Bayesian Particle Tracking of Traffic Flows

TL;DR

This paper introduces a Bayesian particle filter for real-time traffic flow tracking that captures non-linearities and regime shifts, demonstrated on highway traffic data.

Contribution

It presents a novel particle learning algorithm for online inference of traffic states and regimes, handling abrupt changes in traffic flow dynamics.

Findings

Effective detection of traffic regime shifts from detector data

Real-time inference of traffic flow states and parameters

Demonstrated on Illinois highway traffic data

Abstract

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.