Airport Ground Access Mode Choice Models (2008)

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105 This appendix contains detailed technical summaries of selected airport ground access mode choice models discussed in the body of the report. The summaries document the technical details of the models, including the functional form adopted, market segmenta- tion, causal variables included in the model, estimated parameter values, and goodness-of-fit measures where these have been re- ported in the literature or model documentation. The summaries also include statistical information on the airports for which the models have been developed. The total annual passengers for each U.S. airport shown in the summary table for each technical summary are obtained from FAA statistics for primary and nonprimary commercial service airports for the calendar year 2005. These statistics typically differ slightly from those published by each airport on their websites. The percentage of passengers at each airport for whom the airport is their origin or des- tination was estimated from data reported to the U.S. Department of Transportation by U.S. airlines in the Airline Origin and Destination (O&D) Survey and published by the Bureau of Transportation Statis- tics (BTS) on their TranStats website (http://www.transtats.bts.gov). Because the origin-destination (O-D) data on the BTS website is restricted to U.S. domestic itineraries, the calculated O&D percent- ages were adjusted to account for passengers connecting between domestic and international flights. The sources for passenger statistics for non-U.S. airports are noted on the summary tables. LIST OF TECHNICAL SUMMARIES D1 Atlanta Regional Commission Model D2 Boston Logan International Airport Model D3 Chicago Airport Express Ridership Forecasting Study D4 Miami Intermodal Center Travel Demand Forecast Study D5 Oakland International Airport BART Connector Study D6 Portland International Airport Alternative Mode Study D7 San José International Airport Model D8 Toronto Air Rail Link Revenue and Ridership Study D9 United Kingdom SERAS Study Air Passenger Surface Access Model D1 ATLANTA REGIONAL COMMISSION MODEL Summary Airport Hartsfield–Jackson Atlanta International Airport (H-JAIA) Model Developer Atlanta Regional Commission Date Developed 2003 (updated 2006) Market Addressed Air passengers Model Type Revealed preference data Model Structure Nested logit Survey Data Used 2000 H-JAIA Peak Week Survey Airport Profile Total annual passengers (2005): 84.8 million Percentage O&D: 35% Ground access mode split (2005 H-JAIA Peak Week Survey): Private vehicle—drop off 25% Private vehicle—parked 31% Rental car 16% Taxi 8% Limousine 2% Public transit 10% Commercial shuttle/van 4% Hotel/motel courtesy vehicle 4% Other <1% Market Segmentation Residents—Business trips Residents—Non-business trips Non-residents—Business trips Non-residents—Non-business trips Explanatory Variables Off-peak highway travel time Private vehicle cost (operating, parking) Walk time (transit) Wait time (transit) In-vehicle time (transit) Transit fare Taxi fare Description The Atlanta Regional Commission (ARC) Airport Passenger Model (APM) comprises a component of the overall regional travel demand model that models air passenger trips to and from Hartsfield–Jackson Atlanta International Airport (H-JAIA) (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 reestimation of the mode choice model only changed the alternative-specific constants (ASCs) for three of the modes and did not affect the coefficients for the continuous variables, as ex- plained here. The ARC/APM consists of two components: a trip genera- tion/distribution model that assigns the total originating air passen- ger traffic at H-JAIA to regional transportation analysis zones (TAZs) and a mode choice model that predicts the ground access mode use of those air passenger trips. A subsequent step converts air passenger trips to vehicle trips for inclusion on the traffic as- signment step of the regional transportation demand model. The trip generation model (and subsequent prediction of air passenger trips by mode and resulting vehicle trips) is based on the airport passen- ger traffic for an average day of the year, which is assumed to be the same as the average weekday, and is discussed in more detail here. The ARC/APM predicts air passenger and vehicle trips by four market segments that distinguish between residents of the Atlanta region and visitors to the region (non-residents) and between busi- ness and non-business (personal) trips, giving the following 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, tran- sit, and taxi. Although not explicitly stated in the model documen- tation, it appears from the existing mode use data given in the documentation that the transit mode includes the Metropolitan Atlanta Rapid Transit Authority (MARTA) rail and bus services, commercial shared-ride shuttle van services, and other high- occupancy shared-ride modes such as charter bus. The taxi mode appears to include exclusive ride limousine services and hotel and APPENDIX D Mode Choice Model Technical Summaries

