Bayesian inference on dynamic linear models of day-to-day origin-destination flows in transportation networks

TL;DR

This paper introduces a Bayesian dynamic linear model for estimating day-to-day origin-destination flows in transportation networks, accounting for non-stationarity and hierarchical relationships, using MCMC methods.

Contribution

It develops a novel Bayesian state-space approach with a specialized MCMC algorithm for dynamic OD flow estimation in complex, congested networks.

Findings

Effective in congested networks

Handles partial link data

Captures non-stationary flow patterns

Abstract

Estimation of origin-destination (OD) demand plays a key role in successful transportation studies. In this paper, we consider the estimation of time-varying day-to-day OD flows given data on traffic volumes in a transportation network for a sequence of days. We propose a dynamic linear model (DLM) in order to represent the stochastic evolution of OD flows over time. DLM's are Bayesian state-space models which can capture non-stationarity. We take into account the hierarchical relationships between the distribution of OD flows among routes and the assignment of traffic volumes on links. Route choice probabilities are obtained through a utility model based on past route costs. We propose a Markov chain Monte Carlo algorithm, which integrates Gibbs sampling and a forward filtering backward sampling technique, in order to approximate the joint posterior distribution of mean OD flows and parameters of the route choice model. Our approach can be applied to congested networks and in the case when data are available on only a subset of links. We illustrate the application of our approach through simulated experiments on a test network from the literature.