Designing Heterogeneous Sensor Networks for Estimating and Predicting Path Travel Time Dynamics: An Information-Theoretic Modeling Approach

With a particular emphasis on the end-to-end travel time prediction problem, this paper proposes an informationtheoretic sensor location model that aims to maximize information gains from a set of point, point-to-point and probe sensors in a traffic network. Based on a Kalman filtering structure, the proposed measurement and information quantification models explicitly take into account several important sources of errors in the travel time estimation/prediction process, such as the uncertainty associated with prior travel time estimates, measurement errors and sampling errors. After thoroughly examining a number of possible measures of information gain, this paper selects a path travel time prediction uncertainty criterion to construct a joint sensor location and travel time estimation/prediction framework. We further discuss how to quantify information gain for steady state historical databases and point-to-point sensors with multiple paths, and a heuristic beam-search algorithm is developed to solve the combinatorial sensor selection problem. A number of illustrative examples are used to demonstrate the effectiveness of the proposed methodology.