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Improving travel information products via robust estimation techniques : final report, March 2009.
  • Published Date:
    2009-03-01
  • Language:
    English
Filetype[PDF-1.31 MB]


Details:
  • Publication/ Report Number:
    OTREC-RR-09-04
  • Resource Type:
  • Geographical Coverage:
  • OCLC Number:
    691272183
  • NTL Classification:
    NTL-OPERATIONS AND TRAFFIC CONTROLS-Traffic Flow ; NTL-INTELLIGENT TRANSPORTATION SYSTEMS-INTELLIGENT TRANSPORTATION SYSTEMS ;
  • Format:
  • Abstract:
    Traffic-monitoring systems, such as those using loop detectors, are prone to coverage gaps, arising from sensor noise, processing errors and

    transmission problems. Such gaps adversely affect the accuracy of Advanced Traveler Information Systems. This project will explore models

    based on historical data that can provide estimates to fill such gaps. We build on an initial study by Mr. Rafael J. Fernandez-Moctezuma, using

    both a linear model and an artificial neural network (ANN) trained on historical data to estimate values for reporting gaps. These initial models

    were 80% and 89% accurate, respectively, in estimating the correct speed range, and misclassifications were always between adjacent speed

    ranges (in paricular, the free-flow range and congested range were never confused). Going forward, we will investigate other non-linear models,

    such as Gaussian Mixtures, that provide further statistical metrics, in contrast to the uninterpreted weights of ANNs.

    This work will exploit the Portland Transportation Archive Listing (PORTAL) at the Intelligent Transportation Systems Laboratory at PSU. Dr.

    Tufte helps supervise development of PORTAL, and Mr. Fernandez used PORTAL data in his study. PORTAL holds more than two years of

    Portland-area freeway-loop-detector data at both detailed and aggregated levels, and is an ideal resource for the proposed work.

    Initially we will be building and testing estimators in off-line mode. We will select a highway segment (comprising multiple detector stations)

    that is representative in terms of pattern of outages. We will build models for this segment, then examine their performance on estimates for

    synthetic gaps (so we can compare estimates to reported values). Later, using live loop-detector data (which PORTAL supports), we will work

    towards on-line estimation over the local freeway network, which requires computing estimates in a timely manner. Our end target is

    improvements in end-user travel information products, such as the Portland-Metro Speed Map on ODOT's Trip Check.

    Our main evaluation metric will be the trade-off curve bewteen accuracy of prediciton and percentage of gaps that can be filled.

    This research supports national surface-transportation research priorities, including the Systems Management Information area (ITS JPO).

    Within that area, it relates to (2) Data Management (techniques and guidance for processing and managing data associated with highway and

    transit monitoring) and (5) Data Dissemination (exchanging information about transportation services and providing that information to

    travelers). [Page 3-15, U.S. Department of Transportation Research, Development, and Technology Plan, 6th Edition]

  • Funding:
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