Traffic Flow Reconstruction from Limited Collected Data
This work addresses traffic management challenges for urban planners by providing a method to estimate density with low data penetration, though it appears incremental as it builds on existing dynamical systems and learning techniques.
The paper tackles the problem of reconstructing traffic density from limited probe vehicle data by using initial and final positions of a small number of cars generated via microscopic dynamical systems, and it proves that the learned model's approximate density converges to a known macroscopic traffic flow model as vehicle count increases.
We propose an efficient method for reconstructing traffic density with low penetration rate of probe vehicles. Specifically, we rely on measuring only the initial and final positions of a small number of cars which are generated using microscopic dynamical systems. We then implement a machine learning algorithm from scratch to reconstruct the approximate traffic density. This approach leverages learning techniques to improve the accuracy of density reconstruction despite constraints in available data. For the sake of consistency, we will prove that, if only using data from dynamical systems, the approximate density predicted by our learned-based model converges to a well-known macroscopic traffic flow model when the number of vehicles approaches infinity.