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Title:

Modeling Car-Following Heterogeneities by Considering Leader–Follower Compositions and Driving Style Differences

Accession Number:

01764209

Record Type:

Component

Availability:

Find a library where document is available


Order URL: http://worldcat.org/issn/03611981

Abstract:

To better understand the behavioral heterogeneities of human-operated vehicles, the paper proposes a method to distinguish car-following behaviors in specific leader–follower contexts. Using the Next-Generation Simulation dataset, the car-following data are first classified into four leader–follower compositions, namely, truck–car, car–car, car–truck, and truck–truck. Based on the classified data, we calibrate the parameters of a few well-known car-following models, including Full Velocity Difference model, Intelligent Driver Model, and Gazis–Herman–Rothery model. Principal component analysis and clustering analysis are then applied to the calibrated parameters to discover the behavioral patterns and to find the probabilistic distributions of the parameters for the classified car-following (CCF) models. Simulation results show that compared with the unified car-following models, the estimation errors of calibrated CCF models are reduced by 20.79% to 49.05%, which indicates that the proposed method provides a more accurate description of car-following heterogeneities. The proposed framework could help highway traffic operators better know the traffic users.

Supplemental Notes:

Zhanbo Sun https://orcid.org/0000-0001-9617-7676 © National Academy of Sciences: Transportation Research Board 2021.

Report/Paper Numbers:

TRBAM-21-04385

Language:

English

Authors:

Sun, Zhanbo

ORCID 0000-0001-9617-7676

Yao, Xue

ORCID 0000-0002-6575-8758

Qin, Ziye

ORCID 0000-0001-5785-5331

Zhang, Peitong
Yang, Ze

ORCID 0000-0002-3445-2799

Pagination:

pp 851-864

Publication Date:

2021-11

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2675
Issue Number: 11
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Web

Features:

Figures; References (53) ; Tables

Identifier Terms:

Principal Component Analysis

Subject Areas:

Highways; Safety and Human Factors; Vehicles and Equipment

Files:

TRIS, TRB, ATRI

Created Date:

Dec 23 2020 11:22AM