Paschalidis, E, Choudhury, CF orcid.org/0000-0002-8886-8976 and Hess, S orcid.org/0000-0002-3650-2518 (2021) From driving simulator experiments to field traffic application: Improving the transferability of car-following models. Journal of Transportation Engineering, Part A: Systems, 147 (1). ISSN 0733-947X
Abstract
Over the last few decades, there have been two main streams of data used for driving behavior research: trajectory data collected from the field [such as using video recordings and global positioning systems (GPS)] and experimental data from driving simulators (where the behaviors of the drivers are recorded in controlled laboratory conditions). Previous research has shown that the parameters of car-following models developed using simulator data are not directly transferable to the field. In this research, we investigate the differences in detail and compare alternative methods to overcome the problem. Two types of approaches are tested in this regard: (1) econometric approaches for increasing model transferability—Bayesian updating and combined transfer estimation—and (2) joint estimation using both data sources simultaneously. Car-following models based on a stimulus-response framework are developed in this regard, using experimental data collected at the University of Leeds Driving Simulator (UoLDS) and detailed trajectory data collected at California Interstate 80 (I-80), in the US, and the UK Motorway 1 (M1). The estimation results of the initial models show that car-following models using driving-simulator data are closer to the UK (M1) data than the I-80 data but not directly transferable. Performances of the proposed approaches for improving transferability are evaluated using t-tests for individual parameter equivalence and transferability test statistics (TTS). The results indicate that the transferability can be improved after parameter updating, and the combined transfer estimation is found to outperform the other approaches. The findings of this study will enable a more effective usage of the driving simulator data for the estimation of mainstream mathematical models of driving behavior while the techniques used can be applied to other types of econometric models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 American Society of Civil Engineers. This is an author produced version of a journal article published in Journal of Transportation Engineering, Part A: Systems. Uploaded in accordance with the publisher's self-archiving policy. This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://doi.org/10.1061/JTEPBS.0000468. |
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: Choice Modelling The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number EU - European Union GA 631782 EU - European Union 615596 |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Aug 2020 14:35 |
Last Modified: | 19 Nov 2020 10:29 |
Status: | Published |
Publisher: | ASCE |
Identification Number: | 10.1061/JTEPBS.0000468 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164282 |