White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Calibration of second order traffic models using kernel based cross entropy method

Maher, MJ and Ngoduy, D (2012) Calibration of second order traffic models using kernel based cross entropy method. Transportation Research. Part C: Emerging Technologies, 24. 102 - 121 . ISSN 0968-090X (In Press)

Full text available as:

Abstract

Second order macroscopic traffic flow models are superior to the(first order) LWR traffic model in replicating non-linear traffic flow phenomena such as phantom traffic jams or stop-and-go waves. In contrast to the LWR model, which assumes an equilibrium speed-density relationship (or socalled fundamental diagram), the second order model uses one more dynamic equation to describe the evolution of the speed, therefore allows the speed to fluctuate around the equilibrium diagram. However, the second order model is so sensitive to model parameters that for a given model parameter set it may exhibit traffic instabilities due to a small initial traffic perturbation (e.g. lanechanging or sudden deceleration). Therefore, small changes of parameter set will lead to completely different model performance, which consequently leads to a more complex calibration effort and hence prohibits its real-life application as compared to the LWR model. So far relatively few calibration results for second order macroscopic traffic flow models have been reported. To contribute to the state-of-the-art, this paper puts forward an effort to find global optimal parameters of a second order macroscopic traffic model using a stochastic optimization approach, namely cross entropy method (CEM). Basically, the CEM is set up to solve discrete combinatorial optimization problems so the main novelty of this paper is to apply the CEM to solve continuous combinatorial optimization problems in transportation through the use of the kernel density estimation method. Numerical studies are carried out to show that the Kernel-based CEM can search for the global optimal model parameters and is a promising method for the calibration of second order traffic models

Item Type: Article
Keywords: traffic flow models, cross entropy method, kernel density estimation, continuous combinatorial optimization
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 26 Mar 2012 10:13
Last Modified: 08 Feb 2013 17:37
Published Version: http://dx.doi.org/10.1016/j.trc.2012.02.007
Status: In Press
Publisher: Elsevier
Identification Number: 10.1016/j.trc.2012.02.007,
URI: http://eprints.whiterose.ac.uk/id/eprint/43780

Actions (repository staff only: login required)