AN INVESTIGATION INTO INCIDENT DURATION FORECASTING FOR FLEETFORWARD

Traffic condition forecasting is the process of estimating future traffic conditions based on current and archived data. Real-time forecasting is becoming an important tool in Intelligent Transportation Systems (ITS). This type of forecasting allows ITS to enact control and management strategies that are "one step ahead" rather than "one step behind" the onset of traffic conditions. For example, an ITS traffic management system can take measures to anticipate congestion rather than reacting to congestion once it is present. Real-time forecasting has benefits to many research fields including route guidance, incident management, public transportation operation, and traveler information. The most common traffic conditions that are forecasted on a real-time basis are flow rate and travel time. The specific traffic condition that the University of Virginia Smart Travel Laboratory is attempting to forecast in this research effort is incident duration, a relatively new area of research for transportation forecasting. To date, there has been limited research into models that can predict how long a certain incident will affect traffic. It has been said that the target audiences of predictive traffic information are commuters and motorists on business. Motor carriers fit nicely in this category, as their business is to provide transportation services. Incident duration forecasts will be extremely important to motor carriers and thus will be a useful tool for FleetForward, a traveler information system for motor carrier operations. Knowing how long an incident will affect traffic allows motor carrier dispatchers and drivers to more intelligently schedule and route shipments

  • Record URL:
  • Supplemental Notes:
    • Publication Date: August 2000. National ITS Implementation Research Center, George Mason University, Fairfax VA. Format: website
  • Corporate Authors:

    National ITS Implementation Research Center

    ,    
  • Authors:
    • Smith, Brian Lee
    • Smith, Kevin W
  • Publication Date: 2000

Language

  • English

Subject/Index Terms

Filing Info

  • Accession Number: 00962436
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Report/Paper Numbers: UVA-CE-ITS-01-5
  • Files: UTC, NTL
  • Created Date: Sep 2 2003 12:00AM