Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization

arXiv:2404.14701v1 Announce Type: new
Abstract: Deep neural networks (DNNs) frequently present behaviorally irregular patterns, significantly limiting their practical potentials and theoretical validity in travel behavior modeling. This study proposes strong and weak behavioral regularities as novel metrics to evaluate the monotonicity of individual demand functions (a.k.a. law of demand), and further designs a constrained optimization framework with six gradient regularizers to enhance DNNs' behavioral regularity. The proposed framework is applied to travel survey data from Chicago and London to examine …

abstract analysis arxiv behavior cs.lg demand functions gradient law metrics modeling networks neural networks novel patterns practical regularization study travel type