Airport Ground Access Mode Choice Models (2008)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

The prediction of air passenger and airport employee mode choice decisions for travel to and from the airport forms a key analytical component of airport landside planning, as well as air- port system planning. However, there is currently no generally accepted and validated approach to modeling how airport users will change their access or egress mode in response to changes in the airport ground transportation system. The factors affecting airport travel are recognized as being significantly different from those affecting typical trips accounted for in regional transportation models. Therefore, it is difficult to determine the economic feasibil- ity of proposed projects to improve airport ground transportation or effectively manage the existing airport ground transportation system using traditional regional models. Travel forecasting models, particularly those developed to address airport ground access and egress trips, are highly specialized and not well understood by most airport managers and planners. With increasing emphasis on intermodal connections, there is a pressing need for more widely accepted and accessible reference material and information on such models. This report has been undertaken to update and extend previous efforts to document the state of practice for airport ground access mode choice models. It examines the characteristics of existing models and discusses the issues involved in the development and use of such mod- els, as well as research and development efforts that are needed to improve the state of the art of modeling airport ground access mode choice and address technical issues that are not cur- rently well handled. The synthesis project has undertaken a comprehensive review of the relevant literature in the field. This was supplemented by an extensive survey of airport authorities, metropolitan planning organizations (MPOs), consulting firms and research organizations, and other rele- vant government agencies to document the extent of the recent use of airport ground access mode choice models and to identify sources of technical information on existing models. Based on the findings of the literature review and survey, the report summarizes the current state of practice of both air passenger and airport employee ground access mode choice mod- els and discusses the type of planning issues to which the models have been applied, the technical approach adopted, the ground access modes included in the models, and the ex- planatory variables and market segmentation used to account for air passenger ground access behavior. The report also addresses the extent to which air passenger ground access mode choice models may be transferable to situations different from the one for which they have been originally developed and issues that arise in integrating air passenger and airport employee ground access mode choice models into more general regional transportation plan- ning models. The development of air passenger ground access mode choice models has been the sub- ject of ongoing research for more than 30 years, during which time the state of practice has evolved from relatively simple multinomial logit (MNL) models to more complex nested logit (NL) models involving several levels of nesting and four or more market segments. However, no clear consensus has yet emerged as to what explanatory variables should be included or how the various modes and sub-modes should be nested and a number of prob- lematic issues have not yet been addressed in a meaningful way, including how to treat rental SUMMARY AIRPORT GROUND ACCESS MODE CHOICE MODELS

car use by non-residents of a region and how best to account for the role of traveler income in the mode choice process. In addition, there has been almost no attention given to how reliably existing models predict air passenger access mode use when circumstances change from those from which the model was developed. In contrast to air passenger mode choice models, there has been very little effort directed at developing mode choice models for airport-based employees, including airline personnel and employees of the many other organizations that are involved in airport operations. The majority of MPOs model airport employee trips the same way as they model any other journey-to-work trips, although the characteristics of airport employee trips are often very different from regular journey-to-work trips because major airports operate 24 hours a day, 7 days a week. Thus, many airport-based employees work shift patterns outside the usual commute periods and airline flight and cabin crews may be away from their crew base for several days at a time. Airport access mode choice decisions by air passengers and airport employees affect a wide range of airport planning and operational management decisions, including the development of landside facilities, airport revenue from parking and other ground transportation services, and programs to reduce the growth in vehicle trips generated by the airport and the associated emissions. Potential uses of models that can predict the effect on access mode use of proposed changes to the system include sizing new planned facilities, evaluating the financial implica- tions of proposed changes in parking rates or other ground transportation fees, determining the expected air quality impacts of planned new facilities or proposed mitigation measures, and assessing the feasibility of proposed projects to improve airport access. Airport accessibility is also a significant factor in airport choice in multi-airport regions or situations where air ser- vice competition exists between local and more distant airports. Airport access mode choice is often embedded within models of airport