01628757

Component

The calibration and validation of second-order macroscopic traffic flow models constitutes a difficult task; and in fact, relatively few calibration results for such macroscopic traffic flow models have been reported so far. This work evaluates a multi-lane second-order macroscopic gas-kinetic traffic (GKT) flow model and its numerical discretization, using real traffic data from a motorway network in the U.K.; where recurrent congestion originated from high on-ramp flows during the morning peak hours is occurring. In the model, the lane-changing terms, simulating lane-changes due to vehicle interactions as well as spontaneous ones, are introduced as source and sink terms in the model equations. The model provides the ability to use different calibration parameters per lane. A high-order finite volume scheme is implemented for spatial discretization, while time integration is based on a high-order implicit-explicit Runge-Kutta method. A relatively new optimization algorithm, namely a parallel, metamodel-assisted Differential Evolution (DE) algorithm, is employed for the calibration of the model parameters by searching for the global optimal solution. Numerical simulations demonstrate that the proposed model is reasonably accurate in reproducing traffic dynamics in the multi-lane framework, while the DE algorithm can be effectively used for its calibration, as well as for other similar macroscopic models.

This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.

01618707

17-01340

English

17p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Genetic algorithms; Macroscopic traffic flow; Multilane highways

United Kingdom

Highways; Operations and Traffic Management

Transportation Research Board Annual Meeting 2017 Paper #17-01340

TRIS, TRB, ATRI

Dec 8 2016 10:25AM