Analysis and prediction of spatiotemporal impact of traffic incidents for better mobility and safety in transportation systems.

Details

• Creators:
  Shahabi, Cyrus ; Demiryurek, Ugur
• Corporate Creators:
  University of Southern California. Dept. of Computer Science
• Corporate Contributors:
  United States. Department of Transportation. Research and Innovative Technology Administration
• Subject/TRT Terms:
  • Traffic incidents
  • Traffic delays
  • Traffic models
  • Traffic data
  • Crash data
  • Forecasting
  • Police reports
  • Algorithms
  • Analysis
• Publication/ Report Number:
  METRANS Project 14-04
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  METRANS Transportation Center (Calif.)
• Abstract:
  The goal of this research is to develop a machine learning framework to predict the spatiotemporal impact ; of traffic accidents on the upstream traffic and surrounding region. The main objective of the framework ; is, given a road accident, to forecast when and how the travel-time delay will occur on transportation ; network. Towards this end, we have developed a Dynamic Topology-aware Temporal (DTT) machine ; learning algorithm that learns the behavior of traffic in both normal conditions and during accidents from ; the historical traffic sensor datasets. This research exploits four years of real-world Los Angeles traffic ; sensor data and California Highway Patrol (CHP) accidents logs collected from Regional Integration of ; Intelligent Transportation Systems (RIITS) under Archived Traffic Data Management System (ADMS) ; project.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29740/dot_29740_DS1.pdf
• File Type:
  [PDF - 1.01 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:71e839eb2a688dc59dc4227f0f5299c0700a539cb1da33ca983c8e0284705b6de1039ed51111f7c4528060bed3e7e66262910d3477c101a5cf1ddbffcc608719