choice and improved representation of airport access mode choice behavior should benefit those applications as well. This report has been prepared to meet the needs of a wide range of airport managers and planners, transportation planning professionals, researchers, and others interested in model- ing airport ground access mode choice, some of whom are primarily interested in a brief overview of the topic, whereas others are more interested in technical details of specific mod- els. This executive summary provides a nontechnical introduction to the issues involved in airport access mode choice modeling and summarizes the current state of practice that is described in more detail in the remainder of the report. Airport planners involved in landside planning and regional transportation planners involved in travel demand modeling who are interested in the airport access mode choice modeling process in general, the details of spe- cific models, or the way in which airport access travel can be integrated into the regional travel demand model will find a more thorough discussion in the relevant chapters of the report. Consultants and transportation modeling specialists may be more interested in the detailed case studies of a number of recent airport access mode choice models that are con- tained in an appendix to the report. Finally, members of the aviation or transportation research community who are interested in airport access mode use modeling may be inter- ested in the potential areas for future research suggested in the report. • Airport Ground Access Mode Choice Modeling Process In broad terms, the general approach to developing a mode choice model is no different from the development of any other mathematical model of a physical or behavioral process. A set of data is assembled that describes the process being modeled. Next, a suitable func- tional form for the mathematical model is defined that expresses the value of the variable that the model is intended to predict in terms of some number of explanatory variables and model coefficients (sometimes referred to as parameters), the values of which are to be determined from the data. Statistical model estimation software is then used to estimate the values of 2

model coefficients that best explain the observed values of the variable that the model is intended to predict, termed the dependent variable, using the observed values of the ex- planatory variables, termed the independent variables. The extent to which the model is able to reproduce the observed values of the dependent variable for any given set of values of the independent variables is referred to as the goodness-of-fit of the model and is an important measure of its usefulness. In the case of airport access mode choice models, there are two types of data that are required to estimate a model. The first consists of the access mode choices made by a repre- sentative sample of airport users, together with explanatory data about their characteristics (such as their household income) or the characteristics of their trip (such as where they com- menced their journey to the airport or the purpose of their air trip). These data are typically obtained from surveys of air passengers or airport employees. The second type of data con- sists of the transportation characteristics (such as travel time and cost) of the various access modes between which the mode choice decisions were made. Because of the wide range of factors that affect airport access mode choice and the num- ber of alternative modes that are typically available to a decision maker, airport access mode choice models are usually disaggregate models that attempt to predict how an individual de- cision maker with a given set of characteristics will behave. These models attempt to predict the probability that a given airport user will choose a particular mode, because two airport users with apparently identical characteristics may well choose different access modes. Because a disaggregate mode choice model predicts the probability of a decision maker choosing a given mode from among a defined set of alternatives, these models are also referred to as discrete choice models. If the probability of choosing each access mode is estimated for each airport user in a given sample of users, then the percentage of any group of such users choosing a given mode can be calculated. There are two different approaches to assembling the necessary data on airport access mode choice behavior to develop mode choice models: revealed preference and stated pref- erence surveys. Revealed preference surveys identify the travel choices actually made by air- port travelers as well as collect information on other traveler characteristics and details of the trip that are believed to influence the choice. The model estimation process attempts to develop a model that explains the mode choice decisions in terms of the traveler characteris- tics and the service characteristics of the different airport ground access modes available to the traveler. Stated preference surveys follow a similar process, except that the respondent is presented with a set of hypothetical choices and asked to select from them. For realism, the stated preference experiment is usually structured so that the choices presented to the respondent correspond to their current trip or a recent actual trip, but change the characteris- tics of the ground transportation options available. This allows the model to incorporate ground access options that do not currently exist or to explore the effect of changing factors that do not exhibit much variation in the real world. Once an estimation dataset has been assembled, model specification involves selecting an appropriate functional form and market segmentation for the model and defining relevant explanatory variables. Model estimation software is then used to obtain the estimated values of the model coefficients. The statistical significance of these estimated values and the over- all goodness-of-fit of the model are examined and the model specification revised as neces- sary to address any problems with the model coefficients or statistical fit. Once a satisfactory model has been estimated, model calibration involves making any necessary adjustments to the model so that the model predictions agree with the observed pattern of mode use. Model validation is the final step in model development and involves comparing the predictions of the model with actual values of the phenomena being modeled, ground access mode use in this case, under different conditions from those under which the model was developed, usu- ally after some change has occurred in the system being modeled. 3

