Airport Ground Access Mode Choice Models (2008)

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31 AIR PASSENGER MODE CHOICE MODELS The ability to predict how airport users will respond to changes in the service characteristics of the ground access modes serving the airport or the addition of new ground access services is clearly an essential part of any effective analysis of proposals to enhance the airport ground trans- portation system. In addition, airport ground access models play an important role in studies addressing how future air travel demand will be distributed among airports in a multi- airport region. The relative accessibility of airports serving a region is recognized as one of the key determinants of air passenger airport choice (in addition to the air service offered at the airports). Whereas early airport choice models simply used highway travel times as a measure of airport accessibil- ity, later models have recognized that appropriate measures of airport accessibility need to account for the range of ground transportation services available and the proportion of airport users who choose different ground transportation modes for their travel to and from the airport. Because airport ground transportation involves trips both to and from the airport, ideally what are required are airport ground access and egress models. However, in practice, most modeling efforts to date have only addressed airport access, and it has been assumed (often implicitly) that the reverse trip reflects a symmetrical pattern of mode use. Personal experience and a growing body of evidence sug- gests that this is not the case, at least on the basis of the behavior of individual air parties, but this report focuses pri- marily on airport ground access mode choice models, because those are the models that are generally available. The review of the literature undertaken as part of this study found no examples of prior studies that explicitly modeled airport ground egress travel behavior. The focus on ground access trips no doubt results primarily from the use of air passenger surveys as the source of data on which to base the development of airport ground travel mode choice models. Because it is much easier to survey departing passengers rather than arriving passengers, air passenger surveys have generally addressed the ground access trip that the respon- dent has just completed, rather than the egress trip that a respondent beginning their air trip will make on their return or the egress trip that a visiting air traveler made on their arrival in the area some time before. The issue of the differ- ence between airport ground access and egress travel behavior is discussed later in this report. Therefore, given the importance of understanding air pas- senger airport ground access mode use it is not surprising that there have been a number of studies over the years that have developed air passenger ground access mode choice models. One of the earliest efforts to develop a formal model of air passenger airport ground access mode choice was under- taken in the early 1970s (Ellis et al. 1974). 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 (Leake and Underwood 1977; Sobieniak et al. 1979; Gosling 1984; Spear 1984; Harvey 1986). 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 (Ben-Akiva and Lerman 1985). 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 (Howard Humphreys and Partners 1987), followed shortly thereafter by a study by Harvey (1988) that used a NL structure to develop an integrated model of airport choice and ground access mode choice for the San Francisco Bay Area. Subse- quent air passenger ground access mode choice models developed for Boston, Massachusetts (Harrington et al. 1996; Harrington 2003); Portland, Oregon (PDX Ground Access . . . 1998); and airports in the southeast and east of England (Halcrow Group Ltd. 2002b) used a nested structure, whereas other studies continued to use MNL models to rep- resent air passenger ground access mode choice (Tambi and Falcocchio 1991; Dowling Associates, Inc. 2002; Psaraki and Abacoumkin 2002). In addition to models that have exclusively addressed airport access mode choice, a number of recent studies have used NL models to represent air pas- senger airport choice, with airport ground access mode choice as a lower-level nest (Bondzio 1996; Monteiro and Hansen 1996; Mandel 1999; Pels et al. 2003). 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. The technical details of many of these models have been re- viewed by Lunsford and Gosling (1994) and later by Gosling et al. (2003). However, the level of detail reported in the liter- ature for each of the models varies, with some authors only providing partial information on estimated parameter values or even on the independent variables included in the model. It is common to estimate separate sets of model parameters, or CHAPTER THREE REVIEW OF THE LITERATURE

