@article{ref1,
title="Can we trust the speed-spacing relationship estimated by car-following model from non-stationary trajectory data?",
journal="Transportmetrica A: transport science",
year="2019",
author="Wang, Hao and Wang, Wei and Chen, Jun and Xu, Chengcheng and Li, Ye",
volume="15",
number="2",
pages="263-284",
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.<p /> <p>Language: en</p>",
language="en",
issn="2324-9935",
doi="10.1080/23249935.2018.1466211",
url="http://dx.doi.org/10.1080/23249935.2018.1466211"
}