@article{ref1,
title="A probability lane-changing model considering memory effect and driver heterogeneity",
journal="Transportmetrica B: transport dynamics",
year="2020",
author="Pang, Meng-Yuan and Jia, Bin and Xie, Dong-Fan and Li, Xin-Gang",
volume="8",
number="1",
pages="72-89",
abstract="Lane changing is one of the basic driving behaviours, which may induce traffic oscillations and incidents. However, it is difficult to well model the lane-changing decision process due to the complex traffic status. To promote the prediction accuracy of lane-changing decisions, this paper presents a probability lane-changing model by taking into account the memory effect. That is, the lane-changing decision model considers a series of trajectory data rather than the data of a specific time utilized in most existing models. Furthermore, the drivers are classified in terms of lane-changing trajectories, which is expected to further promote the prediction accuracy of the lane-changing decision model. Calibrations and validations are carried out based on the NGSIM data, which indicate that the proposed model can significantly promote the prediction accuracy of lane-changing decisions.<p /> <p>Language: en</p>",
language="en",
issn="2168-0566",
doi="10.1080/21680566.2020.1715310",
url="http://dx.doi.org/10.1080/21680566.2020.1715310"
}