Pattern Recognition in Urban Traffic Flows

Accurate predictions of traffic volumes allow authorities to optimize local and network-wide traffic management systems. Traffic flows show variations with respect to different time scales. The authors argue that many of the long- and short-term fluctuations are predictable, because they are recurrent. Due to noise, it is however not trivial to disentangle all temporal patterns. In this study, we propose a novel machine learning method that decomposes a traffic flow time series into multiple interpretable temporal patterns, so-called profiles. We use a data-driven neural network approach that automatically finds (1) the long- and short-term profiles in the presence of noise, (2) the magnitude of each profile at any given time, and (3) the linear combination of these profiles to construct a ‘denoised’ reproduction of the observed traffic volume time series. We apply our approach to traffic volumes in the city of Enschede, the Netherlands, using two years of data..

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Eikenbroek, Oskar A L
  • Mes, Martijn
  • Van Berkum, Eric C
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 18p

Subject/Index Terms

• TRT Terms: Neural networks; Pattern recognition systems; Traffic data; Traffic flow; Traffic volume; Urban highways
• Geographic Terms: Enschede (Netherlands)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01697443
• Record Type: Publication
• Report/Paper Numbers: 19-01430
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:50PM