Baysian Updating of Trip Generation Parameters

The use of borrowed models from similar urban areas is one way of reducing the costs associated with conducting full-scale travel surveys. However, in order to tune borrowed models to reflect the local conditions of the study area, these models can be updated using low-cost information (objective or subjective) from the study area. A method based on Bayesian statistics for updating cross-classified household trip generation rates is presented. Some updating techniques are briefly reviewed. Previous applications of Bayesian statistics in the transportation field are also discussed; a methodology for their use to update cross-classified trip rates is presented along with sample application.