rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: The
    title: prediction of bus arrival time using Automatic Vehicle Location Systems data
  name:
    name part: Jeong, Ran Hee, 1968-
    role:
      role term: creator
  name:
    name part: Rilett, Laurence R.
    role:
      role term: thesis advisor.
  name:
    name part: Epps Martin, Amy
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: remote
  abstract: Advanced Traveler Information System (ATIS) is one component of Intelligent Transportation Systems (ITS), and a major component of ATIS is travel time information. The provision of timely and accurate transit travel time information is important because it attracts additional ridership and increases the satisfaction of transit users. The cost of electronics and components for ITS has been decreased, and ITS deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which is a part of ITS, have been adopted by many transit agencies. These allow them to track their transit vehicles in real-time. The need for the model or technique to predict transit travel time using AVL data is increasing. While some research on this topic has been conducted, it has been shown that more research on this topic is required. The objectives of this research were 1) to develop and apply a model to predict bus arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and the probabilty of a bus being on time. In this research, the travel time prediction model explicitly included dwell times, schedule adherence by time period, and traffic congestion which were critical to predict accurate bus arrival times. The test bed was a bus route running in the downtown of Houston, Texas. A historical based model, regression models, and artificial neural network (ANN) models were developed to predict bus arrival time. It was found that the artificial neural network models performed considerably better than either historical data based models or multi linear regression models. It was hypothesized that the ANN was able to identify the complex non-linear relationship between travel time and the independent variables and this led to superior results because variability in travel time (both waiting and on-board) is extremely important for transit choices, it would also be useful to extend the model to provide not only estimates of travel time but also predicti…
  note: by Ran Hee Jeong.
  note: "Major Subject: Civil Engineering"
  note: Title from author supplied metadata (automated record created on Feb. 17, 2005.)
  note: Vita.
  note: Abstract.
  note: Electronic resource.
  note: Mode of access: World Wide Web.
  note: System requirements: World Wide Web access and Adobe Acrobat Reader.
  note: Ph. D. Texas A&M University 2004.
  note: Includes bibliographical references.
  note: Text (Dissertation).
  subject:
    topic: Major civil engineering
  subject:
    topic: prediction model
  subject:
    topic: Neural Network model
  subject:
    topic: Automatic Vehicle Location (AVL) Systems
  subject:
    topic: prediction interval
  subject:
    topic: GPS
  subject:
    topic: bus arrival time
  identifier: http://hdl.handle.net/1969.1/1458
  record info:
    record content source: TXA
    record creation date: 050411
    record change date: 20151101055808.0
    record identifier: in00002040656