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Found 6 papers, 1 papers with code
Fairness-enhancing deep learning for ride-hailing demand prediction
When coupled with the bias mitigation regularization method, the de-biasing SA-Net effectively bridges the mean percentage prediction error gap between the disadvantaged and privileged groups, and also protects the disadvantaged regions against systematic underestimation of TNC demand.
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
This Prob-GNN framework is substantiated by deterministic and probabilistic assumptions, and empirically applied to the task of predicting the transit and ridesharing demand in Chicago.
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
The latent space in deep hybrid models can be interpreted, because it reveals meaningful spatial and social patterns.
Estimating air quality co-benefits of energy transition using machine learning
Applications of the framework with Chinese data reveal highly heterogeneous health benefits of reducing fossil fuel use in different sectors and regions in China with a mean of \$34/tCO2 and a standard deviation of \$84/tCO2.
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data
This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints.
Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation
To demonstrate the strength and challenges of DNNs, we estimated the DNNs using a stated preference survey, extracted the full list of economic information from the DNNs, and compared them with those from the DCMs.
