Deep neural networks for choice analysis: A statistical learning theory perspective

•Used statistical learning theory to evaluate DNNs in choice analysis.•Operationalized DNN interpretability by using the choice probability functions.•Provided a tight upper bound on the estimation error of DNNs.•Conducted experiments to identify when DNNs outperform classical models.•DNNs can be mo...

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Bibliographic Details
Published in:	Transportation research. Part B: methodological Vol. 148; pp. 60 - 81
Main Authors:	Wang, Shenhao, Wang, Qingyi, Bailey, Nate, Zhao, Jinhua
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.06.2021 Elsevier Science Ltd
Subjects:	Approximation Artificial neural networks Choice learning Choice modeling Deep neural networks Economic forecasting Interpretability Learning Learning theory Logit models Machine learning Mathematical analysis Mathematical models Neural networks Predictions Statistical analysis Statistical learning theory Statistical models Trip surveys Upper bounds Statistical learning theory Deep neural networks Interpretability Choice modeling
ISSN:	0191-2615, 1879-2367
Online Access:	Get full text
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