Anticipating Welfare Impacts via Travel Demand Forecasting Models

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 significant advances have been made in recent years. However, travel demand forecasting tools used in practice have lagged and rely on traditional, aggregate four- or five-step approaches. One reason behind the divergence in methods is the lack of work that directly compares performance of the two approaches. This research provides such a comparison, with an emphasis on calculations of traveler welfare. A traditional, aggregate model and an activity-based microsimulation model of travel demand were developed in parallel by using the same data for Austin, Texas. The models were applied for a base scenario and for several policy scenarios to test model performance and sensitivity to inputs. The spatial distribution of traveler welfare implied by these scenarios illuminates a variety of key differences in the models’ performance and suggests that the activity-based model enjoys a greater sensitivity to inputs. Additional outputs demonstrate the level of segmentation that can be attained in model outputs using microsimulation methods. The comparative analysis of these two competing approaches to travel demand forecasting also offers some insight into the practical benefits of an activity-based approach.