Route Planning with Statistical Models

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dc.contributor.advisorRyzhov, Ilyaen_US
dc.contributor.authorHuang, Yufeien_US
dc.contributor.departmentSystems Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2018-07-17T06:25:15Z
dc.date.available2018-07-17T06:25:15Z
dc.date.issued2018en_US
dc.description.abstractOne difficulty to find the fastest route in route planning is how to determine the precise travel time on each road. In the real world, the travel time of each road varies with time, weather condition and many other factors. The thesis aims at studying route planning algorithms that use statistical models to predict the changes of travel time for each road and calculate the fastest route. Using the historical data of main roads in Washington D.C. area, the thesis studied major factors that would affect the travel time. Different statistical models are presented and compared to fit the travel time of each road. Then the LASSO regression model is chosen, and different predictive route planning algorithms are introduced to fulfill our goal. Finally, deterministic approximate dynamic programming is recommended to solve our problem.en_US
dc.identifierhttps://doi.org/10.13016/M2M03Z13V
dc.identifier.urihttp://hdl.handle.net/1903/21031
dc.language.isoenen_US
dc.subject.pqcontrolledOperations researchen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledApproximate Dynamic Programmingen_US
dc.subject.pquncontrolledDijkstra'a Algorithmen_US
dc.subject.pquncontrolledLASSO Regressionen_US
dc.subject.pquncontrolledRoute Planningen_US
dc.subject.pquncontrolledStatistical Modelsen_US
dc.subject.pquncontrolledSure Independence Screeningen_US
dc.titleRoute Planning with Statistical Modelsen_US
dc.typeThesisen_US

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