University of Illinois Urbana-Champaign

Graph signal processing for traffic analysis and forcasting

Gacek, Andrew

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https://hdl.handle.net/2142/107803

Description

Title
Graph signal processing for traffic analysis and forcasting
Author(s)
Gacek, Andrew
Contributor(s)
Do, Minh
Issue Date
2020-05
Keyword(s)
  • graph signal processing
  • GSP
  • traffic network
  • wavelets
  • anomaly
Abstract
The application of Graph Signal Processing (GSP) techniques to traffi c 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 traffi c states with the goal of discovering patterns in traffi c 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 tra ffic incidents via graph wavelet decompositions.
Type of Resource
text
Language
en
Permalink
http://hdl.handle.net/2142/107803

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