dc.contributor.advisor	Laval, Jorge A.
dc.contributor.author	Xu, Tu
dc.contributor.committeeMember	Hunter, Michael
dc.contributor.committeeMember	Mokhtarian, Patricia
dc.contributor.committeeMember	Liu, Haobing
dc.contributor.committeeMember	Maguluri, Siva Theja
dc.contributor.department	Civil and Environmental Engineering
dc.date.accessioned	2020-09-08T12:46:48Z
dc.date.available	2020-09-08T12:46:48Z
dc.date.created	2020-08
dc.date.issued	2020-07-14
dc.date.submitted	August 2020
dc.date.updated	2020-09-08T12:46:49Z
dc.description.abstract	This thesis presents the formulation of a family of two-regime car-following models where both free-flow and congestion regimes obey random processes. This formulation generalizes previous efforts based on Brownian and geometric Brownian acceleration processes, each reproducing a different feature of traffic instabilities. We show that the unified model is able to capture virtually all types of traffic instabilities consistently with empirical data, including formation and propagation of oscillations, capacity drop in the absence of lane changes, and the concave growth pattern of vehicle speeds along a platoon. The probability density of vehicle positions turns out to be analytical in our model, and therefore parameters can be estimated using maximum likelihood. This allows us to test a wide variety of hypotheses using statistical inference methods, such as the homogeneity of the driver/vehicle population and the statistical significance of the impacts of roadway geometry. Using data from two controlled car-following experiments and one uncontrolled car-following dataset, we find that (i) model parameters are similar across repeated experiments within the same dataset but different across datasets, (ii) the acceleration error process is closer to a Brownian motion, and (iii) drivers press the gas pedal harder than usual when they come to an upgrade segment. The model is flexible so that newer vehicle technologies can be incorporated to test such hypotheses as differences in the car-following parameters of automated and regular vehicles, when data becomes available.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/63635
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Car-following model
dc.subject	Traffic instability
dc.subject	Maximum likelihood estimation
dc.title	Parameter estimation and statistical inference of a two-regime car-following model
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Laval, Jorge A.
local.contributor.corporatename	School of Civil and Environmental Engineering
local.contributor.corporatename	College of Engineering
relation.isAdvisorOfPublication	f3602e94-b854-41e9-b48f-6a83639ec469
relation.isOrgUnitOfPublication	88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral

Title: Parameter estimation and statistical inference of a two-regime car-following model

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Parameter estimation and statistical inference of a two-regime car-following model