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Found 14 papers, 5 papers with code
Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization
Moreover, the proposed framework is applicable to other NN-based choice models such as TasteNets.
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction
This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity.
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems.
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.
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.
Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks
Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy.
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.
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.
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
The relative ranking of the ML and DCM classifiers is highly stable, while the absolute values of the prediction accuracy and computational time have large variations.
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks
However, the two methods are highly complementary because data-driven methods are more predictive but less interpretable and robust, while theory-driven methods are more interpretable and robust but less predictive.
Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions
Overall, this study demonstrates that prior behavioral knowledge could be used to guide the architecture design of DNN, to function as an effective domain-knowledge-based regularization method, and to improve both the interpretability and predictive power of DNN in choice analysis.
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.
