STOCHASTIC PROCESSES IN FREEWAY TRAFFIC PART I. ROBUST PREDICTION MODELS

This two-part series of papers presents the results of recent research work on the construction of stochastic process models from empirical time series of freeway traffic state variables along with their application to the automatic detection of traffic incidents (accidents, disabled vehicles, spilled loads, etc.). Part I (this paper) summarises the development of an autoregressive integrated moving average model of the form ARIMA (0,1,3) using surveillance data recorded at the Los Angeles, Minneapolis and Detroit freeway systems. Part II describes a new incident detection algorithm based on the ARIMA (0,1,3) model, amd evaluates the performance of this algorithm using data on 50 lane blockage incidents.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00410683
• Corporate Authors:

  Printerhall Limited

  29 Newmart Street
  London W1P 3PE,   England 
• Authors:
  • Ahmed, S A
• Publication Date: 1983-6

Media Info

• Features: Figures; References; Tables;
• Pagination: 4 p.
• Serial:
  • Traffic Engineering & Control
  • Volume: 24
  • Issue Number: 6-7
  • Publisher: Hemming Group, Limited
  • ISSN: 0041-0683
  • Serial URL: http://www.tecmagazine.com/

Subject/Index Terms

• TRT Terms: Algorithms; Detection and identification; Detectors; Forecasting; Freeways; Stochastic processes; Traffic incidents; Traffic simulation
• Uncontrolled Terms: Incidents
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 00385137
• Record Type: Publication
• Source Agency: National Highway Traffic Safety Administration
• Report/Paper Numbers: HS-035 775
• Files: HSL, TRIS, USDOT
• Created Date: May 30 1984 12:00AM