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dc.contributor.authorHu, Xianbiao
dc.contributor.authorChiu, Yi-Chang
dc.contributor.authorShelton, Jeff
dc.date.accessioned2017-03-10T17:47:30Z
dc.date.available2017-03-10T17:47:30Z
dc.date.issued2016-03-30
dc.identifier.citationDevelopment of a behaviorally induced system optimal travel demand management system 2016, 21 (1):12 Journal of Intelligent Transportation Systemsen
dc.identifier.issn1547-2450
dc.identifier.issn1547-2442
dc.identifier.doi10.1080/15472450.2016.1171151
dc.identifier.urihttp://hdl.handle.net/10150/622793
dc.description.abstractThe basic design concept of most advanced traveler information systems (ATIS) is to present generic information to travelers, leaving travelers to react to the information in their own way. This passive way of managing traffic by providing generic traffic information makes it difficult to predict the outcome and may even incur an adverse effect, such as overreaction (also referred to as the herding effect). Active traffic and demand management (ATDM) is another approach that has received continual attention from both academic research and real-world practice, aiming to effectively influence people's travel demand, provide more travel options, coordinate between travelers, and reduce the need for travel. The research discussed in this article deals with how to provide users with a travel option that aims to minimize the marginal system impact that results from this routing. The goal of this research is to take better advantage of the available real-time traffic information provided by ATIS, to further improve the system level traffic condition from User Equilibrium (UE), or a real-world traffic system that is worse than UE, toward System Optimal (SO), and avoid passively managing traffic. A behaviorally induced, system optimal travel demand management model is presented to achieve this goal through incremental routing. Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona, as well as on the Capital Area Metropolitan Planning Organization (CAMPO) network in Austin, TX. The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO, and results in substantial economic savings.
dc.language.isoenen
dc.publisherTAYLOR & FRANCIS INCen
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/15472450.2016.1171151en
dc.rightsCopyright © 2017 Taylor & Francis.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectactive traffic and demand management (ATDM)en
dc.subjectbehaviorally induced system optimalen
dc.subjectdynamic traffic assignmenten
dc.subjectsystem optimalen
dc.subjecttravel behaviouren
dc.subjecttravel demand management (TDM)en
dc.titleDevelopment of a behaviorally induced system optimal travel demand management systemen
dc.typeArticleen
dc.contributor.departmentDepartment of Civil Engineering and Engineering Mechanics, University of Arizonaen
dc.identifier.journalJournal of Intelligent Transportation Systemsen
dc.description.note12 month embargo; Published online: 30 Mar 2016en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal accepted manuscripten
refterms.dateFOA2017-03-31T00:00:00Z
html.description.abstractThe basic design concept of most advanced traveler information systems (ATIS) is to present generic information to travelers, leaving travelers to react to the information in their own way. This passive way of managing traffic by providing generic traffic information makes it difficult to predict the outcome and may even incur an adverse effect, such as overreaction (also referred to as the herding effect). Active traffic and demand management (ATDM) is another approach that has received continual attention from both academic research and real-world practice, aiming to effectively influence people's travel demand, provide more travel options, coordinate between travelers, and reduce the need for travel. The research discussed in this article deals with how to provide users with a travel option that aims to minimize the marginal system impact that results from this routing. The goal of this research is to take better advantage of the available real-time traffic information provided by ATIS, to further improve the system level traffic condition from User Equilibrium (UE), or a real-world traffic system that is worse than UE, toward System Optimal (SO), and avoid passively managing traffic. A behaviorally induced, system optimal travel demand management model is presented to achieve this goal through incremental routing. Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona, as well as on the Capital Area Metropolitan Planning Organization (CAMPO) network in Austin, TX. The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO, and results in substantial economic savings.


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