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Article overview
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Comparative model accuracy of a data-fitted generalized Aw-Rascle-Zhang model | | Shimao Fan
; Michael Herty
; Benjamin Seibold
; | | Date: |
30 Oct 2013 | | Abstract: | The Aw-Rascle-Zhang (ARZ) model can be interpreted as a generalization of the
Lighthill-Whitham-Richards (LWR) model, possessing a family of fundamental
diagram curves, each of which represents a class of drivers with a different
empty road velocity. A weakness of this approach is that different drivers
possess vastly different densities at which traffic flow stagnates. This
drawback can be overcome by modifying the pressure relation in the ARZ model,
leading to the generalized Aw-Rascle-Zhang (GARZ) model. We present an approach
to determine the parameter functions of the GARZ model from fundamental diagram
measurement data. The predictive accuracy of the resulting data-fitted GARZ
model is compared to other traffic models by means of a three-detector test
setup, employing two types of data: vehicle trajectory data, and sensor data.
This work also considers the extension of the ARZ and the GARZ models to models
with a relaxation term, and conducts an investigation of the optimal relaxation
time. | | Source: | arXiv, 1310.8219 | | Services: | Forum | Review | PDF | Favorites | |
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