Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors

Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, the authors evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. The authors consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. The authors assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, the authors use microscopic traffic simulation, where the authors compare the estimates with the ground truth using Edie's definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, the authors find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.


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  • Accession Number: 01666111
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 16 2018 11:22AM