Zhong, RX, Fu, KY, Sumalee, A et al. (2 more authors) (2016) A cross-entropy method and probabilistic sensitivity analysis framework for calibrating microscopic traffic models. Transportation Research Part C: Emerging Technologies, 63. pp. 147-169. ISSN 0968-090X
Abstract
Car following modeling framework seeks for a more realistic representation of car following behavior in complex driving situations to improve traffic safety and to better understand several puzzling traffic flow phenomena, such as stop-and-go oscillations. Calibration and validation techniques pave the way towards the descriptive power of car-following models and their applicability for analyzing traffic flow. However, calibrating these models is never a trivial task. This is caused by the fact that some parameters, such as reaction time, are generally not directly observable from traffic data. On the other hand, traffic data might be subject to various errors and noises. This contribution puts forward a Cross-Entropy Method (CEM) based approach to identify parameters of deterministic car-following models under noisy data by formulating it as a stochastic optimization problem. This approach allows for statistical analysis of the parameter estimations. Another challenge arising in the calibration of car following models concerns the selection of the most important parameters. This paper introduces a relative entropy based Probabilistic Sensitivity Analysis (PSA) algorithm to identify the important parameters so as to reduce the complexity, data requirement and computational effort of the calibration process. Since the CEM and the PSA are based on the Kullback–Leibler (K–L) distance, they can be simultaneously integrated into a unified framework to further reduce the computational burden. The proposed framework is applied to calibrate the intelligent driving model using vehicle trajectories data from the NGSIM project. Results confirm the great potential of this approach.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Elsevier Ltd. All rights reserved. This is an author produced version of a paper published in Transportation Research Part C: Emerging Technologies. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Car-following model; Model calibration; Cross-entropy method; Probabilistic sensitivity analysis; Relative entropy; Kullback–Leibler distance |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/J002186/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Feb 2016 16:17 |
Last Modified: | 16 Nov 2016 09:20 |
Published Version: | http://dx.doi.org/10.1016/j.trc.2015.12.006 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.trc.2015.12.006 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:94667 |