Estimating current modal splits.

Details

• Creators:
  Racca, David P. ; Ratledge, Edward C.
• Corporate Contributors:
  University of Delaware. Delaware Transportation Institute ; Delaware. Dept. of Transportation
• Subject/TRT Terms:
  • Modal split
  • Commuting
  • Mode choice
  • Commuting--Delaware
  • Choice of transportation
• Resource Type:
  Tech Report
• Geographical Coverage:
  Delaware
• Corporate Publisher:
  Delaware Center for Transportation
• Abstract:
  This project is the second part in a two part modeling effort. In previous work*, mode ; choice was modeled by examining characteristics of individuals and the trips they make. ; A study of the choices of individuals is necessary for a fundamental understanding of ; travel mode choice. Models were built to estimate mode split at the State and County ; level. Where transit or walk trips often account for only 1 to 5% of all trips, the main ; problem in modeling the use of other choices of travel besides the personal auto is that ; there is very little data available. The modeling difficulty becomes greater as estimates ; of mode split are desired for smaller levels of geography, such as for a traffic zone, rather ; than a County. ; For use in travel demand forecasting and examination of transit markets, almost all mode ; choice models used by transportation agencies are developed using aggregate level data, ; typically at the level of a traffic zone, such as population totals, mean incomes, average ; household characteristics, and other summary data. The reason for this is that aggregate ; data, such as provided by the U.S. Census, is typically more available. For the most part, ; estimates of travel mode split used in travel demand models are not very sophisticated ; and often consist of an estimate based on fixed percentage of trips (e.g. 1% of trips in a ; zone will be accomplished by using transit) rather than a model considering a number of ; factors. ; This project investigates how travel mode split can be modeled using aggregate data at ; smaller levels of geography like traffic zones for use in route planning and travel demand ; forecasting. It starts with models based on individual data developed at the county level ; and investigates the applicability of these models at smaller levels of geography where ; aggregate estimates of the factors are available.
  More +
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/17967/dot_17967_DS1.pdf
• File Type:
  [PDF - 308.82 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:31f7d5390f6166620fe7ca1f9659a6dce44d70e4c023742ee71d203bc101d545