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Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability

Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability

I. Laña
Ignacio
I. Olabarrieta
Javier Del Ser
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Papers citing "Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability"

3 / 3 papers shown
Title
Uncertainty Quantification for Image-based Traffic Prediction across Cities
Uncertainty Quantification for Image-based Traffic Prediction across Cities
Alexander Timans
Nina Wiedemann
Nishant Kumar
Ye Hong
Martin Raubal
11
1
0
11 Aug 2023
Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching
Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching
Lu Zhang
Wenchao Ding
Jia Chen
Shaojie Shen
24
36
0
05 Mar 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,042
0
06 Jun 2015