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 (AVI) readers in a network, so as to maximize the expected information gain for the subsequent origin-destination (OD) demand estimation problem. The proposed sensor design model explicitly takes into account several important error sources in traffic OD 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, this paper derives analytical formulations to describe estimation variance propagation for a set of linear measurement equations. A scenario-based (SB) stochastic optimization procedure and a beam search algorithm are developed to find suboptimal point and point-to-point sensor locations subject to budget constraints. This paper also provides a number of illustrative examples to demonstrate the effectiveness of the proposed methodology.