even different model specifications, for different market seg- ments, such as residents of the area versus visitors, or air travelers on business trips versus those on leisure trips. Some published articles describing these models only present the estimated values of the model coefficients for some of the mar- ket segments. This makes comparison of the different models difficult. However, detailed results are available for a number of recent models. The principal features of these models are briefly described in the following sections. More detailed doc- umentation of each of these models is provided in Appendix D included in the web version only. Atlanta Regional Commission Model The Atlanta Regional Commission (ARC), the MPO for the Atlanta region, has developed an Airport Passenger Model (APM) to model air passenger trips to and from Hartsfield– Jackson Atlanta International Airport (H-JAIA) as a compo- nent of the overall regional travel demand modeling process (Model Documentation . . . 2005). The model was originally developed in 2003 using data from the H-JAIA Peak Week Air Passenger Survey performed in 2000 and was updated in 2006 for new income groups for 2000 (Travel Demand . . . 2006). The ARC Airport Passenger Model (ARC/APM) consists of two components: a trip generation/distribution model that as- signs the total originating air passenger traffic at H-JAIA to regional TAZs and a mode choice model that predicts the ground access mode use of those air passenger trips. A sub- sequent step converts air passenger trips to vehicle trips for inclusion on the traffic assignment step of the regional trans- portation demand model. The model is one of the few exam- ples (and the only one documented in this study) of a special- generator airport access mode choice model fully integrated into a regional travel demand modeling process. The ARC/APM predicts air passenger and vehicle trips using four market segments: resident business, resident non-business, non-resident business, and non-resident non- business. The mode choice model considers five modes: air passengers dropped off by private vehicle, private vehicle parked at the airport for the duration of the air trip (termed drive self), rental car, transit, and taxi. Although not explicitly stated in the model documentation, it appears from the documentation that the transit mode includes the Metropolitan Atlanta Rapid Transit Authority rail and bus services, commercial shared-ride shuttle van services, and other high-occupancy shared-ride modes such as charter bus, whereas the taxi mode includes exclusive ride limou- sine services and hotel and motel courtesy vehicles, as well as conventional taxi use. The basic form of the mode choice model is a NL model with a separate structure for trips by residents of the region from that for non-residents. The model does not take the type of ground access trip origin into account. Therefore, all visi- tors to the region are considered to include drop off by pri- 32 vate vehicle and rental car in their choice set, whether or not they are staying with residents of the region and thus have someone who could drop them off at the airport or the need for a rental car during their visit. The explanatory variables consist of the travel times and costs for each mode. Separate variables are defined for in-vehicle, walk, and wait times. The transit in-vehicle times use the total in-vehicle time for the trip from the origin zone to the airport zone, transit walk times combine access, egress, and sidewalk times from the transit network, and the transit wait times combine the initial wait with any transfer wait times. Boston Logan International Airport Model This model was developed by the Central Transportation Plan- ning Staff (CTPS) in Boston using a 1993 air passenger survey done at Boston Logan International Airport (Harrington et al. 1996; Harrington 2003). Separate sub-models were developed for resident business trips, resident non-business trips, non- resident business trips, and non-resident non-business trips. The two resident sub-models consist of a NL model, with sep- arate nests for door-to-door modes (taxi and limousine) and automobile modes (drop off, short-term parking, long-term parking, and off-airport parking). There are four shared-ride public modes at the top level (regular transit, scheduled airport bus, the Logan Express service to off-airport terminals in the