Real-time incident detection using social media data.

Details

• Creators:
  Qian, Zhen (Sean)
• Corporate Creators:
  Carnegie-Mellon University
• Contributors:
  Kopko, Mark
• Corporate Contributors:
  Pennsylvania. Dept. of Transportation. Bureau of Planning and Research
• Subject/TRT Terms:
  • Data mining
  • Geographic information systems
  • Incident detection
  • Real time information
  • Social media
  • Traffic incidents
  • Web applications Twitter
• Publication/ Report Number:
  FHWA-PA-2016-004-CMU WO 03;WO-003
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States;Pennsylvania;Philadelphia (Pennsylvania)
• Edition:
  Final report
• Corporate Publisher:
  Pennsylvania. Department of Transportation
• Abstract:
  The effectiveness of traditional incident detection is often limited by sparse sensor coverage, and reporting incidents to emergency response systems ; is labor-intensive. This research project mines tweet texts to extract incident information on both highways and arterials as an efficient and cost-effective ; alternative to existing data sources. This research report presents a methodology to crawl, process and filter tweets that are accessible by ; the public for free. Tweets are acquired from Twitter using the REST API in real time. The process of adaptive data acquisition establishes a ; dictionary of important keywords and their combinations that can imply traffic incidents (TI). A tweet is then mapped into a high dimensional binary ; vector in a feature space formed by the dictionary, and classified into either TI related or not. All the TI tweets are then geocoded to determine their ; locations, and further classified into one of the five incident categories. We apply the methodology in two regions, the Pittsburgh and Philadelphia ; Metropolitan Areas. Overall, mining tweets holds great potentials to complement existing traffic incident data in a very cheap way. A small sample of ; tweets acquired from the Twitter API cover most of the incidents reported in the existing data set, and additional incidents can be identified through ; analyzing tweets text. Twitter also provides ample additional information with a reasonable coverage on arterials. A tweet that is related to TI and ; geocodable accounts for approximately 10% of all the acquired tweets. Of those geocodable TI tweets, the majority are posted by influential users ; (IU), namely public Twitter accounts owned by public agencies and media, while a small number is contributed by individual users. There is more ; incident information provided by Twitter on weekends than on weekdays. Within the same day, both individuals and IUs tend to report incidents more ; frequently during the day time than at night, especially during traffic peak hours. Individual tweets are more likely to report incidents near the center of ; a city, and the volume of information significantly decays outwards from the center. We develop a prototype web application to allow users extract ; both real-time and historical incident information and visualize it on the map. The web application will be tested in PennDOT transportation ; management centers. ; Author ORCID information: http://orcid.org/0000-0001-8716-8989
  More +
• Format:
  PDF
• Funding:
  CMUIGA2012 - CMU WO 03
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/30972/dot_30972_DS1.pdf
• File Type:
  [PDF - 4.40 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:d9629151c88b0dfbdae9c5761eb99b2c5b60d06b8e2f4aaafad0780c42024b4f036beb7c9d70d39024aaec593dc6dd00914aecf4b9b5e81908fd3cc5f9d2d5fd