Crowdsourced Social Media Monitoring System Development

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Kumar, Amit
Ross, Catherine L.
Karner, Alex
Katyal, Rohan
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Crowdsourcing is a relatively new area of research, but it is already generating an enormous amount of interest among both researchers and practitioners, and is finding applications in multiple domains. It is particularly useful for efficient traffic management and increasing public participation. Many state departments of transportation (DOTs) are already using crowdsourced technologies and others are exploring its applications for traffic management. Researchers are using sensor-rich mobile phones and online social networks for fetching data from network users. Despite recent advancements, there remain gaps between the state of the art and practice that need to be bridged. Programs like the Waze Connected Citizens Program and Strava Metro Data Program are success stories in practice. This study explores the implementation of crowdsourced traffic management by Georgia DOT (GDOT) and the challenges specific to them. The reliability of data and filtering high volumes of information were found to be the two primary concerns. The team proposed a system which can potentially tackle those challenges. The system consists of a mobile application and a text mining application that together leverage the existing Twitter technology stack. Based on interviews with traffic management professionals and a visit to GDOT, the report contains recommendations that would improve the workflow at the traffic management center (TMC). Computer vision, data management and social media analytics would be particularly beneficial to decrease operator burden. A system with multiple sources of information integrated into one would be particularly beneficial. We are on the cusp of a revolution with respect to big data and crowdsourcing. This is the ideal time for GDOT to invest in crowdsourcing technologies to reap the benefits in the future.
Georgia. Department of Transportation. Office of Research
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Technical Report
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