Reconstructing the Traffic State by Fusion of Heterogeneous Data

Abstract

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on stationary detector data as typically collected by traffic control centres, and may be augmented by floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: 1. compensation for gaps in data caused by detector failure; 2. separation of noise from dynamic traffic information; and 3. the fusion of floating car data with stationary detector data.

For more information see http://www.mtreiber.de or http://www.akesting.de

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• Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts
• Theoretical vs. Empirical Classification and Prediction of Congested Traffic States

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• A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation
• An Open-Source Microscopic Traffic Simulator
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• Refining time-space traffic diagrams: A simple multiple linear regression model
• Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
• Learning Traffic Speed Dynamics from Visualizations
• Stop-and-go wave super-resolution reconstruction via iterative refinement
• Generalized adaptive smoothing based neural network architecture for traffic state estimation
• Scalable analysis of stop-and-go waves: Representation, measurements and insights
• Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction