Abstract
In this paper, the conventional disaggregate travel demand model, a probability model for the modeling of multiple modes, generally called random utility maximization (RUM), is expanded to a model of count of mode choice. The extended travel demand model is derived from general economic theory—maximizing instantaneous utility on the time horizon, subject to a budget constraint—and can capture the dynamic behavior of countable travel demand. Because the model is for countable dependent variables, it has a more realistic set of assumptions to explain travel demand than the RUM model. An empirical test of the theoretical model using a toll facility user survey in the New York City area was performed. The results showed that the theoretical model explained more than 50% of the trip frequency behavior observed in New York City toll facility users. Travel demand for toll facility users increased with respect to household employment, household vehicle count, and employer payment of tolls and decreased in terms of travel time, road pricing, travel distance, and mass transit access.
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