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    Comparative model accuracy of a data-fitted generalized Aw-Rascle-Zhang model

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    Genre
    Journal Article
    Date
    2014-01-01
    Author
    Fan, S
    Herty, M
    Seibold, B
    Subject
    Traffic model
    Lighthill-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
    
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    DOI
    10.3934/nhm.2014.9.239
    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. © American Institute of Mathematical Sciences.
    Citation to related work
    American Institute of Mathematical Sciences (AIMS)
    Has part
    Networks and Heterogeneous Media
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    For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
    ae974a485f413a2113503eed53cd6c53
    http://dx.doi.org/10.34944/dspace/5906
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