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- W2887782725 abstract "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." @default.
- W2887782725 created "2018-08-22" @default.
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- W2887782725 date "2018-01-04" @default.
- W2887782725 modified "2023-09-27" @default.
- W2887782725 title "Traffic Monitoring on Sparse-Measurement Network-Wide Fundamental Diagrams" @default.
- W2887782725 hasPublicationYear "2018" @default.
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