Physics Informed Deep Learning for Traffic State Estimation

The challenge of traffic state estimation (TSE) lies in the sparsity of observed traffic data and the sensor noise present in the data. This paper presents a new approach - physics informed deep learning (PIDL) method - to tackle this problem. PIDL equips a deep learning neural network with the stre...

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Bibliographic Details
Published in2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) pp. 1 - 6
Main Authors Huang, Archie J., Agarwal, Shaurya
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2020
Subjects
Biological neural networks
Deep learning
Physics
Physics Informed Machine Learning
Real-time systems
Roads
Sensor Placement
State estimation
Traffic State Estimation
Training
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DOI10.1109/ITSC45102.2020.9294236

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Summary:The challenge of traffic state estimation (TSE) lies in the sparsity of observed traffic data and the sensor noise present in the data. This paper presents a new approach - physics informed deep learning (PIDL) method - to tackle this problem. PIDL equips a deep learning neural network with the strength of the physical law governing traffic flow to better estimate traffic conditions. A case study is conducted where the accuracy and convergence-time of the algorithm are tested for varying levels of scarcely observed traffic density data - both in Lagrangian and Eulerian frames. The estimation results are encouraging and demonstrate the capability of PIDL in making accurate and prompt estimation of traffic states.
DOI:10.1109/ITSC45102.2020.9294236