Bayesian Particle Tracking of Traffic Flows

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.

References in corpus (7)

• Travel time estimation for ambulances using Bayesian data augmentation
• Dynamical functional prediction and classification, with application to traffic flow prediction
• Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service
• Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data
• Particle Learning and Smoothing
• Scaling the Kalman filter for large-scale traffic estimation
• Mixtures, envelopes, and hierarchical duality

Cited by in corpus (5)

• Deep Learning for Short-Term Traffic Flow Prediction
• Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
• Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
• STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph
• Data-Driven Intersection Management Solutions for Mixed Traffic of Human-Driven and Connected and Automated Vehicles