  This work presents a collapsed generalized Aw-Rascle-Zhang (CGARZ) model,
which fits into a generic second order model (GSOM) framework. GSOMs augment
the evolution of the traffic density by a second state variable characterizing
a property of vehicles or drivers. A cell transmission model for the numerical
solution of GSOMs is derived, which is based on analyzing the sending and
receiving functions of the traffic density and total property. The predictive
accuracy of the CGARZ model is then compared to the classical first-order LWR
and four second-order models. To that end, a systematic approach to calibrate
model parameters from sensor flow-density data is introduced and applied to all
models studied. The comparative model validation is conducted using two types
of field data: vehicle trajectory data, and loop detector data. It is shown
that the CGARZ model provides an intriguing combination of simple model
dynamics in the free-flow regime and a representation of the spread of
flow-density data in the congested regime, while possessing a competitive
prediction accuracy.
