Event
MS Thesis Defense: Yitian Wang
Tuesday, November 14, 2017
1:00 p.m.
AVW2168
Maria Hoo
301 405 3681
mch@umd.edu
ANNOUNCEMENT: MS Thesis Defense
Name: Yitian WangProfessor Gang Qu
Professor Tudor Dumitras
Date/Time: Tuesday, November 14, 2017, at 1pm
Place: AVW 2168
Title:Analysis and Forecasting for Traffic Flow Data
Abstract:
In this thesis, a number of techniques related to Principal Component Analysis (PCA) are used to derive core traffic patterns from streams of traffic data on a large number of road segments. Using a few number of khidden variables, we show that the traffic information on the road segments can be captured by k traffic patterns. The dimensionality of the correlated road segments is successfully reduced from n to a much smaller number kby applying techniques related to Principal Component Analysis (PCA), where n is the number of road segments while k is the number of hidden variables. We use the knearest neighbor(KNN) method to predict the values of the hidden variables over small time windows. As a result, we are able to forecast the speeds for n road segments very quickly. Our results are aimed at network-level and real-time prediction. In general, the computation of PCA is computationally demanding when n is large. A more efficient online version of PCA, called PASTd algorithm isused to reduce data dimension. As a result, our forecasting method is efficient, flexible, and robust.