Mat Jusoh, R. and Ampountolas, K. (2019) Optimal Selection of Traffic Sensors: an Information-Theoretic Framework. In: 2019 American Control Conference (ACC), Philadelphia, PA, USA, 10-12 Jul 2019, pp. 3297-3302. ISBN 9781538679265 (doi: 10.23919/ACC.2019.8815159)
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Abstract
This paper presents an information-theoretic framework for the optimal selection of sensors across a traffic network. For the selection of sensors a set covering integer programming (IP) problem is developed. A measure of correlation between random variables, reflecting a variable of interest, is introduced as a “distance” metric to provide sufficient coverage and information accuracy. The ultimate goal is to select sensors that are most informative about unsensed locations. The Kullback-Leibler divergence (relative entropy) is used to measure the dissimilarity between probability mass functions corresponding to different solutions of the IP program. Efficient model selection is a trade-off between the Kullback-Leibler divergence and the optimal cost of the IP program. The proposed framework is applied to the problem of developing sparse-measurement traffic flow models with empirical inductive loop-detector data of one week from a central business district with about sixty sensors. Results demonstrate that the obtained sparse-measurement rival models are able to preserve the shape and main features of the full-measurement traffic flow models.
| Item Type: | Conference Proceedings |
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| Keywords: | Entropy, information theory, mutual information, integer programming, optimisation, probability, road traffic, sensors, Kullback-Leibler divergence. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | BINTI MAT JUSOH, RUZANNA and Ampountolas, Dr Konstantinos |
| Authors: | Mat Jusoh, R., and Ampountolas, K. |
| College/School: | College of Science and Engineering > School of Engineering > Infrastructure and Environment |
| ISSN: | 2378-5861 |
| ISBN: | 9781538679265 |
| Published Online: | 29 August 2019 |
| Copyright Holders: | Copyright © 2019 AACC |
| First Published: | First published in 2019 American Control Conference (ACC): 3297-3302 |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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