1 citations · 1 across the 2 of their papers we have counts for
7 papers
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models
Yunhan Zheng, Shenhao Wang, Jinhua Zhao
Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in mach…
Estimating air quality co-benefits of energy transition using machine learning
Da Zhang, Qingyi Wang, Shaojie Song +6
Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is…
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks
Shenhao Wang, Baichuan Mo, Jinhua Zhao
Researchers often treat data-driven and theory-driven models as two disparate or even conflicting methods in travel behavior analysis. However, the two methods are highly complemen…
Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions
Shenhao Wang, Baichuan Mo, Jinhua Zhao
Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to impro…
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data
Shenhao Wang, Qingyi Wang, Jinhua Zhao
It is an enduring question how to combine revealed preference (RP) and stated preference (SP) data to analyze travel behavior. This study presents a framework of multitask learning…
Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation
Shenhao Wang, Qingyi Wang, Jinhua Zhao
While deep neural networks (DNNs) have been increasingly applied to choice analysis showing high predictive power, it is unclear to what extent researchers can interpret economic i…