Bridging the gap: unifying transportation planning and operations through enhanced travel demand modeling

Simple item page
dc.contributor.advisorBoyles, Stephen David, 1982-
dc.contributor.committeeMemberBhat, Chandra R.
dc.contributor.committeeMemberHasenbein, John J.
dc.contributor.committeeMemberHainen, Alexander M.
dc.contributor.committeeMemberMachemehl, Randy B.
dc.creatorAlexander, William Eric
dc.date.accessioned2024-07-16T14:49:10Z
dc.date.available2024-07-16T14:49:10Z
dc.date.created2024-05
dc.date.issued2024-05
dc.date.submittedMay 2024
dc.date.updated2024-07-16T14:49:11Z
dc.description.abstractTravel demand modeling is a necessary and useful tool for municipalities to better allocate resources, and much research has been developed on how best to calibrate and apply these models. This dissertation identifies and addresses a disconnect which exists between the modeling and operations fields, seeking to bridge the gap for improved performance in both the predictive capabilities of these models as well as the operational efficiency of the ensuing equilibrium that will develop between road users’ route choices and managers’ traffic signal timing decisions made based on these models. A major engineering contribution we detail is the development of wrap, a cross-platform, free-and-open-source travel demand modeling software package. Scientific contributions presented in this dissertation include a novel method for calibrating trip generation rates based on a sample of roadway volume data as well as a detailed investigation of pressure-based traffic signal timing optimization. This latter investigation introduces three novel pressure functions, offering improved route choice equilibria by balancing signal timings and drivers’ anticipated efforts to minimize their cost of travel. This work also introduces preliminary contributions of practical benefit, including the use of machine learning for corridor travel time predictions and reinforcement learning for traffic signal control to better position this research for application in the field. The collaborative potential between improved travel demand models and transportation operations policies is strongly emphasized, with the contributions of this work intended to facilitate synergy in these two fields for the development of a more efficient transportation network.
dc.description.departmentCivil, Architectural and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/2152/126048
dc.identifier.urihttps://doi.org/10.26153/tsw/52593
dc.language.isoEnglish
dc.subjectTraffic assignment
dc.subjectTravel demand modeling
dc.subjectTraffic signal timing optimization
dc.subjectAlgorithm B
dc.subjectTravel demand model calibration
dc.subjectWrap
dc.subjectOpen-source software
dc.titleBridging the gap: unifying transportation planning and operations through enhanced travel demand modeling
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeCockrell School of Engineering
thesis.degree.departmentCivil, Architectural and Environmental Engineering
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.nameDoctor of Philosophy
thesis.degree.programTransportation Engineering
thesis.degree.schoolThe University of Texas at Austin

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
ALEXANDER-PRIMARY-2024.pdf
Size:
2.91 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
2 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
proquest_license.txt
Size:
6.38 KB
Format:
Plain Text
Description:
Download

Collections

UT Electronic Theses and Dissertations