region, and the Water Shuttle between the airport and the downtown Boston waterfront). The visitor sub-models are MNL models and omit the long-term parking alternatives but add a hotel shuttle mode. This model is particularly relevant to studies involving improved public transportation access to airports because it includes both a rail access mode, the Massachusetts Bay Transportation Authority (MBTA) regional rail transit sys- tem, and off-airport terminals, the Logan Express service operated by the Massachusetts Port Authority (Massport), the airport authority for Logan Airport. The MBTA Airport Station is adjacent to the airport and linked to the passenger terminals by a free shuttle bus service operated by Massport. Unlike many other airport access mode choice models, the CTPS model is also interesting in that it treats rental car use as an independent decision and excludes it from the mode choice decision process. Chicago Airport Express Ridership Forecasting Study In 2003, the Chicago Department of Transportation retained Resource Systems Group, Inc. and Wilbur Smith Associates to undertake a ridership and revenue forecasting study of a proposed Airport Express train service between downtown Chicago and O’Hare International and Midway Airports (Wilbur Smith Associates 2004). To identify the pattern of air passenger trip ends, travel party characteristics, and exist- ing access mode use, and to understand how airport travelers

33 might change their access mode choice if the Airport Express train were available, two surveys of air travelers were under- taken by Resource Systems Group, Inc. at O’Hare and Mid- way Airports, an origin–destination survey and a stated pref- erence survey (Resource Systems Group Inc. 2004). The origin–destination survey results were used to develop a pro- file of existing originating air passenger characteristics, in- cluding trip purpose, trip origin, and ground access mode use, as well as current and future trip tables that predicted the number of trips by market segment originating in each of 145 TAZs within the study area. The stated preference survey interviewed 1,110 air travel- ers in the two airports, who were asked about details of their trip to the airport, and then asked to complete eight stated preference choice experiments in which they were presented with a choice between three trip modes, including the mode they had just used and the Airport Express train. The results of the stated preference survey were used to estimate a mode choice model that defined nine airport access modes: private vehicle parked at the airport for the duration of the air trip, drop off at airport by private vehicle, rental car, taxi, other private mode, Airport Express train, Airport Bus, Chicago Transit Authority train, and other public mode. The “other private mode” included limousine, hotel/motel courtesy shuttle, and shared-ride airport van service, whereas the “other public mode” included local bus, region train and bus service, and charter bus. The mode choice model used a NL form with a somewhat different nest structure for travelers on business and non-business trips, as shown in Figure 4. The utility functions for each airport access mode included two continuous variables, total travel time and travel cost, in addition to alternative specific constants. The estimated coef- ficients for each of the two continuous variables within a given market segment were constrained to have the same value for each mode. Traveler income was not explicitly in- cluded in the model, but travelers were divided into two income categories on the basis of household income and sep- arate travel cost coefficients estimated for each category. Although a single travel time variable was used in each utility function, weights were applied to various components of the total travel time that was used in the model estimation to account for different disutility of access, transfer, and waiting Public Transport Mode Other Public Mode Public Transport Mode Other Private Mode Drove & Park Drive & Park CTA Service Free Van Dropped Off Rental Car Taxi TaxiWalk CTA Train Airport Bus Airport Ground Access Mode Choice Airport Express Train Ex pr es s Tr ai n Ac ce ss M od e Business Mode Choice Model Structure Non-Business Mode Choice Model Structure Airport Express Train Public Transport Mode Other Private Mode Drove & Park Dropped Off Rental Car Taxi Airport Ground Access Mode Choice Public Transport Mode Other Public Mode CTA Train Airport Bus Drive & Park CTA Service Free Van Taxi Ex pr es s Tr ai n Ac ce ss M od e Walk FIGURE 4 Chicago Airport Express mode choice model nesting structure (Source: Wilbur Smith Associates 2004).

time (as applicable) for each mode. The weights were deter- mined by iteratively adjusting their value to obtain the best overall model estimation result. In addition to the two contin- uous variables, a dummy variable for the availability of bag- gage check-in at the downtown terminal was included in the Airport Express and Airport Bus modes, and a second dummy variable was included in the Airport Express mode for those passengers boarding at an intermediate station. Separate model coefficients were estimated for business and non-business travelers. The same model coefficients were used for residents and non-residents of the Chicago region, although the available modes for these two market segments were different. After the model coefficients were estimated using the stated preference data, the model was calibrated to correspond to the mode shares for the study area obtained from the origin–destination survey data by adjust- ing several of the ASCs until the model predicted the ob- served mode shares from the study area. The calibrated mode choice model was applied to forecast ridership and revenue for the Airport Express train for vari- ous service scenarios for 2009 and 2020, including two dif- ferent fare levels, several different travel time assumptions for the service to O’Hare Airport, two different growth rates for off-peak highway travel time, and whether or not a free downtown shuttle or baggage check at the downtown termi- nal would be provided (Wilbur Smith Associates 2004). Miami Intermodal Center Travel Demand Forecast Study The Miami Intermodal Center (MIC) is being planned as a major transportation interchange facility located immedi- ately to the east of the Miami International Airport (MIA) to provide an integrated terminal for several intercity trans- portation services, including Amtrak, Tri-Rail commuter rail, and Greyhound buses, as well as Metrobus and Metro- rail transit services (Miami Intermodal Center . . . 1997). An APM (the MIC/MIA Connector) will link the MIC to the air- port. Provision has also been made in the planning for future High Speed Rail and East–West Corridor rail lines as well as an Airport/Seaport Connector rail link. The MIC will also help accommodate growth of MIA by providing expanded airport landside facilities, including rental car and long-term parking facilities. As part of the planning for the MIC, a travel demand forecast was prepared by ICF Kaiser Engi- neers (1995) that incorporated an airport access mode choice model that was used to forecast ridership on the MIC/MIA Connector. The mode choice model was based on a model originally developed by KPMG Peat Marwick for a study for Newark International Airport in New Jersey. Because of the large number of modes available at MIA and the complexity of the choices available as a result of the MIC project, the mode choice model was expanded to in- clude the following nine modes: drop off by private vehicle, 34 private vehicle parked for the air trip duration, rental car, taxi, limousine, premium transit, local transit, shared-ride van, and hotel courtesy shuttle. The model used a NL struc- ture with the first five modes grouped into a nest called Non- Group modes and the other four modes grouped into a nest called Group modes. The model defined four market seg- ments: resident business trips, resident non-business trips, non-resident business trips, and non-resident non-business trips. The model has only three explanatory variables, apart from the ASCs: in-vehicle travel time, out-of-vehicle travel time (including waiting time and terminal time, such as walk- ing from the parking lot to the airport terminal or returning a rental car), and travel cost. There is no consideration given to the effect of income differences in the model. The values for the coefficients for the continuous variables were not esti- mated from the air passenger survey data, but rather adopted from the values estimated for Newark International Airport in the original model with the same coefficient values being used for each market segment. Values for ASCs for each mode were estimated from air passenger survey data to fit the model predictions of mode use to the observed data. Bay Area Rapid Transit–Oakland International Airport Connector Study Since the 1970s, a number of studies have been undertaken by the Port of Oakland (the operator of Oakland International Airport), Bay Area Rapid Transit (BART), and other agen- cies to explore the feasibility of developing an APM connec- tion to replace the current AirBART shuttle bus link between the airport and the Coliseum BART station located approxi- mately 2.5 miles from the airport (BART–Oakland . . . 2002, Executive Summary). As part of on-going efforts to imple- ment the Oakland Airport Connector, a Final Environmental Impact Report/Environmental Impact Statement (EIR/EIS) was completed and approved in March 2002. The analysis for the EIR/EIS included the development of an airport access mode choice model by CCS Planning and Engineer- ing, Inc., that was applied to generate ridership projections for the Connector (BART–Oakland . . . 2002, Appendix B: Transit Ridership Procedures and Inputs). The mode choice model addressed both air passenger trips and airport em- ployee trips, with the employee trips treated as a separate market segment. The general form of the model is a MNL logit model with air passenger trips divided into four market segments: resident business trips, resident personal trips, vis- itor business trips, and visitor personal trips. Data on the air party characteristics for each market seg- ment were obtained from the 1995 Air Passenger Survey un- dertaken for the Metropolitan Transportation Commission (MTC) at the three Bay Area airports (including Oakland International Airport), supplemented by surveys of AirBART passengers performed by CCS Planning and Engineering, Inc., in December 1999 and May 2000 as part of the study.

