Reliable Real-time Destination Prediction

Abstract

In this paper, a reliable online destination prediction methodology is presented. The destination prediction methodology consists of a novel sequential complete diameter distance limited clustering method and an ensemble of random forest classifiers employing a one-vs-rest binarization strategy. Through the use of a novel OvR Uncertainty metric, predictions with high uncertainty could be withheld, thus increasing the overall reliability of the predictions made. The methodology was validated on 778 journeys from two real non-commuter vehicles based in the UK. These datasets allowed the methodology to be tested on real, yet challenging-to-predict journeys and irregular driver behavior. The sequential complete diameter distance limited clustering method was found to be a fast and effective method for sequentially clustering GPS coordinates into clusters that correspond to geographical locations. Prediction results showed that while only an overall mean prediction accuracy of 52% and 34% could be achieved on the two datasets, mean prediction accuracy could be significantly increased to over 90% and 73% respectively by only providing predictions with low uncertainty.