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.
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.