Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks
This study conducts a comprehensive comparative analysis of regression-based multinomial models and artificial neural network models in intercity travel mode choices. The four intercity travel modes of airplane, high-speed rail (HSR), train, and express bus were used for analysis. Passengers’ activi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/9219176 |
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| _version_ | 1832549770065870848 |
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| author | Xiaowei Li Yuting Wang Yao Wu Jun Chen Jibiao Zhou |
| author_facet | Xiaowei Li Yuting Wang Yao Wu Jun Chen Jibiao Zhou |
| author_sort | Xiaowei Li |
| collection | DOAJ |
| description | This study conducts a comprehensive comparative analysis of regression-based multinomial models and artificial neural network models in intercity travel mode choices. The four intercity travel modes of airplane, high-speed rail (HSR), train, and express bus were used for analysis. Passengers’ activity data over the process of intercity travel were collected to develop the models. The standard multinomial logit (MNL) regression and Bayesian multinomial logit (BMNL) regression were compared with the radial basis function (RBF) and multilayer perceptron (MLP). The results show that MLP performs best in terms of predictive accuracy, followed by BMNL and MNL, and RBF is the least accurate. The performances of all models were examined against changes in data balance, and it was found that rebalancing can improve fitting performance while slightly reducing the predictive performance. This comparative study and its parameter estimation shed new light on the comparison of traditional and emerging models in travel behavior studies, and the findings can be used as heuristic guidance for all stakeholders. |
| format | Article |
| id | doaj-art-12f6f9df8bb6414c9074d77d90c55d54 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-12f6f9df8bb6414c9074d77d90c55d542025-02-03T06:08:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/92191769219176Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural NetworksXiaowei Li0Yuting Wang1Yao Wu2Jun Chen3Jibiao Zhou4School of Civil Engineering, Xi’an University of Architecture & Technology, Xi’an 710055, ChinaSchool of Civil Engineering, Xi’an University of Architecture & Technology, Xi’an 710055, ChinaSchool of Modern Posts and Institute of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Civil Engineering, Xi’an University of Architecture & Technology, Xi’an 710055, ChinaCollege of Transportation Engineering, Tongji University, Shanghai 201804, ChinaThis study conducts a comprehensive comparative analysis of regression-based multinomial models and artificial neural network models in intercity travel mode choices. The four intercity travel modes of airplane, high-speed rail (HSR), train, and express bus were used for analysis. Passengers’ activity data over the process of intercity travel were collected to develop the models. The standard multinomial logit (MNL) regression and Bayesian multinomial logit (BMNL) regression were compared with the radial basis function (RBF) and multilayer perceptron (MLP). The results show that MLP performs best in terms of predictive accuracy, followed by BMNL and MNL, and RBF is the least accurate. The performances of all models were examined against changes in data balance, and it was found that rebalancing can improve fitting performance while slightly reducing the predictive performance. This comparative study and its parameter estimation shed new light on the comparison of traditional and emerging models in travel behavior studies, and the findings can be used as heuristic guidance for all stakeholders.http://dx.doi.org/10.1155/2021/9219176 |
| spellingShingle | Xiaowei Li Yuting Wang Yao Wu Jun Chen Jibiao Zhou Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks Journal of Advanced Transportation |
| title | Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks |
| title_full | Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks |
| title_fullStr | Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks |
| title_full_unstemmed | Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks |
| title_short | Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks |
| title_sort | modeling intercity travel mode choice with data balance changes a comparative analysis of bayesian logit model and artificial neural networks |
| url | http://dx.doi.org/10.1155/2021/9219176 |
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