APPLICATION OF DISAGGREGATE MODAL-CHOICE MODELS TO TRAVEL DEMAND FORECASTING FOR URBAN TRANSIT SYSTEMS

Abstract

This paper describes the development and application of disaggregate models to travel demand forecasting for transit systems in the Niagra Frontier region in western New York State. In this study, the disaggregate modal-choice models are developed from trip information from standard home- interview surveys. The data set used is a 12 percent subsample of the home interview conducted in 1962. Binary logit models are calibrated for four types of trips, classified by trip purpose and automobile availability. For each model, the individual's probability of using transit is related to two system variables: the ratio of transit-to- automobile travel time and trip length. Aggregated transit-use proportions for the entire system are then obtained by combining the disaggregate probabilities with an approximation term, which reflects the within-zone variance of system characteristics. The prediction performance of the disaggregate modeling approach is compared to that of the conventional modal-split model. Results suggest that, by using the same type of information but only 12 percent of the sample, the disaggregate modal- choice method can produce at least as accurate modal-split predictions as the conventional method. The paper also describes the precedures necessary to integrate the disaggregate modal-choice method into typical urban transportation planning modeling systems.

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