Analysis and Prediction of Spatiotemporal Impact of Traffic Incidents for Better Mobility and Safety in Transportation System [Research Project]

Details

• Creators:
  Shahabi, Cyrus ; Demiryurek, Ugur
• Corporate Creators:
  University of Southern California. Dept. of Computer Science ; METRANS Transportation Center (Calif.)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Algorithms
  • Analysis
  • Crash data
  • Forecasting
  • Police reports
  • Traffic data
  • Traffic delays
  • Traffic incidents
  • Traffic models
• Resource Type:
  Brief
• Geographical Coverage:
  Los Angeles (California);United States
• Edition:
  Research Project
• 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. We propose a Latent Space Model for Road Networks (LSM-RN), which enables more accurate and scalable traffic prediction by utilizing both topology similarity and temporal correlations.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/67672/dot_67672_DS1.pdf
• File Type:
  [PDF - 231.36 KB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:e390e5e03979ef9b1e19f259b19b54c6ee5ba320bb49a258951521ef27f219731a83d39a0035733c60c3158da9ff42d024b71dcb8055efcca0ad4f8af806ac1a