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Title:

Vehicle Dynamics Model for Estimating Typical Vehicle Accelerations

Accession Number:

01557772

Record Type:

Component

Availability:

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Order URL: http://www.trb.org/Main/Blurbs/173559.aspx

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Order URL: http://worldcat.org/isbn/9780309369275

Abstract:

Developing mathematical models for accurate estimation of the longitudinal acceleration behavior of drivers and vehicles is an important challenge in traffic engineering. The modeling of vehicle acceleration is complex because of its dependence on vehicle type and human driving behavior. Existing acceleration dynamics models have tied typical acceleration (the maximum acceleration that drivers would use in a free-flow condition) to maximum acceleration models that are descriptive of the maximum acceleration capability of the vehicle. Although this approach generally results in a better fitting of field data that are understandable and predictable, the proposed models do not take into account differing driving behaviors. The research presented in this paper develops a model that overcomes this limitation by explicitly incorporating driver behavior in the mathematical expression of a dynamics-based acceleration model. The proposed model has a flexible shape that allows it to incorporate driver variations. Furthermore, the model is demonstrated to be superior to similar models because it predicts more accurate acceleration levels in all domains.

Monograph Accession #:

01586813

Report/Paper Numbers:

15-3970

Language:

English

Authors:

Fadhloun, Karim
Rakha, Hesham
Loulizi, Amara
Abdelkefi, Abdessattar

Pagination:

pp 61–71

Publication Date:

2015

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Issue Number: 2491
Publisher: Transportation Research Board
ISSN: 0361-1981

ISBN:

9780309369275

Media Type:

Print

Features:

Figures (5) ; References (12) ; Tables (4)

Subject Areas:

Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

Files:

TRIS, TRB, ATRI

Created Date:

Dec 30 2014 1:18PM

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