We propose eigen-based and Markov-based methods to explore the global and local structure of patterns in real-world GPS taxi trajectories. Our primary goal is to predict the subsequent path of an in-progress taxi trajectory. The exploration of global and local structure in the data differentiates this work from the state-of-the-art literature in trajectory prediction methods, which mostly focuses on local structures and feature selection. We propose four algorithms: a frequency based algorithm FreqCount, which we use as a benchmark, two eigen-based (EigenStrat, LapStrat), and a Markov-based algorithm (MCStrat). Pairwise performance analysis on a large real-world data set reveals that LapStrat is the best performer, followed by MCStrat.
|Original language||English (US)|
|Number of pages||5|
|Journal||CEUR Workshop Proceedings|
|State||Published - Jan 1 2013|