Bayesian Networks Methods for Traffic Flow Prediction

Abstract

During the last decades, "Transport Demand" and "Mobility" has been a continuously developing branch in the transport literature. This is reflected in the great amount of research papers published in scientific magazines dealing with trip matrix estimation (see (Doblas & Benítez, 2005)) and traffic assignment problems (see (Praskher & Bekhor, 2004)). Current traffic models reproduce the mobility using several data inputs, in particular prior trip matrix, link counts, etc. (see (Yang & Zhou, 1998)) which are data only from a subset of the problem variables, and its size will depend on the available budget for the study being carried on. Among the problems faced for solving these models we can emphasize the high number of possible solutions which is usually solved by choosing the solution where the model results best fits real data. Nevertheless a model does not have to reproduce only the real data, but also must reproduce accurately all the variables. To this end, the aim of this paper consists of presenting two Bayesian network models for traffic estimation, trying to bring a new tool to the transportation field. The first one deals with the problem of link flows, trip matrix estimation and traffic counting location and in the second one we propose a Bayesian network for route flow estimation (and hence link flows and OD flows) using data from plate scanning technique together with a model for optimal plate scanning device location. Since a Gaussian Bayesian network is used, these models allow us to update the predictions from a small subset of real data and probability intervals or regions are obtained to get an idea of the associated uncertainties. In addition dealing with data from the plate scanning approach we improve the under-specification level of the traffic flow estimation problem.

Sparsifying priors for Bayesian uncertainty quantification in model discovery

“…A common approach to remedy this is to use the Bayesian probability framework where uncertainty is quantified in terms of probability and where priors are employed to encode assumptions and knowledge about model parameters [26,27]. Bayesian methods have been widely used for uncertainty quantification in time series models, with applications to weather forecasting [28][29][30], disease modelling [31][32][33], traffic flow [34][35][36] and finance [37][38][39], among many others. By leveraging informative priors Bayesian inference can achieve better results for limited data compared to frequentist approaches [40][41][42].…”