Large scale estimation of arterial traffic and structural analysis of traffic patterns using probe vehicles

Abstract : Estimating and analyzing traffi c conditions on large arterial networks is an inherently diffi cult task. The fi rst goal of this article is to demonstrate how arterial tra c conditions can be estimated using sparsely sampled GPS probe vehicle data provided by a small percentage of vehicles. Traffi c signals, stop signs, and other flow inhibitors make estimating arterial traffi c conditions significantly more diffi cult than estimating highway traffi c conditions. To address these challenges, we propose a statistical modeling framework that leverages a large historical database and relies on the fact that tra ffic conditions tend to follow distinct patterns over the course of a week. This model is operational in North California, as part of the Mobile Millennium tra ffic estimation platform. The second goal of the article is to provide a global network-level analysis of tra ffic patterns using matrix factorization and clustering methods. These techniques allow us to characterize spatial tra ffic patterns in the network and to analyze traffi c dynamics at a network scale. We identify tra ffic patterns that indicate intrinsic spatio-temporal characteristics over the entire network and give insight into the traffi c dynamics of an entire city. By integrating our estimation technique with our analysis method, we achieve a general framework for extracting, processing and interpreting traffi c information using GPS probe vehicle data.
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Communication dans un congrès
Transportation Research Board 91st Annual Meeting (TRB'2012), Jan 2012, Washington, United States. 2012
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Aude Hofleitner, Ryan Herring, Alexandre Bayen, Yufei Han, Fabien Moutarde, et al.. Large scale estimation of arterial traffic and structural analysis of traffic patterns using probe vehicles. Transportation Research Board 91st Annual Meeting (TRB'2012), Jan 2012, Washington, United States. 2012. 〈hal-00741497〉

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