35 The model assigns airport trips among the following eight modes: private vehicle, rental car, scheduled airport bus, pub- lic transit, shared-ride van, hotel courtesy shuttle, taxi or lim- ousine, and other. Public transit included both the use of BART by means of the AirBART shuttle (or the Connector in the future), as well as local transit bus service directly to the airport. The transit alternative for travelers with trip origins in zones near the airport was assumed to be local bus, whereas the transit alternative for those from more distant zones was assumed to be BART. The use of “other” modes was not explicitly modeled, but rather the use of those modes was assumed to remain constant from the mode share observed in the 1995 Air Passenger Survey. The model utility functions included the following six variables: highway travel time, travel time by rail transit, travel time by bus transit, walking distances, waiting times, and travel costs, although not all variables applied to each mode. The distinction between rail transit travel time and bus transit travel time allowed the analysis to consider the effect of replacing the AirBART shuttle bus with the planned APM, as well as the different level of service between BART and local bus. Household income was included in the model by dividing the costs for personal trips by the household income in thousands of dollars raised to the power 1.5. This adjust- ment was not applied to business trips as it was considered that business travel decisions are unaffected by income because business travelers are usually reimbursed for travel expenses. The model coefficients for the continuous vari- ables were adopted directly from an earlier airport ground access mode choice model for the Bay Area developed by Harvey (1988). The values of the ASCs were then estimated to fit the model to the mode use data from the 1995 MTC Air Passenger Survey. The mode choice model market segment for airport em- ployees included only two modes, private vehicle and public transit, and is discussed further in the following section on airport employee travel choice. Portland International Airport Alternative Mode Study Soon after the Boston Logan model was developed, a similar modeling effort was undertaken in Portland, Oregon, as part of a ground access study for Portland International Airport, jointly undertaken by the Port of Portland and Metro, the regional MPO, with the assistance of Cambridge Systemat- ics, Inc. (Bowman 1997; Cambridge Systematics 1998; PDX Ground Access . . . 1998). The primary purpose of the model was to forecast the potential ridership on potential ground access enhancements, including a planned extension of the Portland MAX (Metropolitan Area Express) light rail system to the airport. An air passenger survey was done at the airport that combined a revealed preference survey that examined air passengers’ actual mode use and a stated preference survey that was designed to determine travelers’ preferences for modes that were not then available, namely light rail, express bus, and shared-ride transit (it is unclear from the documen- tation how this was defined). An initial model estimation was done by Cambridge Sys- tematics (Bowman 1997; Cambridge Systematics 1998) that jointly estimated MNL models using both the revealed pref- erence and stated preference data for the same four market segments as the Boston Logan model. Separate ASCs were estimated for each mode for trips originating within the Port- land metropolitan area (termed internal trips) and those orig- inating outside the metropolitan area (termed external trips). Two different sets of model parameters were estimated for each market segment, reflecting different assumptions for the ASCs for the light rail and express bus modes. The models were subsequently revised by Metro staff to combine some of the choice alternatives and recalibrate the models by ad- justing the ASCs (PDX Ground Access . . . 1998). In addition to the development of a mode choice model, a review of the experiences of other U.S. airports with a range of airport ground access strategies was undertaken as part of the overall study of alternative airport access modes (Coogan 1997). This report included statistics on the ground access mode shares of various airports that had implemented ground transportation services similar to those being considered for Portland, as well as a discussion of the operational experi- ence of those airports with the ground transportation services and the lessons that might be applicable to the Portland situ- ation. Although there was no explicit comparison of the results of the mode choice analysis with the experience at other airports, this study put the results of the mode choice modeling into a larger context and served to provide some as- surance of the likely validity of the modeling results. San José International Airport Model This model was developed by Dowling Associates (2002) and was designed to estimate the ridership on a planned APM to connect the airport to a nearby Santa Clara Valley Trans- portation Authority light rail line. The model was estimated using data from an air passenger survey performed at the air- port for the Bay Area Metropolitan Transportation Commis- sion in 1995 and supplemented with the results of stated pref- erence surveys that were conducted as part of the study to determine how air passenger mode choice might be influ- enced by the availability of the APM and to compensate for the limited number of users of the light rail line in the 1995 survey sample. The model used a MNL form with separate coefficients for the same four market segments used in the Oakland International Airport–BART Connector model, as well as an airport employee segment. Each air passenger market segment included six modes: private vehicle, rental car, scheduled airport bus, shared-ride door-to-door van, taxi, and public transit. In addition, the visitor market seg- ments included hotel shuttle. The model implementation allowed for up to four different public transit routes from any

