The quality of information available on a network is crucial for different transportation planning and management applications. The problem of where to strategically obtain this information has long tradition, and normally can be subdivided into observability problems, focusing on the topological properties of the network, and flow-estimation problems, where (prior) information of traffic states or which type of application is making use of the sensor data are needed to identify optimal sensor locations.
This paper focuses mainly on the first problem type: it presents a new methodology and an intuitive metric for assessing the information quality of a set of sensors in a network. This methodology is based on existing approaches that can efficiently find solutions for full observability (i.e., the set of sensors needed to make the system fully determined). The main contribution is the development of a novel metric that can quantify the quality of a solution in case of partial observability, i.e. when not all sensors characterizing full observability solutions are available. This is an important contribution in this field, since even in small sized networks the solution for full observability requires an exceedingly large amount of sensors.
We tested this new methodology both on toy networks, in order to analyze the properties of the metric and illustrate its logic, and to explain and test a local greedy search algorithm for optimal sensor positioning, and on a real-sized network, to show how the method performs and selects the most informative links where to install the sensors. Analysis of partial observability solutions shows that the local search algorithm succeeds in finding the links that contain the largest deal of information in a network, and to classify families of full observability solutions.