GRAPH SIGNAL PROCESSING FOR TRAFFIC ANALYSIS AND FORECASTING
Gacek, Andrew
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https://hdl.handle.net/2142/125096
Description
Title
GRAPH SIGNAL PROCESSING FOR TRAFFIC ANALYSIS AND FORECASTING
Author(s)
Gacek, Andrew
Issue Date
2020-05-05
Keyword(s)
Graph Signal Processing
Date of Ingest
2024-11-12T10:39:30-06:00
Abstract
The application of Graph Signal Processing (GSP) techniques to traffic networks in urban settings is a challenging problem that has become the topic of many studies in recent works. In this paper, we analyze the effectiveness of these techniques to sparse image data collected in suburban environments using the STREETS dataset. We start with spectral analysis on these traffic states with the goal of discovering patterns in traffic data not obvious to the casual observer. Second, we introduce structure into the traffic forecasting problem with an adaptive graph filter. The performance of this filter is compared to non-graphical methods. Finally, we attempt to classify anomalous traffic incidents via graph wavelet decompositions.
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