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dc.contributor.authorGao, Jason Hao
dc.contributor.authorPeh, Li-Shiuan
dc.date.accessioned2014-09-17T14:25:42Z
dc.date.available2014-09-17T14:25:42Z
dc.date.issued2014-09
dc.identifier.urihttp://hdl.handle.net.ezproxyberklee.flo.org/1721.1/89791
dc.description.abstractRoadRunner is an in-vehicle app for traffic congestion control without costly roadside infrastructure, instead judiciously harnessing vehicle-to-vehicle communications, cellular connectivity, and onboard computation and sensing to enable large-scale traffic congestion control at higher penetration and finer granularity than previously possible. RoadRunner limits the number of vehicles in a congested region or road by requiring each to possess a token for entry. Tokens can circulate and be reused among multiple vehicles as vehicles move between regions. We built RoadRunner as an Android app utilizing LTE, 802.11p, and 802.11n radios, deployed it on 10 vehicles, and measured cellular access reductions of up to 84% and response time improvements of up to 80%. In a microscopic agent-based traffic simulator, RoadRunner achieved travel speed improvements of up to 7.7% over an industry-strength electronic road pricing system.en_US
dc.description.sponsorshipSingapore-MIT Alliance for Research and Technologyen_US
dc.description.sponsorshipAmerican Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshipen_US
dc.language.isoen_US
dc.publisherIntelligent Transport Systemsen_US
dc.relation.isversionofhttp://itsworldcongress.org/sessions/advanced-traffic-control-strategies/en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleRoadRunner: Infrastructure-less vehicular congestion controlen_US
dc.typeArticleen_US
dc.identifier.citationGao, Jason H., and Li-Shiuan Peh. "RoadRunner: Infrastructure-less Vehicular Congestion Control." The 21st Intelligent Transport Systems World Congress, Detroit, Michigan, September 7-11, 2014.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorGao, Jason Haoen_US
dc.contributor.mitauthorPeh, Li-Shiuanen_US
dc.relation.journalProceedings of the 21st Intelligent Transport Systems World Congressen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsGao, Jason H.; Peh, Li-Shiuanen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-9010-6519
dc.identifier.orcidhttps://orcid.org/0000-0002-3405-1418
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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