Optimal Sensor Placement in Road Transportation Networks using Virtual Variances

International audienceThis paper addresses the problem of Optimal Sensor Placement in Road Transportation Networks. The per formance of the sensors is measured in terms of estimation error covariance of the Best Linear Unbiased Estimator of cumulative flows in the network over a long period. Sensors are to be placed in such a way that the sum of the error covariance and of a cost penalizing the number of sensors is minimized. The problem, inherently combinatorial, is relaxed using the concept of Virtual Variance. The resulting problem can be cast as a convex problem, whose computational load ismuch lower than the original combinatorial problem. Several variations are discussed, and the algorithm is applied to a regular grid network, for which an explicit comparison with the true optimum is offered, and, using data from the Grenoble Traffic Lab sensor network, to the real-world scenario ofRocade Sud in Grenoble, France

Optimal Sensor Placement in Road Transportation Networks using Virtual Variances

International audienceThis paper addresses the problem of Optimal Sensor Placement in Road Transportation Networks. The per formance of the sensors is measured in terms of estimation error covariance of the Best Linear Unbiased Estimator of cumulative flows in the network over a long period. Sensors are to be placed in such a way that the sum of the error covariance and of a cost penalizing the number of sensors is minimized. The problem, inherently combinatorial, is relaxed using the concept of Virtual Variance. The resulting problem can be cast as a convex problem, whose computational load ismuch lower than the original combinatorial problem. Several variations are discussed, and the algorithm is applied to a regular grid network, for which an explicit comparison with the true optimum is offered, and, using data from the Grenoble Traffic Lab sensor network, to the real-world scenario ofRocade Sud in Grenoble, France

Estimation of fundamental diagrams in large-scale traffic networks with scarce sensor measurements

International audienceThe macroscopic fundamental diagram (MFD) relates space-mean flow density and the speed of an entire network. We present a method for the estimation of a "normalized" MFD with the goal to compute specific Fundamental Diagram in places where loop sensors data is no available. The methodology allows using some data from different points in the city and possibly combining several kinds of information. To this aim, we tackle at least three major concerns: the data dispersion, the sparsity of the data, and the role of the link (with data) within the network. To preserve the information we decided to treat it as two-dimensional signals (images), so we based our estimation algorithm on image analysis, preserving data veracity until the last steps (instead of first matching curves that induce a first approximation). Then we use image classification and filtering tools for merging of main features and scaling. Finally, just the Floating Car Data (FCD) is used to map back the general form to the specific road where sensors are missing. We obtained a representation of the street by means of its likelihood with other links within the same network