  We develop a Bayesian particle filter for tracking traffic flows that is
capable of capturing non-linearities and discontinuities present in flow
dynamics. Our model includes a hidden state variable that captures sudden
regime shifts between traffic free flow, breakdown and recovery. We develop an
efficient particle learning algorithm for real time on-line inference of states
and parameters. This requires a two step approach, first, resampling the
current particles, with a mixture predictive distribution and second,
propagation of states using the conditional posterior distribution. Particle
learning of parameters follows from updating recursions for conditional
sufficient statistics. To illustrate our methodology, we analyze measurements
of daily traffic flow from the Illinois interstate I-55 highway system. We
demonstrate how our filter can be used to inference the change of traffic flow
regime on a highway road segment based on a measurement from freeway
single-loop detectors. Finally, we conclude with directions for future
research.
