hussein verfasserin aut In Applied Sciences MDPI AG, 2012 11(2021), 24, p 11916 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:11 year:2021 number:24, p 11916 https://doi.org/10.3390/app112411916 kostenfrei https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb kostenfrei https://www.mdpi.com/2076-3417/11/24/11916 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 24, p 11916 |
allfieldsGer |
10.3390/app112411916 doi (DE-627)DOAJ016622804 (DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Yufeng Qian verfasserin aut Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. imbalanced data travel mode choice data hybrid support vector machine-based model Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Mahdi Aghaabbasi verfasserin aut Mujahid Ali verfasserin aut Muwaffaq Alqurashi verfasserin aut Bashir Salah verfasserin aut Rosilawati Zainol verfasserin aut Mehdi Moeinaddini verfasserin aut Enas E. Hussein verfasserin aut In Applied Sciences MDPI AG, 2012 11(2021), 24, p 11916 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:11 year:2021 number:24, p 11916 https://doi.org/10.3390/app112411916 kostenfrei https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb kostenfrei https://www.mdpi.com/2076-3417/11/24/11916 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 24, p 11916 |
allfieldsSound |
10.3390/app112411916 doi (DE-627)DOAJ016622804 (DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Yufeng Qian verfasserin aut Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. imbalanced data travel mode choice data hybrid support vector machine-based model Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Mahdi Aghaabbasi verfasserin aut Mujahid Ali verfasserin aut Muwaffaq Alqurashi verfasserin aut Bashir Salah verfasserin aut Rosilawati Zainol verfasserin aut Mehdi Moeinaddini verfasserin aut Enas E. Hussein verfasserin aut In Applied Sciences MDPI AG, 2012 11(2021), 24, p 11916 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:11 year:2021 number:24, p 11916 https://doi.org/10.3390/app112411916 kostenfrei https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb kostenfrei https://www.mdpi.com/2076-3417/11/24/11916 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021 24, p 11916 |
language |
English |
source |
In Applied Sciences 11(2021), 24, p 11916 volume:11 year:2021 number:24, p 11916 |
sourceStr |
In Applied Sciences 11(2021), 24, p 11916 volume:11 year:2021 number:24, p 11916 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
imbalanced data travel mode choice data hybrid support vector machine-based model Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry |
isfreeaccess_bool |
true |
container_title |
Applied Sciences |
authorswithroles_txt_mv |
Yufeng Qian @@aut@@ Mahdi Aghaabbasi @@aut@@ Mujahid Ali @@aut@@ Muwaffaq Alqurashi @@aut@@ Bashir Salah @@aut@@ Rosilawati Zainol @@aut@@ Mehdi Moeinaddini @@aut@@ Enas E. Hussein @@aut@@ |
publishDateDaySort_date |
2021-01-01T00:00:00Z |
hierarchy_top_id |
737287640 |
id |
DOAJ016622804 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ016622804</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412095556.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/app112411916</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ016622804</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH301-705.5</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC1-999</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QD1-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yufeng Qian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">imbalanced data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">travel mode choice data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hybrid support vector machine-based model</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). Civil engineering (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biology (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Physics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemistry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mahdi Aghaabbasi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mujahid Ali</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Muwaffaq Alqurashi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bashir Salah</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rosilawati Zainol</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mehdi Moeinaddini</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Enas E. Hussein</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Applied Sciences</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">11(2021), 24, p 11916</subfield><subfield code="w">(DE-627)737287640</subfield><subfield code="w">(DE-600)2704225-X</subfield><subfield code="x">20763417</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:24, p 11916</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/app112411916</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2076-3417/11/24/11916</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2076-3417</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2021</subfield><subfield code="e">24, p 11916</subfield></datafield></record></collection>
|
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 |
authorStr |
Yufeng Qian |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)737287640 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
TA1-2040 |
illustrated |
Not Illustrated |
issn |
20763417 |
topic_title |
TA1-2040 QH301-705.5 QC1-999 QD1-999 Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model imbalanced data travel mode choice data hybrid support vector machine-based model |
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 |
topic_unstemmed |
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 |
topic_browse |
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 |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Applied Sciences |
hierarchy_parent_id |
737287640 |
hierarchy_top_title |
Applied Sciences |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)737287640 (DE-600)2704225-X |
title |
Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model |
ctrlnum |
(DE-627)DOAJ016622804 (DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb |
title_full |
Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model |
author_sort |
Yufeng Qian |
journal |
Applied Sciences |
journalStr |
Applied Sciences |
callnumber-first-code |
T |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2021 |
contenttype_str_mv |
txt |
author_browse |
Yufeng Qian Mahdi Aghaabbasi Mujahid Ali Muwaffaq Alqurashi Bashir Salah Rosilawati Zainol Mehdi Moeinaddini Enas E. Hussein |
container_volume |
11 |
class |
TA1-2040 QH301-705.5 QC1-999 QD1-999 |
format_se |
Elektronische Aufsätze |
author-letter |
Yufeng Qian |
doi_str_mv |
10.3390/app112411916 |
author2-role |
verfasserin |
title_sort |
classification of imbalanced travel mode choice to work data using adjustable svm model |
callnumber |
TA1-2040 |
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. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
24, p 11916 |
title_short |
Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model |
url |
https://doi.org/10.3390/app112411916 https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb https://www.mdpi.com/2076-3417/11/24/11916 https://doaj.org/toc/2076-3417 |
remote_bool |
true |
author2 |
Mahdi Aghaabbasi Mujahid Ali Muwaffaq Alqurashi Bashir Salah Rosilawati Zainol Mehdi Moeinaddini Enas E. Hussein |
author2Str |
Mahdi Aghaabbasi Mujahid Ali Muwaffaq Alqurashi Bashir Salah Rosilawati Zainol Mehdi Moeinaddini Enas E. Hussein |
ppnlink |
737287640 |
callnumber-subject |
TA - General and Civil Engineering |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.3390/app112411916 |
callnumber-a |
TA1-2040 |
up_date |
2024-07-03T22:06:32.564Z |
_version_ |
1803597268978237440 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ016622804</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240412095556.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/app112411916</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ016622804</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ34c00e8a6e474957a9b4dbc3ba7fcbcb</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QH301-705.5</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QC1-999</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QD1-999</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Yufeng Qian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">imbalanced data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">travel mode choice data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">hybrid support vector machine-based model</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). Civil engineering (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Biology (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Physics</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Chemistry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mahdi Aghaabbasi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mujahid Ali</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Muwaffaq Alqurashi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Bashir Salah</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rosilawati Zainol</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mehdi Moeinaddini</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Enas E. Hussein</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Applied Sciences</subfield><subfield code="d">MDPI AG, 2012</subfield><subfield code="g">11(2021), 24, p 11916</subfield><subfield code="w">(DE-627)737287640</subfield><subfield code="w">(DE-600)2704225-X</subfield><subfield code="x">20763417</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:24, p 11916</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/app112411916</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/34c00e8a6e474957a9b4dbc3ba7fcbcb</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2076-3417/11/24/11916</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2076-3417</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2021</subfield><subfield code="e">24, p 11916</subfield></datafield></record></collection>
|
score |
7.3987055 |