Traffic prediction at signalised intersections using Integrated Nested Laplace Approximation

TL;DR

This paper presents a Bayesian traffic prediction model for signalised intersections using INLA, a computationally efficient alternative to MCMC, leveraging Gaussian Markov Random Fields for accurate spatiotemporal predictions.

Contribution

It introduces a novel application of INLA for traffic flow prediction at intersections, combining space, time, and covariates within a GMRF framework.

Findings

INLA provides accurate traffic predictions at intersections.

The model efficiently incorporates spatial, temporal, and covariate data.

GMRF assumption is valid for traffic flow modeling.

Abstract

A Bayesian approach to predicting traffic flows at signalised intersections is considered using the the INLA framework. INLA is a deterministic, computationally efficient alternative to MCMC for estimating a posterior distribution. It is designed for latent Gaussian models where the parameters follow a joint Gaussian distribution. An assumption which naturally evolves from an LGM is that of a Gaussian Markov Random Field (GMRF). It can be shown that a traffic prediction model based in both space and time satisfies this assumption, and as such the INLA algorithm provides accurate prediction when space, time, and other relevant covariants are included in the model.