motel courtesy vehicles, as well as conventional taxi use. This grouping of modes is likely to have a significant effect on the esti- mated model coefficients, because it implies that the modes grouped together for the purpose of model estimation have similar service characteristics. However, in reality this is clearly far from the case. Shared-ride shuttle van services provide a door-to-door service for a significantly higher fare than regular transit, whereas hotel/motel courtesy vehicles provide a free service by definition, although their use is typically restricted to guests at the hotel or motel providing the service. The basic form of the mode choice model is a nested logit (NL) model with a separate structure for trips by residents of the region from that for non-residents, as shown in Figure D1. The resident model divides the modes into private vehicle trips and public modes (termed non-auto modes). The private vehicle nest distinguishes between drop-off trips and those where the vehicle was parked for the duration of the air trip. The public mode nest distinguishes between use of transit and taxi. The non-resident model contains three modes at the top level: drop off by private vehicle, rental car, and public modes. As with the resident model, the public mode nest distinguishes between use of transit and taxi. The model does not take the type of ground access trip origin into account. Thus, all visitors to the region are considered to include drop-off by private 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. Explanatory Variables The explanatory variables consist of the travel times and costs for each mode. The travel times for the private vehicle and taxi modes use the off-peak travel time from the highway network in the re- gional travel demand model. Transit travel times are obtained from the a.m. peak transit network in the regional travel demand model. 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 consist 106 of the combined access, egress, and sidewalk times from the transit network, whereas the transit wait times consist of the initial wait plus any transfer wait times from the network. Private vehicle operating costs are assumed at 8.74 cents per mile, based on the off-peak highway distance from the regional net- work. Parking costs for vehicles parked for the duration of the air trip are based on half the daily long-term parking cost at H-JAIA multiplied by the average trip duration in days, assumed as 4 days for business trips and 7 days for non-business trips. Thus, all air par- ties with a given trip purpose are assumed to incur the same parking cost if they choose to park at the airport during their trip, irrespec- tive of their actual air trip duration. Transit fares are obtained from the transit network (the fares are unaffected by the time of day). Taxi fares were estimated from the off-peak highway distance as- suming a flag drop of $1.75 and a rate of $1.75 per mile. There are no explanatory variables in the utility function for the rental car mode, only an ASC. Thus, the model implicitly assumes that the rental car alternative is perceived as providing the same utility to all non-resident air parties, irrespective of their air party characteristics. Apart from estimating different model coefficients for the four market segments and any differences in travel time and cost owing to the different trip origin locations, the model does not consider any air party characteristics, such as household income or air party size. Based on the model documentation, the treatment of air party size appears to be inconsistent, with costs for private vehicle and taxi modes being calculated on an air party basis, but transit fares being calculated on an air passenger basis. Model Coefficients The adopted and estimated model coefficients for each of the mar- ket segments are shown in Table D1, and the corresponding implied values are shown in Table D2. The coefficients for the continuous variables were not esti- mated from the data but rather adopted from other models. The Residents Non-residents Private Drive Self Dropped Off Dropped Off Rental Car Non Auto Transit Taxi Non Auto Transit Taxi FIGURE D1 ARC/APM model choice model nesting structure. [Source: Model Documentation: Mobility 2030 Regional Transportation Plan, Atlanta Regional Commission (2005).]

