Finding Optimal Sensor Placements for Traffic Data Reconstruction Using QR Pivoting and Deep Reinforcement Learning

This paper studies and presents data-driven methods for finding rankings of traffic links in a network for optimal traffic data reconstruction based on measurements taken from a subset of links. The link ranking represents the importance of respective links in terms of reconstructing traffic informa...

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Bibliographic Details
Published in:Transportation research procedia (Online) 2025, Vol.82, p.2885-2897
Main Authors: Pham, Hoang Dat, MN, Sharath, Ashraf, Ahmed, Mehran, Babak
Format: Article
Language:English
Subjects:
Data Reconstruction
Matrix Factorization
Reinforcement Learning
Sensor Placement
Traffic Data
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Summary:This paper studies and presents data-driven methods for finding rankings of traffic links in a network for optimal traffic data reconstruction based on measurements taken from a subset of links. The link ranking represents the importance of respective links in terms of reconstructing traffic information from sparsely placed sensors, connected vehicles, or other state-of-the-art methods. We first present a baseline method based matrix factorization of the eigen-vector basis matrix, followed by column pivoting. Moreover, we propose a reinforcement learning framework to improve the ranking method when the traffic data is used for the purpose of routing. This study utilizes dynamic traffic data that is observed and estimated from simulation.
ISSN:2352-1465
2352-1465
DOI:10.1016/j.trpro.2024.12.225