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Found 8 papers, 3 papers with code
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
AI-based digital twins are at the leading edge of the Industry 4. 0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis.
Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability
The estimation of the amount of uncertainty featured by predictive machine learning models has acquired a great momentum in recent years.
A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles
Depending on the resemblance of the traffic behavior at the sensed road, the generation method can be fed with data from one road only.
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification
Specifically, the study proposes three different techniques capable of eliciting understandable information about the knowledge grasped by these recurrent models, namely, potential memory, temporal patterns and pixel absence effect.
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Deep Learning methods have been proven to be flexible to model complex phenomena.
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances.
Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls
In order to explore this capability, we identify three different levels of data absent scenarios, where TL techniques are applied among Deep Learning (DL) methods for traffic forecasting.
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data.
