Learning Traffic Flow Dynamics using Random Fields

TL;DR

This paper introduces a probabilistic mesoscopic traffic flow model using factor graphs, enabling efficient learning from limited vehicle trajectory data and outperforming existing methods in traffic state estimation.

Contribution

It proposes a novel factor graph-based approach for traffic flow modeling that ensures non-negative speeds and densities, improving estimation accuracy with limited data.

Findings

Accurately reproduces traffic conditions with as low as 10% probe vehicle data.

Outperforms state-of-the-art traffic estimation techniques in accuracy.

Efficiently learns traffic dynamics from limited Lagrangian measurements.

Abstract

This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measurements using an efficient message passing technique. The approach ensures that estimated speeds and traffic densities are non-negative with probability one. The estimation technique is tested using vehicle trajectory datasets generated using an independent microscopic traffic simulator and is shown to efficiently reproduce traffic conditions with probe vehicle penetration levels as little as 10\%. The proposed algorithm is also compared with state-of-the-art traffic state estimation techniques developed for the same purpose and it is shown that the proposed approach can outperform the state-of-the-art techniques in terms reconstruction accuracy.