Passenger demand for air transportation in a hub-and-spoke network

A major transformation of the air transportation system---involving the modernization of technologies, policies, and business models---is currently under way. Knowledge of passenger demand for air service is the key to a successful system transformation. This research develops an air passenger demand model and applies it to the air transportation system of the United States.;The proposed model deals with city-pair demand generation and demand assignment (to routes) in a single model, which is consistent with random utility theory. It also quantifies the "induced" air travel by adding a non-air alternative in the choice set. Using publicly available and regularly collected panel data, the model captures both time series and cross-sectional variation of air travel demand, and can be regularly updated. The empirical analysis explicitly modeled the pattern of correlations among alternatives by a three-level nested logit model. This implies that a route is more likely to compete with another route of the same O-D airport pair in a multiple airport system than the routes of the other O-D airport pairs, and is least likely to be substituted by the non-air alternative. In addition, the endogeneity problem of air fare was identified and remedied by the instrumental variables (IV) method. The IV estimates yield more sensible values-of-time, demand elasticities, and correlations of total utilities for alternatives than those of ordinary least squares method.;Other empirical findings include that (1) the fare elasticities from our estimates accord with the variation of fare elasticities from other studies in the literature; (2) for connecting routes, a proportional flight frequency increase on the segment with lower frequency increases service attractiveness more than an equivalent change on higher frequency segment; (3) travelers avoid connecting at airports with high expected delay; (4) under steady state, a one-minute hub delay increase has a larger impact on demand than an equivalent change in scheduled flight time of a connecting route; (5) air travel demand is strongly sensitive to income; (6) market distance has a concave effect on air route demand; and (7) potential travelers' fare sensitivity has increased relative to frequency sensitivity since 2001.