Airport Ground Access Mode Choice Models (2008) / Chapter Skim
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Pages 14-30
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From page 14... ...
This chapter provides a short introduction to the motivation and methodology for modeling airport ground access mode choice. It is primarily intended to give airport management and planning staff some background on how these models can be used in the planning and decision-making process, and provide an overview of the technical issues involved in developing and applying these models so that they can interact with more technical specialists in an informed way and properly supervise contracts for the development and use of such models.
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From page 15... ...
15 Given the many different factors influencing the proportion of airport travelers using each mode, it is unrealistic to expect planners and decision makers to be able to make quantitative estimates of the effect on mode use of any given change in the system without the use of formal analytical tools that model how airport users respond to changes in the available airport ground transportation services. Thus, airport ground access mode choice models provide the basic input to other analysis tools that are used to support airport landside and ground access planning and decision making, such as traffic flow models, simulation models, or financial planning tools.
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From page 16... ...
model. The trip generation module converts airport passenger traffic forecasts into estimates of person-trips between the airport and each regional travel analysis zone, whereas the mode choice module converts these estimated person-trips into the corresponding vehicle trips for use in the regional travel demand model.
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From page 17... ...
17 among a defined set of alternatives, these models are also referred to as discrete choice models. Because the airport access mode choice decision depends on the characteristics of each individual travel party as well as the transportation characteristics of the different modes faced by that travel party, which in turn vary with the travel party characteristics (e.g., the travel costs typically vary with the access trip origin and the travel party size)
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From page 18... ...
whether or not the decision is made jointly by the members of the party or by one individual within the party. In contrast, airport employees are usually assumed to make airport access mode choice decisions individually, even if they decide to travel to the airport in a group (e.g., a car pool)
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From page 19... ...
19 – Potentially useful (used in some models) Amount of checked baggage Number of air trips from airport in past year Whether trip costs paid by employer or client Time arrived at airport Gender of respondent.
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origin zone and thus constant for every zone, although the cost for parking will depend on how long the vehicle is parked. Some costs, such as fares for some public transportation services will depend on both the origin zone and the number of people in the travel party.
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21 travelers' expressed choices between hypothetical situations that they have not actually encountered correspond to how they would really behave if faced with those situations in practice. To attempt to address this concern, stated preference studies are often combined with analysis of revealed preference to at least ensure that the stated choice behavior is consistent with the actual behavior when applied to situations that have actually been experienced.
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From page 22... ...
typically a few weeks or less, but is being used to predict mode use for a different time period, such as a year. Even if the survey data used for model estimation is an accurate representation of the larger population, the conversion of model predictions from travel party trips to vehicle trips generally will require some model calibration.
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From page 23... ...
23 difference between the utility of alternative 1, U1, and that of some other alternative 2, U2, (termed a binary choice because it involves only two alternatives)
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variables and an error term that accounts for unobserved characteristics and variability in the perceived utility of a given set of characteristics across different individuals, therefore: Ui = Vi + ε where Vi is the deterministic part of the utility and ε is the error term. In logit choice models, the error term is assumed to be a random variable with values that are independent and identically distributed with a Gumbel (double exponential)
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From page 25... ...
25 the probability of an air party choosing to park in the longterm parking lot than on the probability of choosing to use a shared-ride van, because those who would have parked in the short-term lot at the former rates are much more likely to choose to park in the long-term lot instead than to decide to use a shared-ride van. Similarly, changes in one public transportation service are likely to affect the use of other public transportation services to a greater extent than the use of private vehicles.
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From page 26... ...
in terms of those values rather than the real values. This may not be a problem in obtaining a good fit to the data, but will produce biased predictions when the model is later applied to other datasets that do not have the errors or biases.
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27 and passengers need to arrive at the airport earlier than before to ensure being able to clear security in time to make their flight. In addition, airports rigorously enforce the prohibition on leaving vehicles unattended at the terminal curb front or even waiting when not actively loading or unloading passengers.
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From page 28... ...
the feasibility of a major infrastructure investment such as an airport rail link or even an automated people-mover link to a nearby rail station that might cost several hundred million dollars or more, the cost of having a good modeling capability is trivial and would easily be justified by avoiding a poor decision that results in an increase in the cost of the project by even 1%. Indeed, having such a modeling capability is probably a necessary requirement for obtaining environmental approval and funding.
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From page 29... ...
29 income levels rise or fuel prices increase in real terms, then transportation operators will need to raise their prices to cover their higher costs of doing business and the cost of operating private vehicles will rise. Conversely, if transportation operators are able to achieve productivity gains or rising levels of air travel increase the traffic that they carry, allowing them to be more efficient, then they may be able to reduce their prices in real terms.
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From page 30... ...
elasticity of demand for a given mode with respect to a given service variable can be calculated numerically, and this may be a useful thing to do to give planners and managers an easily understood tool to make quick assessments of the likely effect of any proposed change. The important caveat to ensure this is clearly understood is that any given elasticity 30 value is only valid for the particular situation for which it has been calculated and will change as the situation changes.
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