given analysis zone, and the model used separate coefficients for bus travel time and rail travel time. In addition, a separate ASC was used for those routes that included the use of the APM to reflect the greater attractiveness of this link as deter- mined from the stated preference survey. The model is very similar in structure and form to the model used for the Oak- land International Airport–BART Connector analysis and used the same approach of adopting the model coefficients from Harvey (1988) and estimating ASCs. The mode choice model market segment for airport em- ployees included only two modes, private vehicle and public transit, and is discussed further in the following section on airport employee travel choice. Toronto Air Rail Link Revenue and Ridership Study In May 2003, Transport Canada issued a Request for Business Case for a public–private partnership to develop an Air Rail Link between the Toronto Lester B. Pearson International Airport and Toronto Union Station (Request for Business Case . . . 2003). In preparation for the Request for Business Case, a revenue and ridership forecasting study was under- taken in 2002 (Halcrow Group 2002a). The study included the conduct of a stated preference air passenger survey and the development of a mode choice model to predict the diversion of airport access trips from existing modes to the proposed new rail link. The stated preference survey was carried out in February 2002 in the terminal departure lounges and collected data on the air party characteristics, ground trip origin, and ground access mode for the current trip. Some 807 respon- dents were identified as potential air rail link users and com- pleted a stated preference questionnaire, the results of which were then used to estimate a set of binomial logit mode choice models, each of which models the choice between an existing mode and the planned rail link. Air travelers using hotel bus or rental car to access the air- port were excluded from the diversion analysis, because users of a hotel bus were assumed to have a door-to-door ser- vice that was effectively free, whereas those using a rental car were assumed to require the car for other purposes during their visit and thus not consider the use of other modes. The mode choice model relationships use three continuous vari- ables: the in-vehicle travel time on each mode, the service headway for the mode, and the travel cost involved in using the mode. In addition, the utility function for the rail link includes two dummy variables: one that indicated whether the air traveler was accompanied (whether by other members of the air travel party or by well-wishers) or traveling alone and one indicating whether the air traveler(s) intended to check any bags. The model structure does not directly con- sider the type of trip origin. However, the approach of devel- oping separate diversion models for each existing access mode indirectly addresses some of these effects, because air passengers being dropped off by private vehicle would 36 largely have begun their trip from a private residence, whereas those using taxi or airport bus would be more likely to have begun their trip from a hotel or place of business. The model utility functions also do not consider the household income of the air travelers. ASCs were initially included in the model utility func- tions; however, these were found to be not statistically sig- nificant and were dropped from the model. This is surprising given the relatively simple form of the utility functions and the absence from the model of such factors as household income and the access time involved in reaching the rail link station. Separate model coefficients were estimated for four market segments: resident business, resident non-business, non-resident business, and non-resident non-business. In addition to estimating a formal mode choice model, the study included a benchmark comparison analysis that exam- ined the mode share of existing airport rail links in 24 cities in the United States, Europe, and Australia. This analysis developed cross-sectional regression relationships that ex- pressed the rail mode share in terms of a series of market and geographical characteristics, such as the percentage of air passengers with central city origins, the distance of the air- port from the central city, and the ratio of rail travel time to taxi travel time from the city center. These relationships were then used to predict the corresponding rail mode share for Toronto using the same regional characteristics. The result- ing range of rail mode shares (which varied with the charac- teristic chosen) was compared with the results of the formal mode choice modeling process, to provide a reality check on the modeling analysis. United Kingdom South East and East of England Regional Air Service Study Air Passenger Surface Access Model As part of the South East and East of England Regional Air Service (SERAS) study undertaken for the U.K. Department of Transport, Local Government and the Regions, a set of surface access models was developed that included an air passenger mode choice model, an airport employee trip dis- tribution model, and an airport employee mode choice model (Halcrow Group 2002b). The air passenger mode choice model is a NL model that covers 12 defined ground access modes and has separate coefficients and model structures for the following six market segments: • U.K. business passengers on domestic trips, • U.K. business passengers on international trips, • U.K. leisure passengers on domestic trips, • U.K. leisure passengers on international trips, • Non-U.K. passengers on business trips, and • Non-U.K. passengers on leisure trips. The 12 ground access modes consist of several different types of rail links, including a dedicated express rail service