107 ASCs (termed model bias coefficients in the documentation) were then estimated to ensure that the model predicted the ob- served mode shares. Therefore, the model bias coefficients not only account for intrinsic attributes of the different modes not ex- plained by the continuous variables but also any differences that would have existed between the continuous variable coefficients that were adopted for the model and the values that would have been obtained had these coefficients been estimated from the data. The adopted coefficients for the continuous variables all have the expected signs. The implied value of highway travel time varies between $12/h and $16/h, which is significantly less than values of time typically found for air travelers in air travel demand models. Although the implied values of time for non-business trips are lower than those for business trips, as is normally expected, the differ- ences are surprisingly small. The implied values of time for resi- dents of the region on business trips is slightly lower than for non- residents on business trips, which is not unreasonable given the gen- eral income levels in the Atlanta region compared with many other parts of the United States. However, because these coefficients were apparently not estimated from the Atlanta data, this appears coinci- dental. The implied values of transit in-vehicle time are lower than for highway travel time, as expected because those with a higher value of time will tend to use other modes than transit. The implied values of walk and wait time involved in transit trips are signifi- cantly higher than for in-vehicle time, as is generally found in other mode choice models. The constant term for private vehicle parked for the duration of the air trip suggests that this alternative has a lower disutility than being dropped off by private vehicle after allowing for the travel time and cost involved in the two alternatives, equivalent to about $20 per air party. Because the utility functions for the Coefficient Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Variables Highway time (minutes) 0.071 –0.044 –0.068 –0.039 Transit in-vehicle time (min) –0.053 –0.031 –0.050 –0.029 Walk time (minutes) –0.093 –0.051 –0.089 –0.045 Wait time (minutes) –0.107 –0.077 –0.096 –0.071 Cost (cents) –0.00277 –0.002105 –0.00256 –0.001969 Constants Private vehicle parked for trip 5.427 4.517 N/A N/A Rental car N/A N/A –4.061 –3.153 Transita –0.738 0.159 0.720 –1.853 Taxia 8.366 2.876 7.753 3.408 Nest Coefficients Private auto nest 0.3 0.3 N/A N/A Public mode nest 0.3 0.3 0.3 0.3 Note: N/A = not applicable. aIncludes nest constant term. TABLE D1 ATLANTA AIRPORT PASSENGER MODEL COEFFICIENTS Parameter Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Travel Time ($/hour) Highway time 15 13 16 12 Transit in-vehicle time 11 9 12 9 Walk time 20 15 21 14 Wait time 23 22 23 22 Constants ($) Private vehicle parked for trip 20 21 N/A N/A Rental car N/A N/A –16 –16 Transit –3 –91 3 Taxi 30 14 30 17 TABLE D2 IMPLIED VALUES OF ATLANTA AIRPORT PASSENGER MODEL COEFFICIENTS

drop-off alternative do not consider the operating cost of the vehicle for the return trip or assign any disutility to the time of the driver, this does not seem unreasonable, although in reality these effects are not likely to be constant across all air parties. Because the utility functions for the rental car alternative do not include any continuous variables (travel time or cost), the esti- mated values of the constant terms ensure that the model predicts the observed use of rental car in the estimation dataset and have no intrinsic interpretation. Model Fit The documentation on the model provided no information on the overall fit of the model. Model Application The ARC/APM has been designed to be used as an integral part of the regional travel demand modeling process and generates trip tables for air passenger trips and the associated vehicle trips that are subsequently combined with other types of trips in the regional travel demand model (Travel Demand . . . 2006). However, the re- sults of the APM are saved as separate tables and can be presented or exported independently of the regional travel demand model re- sults for use in airport planning or related studies. The ARC/APM is programmed in Fortran and can be called as part of running the regional travel demand modeling software. This was originally programmed using Tranplan software but has re- cently been converted to TP+/Cube Voyager software. The model can read regional travel demand model skim tree tables of highway and transit travel times, highway distances, and transit costs directly for use in modeling air passenger trips. Trip Generation/Distribution Model The first steps in the ARC/APM generate a sample of air passenger trips with their associated air party characteristics, including the trip origin TAZ, for use in modeling the air passenger ground access mode choice. Although strictly not part of the access mode choice model, these steps are sufficiently important to generating the over- all pattern of air passenger and associated vehicle trips that they deserve a fairly detailed explanation. The first step in the trip generation/distribution process is to obtain an estimate of the total air passenger enplanements at H-JAIA for the year in question. This is then adjusted to exclude connecting passen- gers and divided into the four market segments. In the case of histor- ical data, the percentage of originating air passengers in each market segment can be obtained from air passenger survey information and the proportion of connecting passengers can be obtained from airline data reported to the U.S.DOT. For the purposes of developing the APM, the ARC used an estimate of connecting passengers from the H-JAIA Master Plan. When applying the APM to future years, it will be necessary to adjust the proportion of connecting passengers and the percentage of originating passengers in each market segment to reflect any forecast changes in the traffic composition at H-JAIA. The second