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Title:

Parameter Estimation of a Stochastic Microscopic Car-Following Model

Accession Number:

01660986

Record Type:

Component

Abstract:

This paper presents the formulation and parameter estimation of a family of microscopic car following models based on stochastic desired acceleration processes. This formulation generalizes previous separate efforts based on Brownian and geometric Brownian acceleration processes, each reproducing a different feature of traffic instabilities. The single extra parameter needed regulates the type of driver error in a scale from Brownian to geometric Brownian acceleration processes. The model parameters are estimated using maximum-likelihood estimation on a six-vehicle car-following experiment. We find evidence that the error process is closer to the geometric Brownian motion and that it is the same for all six drivers.

Supplemental Notes:

This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.

Report/Paper Numbers:

18-06544

Language:

English

Authors:

Xu, Tu
Laval, Jorge A

Pagination:

14p

Publication Date:

2018

Conference:

Transportation Research Board 97th Annual Meeting

Location: Washington DC, United States
Date: 2018-1-7 to 2018-1-11
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Uncontrolled Terms:

Brownian motion

Subject Areas:

Highways; Operations and Traffic Management

Source Data:

Transportation Research Board Annual Meeting 2018 Paper #18-06544

Files:

TRIS, TRB, ATRI

Created Date:

Jan 8 2018 11:41AM