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Found 4 papers, 1 papers with code
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations.
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.
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.
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models
Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms.
