Incident detection systems typically emphasize incident presence and location over incident severity and incident recovery. Yet, Advanced Traveller Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) rely on the latter states to implement and terminate diversion, and its supportive control strategies. Further, incident detection systems directly benefit from processing measurement vectors rather than scalars. Vectors of lane measurements favor detection through lane imbalances and identification of incident host lanes. Intelligent Transportation Systems promise new sensor data to control centres, including the travel times experienced by probe vehicles. Vectors of new and old sensor inputs may possess enhanced discriminatory values. To accomodate added detection states and the fusion of multi-sensor input vectors, this paper reformulates the arterial incident detection problem as a multiple attribute decision making problem with Bayesian scores. This novel approach utilizes as input the combinations of simulated probe travel times, number of probe reports, lane specific detector occupancies and vehicle counts. Models based solely on probe data lack in performance due to excessive overlaps in class distributions. Models based on detector occupancies and vehicle counts by lane perform outstandingly. They display a propensity to detect through lane measurement imbalances. The probe data is shown to enhance the performance of detector data baed models. (A)

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    The Boulevard, Langford Lane
    Kidlington, Oxford  United Kingdom  OX5 1GB
  • Authors:
    • Thomas, N E
  • Publication Date: 1998


  • English

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Filing Info

  • Accession Number: 00767297
  • Record Type: Publication
  • Source Agency: Transport Research Laboratory
  • Files: ITRD
  • Created Date: Aug 6 1999 12:00AM