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    •   MOspace Home
    • University of Missouri-Kansas City
    • School of Graduate Studies (UMKC)
    • Theses and Dissertations (UMKC)
    • Theses (UMKC)
    • 2019 Theses (UMKC)
    • 2019 UMKC Theses - Freely Available Online
    • View Item
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    Characterizing Road Conditions via Smart Mobile Crowd Sourcing

    Shorter, Dustin
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    [PDF] Characterizing Road Conditions via Smart Mobile Crowd Sourcing (2.194Mb)
    Date
    2019
    Format
    Thesis
    Metadata
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    Abstract
    Bad road conditions can cause vehicle damage and create hazardous driving conditions. Current reporting methods for bad road conditions is a burden for the reporter, so much so that these conditions might not be reported. With current smartphone technology this reporting method can be greatly improved. Smartphones come with an accelerometer and GPS positioning on-board. Utilizing these sensors, once the application is installed and run, a smartphone user can gather bad road conditions automatically while driving. Then the user can upload the data to the server. These road conditions are then classified and optionally displayed on a map while the user is driving. The potential for finding bad road conditions is greatly increases with crowd sourcing. When the road condition data is analyzed an algorithm is used to determine the size of the bad road condition and how many samples are needed. Even though the smartphone might not have the sampling rate of a dedicated accelerometer it’s potential for gather large amounts of data using crowd sourcing and ease of use outweighs this deficiency.
    Table of Contents
    Introduction -- Background -- Implementation -- Experimentation -- Conclusion
    URI
    https://hdl.handle.net/10355/68010
    Degree
    M.S. (Master of Science)
    Thesis Department
    Computer Science (UMKC)
    Collections
    • Computer Science and Electrical Engineering Electronic Theses and Dissertations (UMKC)
    • 2019 UMKC Theses - Freely Available Online

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