  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.
