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Found 5 papers, 2 papers with code
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.
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Deep Learning methods have been proven to be flexible to model complex phenomena.
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.
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Results from this comparison benchmark and the analysis of the statistical significance of the reported performance gaps are decisive: Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.
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.
