Travel demand forecasting models : development, application, and comparison of aggregate and activity-based approaches for the Austin, Texas Region

Abstract

A great disparity exists between the direction of travel demand forecasting by researchers, and the travel demand models used by transportation planning organizations. Activity-based models of travel demand have become increasingly studied in the academic realm and vast developments have been made over the past many years. However, travel demand forecasting tools used in practice by transportation planning organizations, and the like, have lagged behind, relying on the tried and true traditional, aggregate 4-step approach to travel demand modeling. Many reasons for such a paradox are possible, but one cause is that there is little work that directly relates these two approaches from a model performance perspective. The aim of this research is provide just such a comparison. A traditional, aggregate model and an activity-based microsimulation model of travel demand are developed in parallel using the same data for Austin, Texas. The models are applied for both a base scenario and several policy scenarios to test model performance and sensitivity to inputs. Aggregate outputs indicate that there are many key differences between the ways these two models perform, and some evidence suggests that the activity-based model may boast a greater sensitivity to inputs. Additional outputs are produced to demonstrate the level of segmentation that can be attained in the generated outputs using microsimulation methods. The analysis performed in this research serves as a comparison of these two competing approaches to travel demand forecasting and offers some insight into the benefits of the activity-based approach from a practical standpoint.