This study proposes a missing data recovery method based on grey-relational nearest-neighbor substitution techniques for treating with missing data from dual-loop detectors in estimating travel time and evaluates the effects of the missing data on travel-time estimation performance. Field data from the Taiwan national freeway no.1 were used as a case study for testing the proposed model. Study results shown that the travel time estimation with missing data rate up to 33%. It is indicated that the proposed missing data treatment model can ensure the accuracy of travel time estimation with incomplete data sets.
Proceedings of the 2003 IEEE International Conference on Systems, Man, and Cybernetics, p.p 102 - 107