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Whereas deep neural network (DNN) is increasingly applied to choice analysis,it is challenging to reconcile domain-specific behavioral knowledge withgeneric-purpose DNN, to improve DNN's interpretability and predictive power,and to identify effective regularization methods for specific tasks. This studydesigns a particular DNN architecture with alternative-specific utilityfunctions (ASU-DNN) by using prior behavioral knowledge. Unlike a fullyconnected DNN (F-DNN), which computes the utility value of an alternative k byusing the attributes of all the alternatives, ASU-DNN computes it by using onlyk's own attributes. Theoretically, ASU-DNN can dramatically reduce theestimation error of F-DNN because of its lighter architecture and sparserconnectivity. Empirically, ASU-DNN has 2-3% higher prediction accuracy thanF-DNN over the whole hyperparameter space in a private dataset that wecollected in Singapore and a public dataset in R mlogit package. Thealternative-specific connectivity constraint, as a domain-knowledge-basedregularization method, is more effective than the most popular generic-purposeexplicit and implicit regularization methods and architectural hyperparameters.ASU-DNN is also more interpretable because it provides a more regularsubstitution pattern of travel mode choices than F-DNN does. The comparisonbetween ASU-DNN and F-DNN can also aid in testing the behavioral knowledge. Ourresults reveal that individuals are more likely to compute utility by using analternative's own attributes, supporting the long-standing practice in choicemodeling. Overall, this study demonstrates that prior behavioral knowledgecould be used to guide the architecture design of DNN, to function as aneffective domain-knowledge-based regularization method, and to improve both theinterpretability and predictive power of DNN in choice analysis.

Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions