TY - JOUR
T1 - Predicting globally and locally
T2 - A comparison of methods for vehicle trajectory prediction
AU - Groves, William
AU - Nunes, Ernesto
AU - Gini, Maria L
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84923902034&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84923902034&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84923902034
SN - 1613-0073
VL - 1088
SP - 5
EP - 9
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
ER -