Speed and Delay Prediction Models for Planning Applications
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Speed and Delay Prediction Models for Planning Applications

Filetype[PDF-390.25 KB]


English
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  • DOI:
  • Resource Type:
    Research Paper
  • Geographical Coverage:
    United States
  • TRIS Online Accession Number:
    00780089
  • NTL Classification:
    NTL-PLANNING AND POLICY-PLANNING AND POLICY;NTL-OPERATIONS AND TRAFFIC CONTROLS-Congestion;NTL-OPERATIONS AND TRAFFIC CONTROLS-Traffic Flow;
  • Abstract:
    Estimation of vehicle speed and delay is fundamental to many forms of

    transportation planning analyses including air quality, long-range travel

    forecasting, major investment studies, and congestion management systems.

    However, existing planning-level techniques do a poor job of estimating the

    duration and extent of congestion. To improve the state of the practice, a

    simplified queuing-based model, QSIM, was developed. QSIM incorporates several

    features including: the use of temporal distributions as a basis for developing

    hourly traffic estimates; estimation of "peak spreading"; accounting for daily

    variation in traffic by allowing hourly traffic estimates to vary stochastically;

    for freeways, the inclusion of a capacity drop after flow has broken down (i.e.,

    after the onset of queuing) to model the growth and dissipation of queues; for

    arterials, considering the effects of signal density and progression; separate

    functions to estimate speeds in queuing and free-flow conditions based on

    relationships developed with microscopic traffic simulation models; use of the

    concept of highway capacity to determine when traffic operates under free-flow

    and queuing conditions as well as a basis for estimating free-flow speeds and

    the extent of queuing on the test link; and estimating delay rather than speed

    as the predictive variable. (Speed is then developed as a function of delay and

    free-flow speed.) The model was used to develop a dataset from which a series

    of predictive equations were developed. The equations use only a few, readily

    available independent variables. Application of the new procedure shows that

    under congested conditions, it predicts substantially more delay than do

    traditional methods. Future work includes field validation of the models and

    extending them to cover a variety of bottleneck conditions.

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