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  • 标题:Urban network traffic state estimation using a data-based approach ⁎
  • 本地全文:下载
  • 作者:Martin Rodriguez-Vega ; Carlos Canudas-de-Wit ; Hassen Fourati
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2021
  • 卷号:54
  • 期号:2
  • 页码:278-283
  • DOI:10.1016/j.ifacol.2021.06.033
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractIn this paper we propose an estimator of vehicle density in every road section of a large urban traffic network. We assume a limited number of flow and turning ratio sensors can be installed, and that aggregate floating car data (FCD) is available, such that the space-mean speed of each road can be estimated. We propose a method to locate turning ratio sensors, which takes as input previous low-quality estimates of the turn rates, and then assigns to each intersection a weight according to the effect on the total density reconstruction error caused by perturbations between a priori and actual turning ratio values. We evaluate the models and estimator using data from the urban traffic network of Grenoble in France.
  • 关键词:KeywordsTraffic density estimationSensor locationLarge networksTurning ratios
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