Modeling passengers’ flight choice behavior is valuable to understanding the increasingly competitive airline market and predicting air travel demand. Standard and mixed-multinomial logit models of itinerary choice for business travel are estimated on the basis of a stated preference survey conducted in 2001. The results suggest that observed demographic- and trip-related differences are incorrectly manifested as unobserved heterogeneity in a random-coefficient mixed logit model that ignores the demographic- and trip-related characteristics of travelers. Among demographics, gender and income level have the most noticeable effects on sensitivity to service attributes in itinerary choice behavior, but membership in a frequent flyer program, employment status, travel frequency, and group travel also emerge as important determinants. However, residual heterogeneity is significant because of unobserved factors, even after accommodating sensitivity variations due to demographic- and trip-related factors. Consequently, substitution rates for each service attribute show substantial variations in the willingness to pay among observationally identical business passengers.

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Modeling passengers' flight choice behavior is valuable to understanding the increasingly competitive airline market and predicting air travel demands. This paper estimates standard and mixed multinomial logit models of itinerary choice for business travel, based on a stated preference survey conducted in 2001.The results suggest that observed demographic and trip related differences get incorrectly manifested as unobserved heterogeneity in a random coefficients mixed logit model that ignores demographic and trip-related characteristics of travelers. Among demographics, gender and income level have the most noticeable effects on sensitivity to service attributes in itinerary choice behavior, but frequent flyer membership, employment status, travel frequency, and group travel also emerge as important determinants. However, there is significant residual heterogeneity due to unobserved factors even after accommodating sensitivity variations due to demographic and trip-related factors. Consequently, substitution rates for each service attribute show substantial variations in the willingness-to-pay among observationally identical business passengers.

An understanding of residential location choice is fundamental to behavioral models of land use and, ultimately, travel demand. Detailed data and predictive models are needed. This research examined choices of apartment dwellers and their reasons for moving, their priorities when choosing a residential location, and the trade-offs involved. In addition to summary statistics of the data, linear regressions, binary logit, and ordered probit models were used to investigate variations in rent and apartment size and stated preferences of housing, location, transportation, and access. Binary logit and ordered probit models reveal similar results concerning people's preferences for accessibility. For instance, families and other multiperson households tend to place less value on commute times and freeway access and choose apartment improvements over travel savings. Women are more likely to state that they place a higher importance on commute time and freeway access, but, when asked to choose between travel times and apartment size, they are more likely to choose the larger apartment. Other models suggest that being within walking distance of a commercial center increases average rent by $24 per month. Increases in distances to the central business district (CBD) and mean neighborhood commute times reflect lower monthly rents, about $20 per mile from the CBD and $24 per added minute of commute (one way). Apartments in the urban area tend to be, on average, 75 ft2 smaller, all things being equal (including population density, which has an added effect). These results and many others provide several valuable insights regarding the location choice of those residing in apartments.

We present a Dynamic Airline Scheduling (DAS) technique which is able to change departure times and reassign aircraft types during the booking process to meet fluctuating passenger demands. The procedure is tested on several different days before departure, resulting in a significant profit increase for a major European airline. The results also indicate that applying DAS close to departure yields the largest potential.