The overall process of developing an airport access mode choice model is summarized in Figure 1 and discussed in more detail in chapter two of this report. The basic concept underlying most disaggregate discrete choice analysis is that each alter- native in the choice set provides the decision maker with some utility that can be expressed in terms of measurable or observable characteristics of both the decision maker and the alterna- tive (e.g., the travel time involved or the income level of the decision maker). The larger the difference in the utility between two alternatives, the more likely the decision maker is to choose the alternative with the higher utility. Because the probability of choosing a particular alternative cannot be greater than one or less than zero, this results in an S-shaped relationship between the difference in utility between two alternatives and the probability of choosing the alternative with the greater utility for that decision maker. A common mathematical form for this relationship is the logistic function, which for more than two alternatives results in the MNL model that has been widely used for airport access mode choice studies. This model can be expressed as: where P(i) is the probability of a decision maker choosing alternative i, Ui and Uj are the util- ities of alternatives i and j, and J is the number of alternatives. The utility function for a given alternative is assumed to comprise a deterministic part that consists of a function of measured and observed variables and an error term that accounts for unobserved characteristics and variability in the perceived utility of a given set of characteristics across different individu- als. In logit choice models, the error term is assumed to be a random variable and the vari- ance of the error term reflects the goodness-of-fit of the model. The deterministic part of the utility function typically consists of a linear combination of explanatory variables with their associated model coefficients, the values of which are determined in the model estimation process. Therefore, a utility function can be expressed as: Ui = ai + b1x1 + b2x2 + . . . + bnxn + ε where ai and the b’s are the model coefficients, the x’s are the values of the explanatory vari- ables such as travel time and cost, and ε is the error term. In general, the utility function for each alternative will have a constant term ai, known as the alternative-specific constant, which reflects attributes of the alternative that are not accounted for by the other variables. Therefore, a fairly simple utility function for a mode choice model might comprise: Vi = ai + b1(travel time) + b2(waiting time) + b3(walk distance) + b4(cost/income) where Vi is the deterministic part of the utility function (it is common to omit the error term in presenting the components of a utility function). In this example, travel cost is divided by income in the fourth explanatory variable so that the choice process becomes less sensitive to cost for higher-income travelers. Although the MNL model has been widely used, it is vulnerable to problems that arise from a property of the model termed the Independence from Irrelevant Alternatives. This states that including a new alternative in the choice set (or changing the perceived value of one of the alternatives) should not affect the relative probabilities of choosing any of the other alternatives. However, in many situations in airport access mode choice it is quite unlikely that changing the characteristics of one mode or sub-mode will leave the relative probabili- ties of choosing all the other modes and sub-modes unchanged. For example, 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. These limitations can be addressed by P i e e U U j J i j( ) = ∈ ∑ 4

grouping similar modes or sub-modes into separate groups or nests in a choice structure referred to as a NL model, as illustrated by Figure 4 in chapter three. This figure (from a ridership study for a proposed airport express train in Chicago) shows that private transport modes have been grouped together in one nest, whereas public trans- port modes have been grouped in a different nest. It also shows another feature of NL mod- els, that it is possible to define lower-level nests that contain sub-modes of a particular mode, in this case the access mode by which travelers reach the airport express train. The grouping of modes in the NL model requires some changes to the mathematical form of the model, which are not discussed here but are described in chapter two. Once a calibrated or validated model is available, the process of applying the model is technically fairly straightforward, although there are a number of aspects that need to be care- fully considered in developing the required input data and interpreting the results. One is that although a relatively small survey sample size (a few thousand respondents in the case of air passengers and perhaps even fewer for airport