Air Demand in a Dynamic Competitive Context with the Automobile (2019) / Chapter Skim
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Pages 77-90
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From page 77... ...
77 Introduction The research conducted under ACRP Project 03-40 included the creation of several new models to better understand the influence of many causal factors on the choice of automobile or plane for various aspects of a full long-distance trip. The attitude-based SEM was presented in Chapter 5, with other discussions of attitudes, values, and preferences.
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From page 78... ...
78 Air Demand in a Dynamic Competitive Context with the Automobile area as well as combinations of reference trip distance, purpose, and mode. Soft quotas were used to ensure equal representation of gender and a range of incomes and ages, meaning that the sample was targeted proportionally along these dimensions and was monitored during the data collection process, but exact minimum quotas were not set.
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From page 79... ...
Research Methods 79 Respondents meeting these criteria were removed from the dataset, and new respondents were recruited to complete data collection. The tables and charts that follow provide information about the sample makeup.
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80 Air Demand in a Dynamic Competitive Context with the Automobile The estimation work conducted in the research made use of both Multinomial Logit and Mixed Multinomial Logit models. The research team conducted much of the determination of appropriate model specifications (e.g., in terms of specification of cost)
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From page 81... ...
Research Methods 81 Car Constants in the Logit Models As presented in ACRP Web-Only Document 38, several shifts are applied to the car constant in the original logit models, primarily for demographic effects. In addition, there is a key difference if a rental car is shown for the long-distance trip.
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From page 82... ...
82 Air Demand in a Dynamic Competitive Context with the Automobile survey sample: Washington, D.C., Boston, Chicago, and Denver and would depend on the available departure airports and the geography of the sample. Table 6-2 shows the mode share and airport share (of those that fly)
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Research Methods 83 rise by 20%. The $2 increase in gas costs per gallon results in about a 10% increase in air trips.
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84 Air Demand in a Dynamic Competitive Context with the Automobile Latent Variable 1: Automobile Orientation The first latent construct is used to explain the answers to three separate attitudinal statements. The research team examined the impact of the latent variable on the attitudinal statements.
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From page 85... ...
Research Methods 85 • For the Multiday Trips Unpleasant latent variable and the Car Stress latent variable, a negative impact on car was found in both the business and leisure models. • Finally, for the Airport Stress latent variable, there was, as expected, a positive impact on the car choice.
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From page 86... ...
86 Air Demand in a Dynamic Competitive Context with the Automobile COEFFICIENT BUSINESS LEISURE ESTIMATE T-RATIO ESTIMATE T-RATIO Airfare ($) −0.01 −13.10 −0.01 −16.87 Gas cost for access ($)
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From page 87... ...
Research Methods 87 household income of $87,500. The value of time will change with income, based on the income elasticity coefficient.
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88 Air Demand in a Dynamic Competitive Context with the Automobile NEW ACRP PROJECT 03-40 COMPONENTS (Applied to Car and Air Tours) Figure 6-1.
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Research Methods 89 • Access distances sorted by road to/from each airport from each Census tract and zone centroid. • Station-to-station rail networks, based on Amtrak schedules and fare tables.
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From page 90... ...
90 Air Demand in a Dynamic Competitive Context with the Automobile In summary, the ACRP Project 03-40 model is applied to all tours within the continental United States of 100 miles or more in each direction, for which the mode predicted by the FHWA model is either car or airplane. The synthetic population used in the FHWA model is drawn at the Census-tract level, so the Census tract at the home end of the tour is already known.
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