UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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2012 - 20152

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Author: search.filters.author.Krause, CorySubject: search.filters.subject.GPS

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    SHORT TERM TRAVEL BEHAVIOR PREDICTION THROUGH GPS AND LAND USE DATA
    (2015) Krause, Cory; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The short-term destination prediction problem consists of capturing vehicle Global Positioning System (GPS) traces and learning from historic locations and trajectories to predict a vehicle’s destination. Drivers have predictable trip destinations that can be estimated through probabilistic modeling of past trips. This dissertation has three main hypotheses; 1) Employing a tiered Markov model structure will permit a shorter learning period while achieving similar accuracy results, 2) The addition of derived trip purpose information will increase accuracy of the start of trip and in-route models as a whole, and 3) Similar methodologies of travel pattern inference can be used to accurately predict trip purpose and socio-economic factors. To study these concepts, a database of GPS driving traces (120 participants for 70 days) is collected. To model the user’s trip purpose, a new data source was explored: Point of Interest (POI)/land use data. An open source land use/POI dataset is merged with the GPS dataset. The resulting database includes over 20,000 trips with travel characteristics and land use/POI data. From land use/POI data, and travel patterns, trip purpose is calculated with machine learning methods. A new model structure is developed that uses trip purpose when it is available, yet falls back on traditional spatial temporal Markov models when it is not. The start of trip model has an overall increase of accuracy over other start of trip models of 2%. This comes quickly, needing only 30 days to reach this level of accuracy compared to nearly a year in many other models. When adding trip purpose and the start of trip model to in-route prediction methods, the accuracy of the destination prediction increases significantly: 15-30% improvement of accuracy over similar models between 0-50% of trip progression. Certain trips are predicted more accurately than others: work and home based trips average of 90% correct prediction, whereas shopping and social based trips hover around the 50% mark. In all, the greatest contribution of this dissertation is the trip purpose methodology addition and the tiered Markov model structure in gaining fast results in both the start of trip and in-route models.
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    A POSITIVE MODEL OF ROUTE CHOICE BEHAVIOR AND VALUE OF TIME CALCULATION USING LONGITUDINAL GPS SURVEY DATA
    (2012) Krause, Cory; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis approaches the topic of value of time calculation and route choice behavior with a new and innovative methodology using a survey dataset that was uniquely designed and implemented for this purpose. The survey is a 70 day, 218 participant GPS travel survey used to track individual location constantly at one minute intervals. Using a positive behavior theory framework, an in depth knowledge database for each user is created that iteratively updates the learned behavior and experienced travel conditions for each trip the user takes. A new approach for calculating value of time is presented; using the cost and trip duration of previous trips. The bounds (or caps and floors) are averaged to achieve the individual's value of time based upon their route (and therefore cost) decisions. Also using this updating knowledge base, route decision rules are derived using machine learning algorithms to tell why a user has decided to take the toll road option for certain days, and under what conditions the user will not take the toll road option. The final contribution is a model that fully takes advantage of longitudinal GPS data to create an adaptive system for value of time calculation and positive route decision making.