Comparative model accuracy of a data-fitted generalized Aw-Rascle-Zhang model
Genre
Journal ArticleDate
2014-01-01Author
Fan, SHerty, M
Seibold, B
Subject
Traffic modelLighthill-Whitham-Richards
Aw-Rascle-Zhang
generalized
second order
fundamental diagram
trajectory
sensor
data
validation
Permanent link to this record
http://hdl.handle.net/20.500.12613/5924
Metadata
Show full item recordDOI
10.3934/nhm.2014.9.239Abstract
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. © American Institute of Mathematical Sciences.Citation to related work
American Institute of Mathematical Sciences (AIMS)Has part
Networks and Heterogeneous MediaADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.eduae974a485f413a2113503eed53cd6c53
http://dx.doi.org/10.34944/dspace/5906