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A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information
Joint Authors
Zhang, Zhengchao
Ji, Congyuan
Wang, Yineng
Yang, Yanni
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Discrete choice modeling of travel modes is an essential part of traffic planning and management.
Thus far, this field has been dominated by multinomial logit (MNL) models with a linear utility specification.
However, deep neural networks (DNNs), owing to their powerful capacity of nonlinear fitting, are now rapidly replacing these models.
This is because, by using DNNs, mode choice can be assimilated with the classification problems within the machine learning community.
This article proposes a newly designed DNN framework for traffic mode choice in the style of two hidden layers.
First, a local-connected layer automatically extracts an effective utility specification from the available data, and then, a fully connected layer augments the feature representation.
Validated by a practical city-wide multimodal traffic dataset in Beijing, our model significantly outperforms the random utility models and simple fully connected neural network in terms of the prediction accuracy.
Besides the comparison of the predictive power, we also present the interpretability of the proposed model.
American Psychological Association (APA)
Zhang, Zhengchao& Ji, Congyuan& Wang, Yineng& Yang, Yanni. 2020. A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1175737
Modern Language Association (MLA)
Zhang, Zhengchao…[et al.]. A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information. Journal of Advanced Transportation No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1175737
American Medical Association (AMA)
Zhang, Zhengchao& Ji, Congyuan& Wang, Yineng& Yang, Yanni. A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1175737
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1175737