Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Maximum-Entropy Sampling
Operations Research
A Network Based Model for Traffic Sensor Location with Implications on O/D Matrix Estimates
Transportation Science
Models and algorithms for the screen line-based traffic-counting location problems
Computers and Operations Research
Dynamic origin-destination demand estimation using automatic vehicle identification data
IEEE Transactions on Intelligent Transportation Systems
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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.