GRAPH SIGNAL PROCESSING FOR TRAFFIC ANALYSIS AND FORECASTING

Content Files

SP20-ECE499-Thesis-Gacek, Andrew.pdf

Permalink

https://hdl.handle.net/2142/125096

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.
Type of Resource: text
Genre of Resource: dissertation/thesis
Language: eng

The best of ECE undergraduate research