Can we trust the speed–spacing relationship estimated by car-following model from non-stationary trajectory data?

ABSTRACT

This paper studied the reliability of estimating individual speed-spacing relationship by calibrating car-following models with non-stationary data. Empirical trajectories with long durations are used to extract the near stationary data and the dynamic trajectory data. Three car-following models, the Optimal Velocity Model (OVM), the Full Velocity Difference Model, and the Intelligent Driver Model (IDM), are applied for the model calibration. A root mean square error-based indicator is introduced to measure the performance of the model estimation for the stationary speed-spacing relationship. It is found that both the OVM and the IDM perform well in estimating the individual speed-spacing relationship. The IDM has the vantage in the estimation under the situation far away from the stationary traffic state. The results of linear regression indicate that the Stationary-Data-Coverage and Multiple-Dynamic-Type are beneficial to the reliability of the estimation for the individual speed-spacing relationship.

KEYWORDS:

• Speed–spacing relationship
• car-following model
• trajectory-based calibration
• reliability of estimation

Acknowledgement

The authors would also like to thank Professor P. Zheng for providing us the vehicle trajectory data collected in the EC STARDUST project.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Key Research and Development Plan of China: [Grant Number 2016YFE0206800]; National Science Foundation of China: [Grant Number 51478113].