employees) may be adequate to estimate an airport access mode choice model, a much larger sample may be required for a given appli- cation of the model, depending on the issues of interest and the desired level of geographic resolution of the results. Two other considerations involve how to adjust the model to be able to predict behavior in future years, as is typically needed for planning studies. The first of these is how to adjust travel times and, particularly, costs to correspond to future conditions. These adjustments will need to consider expected changes in highway congestion as well as anticipated changes in real costs (in constant dollars) over time. The second consideration involves adjustments for the effect of changes in the levels of real household incomes over time. Implicit in the calibrated coefficients of a mode choice model are assumptions about how travelers trade off time and cost. If real incomes change, these tradeoffs can be expected to change as well. These issues are discussed in more detail in chapter two. • Review of Literature Given the importance of understanding air passenger airport ground access mode use it is not surprising that there have been a number of studies over the years that have developed air pas- senger ground access mode choice models. One of the earliest efforts to develop a formal model of air passenger airport ground access mode choice was undertaken by Ellis et al. in the early 1970s. This study used a MNL model, as did several other studies that developed air passenger ground access mode choice models over the next ten years. However, by the mid-1980s, it was becoming recognized that some of the limitations of the MNL model could be addressed through the use of NL models. One of the first applications of NL models to airport ground access mode choice was undertaken as part of a study of surface access to London Heathrow Airport, followed shortly thereafter by another study that used a NL structure to develop an integrated model of airport choice and ground access mode choice for the San Francisco Bay Area. Subsequent air passenger ground access mode choice models developed for Boston, Massachusetts; Portland, Oregon; and airports in the southeast and east of England used a nested structure, whereas other studies continued to use MNL models to represent air passenger ground access mode choice. In addition to models that have exclusively addressed airport access mode choice, a number of recent studies have used NL models to represent air passenger airport choice, with airport ground access mode choice as a lower level nest. However, these models generally only include a single- level nest for the airport ground access mode choice process and thus are equivalent to MNL models from the perspective of ground access mode choice. In addition to papers in the open literature, the synthesis project identified several studies that had developed airport access mode choice models, the details of which had not been widely reported, in several cases because the models had been documented in technical reports that had restricted distribution or did not obviously involve airport access mode choice. These included the regional travel demand model for the Atlanta region, a ridership 5

forecasting study for a proposed airport express train serving the two Chicago airports, a travel demand forecast study for the planned Miami Intermodal Center, a ridership analysis of a planned automated people mover connection between Oakland International Airport and the nearby Coliseum station of the Bay Area Rapid Transit system, and a revenue and rider- ship forecasting study for a proposed Air Rail Link between Toronto Union Station and Lester B. Pearson International Airport. • Use of Airport Ground Access Models in Airport Planning To better understand the current state of practice with airport access mode choice models, as well as to identify models that may have been developed for specific studies but not reported in the published literature, a web-based survey was undertaken of airport authori- ties, regional and state planning agencies, federal agencies involved in airport or surface transportation planning, airport consulting firms, selected universities and other research organizations, and relevant industry associations. The survey inquired about recent airport ground transportation studies undertaken by the responding organization and whether these involved the use of formal models of airport ground access mode choice. The survey also inquired about respondents’ perceptions of the usability of such a model, as well as their awareness of other organizations that have experience with the use of these models. Survey responses were obtained from 105 different organizations. These responses identified 85 specific studies completed in the past ten years that had included some analysis for airport access mode choice, of which 52 had involved the use of mode choice models. However, only four of these studies were available on the organization’s website. The survey also asked about prior experience with airport access mode choice models, and from these responses it does not appear that there has been a significant increase in the use of analytical models in recent years. Respondents who reported the use of analytical models of airport access mode choice were asked to characterize the current state of practice with these models. Approximately 55% indicated that current models were adequate for their needs, 35% reported that current models are not reliable enough, 30% noted that they are too costly to use, and 10% indicated that they are too complex to use. However, it is worth noting that only 5 of the 13 consulting firms reporting involvement in studies using such models indicated that current models are adequate, whereas 7 of the 