dc.contributor.advisor | Song, Sejun | |
dc.contributor.author | Shorter, Dustin | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019 Spring | |
dc.description | Title from PDF of title page viewed May 14, 2019 | |
dc.description | Thesis advisor: Sejun Song | |
dc.description | Vita | |
dc.description | Includes bibliographical references (pages 44-45) | |
dc.description | Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019 | |
dc.description.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. | eng |
dc.description.tableofcontents | Introduction -- Background -- Implementation -- Experimentation -- Conclusion | |
dc.format.extent | ix, 46 pages | |
dc.identifier.uri | https://hdl.handle.net/10355/68010 | |
dc.publisher | University of Missouri -- Kansas City | eng |
dc.subject.lcsh | Potholes (Roads) | |
dc.subject.lcsh | Crowdsourcing | |
dc.subject.lcsh | Smartphones | |
dc.subject.other | Thesis -- University of Missouri--Kansas City -- Computer science | |
dc.title | Characterizing Road Conditions via Smart Mobile Crowd Sourcing | eng |
dc.type | Thesis | eng |
thesis.degree.discipline | Computer Science (UMKC) | |
thesis.degree.grantor | University of Missouri--Kansas City | |
thesis.degree.level | Masters | |
thesis.degree.name | M.S. (Master of Science) | |