37 (such as the Heathrow Express service from Central London to Heathrow Airport), London Underground, and coach con- nections to nearby mainline rail stations, as well as private automobile (both drop off and park), rental car, taxi, local bus, and charter and intercity coach. The mode choice struc- ture uses a multi-level tree to account for the complex pattern of public modes and alternative rail services, with the modes grouped in a different tree structure for each market segment. A representative choice structure for one of the market seg- ments is shown in Figure 5, and details of the choice struc- ture for the other market segments are included in Appendix D, which can be found in the web version of this report. The utility functions for each mode use a generalized cost ap- proach that considers travel time, out-of-pocket costs, and time penalties for interchanges, with all costs converted to equivalent minutes of travel time. The airport employee mode choice model is discussed further in the following section on airport employee travel choice. Other Recent Studies In addition to the foregoing nine models, the survey of airport authorities, MPOs, and consultants discussed in the next chapter identified several other recent airport access model- ing studies that are not discussed in any detail in this report owing to limited technical documentation. In 2002, the North Central Texas Council of Governments (NCTCOG), together with the Dallas/Fort Worth Interna- tional Airport, Dallas Area Rapid Transit, and Fort Worth Transportation Authority completed a Major Investment Study to evaluate potential rail links to the Dallas/Fort Worth International Airport (DMJM Aviation, Inc. 2002). As part of the study, ridership estimates were prepared for a range of project alternatives that included shuttle bus links to existing commuter rail lines near the airport as well as direct com- muter rail service to the airport and extension of a regional light rail system to the airport. The demand modeling was un- dertaken by the NCTCOG using its regional travel demand model that incorporates an airport trip special-generator model (Dallas–Fort Worth Regional . . . 2007). This model generates home-based non-work and non-home-based trips corresponding to the forecast air passenger traffic at the air- port. These trips are then added to other regional trips in each category to determine the overall distribution of regional trips before mode choice analysis and trip assignment to the regional transportation network. Airport employment is han- dled through the regular home-based work trip generation process. The mode choice models do not treat airport trips differently from other trips in each trip category (Cambridge Systematics 2005). Following the 9-11 attacks on the World Trade Center in New York, the Lower Manhattan Development Corporation was established to rebuild Lower Manhattan and create a sense of place that would revitalize the economy of the area. To improve the transportation links to Lower Manhattan, the Lower Manhattan Development Corporation, in cooperation with the Metropolitan Transportation Authority, the Port Authority of New York & New Jersey, and the New York City Economic Development Corporation, undertook a fea- sibility study of alternative ways to provide a new rail link between Lower Manhattan and suburban commuter markets in Long Island and John F. Kennedy International Airport (Parsons/SYSTRA Engineering 2004). Following an identi- fication and screening process of potential project alterna- tives, two alternatives were selected for further analysis: a new tunnel under the East River between Lower Manhattan and Brooklyn and the use of the existing Montague Street tunnel and realignment of existing subway services. Both alternatives have a number of potential variants. As part of the on-going environmental analysis of these alternatives, a more detailed ridership analysis is being undertaken to eval- uate the likely use of the proposed service by airport travel- ers under the various project alternatives being considered. In 2005, the Aéroports de Montréal undertook a feasibil- ity study for establishing a rail shuttle between the city cen- ter of Montréal and Montréal–Trudeau International Airport (Guilbault et Associés 2005). The rail link would operate largely over existing tracks of the Canadian National Rail- way from the Central Station in Montréal to the vicinity of the airport. The study involved a stated preference survey of 1,000 air passengers at Montréal–Trudeau International Airport and a further 200 air passengers at Montréal–Mirabel International Airport, the results of which were used to de- velop a mode choice model to predict the likely ridership on the rail shuttle at varying train frequencies and fare levels. In July 2000, the Sacramento Area Council of Govern- ments published the findings of a study of transit access to the Sacramento International Airport (Sacramento International . . . 2000). This study reviewed the findings of prior studies ad- dressing the feasibility of extending the Sacramento light rail system to the airport through a developing area to the north of the city known as the Natomas Basin. The study recommended Park & Fly Coach Taxi Rail Routes 0.40598 0.18127 0.13338 0.41689 Kiss & Fly FIGURE 5 SERAS model—Representative mode choice structure: U.K. Business Passengers on International Trips (Source: Halcrow Group 2002b).

