Dynamic Traffic Flow Modeling for Incident Detection and Short-Term Congestion Prediction: Year 1 Progress Report

Details

• Creators:
  Balke, Kevin ; Chaudhary, Nadeem ; Chu, Chi-Lueng ; Kuchangi, Shamanth P. ; Nelson, Paul ; Songchitruksa, Praprut ; Swaroop, Dvahg ; Tyagi, Vipin
• Corporate Creators:
  Texas A&M University System. Texas Transportation Institute
• Corporate Contributors:
  Texas Department of Transportation. Research and Technology Implementation Office ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Forecasting
  • Highway traffic
  • Incident detection
  • Research
  • Traffic congestion
  • Traffic flow
  • Traffic models
  • Weather
• Publication/ Report Number:
  FHWA/TX-06/0-4946-1
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  September 2004 – August 2005
• Corporate Publisher:
  Texas. Department of Transportation
• Description:
  The purpose of this report is to summarize the research activities that were performed during the first year of this research project. In conducting this research, the research team split into several independent groups, each focusing on different aspects of the problem. One group has been focused on using weather and traffic flow conditions as predictors of incident conditions. Their activities are summarized in Chapter II. Other groups have been focused on developing models for producing short-term forecasts of potential congestion, using current measured traffic conditions. The results of these activities are summarized in Chapter III. Finally, the authors are beginning the process of developing a prototype tool that operators can use in a control center to display forecasted conditions. The beginnings of a high-level, functional specification for the tool are provided in Chapter IV.
• Format:
  PDF
• Funding:
  0-4946
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88684/dot_88684_DS1.pdf
• File Type:
  [PDF - 3.37 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:33d53745bdcd26dad5cb1daf4f56a250d54da4d4d84c7eabb56fdac832297f754a6deea0f384f3b72dd48f13c8fae1f6c902be793e6beacd21266562b62829ae