Traffic Flow Model Validation Using METANET, ADOL-C and RPROP | Zendy

Adam Poole | Zendy; Apostolos Kotsialos | Zendy

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Traffic Flow Model Validation Using METANET, ADOL-C and RPROP

Author(s) -

Adam Poole,

Apostolos Kotsialos

Publication year - 2016

Publication title -

ifac-papersonline

Language(s) - English

Resource type - Journals

eISSN - 2405-8971

pISSN - 2405-8963

DOI - 10.1016/j.ifacol.2016.07.049

Subject(s) - flow (mathematics) , computer science , model validation , rprop , mechanics , simulation , environmental science , physics , artificial intelligence , types of artificial neural networks , artificial neural network , time delay neural network , data science

Macroscopic traffic flow model calibration is an optimisation problem typically solved by a derivative-free population based stochastic search methods. This paper reports on the use of a gradient based algorithm using automatic differentiation. The ADOL-C library is coupled with the METANET source code and this system is embedded within an optimisation algorithm based on RPROP. The result is a very efficient system which is able to be calibrate METANET's second order model by determining the density and speed equation parameters as well as the fundamental diagrams used. Information obtained from the system's Jacobian provides extra insight into the system dynamics. A 22 km site is considered near Sheffield, UK and the results of a typical calibration and validation process are reported

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