Large Scale Estimation of Arterial Traffic and Structural Analysis of Traffic Patterns from Probe Vehicles

Bibtex entry :

@conference { trb12,
    author = { Aude Hofleitner and Ryan Herring and Alexandre Bayen and Yufei Han and Fabien Moutarde and Arnaud de La Fortelle },
    title = { Large Scale Estimation of Arterial Traffic and Structural Analysis of Traffic Patterns from Probe Vehicles },
    booktitle = { TRB 2012 },
    year = { 2012 },
    abstract = { Estimating and analyzing trac conditions on large arterial networks is an inherently dif-  cult task. The rst goal of this article is to demonstrate how arterial trac conditions can  be estimated using sparsely sampled GPS probe vehicle data provided by a small percentage  of vehicles. Trac signals, stop signs, and other  ow inhibitors make estimating arterial trac  conditions signicantly more dicult than estimating highway trac conditions. To address  these challenges, we propose a statistical modeling framework that leverages a large historical  database and relies on the fact that trac conditions tend to follow distinct patterns over the  course of a week. The second goal of the article is to provide a global network-level analysis of  trac patterns using matrix factorization and clustering methods. These techniques allow us  to characterize important spatial congurations in the network and to analyze trac dynamics  at a network scale. We identify trac patterns that indicate intrinsic spatio-temporal charac-  teristics over the entire network and give insight into the trac dynamics of an entire city. By  integrating our estimation technique with our analysis method, we achieve a general framework  for extracting, processing and interpreting trac information using GPS probe vehicle data. },
    owner = { arnaud },
    timestamp = { 2012.01.30 },
}