Street Sensors Set Selection through Road Network Modeling and Observability Measures

Nowadays, vehicle flow monitoring, model-based traffic management and congestion prediction are becoming fundamental elements for the realization of the Smart City paradigm. These tasks usually require wide sensor deployments, but, due to economical, practical and environmental constraints, they must be accomplished with a limited number of sensors. Thus motivated, this work addresses the sensors selection problem for urban street monitoring, by employing vector maps as the basic information and considering the placement of at most one sensor along each road, but with a fixed number of available devices. To solve the problem, the concept of system observability is exploited as the criterion for optimal sensor placement, specifically related to the capability of estimating the traffic flow in each road using the available output measurements. In this framework, different integer nonlinear programming problems are proposed, whose solutions are studied and analyzed by means of numerical simulations on a real case scenario.