that the Sacramento Regional Transit District pursue more de- tailed analysis of the feasibility of an extension of the light rail system to North Natomas and the airport and to preserve the right-of-way identified in prior studies. In October 2001, the Regional Transit District initiated the Downtown/Natomas/ Airport Transit Alternatives Analysis/Draft Environmental Impact Statement and Report project. This included the devel- opment of a travel demand forecasting methodology (DKS Associates 2002), the conduct of a combined revealed prefer- ence and stated preference survey, and the development of an air passenger mode choice model (DKS Associates 2004). AIRPORT EMPLOYEE TRAVEL CHOICE In contrast to air passenger ground access mode choice, there has been very little attention paid to airport employee ground access mode use in the literature. In one of the earliest stud- ies to explicitly address airport employee access trip patterns, Dunlay (1978) developed a model of airport employee vehi- cle trips based on information on employee shifts from a sur- vey of airport employees at Dallas/Fort Worth International Airport. However, the model did not address mode choice and assumed that the mode split found in the survey (which was almost exclusively private vehicle use anyway) would remain constant, or at least could be adjusted from exoge- nous data if the model were to be applied in another situation. Instead, the objective of the model was to predict access and egress vehicle traffic flows by time of day, reflecting that air- port employees do not enter or leave the airport exactly at the start and end of their shift. A later study by Boyle and Gawkowski (1992) examined the changes in ridership on the Q3 bus in Queens, New York, when the bus route was extended to better serve employment areas at John F. Kennedy International Airport, and service was subsequently improved to provide higher frequency and earlier and later hours. Because a large part of the increase in ridership was comprised of employees at the airport, it fol- lows that this improvement in service resulted in a change of journey-to-work mode split. However, the study did not at- tempt to collect data on what other modes airport employees 38 were using either before or after the improvement in service and did not develop a formal model of airport employee access mode choice. A subsequent paper by Riccard (1995) described an air- port employee commute program at Boston Logan Interna- tional Airport that was instituted to encourage employees to use alternatives to single-occupant vehicles for their journey to work. The paper contains an extensive discussion on fac- tors that affect airport employee decisions on how to get to and from work as well as a large amount of data on airport employee commute patterns at the airport, but no formal modeling of the mode choice process. In general, it appears that most studies addressing airport employee mode choice have simply used the journey-to- work component of the regional travel demand model for the area, considering the airport as no different from any other employment center. However, three specific models of air- port employee travel have been identified in the literature. A special-purpose airport employee mode choice model was developed for the Greater London region as part of the U.K. SERAS study (Halcrow Group 2002b) and airport employee market segments were included in two mode choice models that were developed to analyze planned APM connections, one between the Oakland International Airport and the Coli- seum station of the BART system (BART–Oakland . . . , Appendix B: Transit Ridership Procedures and Inputs 2002) and one between the San José International Airport and a nearby light rail station of the Santa Clara Valley Trans- portation Authority (Dowling Associates 2002). However, none of these models was estimated directly on airport em- ployee travel data. The SERAS model was based on one developed for a different study covering the area to the south and west of London Heathrow Airport that included all journey-to-work travel, not just airport employees. The Oak- land International Airport and San José International Airport models adapted regional travel demand models for home- based work trips, but calibrated these to airport employee mode use data. All three models are discussed in more detail in chapter six and the relevant sections of Appendix D (web version only).