8 air- port authorities and all 5 of the MPOs reporting the use of such models indicated that current models are adequate for their needs. Because in many cases the actual modeling is done by con- sultants rather than by airport authority or MPO staff, the limitations of the current models may not be fully appreciated by the organization sponsoring the studies. The survey also explored how airport trips were modeled in the regional travel modeling process. Of the 23 MPOs responding to the survey, 15 (65%) reported using a special-generator sub-model for air passenger trips, whereas about 50% reported using a special-generator sub- model for airport employee trips. The other MPOs either treated airport trips the same way as other regional trips or did not consider airport trips at all. • Air Passenger Mode Choice Models Although the details of the different air passenger airport access mode choice models iden- tified in the literature review vary widely, it is clear that a standard of best practice has evolved, although by no means is it always followed. This standard of best practice uses NL choice models with separate coefficients (and possibly including different variables) for at least four market segments: • Resident business trips, • Resident non-business trips, • Non-resident business trips, and • Non-resident non-business trips. 6

The modes available for resident and non-resident trips will generally be different because non-residents do not have the option of parking a private vehicle at the airport (indeed this would make no sense because their visit to the region begins at the airport and they return to the airport at the end of their stay). On the other hand, many non-residents rent a car on their arrival at the airport to provide local transportation during their stay in the region. Although no generally accepted practice has yet emerged for how to structure the nests of a NL model, this should largely be determined by the characteristics of the different modes because the primary purpose of using a NL model is to allow higher rates of substitution between modes that have similar characteristics. Therefore, it would appear logical to group private vehicle modes in one nest, with different parking options as a second-level nest, group exclusive ride on-demand modes (taxi and limousine) together in a second first-level nest, and group shared-ride scheduled modes (public transit and scheduled airport bus) together in a third first-level nest, possibly with different transit options (e.g., rail and bus) as a second- level nest. It is not clear where door-to-door shared-ride van should best fit in this structure, as a separate mode at the top level, in the on-demand nest with taxi and limousine, or in the shared-ride nest with the scheduled modes. This may be an issue to resolve empirically by exploring which option gives the best fit to the data. Alternative access modes to scheduled services can also be included as lower-level nests to each mode. Rental car and hotel shuttle use by non-residents is best modeled outside this choice process, because use of both modes is determined by factors that are largely independent of the service levels of other modes. Rental car use is often determined by local travel needs other than the airport egress and access trips. Therefore, visitors to the region may rent a car even if they are staying at a nearby hotel that has a free shuttle to and from the airport. The form of the utility functions for each choice alternative will generally be a linear com- bination of explanatory variables with their associated coefficients. However, some variables are best entered in the utility function as an inverse or ratio. For example, the service head- way of scheduled modes, which is a direct measure of average waiting time, is the inverse of the service frequency. The effect of household income may best be entered in the utility func- tion by expressing direct travel costs as a ratio of the cost to some function of the per capita or total household income. Thus, higher-income travelers will be less influenced by cost than lower-income travelers. • Airport Employee Mode Choice Airport employee ground access and egress mode choice has received much less attention in the literature than that of air passengers, and only three studies were identified that describe an airport access mode choice model developed to account for airport employee access mode choice behavior. These three models each adapted other journey-to-work mode choice mod- els to predict airport employee mode use rather than developing an entirely new model from airport employee travel data. A special-purpose airport employee mode choice model was developed for the Greater London region as part of the U.K. South East and South of England Regional Air Service (SERAS) Study. This model was a fairly simple binary (two-mode) logit model that predicted the percentage use of private vehicle and public transport and was based directly on one developed for the South and West London Transport Conference for a study covering the area to the south and west of London Heathrow Airport that was felt to provide a good basis for the SERAS work. A study of the potential ridership on a proposed automated people mover link between Oakland International Airport and a nearby rail transit station and a second study of a similar link between San Jose International Airport and a nearby light-rail stop) both used a similar approach of adopting model coefficients from regional travel demand 7

