A Gaussian-process-based data-driven traffic flow model and its application in road capacity analysis

Abstract

To estimate the accurate fundamental relationship in traffic flow, this paper proposes a novel framework that extends classical fundamental diagram (FD) models to incorporate more dimensions of traffic state variables and allow for the impact of the supply-side factors of roads. The proposed framework is suitable for real-time traffic management, especially in urban areas, due to its reliance on minimal assumptions, its flexibility in adapting to various data sources, and its scalability to higher-dimensional data. The Gaussian process (GP) model is adopted as the base model for learning the optimal mapping from these input features to traffic volume. To enhance the GP model, an in-depth analysis of the properties of its kernel and likelihood function is provided. To cope with the hyperparameter optimisation of the GP, a modified Newton method for GP-based traffic flow model is also designed, which can jump over regions with small gradients. Experiments based on simulation data demonstrate the ability of the proposed framework to capture complex relationships between traffic state variables and supply-side factors, and show its value for estimating dynamic road capacity.