Tests of a family of trip table refinements for long-range, quick-response travel forecasting
AJ Horowitz - Transportation research record, 2005 - journals.sagepub.com
AJ Horowitz
Transportation research record, 2005•journals.sagepub.comThis paper addresses the problem of using traffic counts to ascertain zonal trip generation
characteristics when performing quick-response travel forecasts. A family of origin–
destination (OD) trip table estimation methods containing three unexplored members
(biproportional, uniproportional, and dynamic biproportional) is proposed to solve this
problem. The family is tested on static planning networks for Tallahassee, Florida; Northfield,
Minnesota; and Fredericton, New Brunswick, Canada. Results indicate that travel …
characteristics when performing quick-response travel forecasts. A family of origin–
destination (OD) trip table estimation methods containing three unexplored members
(biproportional, uniproportional, and dynamic biproportional) is proposed to solve this
problem. The family is tested on static planning networks for Tallahassee, Florida; Northfield,
Minnesota; and Fredericton, New Brunswick, Canada. Results indicate that travel …
This paper addresses the problem of using traffic counts to ascertain zonal trip generation characteristics when performing quick-response travel forecasts. A family of origin–destination (O-D) trip table estimation methods containing three unexplored members (biproportional, uniproportional, and dynamic biproportional) is proposed to solve this problem. The family is tested on static planning networks for Tallahassee, Florida; Northfield, Minnesota; and Fredericton, New Brunswick, Canada. Results indicate that travel forecasting models can be made to match ground counts better by a simple factoring of origins, destinations, or both. The three methods that directly solve for origin or destination factors have computational and statistical advantages over full-matrix O-D trip table estimation procedures, and the results are qualitatively and quantitatively interpretable.