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UNCERTAINTY ASSOCIATED WITH TRAVEL TIME PREDICTION: ADVANCED VOLATILITY APPROACHES AND ENSEMBLE METHODS

dc.contributor.advisorHaghani, Alien_US
dc.contributor.authorZhang, Yanruen_US
dc.date.accessioned2015-06-25T05:58:30Z
dc.date.available2015-06-25T05:58:30Z
dc.date.issued2015en_US
dc.identifierhttps://doi.org/10.13016/M2CD1K
dc.identifier.urihttp://hdl.handle.net/1903/16570
dc.description.abstractTravel time effectively measures freeway traffic conditions. Easy access to this information provides the potential to alleviate traffic congestion and to increase the reliability in road networks. Accurate travel time information through Advanced Traveler Information Systems (ATIS) can provide guidance for travelers' decisions on departure time, route, and mode choice, and reduce travelers' stress and anxiety. In addition, travel time information can be used to present the current or future traffic state in a network and provide assistance for transportation agencies in proactively developing Advanced Traffic Management System (ATMS) strategies. Despite its importance, it is still a challenging task to model and estimate travel time, as traffic often has irregular fluctuations. These fluctuations result from the interactions among different vehicle-driver combinations and exogenous factors such as traffic incidents, weather, demand, and roadway conditions. Travel time is especially sensitive to the exogenous factors when operating at or near the roadway's capacity, where congestion occurs. Small changes in traffic demand or the occurrence of an incident can greatly affect the travel time. As it is impossible to take into consideration every impact of these unpredictable exogenous factors in the modeling process, travel time prediction problem is often associated with uncertainty. This research uses innovative data mining approaches such as advanced statistical and machine learning algorithms to study uncertainty associated with travel time prediction. The final objective of this research is to develop more accurate and reliable travel time prediction models.en_US
dc.language.isoenen_US
dc.titleUNCERTAINTY ASSOCIATED WITH TRAVEL TIME PREDICTION: ADVANCED VOLATILITY APPROACHES AND ENSEMBLE METHODSen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentCivil Engineeringen_US
dc.subject.pqcontrolledCivil engineeringen_US
dc.subject.pquncontrolledForecastingen_US
dc.subject.pquncontrolledITSen_US
dc.subject.pquncontrolledTraffic Engineeringen_US
dc.subject.pquncontrolledTravel Timeen_US
dc.subject.pquncontrolledUncertainty Predictionen_US


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