Traffic Monitoring on Sparse-Measurement Network-Wide Fundamental Diagrams

Mat Jusoh, R. and Ampountolas, K. (2018) Traffic Monitoring on Sparse-Measurement Network-Wide Fundamental Diagrams. 50th Annual UTSG Conference, London, UK, 2-4 Jan 2018.

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This paper presents a rigorous information theoretic-based framework for the optimal place- ment of sensors across a transport network, the efficient model selection, and construction of sparse-measurement network-wide fundamental diagrams. For the optimal placement of sen- sors across the transport network a set cover integer programming (IP) problem is developed. A measure of correlation between random variables, reflecting occupancy observations, is in- troduced as a “distance” metric to provide sufficient coverage and information accuracy. The relative entropy or Kullback-Leibler divergence 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 evaluated with experimental loop-detector data of one week from a central business district with around sixty sensors. Results demonstrate that the obtained sparse-measurement rival diagrams are able to preserve the shape and main features of the operational full-measurement diagram. Therefore approximated fundamental diagrams, which are in principle less costly, can be used for the efficient monitoring and control of congested urban areas.

Item Type:Conference or Workshop Item
Keywords:Perimeter flow control, sparse-measurement network fundamental diagram, information theory, Kullback-Leibler divergence, integer programming
Glasgow Author(s) Enlighten ID:Ampountolas, Dr Konstantinos and BINTI MAT JUSOH, RUZANNA
Authors: Mat Jusoh, R., and Ampountolas, K.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Infrastructure and Environment

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