Developing a commodity-based truck origin-destination matrix estimation model using input output data and genetic algorithms.
Published: 2004
Language: eng
Number of pages: 92
Description: A commodity-based model to estimate truck origin-destination (O-D) matrix is presented. The model takes advantage of the genetic algorithm global search method to find the best O-D matrix that, when assigned to the network, gives the minimum deviation between the observed and estimated values. The model is flexible with respect to the type of input data. Flows are treated as commodity dollar value; therefore the estimated O-D matrix entries consist of the value of the commodity shipped by truck from the origin zone to the destination zone. The method is composed of two submodels. The first sub-model, the trip generation model, uses Input-Output (IO) data with employment and population data to estimate the zonal level of commodity attraction and production. The second sub-model, the genetic algorithm model, searches globally for the optimum O-D matrix. Case study and a numerical example have improved the functionality of the proposed model.
Notes: Adviser:  Isam Kaysi.

Thesis (M.A.Sc.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Masters Abstracts International, Volume: 43-03, page: 0912.

MICR copy on microfiche (2 microfiches).