5 papers
A Graph-based Methodology for the Sensorless Estimation of Road Traffic Profiles
Eric L. Manibardo, Ibai Laña, Esther Villar +1
Traffic forecasting models rely on data that needs to be sensed, processed, and stored. This requires the deployment and maintenance of traffic sensing infrastructure, often leadin…
Deep Learning for Road Traffic Forecasting: Does it Make a Difference?
Eric L. Manibardo, Ibai Laña, Javier Del Ser
Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas su…
Transfer Learning and Online Learning for Traffic Forecasting under Different Data Availability Conditions: Alternatives and Pitfalls
Eric L. Manibardo, Ibai Laña, Javier Del Ser
This work aims at unveiling the potential of Transfer Learning (TL) for developing a traffic flow forecasting model in scenarios of absent data. Knowledge transfer from high-qualit…
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Javier Del Ser, Ibai Lana, Eric L. Manibardo +5
In short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest occurring shortly after the prediction is queried. The activit…
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
Eric L. Manibardo, Ibai Laña, Jesus L. Lobo +1
This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predict…