FORECASTING URBAN TRAVEL DEMAND FOR QUICK POLICY ANALYSIS WITH DISAGGREGATE CHOICE MODELS: A MONTE CARLO SIMULATION APPROACH

A spatial aggregation methodology based on continuous mathematical functions is employed in urban passenger travel demand prediction. The approach derives aggregate travel demand from disaggregate choice models in the form of multi-dimensional integrals which are solved by Monte Carlo simulation. Approximate empirical relationships are developed to examine the statistical properties of biases and random errors in Monte Carlo prediction. The methodology has been used in developing a comprehensive urban travel demand prediction model suitable for policy-sensitive sketch planning.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Corporate Authors:

  Pergamon Press, Incorporated

  Headington Hill Hall
  Oxford OX30BW,    
• Authors:
  • Watanatada, T
  • Ben-Akiva, Moshe
• Publication Date: 1979-8

Media Info

• Features: References;
• Pagination: p. 241-248
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 13A
  • Issue Number: 4
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: City planning; Mathematical models; Monte Carlo method; Passenger transportation; Passengers; Statistical analysis; Transportation; Transportation planning; Travel demand; Urban transportation
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 00312140
• Record Type: Publication
• Source Agency: Engineering Index
• Files: TRIS
• Created Date: Jul 22 1981 12:00AM