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This work presents a collapsed generalized Aw-Rascle-Zhang (CGARZ) model,which fits into a generic second order model (GSOM) framework. GSOMs augmentthe evolution of the traffic density by a second state variable characterizinga property of vehicles or drivers. A cell transmission model for the numericalsolution of GSOMs is derived, which is based on analyzing the sending andreceiving functions of the traffic density and total property. The predictiveaccuracy of the CGARZ model is then compared to the classical first-order LWRand four second-order models. To that end, a systematic approach to calibratemodel parameters from sensor flow-density data is introduced and applied to allmodels studied. The comparative model validation is conducted using two typesof field data: vehicle trajectory data, and loop detector data. It is shownthat the CGARZ model provides an intriguing combination of simple modeldynamics in the free-flow regime and a representation of the spread offlow-density data in the congested regime, while possessing a competitiveprediction accuracy.

A Collapsed Generalized Aw-Rascle-Zhang Model and Its Model Accuracy