Crowdsourced Social Media Monitoring System Development

Kumar, Amit; Ross, Catherine L.; Karner, Alex; Katyal, Rohan

Title:

Crowdsourced Social Media Monitoring System Development

dc.contributor.author	Kumar, Amit
dc.contributor.author	Ross, Catherine L.
dc.contributor.author	Karner, Alex
dc.contributor.author	Katyal, Rohan
dc.contributor.corporatename	Georgia Institute of Technology. Center for Quality Growth and Regional Development	en_US
dc.contributor.corporatename	Georgia Institute of Technology. College of Design	en_US
dc.contributor.corporatename	Georgia Tech Research Corporation	en_US
dc.contributor.corporatename	Georgia. Department of Transportation. Office of Research	en_US
dc.contributor.corporatename	United States. Department of Transportation	en_US
dc.contributor.corporatename	United States. Federal Highway Administration	en_US
dc.date.accessioned	2018-04-24T18:58:10Z
dc.date.available	2018-04-24T18:58:10Z
dc.date.issued	2017-08
dc.description	Prepared in cooperation with the U.S. Department of Transportation, Federal Highway Administration.	en_US
dc.description.abstract	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.	en_US
dc.description.sponsorship	Georgia. Department of Transportation. Office of Research	en_US
dc.identifier.uri	http://hdl.handle.net/1853/59633
dc.language	en
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.subject	Crowdsourced traffic management	en_US
dc.subject	Crowdsourcing	en_US
dc.subject	Incident management	en_US
dc.subject	Social media monitoring	en_US
dc.subject	Traffic management	en_US
dc.title	Crowdsourced Social Media Monitoring System Development	en_US
dc.type	Text
dc.type.genre	Technical Report
dspace.entity.type	Publication
local.contributor.author	Ross, Catherine L.
local.contributor.corporatename	College of Design
local.contributor.corporatename	Center for Quality Growth and Regional Development
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