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The MTC truck trip generation models are directly from the study "I-880 Intermodal Corridor Study: Truck Travel in the San Francisco Bay Area" prepared by Barton Aschman Associates, December 1992. Published by Caltrans District 4 and Alameda County. Trip generation coefficients are on page 223 of this report.The "NHB" trips are "non-home-based" trips, referred to as "linked" trips in the report.The "GAR" trips are "garage-based" trips, also called garage-based in the report.bIMPORTANT: Two-Axle trucks ("Small") are two-axle, six tire trucks,band exclude two-axle, four tire trucks ("Very Small"). In the variable names "P" is for Productions; "A" is for Attractions, 3 is for three-axle trucks ("Medium"), 4 is for four-plus axle "combination" trucks. The Alameda County model uses the MTC model for Very Small trucks but as of the P09 update replaces the MTC model for Small, Medium and Combo trucks with new models developed by Cambridge Systematics in 2009

Friction Factors

The trip distribution step uses a standard gravity model with a blended travel time impedance measure. This file sets the friction factors, which are vehicle type specific, using an ASCII fixed format text files. The the Ala-CC Bicounty Model, there are separate FF files for Internal-Internal, Internal-External trips, and one for Very Small Trucks. The FF for II trips come from MTC's Travel Model 1.5 configuration.

  • Travel time in minutes (integer, starting in column 1, left justified);
  • Friction factors for very small trucks (integer, starting in column 9, left justified);
  • Friction factors for small trucks (integer, starting in column 17, left justified);
  • Friction factors for medium trucks (integer, starting in column 25, left justified); and,
  • Friction factors for large trucks (integer, starting in column 33, left justified).

External Truck Trips and Special Generator

The trip distribution step also uses two files for special generators for ports and truck trips to external stations.

K-Factors

The trip distribution step also uses a matrix of K-factors to adjust the distribution results to better match observed data. This matrix contains a unit-less adjustment value; the higher the number, the more attractive the production/attraction pair.