Characterizing Road Conditions via Smart Mobile Crowd Sourcing
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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.
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Introduction -- Background -- Implementation -- Experimentation -- Conclusion
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M.S. (Master of Science)