Predicting globally and locally: A comparison of methods for vehicle trajectory prediction

William Groves, Ernesto Nunes, Maria L Gini

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 languageEnglish (US)
Pages (from-to)5-9
Number of pages5
JournalCEUR Workshop Proceedings
Volume1088
StatePublished - Jan 1 2013

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