Data-driven aggregative schemes for multisource estimation fusion: a road travel time application

The principal motivation for combining estimators has been to avoid the a priori choice of which estimation method to use, by attempting to aggregate all the information which each estimation model embodies. In selecting the 'best' model, one is often discarding useful independent evidence in those models which are rejected. This paper deals with estimation fusion; that is, data fusion for the purpose of estimation. More specifically. estimation fusion is studied under heterogeneous data source configurations. Two estimation fusion schemes could be considered: projective and aggregative. An unified linear model and general framework for later schemes are established. Explicit optimal fusion strategies in the sense of the best linear estimation and weighted least squares are presented. The evaluation of the effectiveness of the proposed schemes was conducted on the traffic application, namely, travel time estimation in a given path of a road network. In this problem, data comes from sensors and other sources of information geographically distributed where communication limitations and other considerations often eliminate the possibility of transmitting the observations into a central node processing where computation is performed.