models for home-based work trips and then estimating alternative specific constants to cali- brate the model predictions to survey data on airport employee travel. The most common way to model employee travel to and from airports is to treat the air- port in exactly the same way as any other transportation analysis zone in the regional travel demand model and use the trip generation, trip distribution, and mode choice sub-models for home-based work trips to generate the number of person and vehicle trips associated with air- port employee travel. Those MPOs using special-generator models for airport employee travel tailor this process to better fit the number of airport employee trips, typically through the use of airport employment data and surveys of airport employee travel. • Transferability of Airport Ground Access Mode Choice Models Given the considerable cost and effort required to develop models of airport ground access mode choice, it is natural to ask whether separate models need to be developed for every air- port or whether it would be possible to adapt or apply a model developed for one airport for use at another. Indeed, several of the existing models described in this report did just that. In general, experience in applying models of transportation behavior in situations that are dif- ferent from the one for which they were developed has not been very encouraging. However, this experience has largely focused on models of general urban travel behavior, and airport ground access travel behavior may be more consistent. In principle, one would expect that air travelers would behave similarly when faced with a similar choice situation, controlling for differences in ground access service characteristics (e.g., fares or travel times) and differences in traveler characteristics (e.g., trip purpose and duration, household composition, and income). Therefore, to the extent that a model accu- rately reflects the effect of these variables, it should explain the behavior of air parties in other geographical regions. However, this is a significant caveat, because many models are heav- ily dependent on alternative specific constants that at best reflect a range of local factors that are not explicitly included in the model and at worst correct for problems in the model spec- ification. In particular, there may be regional differences in attitudes toward the service char- acteristics of different modes as well as differences in the nature of the services offered. One important consideration is the way in which different modes are included in NL mod- els. Because the nesting structure of the limited number of models that have used a NL struc- ture is very dependent on the particular modes that exist in the region being modeled, it is difficult to generalize about how the various models have grouped the modes. It is unclear whether differences in the nesting structure of different modes reflects fundamental differences in choice behavior across different regions is a consequence of the modes included in the mod- els or the explanatory variables used, or merely reflects different modeling philosophies by different developers. The way in which household income is included (or not included) in the models will also affect how well they can be expected to explain behavior in other regions where the distribution of household incomes is different. Given the current lack of consensus over model specification and typical coefficient values between different airport access mode choice models, it can be assumed that the transferabil- ity of these models is highly suspect. Although it seems plausible that the underlying airport traveler behavior may not differ that much from region to region, after taking into account dif- ferences in air passenger or airport employee characteristics and transportation system service levels, it appears unlikely that current airport access mode choice models do this in a way that is transferable to other regions, based on the significant differences between the different mod- els. There is an urgent need to better understand how well current models reflect the factors influencing the underlying travel behavior and how they can be improved to better reflect this behavior, both because of the obvious value of being able to apply airport access mode choice models in different situations from those for which they were originally developed, as well as 8

the concerns about the reliability of even applying them to different situations at the airports for which they were developed. • Airport Ground Access Models in the Regional Planning Process Although modeling airport access mode choice by air passengers and, to a lesser extent, airport employees, has largely been restricted to specialized studies addressing airport land- side and system planning issues, there is a growing interest in explicitly modeling such trips in the regional transportation planning process. A number of MPOs have begun to address air passenger trips using a special-purpose mode choice model or special-generator sub-model and a somewhat smaller number of MPOs have begun to do the same for airport employee trips. However, the majority continues to model trips to and from airports as regular regional travel using a standard trip classification such as home-based non-work trips. Because these standard trip classifications encompass a very wide range of activities, most of which have very little in common with airport travel, it would be surprising if the model components did a very good job of predicting airport access mode choice. This is compounded by the concept that air passengers in particular typically have access to a much larger number of alternative modes for airport access and egress trips than are usu- ally modeled in regional travel demand models, including taxi and limousine services, shared-ride door-to-door van services, and scheduled airport express bus services. Further- more, a significant fraction of all airport access and egress trips is made by visitors to the region. Most current regional travel models are only designed to model travel by