Large scale estimation of arterial traffic and structural analysis of traffic patterns using probe vehicles
| العنوان: | Large scale estimation of arterial traffic and structural analysis of traffic patterns using probe vehicles |
|---|---|
| المؤلفون: | Hofleitner, Aude, Herring, Ryan, Bayen, Alexandre, Han, Yufei, Moutarde, Fabien, de La Fortelle, Arnaud |
| المساهمون: | Department of Electrical Engineering and Computer Science [Berkeley] (EECS), University of California [Berkeley], University of California-University of California, California Center for Innovative Transportation, Department of Civil and Environmental Engineering [Berkeley] (CEE), Centre de Robotique (CAOR), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Informatique, Mathématiques et Automatique pour la Route Automatisée (IMARA), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC), Mines Paris - PSL (École nationale supérieure des mines de Paris) |
| المصدر: | proc. Transportation Research Board 91st Annual Meeting (TRB'2012) Transportation Research Board 91st Annual Meeting (TRB'2012), Jan 2012, Washington, United States |
| بيانات النشر: | HAL CCSD, 2012. |
| سنة النشر: | 2012 |
| مصطلحات موضوعية: | [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
| الوصف: | International audience; 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. |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::7c90e70c1b6c8787ba43f9932eec40ed https://hal-mines-paristech.archives-ouvertes.fr/hal-00741497/file/main_trb2012_Gaussian_NMF.pdf |
| رقم الأكسشن: | edsair.dedup.wf.001..7c90e70c1b6c8787ba43f9932eec40ed |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |