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Characterization of accident capacity reduction
  • Published Date:
    2001
  • Language:
    English
Filetype[PDF-417.63 KB]


Details:
  • Resource Type:
  • Geographical Coverage:
  • NTL Classification:
    NTL-INTELLIGENT TRANSPORTATION SYSTEMS-INTELLIGENT TRANSPORTATION SYSTEMS
  • Format:
  • Abstract:
    Incidents are a major cause of urban highway congestion. Incidents include any event that temporarily reduces roadway capacity, such as accidents, debris, disabled vehicles, and hazardous material spills. Incident capacity reduction will be used in the incident management systems, advanced traveler information systems, queuing analysis, and computer simulation models. A study conducted in 1970 estimated that

    an accident or disabled vehicle blocking one lane out of three lanes will reduce traffic flow by an average of 50 percent, an accident blocking two lanes out of three lanes will reduce traffic flow by an average of 79 percent, and an accident or disabled vehicle blocking shoulder lane(s) out of three lanes will reduce traffic flow by an

    average of 33 percent. However, very little other research has comprehensively addressed the impact of incidents on capacity. The premise of this project is that the incident capacity reduction is best modeled as a random variable, not a deterministic value, as is the current practice.

    Extensive traffic flow and incident data for the Hampton Roads region of Virginia in the Smart Travel Laboratory provides us the opportunity to model incident capacity reduction as a random variable. Capacity under prevailing conditions can be estimated by calibrating a speed-flow and /or density-flow curve for a given highway. The peak of this curve defines capacity. When an incident occurs and a bottleneck is

    formed, the reduced capacity of the roadway is reached and can be measured directly as incident capacity. Incident capacity reduction can be computed as the difference between these two values over the capacity under prevailing conditions, and then modeled as a random variable.

    This research focuses on estimating accident capacity reductions with one lane and two lanes out of three lanes blocked, and modeling them as random variables based on the traffic flow and accident data for the Hampton Roads region. The results indicate that accident capacity reduction with one lane out of three lanes blocked can be modeled as Beta distribution with an average of 63 percent, which is fairly higher

    than the result of previous research (50 percent), and accident capacity reduction with two lanes out of three lanes blocked can be modeled as Beta distribution with an average of 77 percent which is slightly lower than the result of previous research (79 percent).

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