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Found 8 papers, 2 papers with code
Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
The results showcase the effectiveness of counterfactual explanations in revealing traffic patterns learned by deep learning models, showing its potential for interpreting black-box deep learning models used for spatiotemporal predictions in general.
Revealing behavioral impact on mobility prediction networks through causal interventions
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending how various aspects of mobility behavior affect predictions.
Uncertainty quantification and out-of-distribution detection using surjective normalizing flows
Reliable quantification of epistemic and aleatoric uncertainty is of crucial importance in applications where models are trained in one environment but applied to multiple different environments, often seen in real-world applications for example, in climate science or mobility analysis.
Is A 15-minute City within Reach in the United States? An Investigation of Activity-Based Mobility Flows in the 12 Most Populous US Cities
Enhanced efforts in the transportation sector should be implemented to mitigate the adverse effects of CO2 emissions resulting from zoning-based planning paradigms.
Spatially-Aware Car-Sharing Demand Prediction
In particular, we compare the spatially-implicit Random Forest model with spatially-aware methods for predicting average monthly per-station demand.
Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities
Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce.
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis
Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems.
National-scale bi-directional EV fleet control for ancillary service provision
Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases.
