From Driving Simulator Experiments to Field-Traffic Application: Improving the Transferability of Car-Following Models
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 1
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.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies (the field data can be found on the NGSIM website at https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm). Some or all data, models, or code generated or used during the study are available from the corresponding author by request (anonymized driving simulator data can be made available based on request).
Acknowledgments
The core part of this research is supported by the Next Generation Driving Behavior Model (NG-DBM) project funded by the FP7 Marie Curie Career Integration Grant of the European Union (PCIG14-GA-2013-631782) and the Economic and Social Research Council, UK. Prof. Stephane Hess’s contribution is supported by the European Research Council through the consolidator Grant No. 615596-DECISIONS. We would like to thank Michael Daly of the UoLDS team for creating the driving simulator scenarios and Dr. Daryl Hibberd for his feedback on the design of the experiments.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 1 • January 2021
Copyright
© 2020 American Society of Civil Engineers.
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Received: Oct 3, 2019
Accepted: Aug 7, 2020
Published online: Oct 30, 2020
Published in print: Jan 1, 2021
Discussion open until: Mar 30, 2021
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