An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications

To design a transportation sensor network, the decision-maker needs to determine what sensor investments should be made, as well as when, how, where and with what technologies. This paper focuses on locating a limited set of traffic counting stations and automatic vehicle identification readers in a network so as to maximize the expected information gain for the subsequent origin-destination demand estimation problem. The proposed sensor design model explicitly takes into account several important error sources in traffic origin-destination demand estimation, such as the uncertainty in historical demand information, sensor measurement errors, as well as approximation errors associated with link proportions. Based on a mean-square measure, the paper derives analytical formulations to describe estimation variance propagation for a set of linear measurement equations. A scenario-based stochastic optimization procedure and a beam search algorithm are developed to find sub-optimal point and point-to-point sensor locations subject to budget constraints. The paper also provides a number of illustrative examples to demonstrate the effectiveness of the proposed methodology.