residents of the region and largely ignore travel by visitors. Therefore, modes such as rental car and hotel courtesy shuttle are typically not included in the models. Finally, the largest single air pas- senger access or egress mode at many airports, for both visitors and residents, is being dropped off or picked up by private vehicle. This option is also typically not explicitly mod- eled in regional travel models. Models predicting private vehicle use that are based on the assumption that the vehicle will be parked at the destination until the return trip will thus underestimate the vehicle-miles of travel involved by a factor of two. These concerns may not be particularly important in terms of total regional travel, because airport trips comprise a fairly small fraction of all regional trips. However, these issues become of much greater concern when the regional travel models are used to predict trips on parts of the transportation network in the vicinity of the airport or are used for airport access and egress studies, including predictions of airport access and egress trips for use in envi- ronmental impact studies. Therefore, a fairly strong case can be made that airport access and egress trips need to be modeled separately from general regional travel patterns (or at least as a special-generator sub-model within the overall modeling framework) and then integrated with other trips in the traffic assignment process. This synthesis project examined some of the technical issues involved in modeling air- port trips within the context of regional travel demand models and identified a range of approaches that has been followed by different MPOs that have explicitly modeled airport trips in their regional travel modeling process. These approaches vary from a special-purpose sub-model within the travel modeling process of the Atlanta Regional Commission, through the external generation of airport trip tables that are combined with the trip tables generated by the regular travel modeling process of the Metropolitan Washington Council of Governments, to two examples of the use of external airport access mode choice mod- els. In the case of the Boston Central Transportation Planning Staff, an air passenger mode choice model was developed in-house in cooperation with the airport authority. In contrast, the Southern California Association of Governments uses a proprietary air passenger mode choice model, the output of which is then used as input to the regular regional travel mod- eling process. Further information on these four approaches is provided in chapter eight of this report. 9

• Conclusions and Further Research Airport ground access and egress mode choice models play a critical role in airport land- side planning studies and modeling traffic on the regional transportation system in the vicin- ity of airports. The ability to predict how air passenger and airport employee access and egress mode use will change in response to changes in the airport landside access system or other anticipated changes in the regional transportation system is essential to the proper eval- uation of proposed measures and projects. However, these decisions are influenced by very different factors from those affecting general regional travel patterns and the transportation options available to airport travelers are often quite different from those for other types of regional trip. Therefore, there is a need for specialized models that can represent these mode choice decisions as well as the means to integrate these models or their output into the regional traveling modeling process. The development of air passenger ground access mode choice models has been the sub- ject of ongoing research for more than 30 years. Over this period, the state of practice has slowly evolved from relatively simple MNL models to more complex NL models involving several levels of nesting and four or more market segments. However, no clear consensus has yet emerged as to what explanatory variables should be included or how the various modes and sub-modes should be nested. In addition, even the most recent models have still not addressed a number of problematic issues in a meaningful way. These include how to treat rental car use by non-residents of a region and how best to account for the role of traveler income in the mode choice process. Aside from these technical considerations, there has been almost no attention given to how reliably existing models predict air passenger access mode use when they are used to predict mode use under very different conditions from those pre- vailing when they were developed, including changes in the physical infrastructure, ground transportation services, and household income levels. There is an urgent need for more research into these specific aspects, as well as continuing research directed at improving the current state of practice. In contrast to air passenger mode choice models, there has been very little effort directed at developing airport employee mode choice models. The majority of MPOs model airport employee trips the same way as they model any other journey-to-work trips. The develop- ment of better airport employee access models is a promising research opportunity. Finally, many existing regional travel models do not explicitly model airport trips, but treat them as general regional travel. Because of the unique characteristics of airport travel and the range of transportation options typically available at airports, this is likely to give fairly poor predictions of airport mode use and the resulting vehicle trips. Further research is needed to explore how well existing regional travel models account for airport trips and to provide guidance on how best to implement explicit modeling of airport access mode choice in the regional travel modeling process. 10