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

다국어 초록 (Multilingual Abstract)

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.