alextimans/traffic4cast-uncertainty
Code repository accompanying the research paper "Uncertainty Quantification for Image-based Traffic Prediction across Cities"
This project helps traffic planners and smart city managers assess the reliability of AI-powered traffic predictions. It takes in traffic data, processes it through various uncertainty quantification methods, and outputs forecasts along with their confidence levels. Users can then make more informed decisions about traffic management based on how certain or uncertain a prediction is.
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Use this if you need to understand the confidence of your traffic predictions, especially for critical decision-making in urban planning or traffic control.
Not ideal if you only need raw traffic predictions without any measure of their uncertainty or if your traffic data is not in an image-based format.
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Aug 14, 2023
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