A NATIONAL TRAVEL DEMAND MODEL FOR THE U.S.: A PERSON-BASED MICROSIMULATION APPROACH

Abstract

Understanding long distance travel behavior and forecasting reliable long distance travel demand are critical in evaluating intercity or regional transportation improvements and infrastructure investment projects. As the nation and various states engage in funding transportation infrastructure improvements to meet future long-distance passenger travel demand, it is imperative to develop effective and practical modeling methods for long-distance passenger travel analysis. This paper proposes an integrated activity-based travel demand model system for individual’s quarterly/yearly long distance or national activities and travel in the U.S at the Metropolitan Statistical Area (MSA)/Non-MSA level. The model system is developed based on a rigorous behavioral framework in long distance travel planning, and takes into account the specific attributes of the long distance travel such as low frequency, long activity duration, different sets of mode alternatives, etc. The system includes three tiers: 1) the yearly long distance activity pattern level predicting the number of different activities a person will choose during one year; 2) the tour level which consists of tour destination choice, time of year choice, tour duration, and tour mode choice; 3) the stop level predicting the intermediate stop frequency, purpose and location. Econometric model developments are conducted for the multiple model components. And estimation results are obtained based on the 1995 American Travel Survey data, transportation origin-destination (OD) skim data, and economic/demographic data. With-in- sample validation is performed for each model prior to model implementation. The model system is implemented in the authors' developed micro-simulation platform which simulates each individual’s yearly long distance activities and travel in the U.S with the input of the 2000 Census Public Use Microdata Sample data, the associated transportation OD skim data and economic/demographic data. Multiple OD tables by time of year and travel mode can be achieved in addition to the disaggregate output of each person’s long distance travel.