Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow
In this paper, we aim at developing new methods to join machine learning techniques and macroscopic
differential models for vehicular traffic estimation and forecast. It is well known that data-driven and model-
driven approaches have (sometimes complementary) advantages and drawbacks. We consider here a dataset
with flux and velocity data of vehicles moving on a highway, collected by fixed sensors and classified by
lane and by class of vehicle.