Abstract
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) |
---|---|
Pages (from-to) | 5-9 |
Number of pages | 5 |
Journal | CEUR Workshop Proceedings |
Volume | 1088 |
State | Published - 2013 |
Event | 3rd Workshop on Ubiquitous Data Mining, UDM 2013 - Co-located with the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China Duration: Aug 3 2013 → … |
Bibliographical note
Publisher Copyright:© 2013 IJCAI.