Citation

Pang MY, Jia B, Xie DF, Li XG. Transportmetrica B: Transp. Dyn. 2020; 8(1): 72-89.

Copyright

(Copyright © 2020, Hong Kong Society for Transportation Studies, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/21680566.2020.1715310

PMID

unavailable

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.

Language: en

Keywords

discrete choice-based model; driver heterogeneity; Lane-changing; memory effect