Multisource Data Fusion for Real-Time and Accurate Traffic Incident Detection via Predictive Analytics

The objectives of this study are to (1) identify data sets available to Massachusetts Department of Transportation (MassDOT) that can be used for real-time incident detection; (2) investigate how data from different sources can be integrated to improve incident detection; and (3) develop guidance for establishing trigger points to alert Highway Operations Center (HOC) operators about incidents on the road. Speed data available through the Regional Integrated Transportation Information (RITIS) platform are used for developing two alternative strategies: (a) an Artificial Intelligence (AI) model using supervised learning based on Long Short-Term Memory (LSTM) and Variational Autoencoders (VAE) layers for classifying records as normal events or incidents, and (b) an empirical rule-based method using historical speeds to establish threshold values, below which an alarm is issued requiring the HOC operator’s attention. Results on the AI model and a verified incident data set indicate a False Alarm Rate (FAR) of 0.0069% and a detection rate of 91.70%. For the empirical rule-based model, a 30-day off-line “field-test” was conducted for June 2021. Most of the events recorded by the MassDOT HOC were detected, and for most of these events the detection time was well before the “SENT-ON” time recorded in the HOC incident database.

• Record URL:
  • https://www.mass.gov/doc/multisource-data-fusion-for-real-time-and-accurate-traffic-incident-detection-via-predictive-analytics-final-report/download
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/73043
• Corporate Authors:

  University of Massachusetts, Lowell

  Department of Civil and Environmental Engineering
  1 University Avenue
  Lowell, MA  United States  01854

  Massachusetts Department of Transportation

  Office of Transportation Planning
  Boston, MA  United States 

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Stamatiadis, Polichronis
  • Gartner, Nathan H
  • Xie, Yuanchang
  • Liu, Ruifeng
• Publication Date: 2023-4

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Appendices; Figures; Maps; References; Tables;
• Pagination: 139p

Subject/Index Terms

• TRT Terms: Artificial intelligence; Data fusion; Incident detection; Traffic incidents
• Identifier Terms: Massachusetts Department of Transportation
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01904431
• Record Type: Publication
• Report/Paper Numbers: 23-040
• Files: NTL, TRIS, USDOT, STATEDOT
• Created Date: Jan 11 2024 1:31PM