step in the trip generation/distribution process allo- cates these trips to TAZs. Analysis of the 2000 H-JAIA air passen- ger survey showed that there were significant differences between resident business trips that started from a residence and those that started from another type of origin, typically the place of work. Almost all resident non-business trips originated from a private res- idence. Similarly, there was a significant difference between non- resident non-business trips that originated from a private residence 108 and those that originated from some other type of trip origin (pri- marily hotels or motels), whereas relatively few non-resident busi- ness trips originated from a private residence. Therefore, the four market segments were further divided into six trip types based on the type of origin, as follows: • Resident business trips from private residences • Resident business trips from other types of trip origin • Resident non-business trips • Non-resident business trips • Non-resident non-business trips from private residences • Non-resident non-business trips from other types of trip origin. The division into these six categories was made on the basis of the proportions in the 2000 H-JAIA air passenger survey. These trips were then allocated to TAZs on the basis of either households (for trips from a private residence) or total employment in a zone (for trips from other origin types). The household allocation equa- tions divided households into four income categories on the basis of the 2000 census. The coefficients of the allocation equations were estimated using linear regression from the results of the 2000 H-JAIA air passenger survey. The form of each equation estimates the number of trips in the survey data in each category from a given zone in terms of the number of households in each income category in a zone or the total employment in the zone. These estimates must then be converted to a percentage of all trips in that category from the zone in question to allocate any other estimate of total trips by category. However, because the survey only gave the trip origin informa- tion by zip code, rather than TAZ, the allocation equations were estimated on a zip code basis. When these equations were applied to TAZs, it was found that the trips from some parts of the region were over-estimated while those from other parts of the region were under-estimated. Therefore the region was divided into three differ- ent groups of zones and different adjustment factors were applied to each group of zones. Details of the allocation equations and adjust- ment factors are provided in the model documentation (Model Documentation . . . 2005), but are not presented here because they do directly affect the mode choice model. Integration with Regional Planning Process The mode choice model within the APM generates a table of air pas- senger trips using each of the five defined modes from each TAZ. These tables of air passenger trips are then converted to associated vehicle trips using assumed values of average vehicle occupancy for each mode. The number of vehicle trips for the private vehicle drop-off mode is doubled to allow for the return trip by the driver dropping off the air party and the total number of vehicle trips dou- bled again to allow for the traffic generated by egress trips, which are assumed to be symmetrical to the pattern of access trips. The resulting tables of air passenger trips using transit and high- way vehicle trips are then added to the trip tables for other types of regional trips generated by the other components of the regional travel demand model before the highway traffic assignment and transit trip assignment steps of the overall modeling process. Although this approach provides a reasonable representation of vehicle trips by private vehicles and rental cars, the combination of several distinct modes in the transit and taxi modes of the APM is more problematical. Trips on public modes other than MARTA do not contribute to MARTA ridership, as implied by including these in the transit assignment step, but rather generate additional highway vehicle trips. Although the various modes included in the APM taxi mode each generate highway vehicle trips, the average vehicle

109 occupancy for hotel/motel courtesy vehicles is likely to be greater than that for conventional taxi and limousine, owing to the shared- ride nature of hotel/motel courtesy vehicles, resulting in an overesti- mate of vehicle trips for the taxi mode. However, the geographic pattern of this error is likely to be very uneven, because most hotel/motel courtesy vehicle trips are from areas fairly close to the airport, whereas taxi and limousine use is more widely distributed throughout the region. Documentation Model Documentation: Mobility 2030 Regional Transportation Plan, Atlanta Regional Commission, Atlanta, Ga., Feb. 11, 2005. Travel Demand Model Documentation, Atlanta Regional Commission, Atlanta, Ga., May 2006. D2 BOSTON LOGAN INTERNATIONAL AIRPORT MODEL Summary Airport Boston Logan International Airport Model Developer Central Transportation Planning Staff, Boston Date Developed 1996 Market Addressed Air passengers Model Type Revealed preference data Model Structure Nested logit (resident), multinomial logit (visitor) Survey Data Used 1993 Boston Logan Air Passenger Survey Airport Profile Total annual passengers (2005): 26.4 million Percentage O&D: 90% Ground access mode split (2003 Logan Air Passenger Survey): Private vehicle—drop-off 21% Private vehicle—parked 11% Rental car 17% Taxi 19% Limousine 7% Logan Express 5% Scheduled bus/limo 4% Public transit—MBTA subway 6% Water shuttle 1% Hotel courtesy shuttle 6% Charter bus 3% Other (including MBTA bus) <1% Market Segmentation Residents—Business trips Residents—Non-business trips Non-residents—Business trips Non-residents—Non-business trips Explanatory Variables In-vehicle time Out-of-vehicle time (walk, wait, transfer) Automobile access time Travel cost (parking, tolls, automobile operating cost, fares) Dummy variables (employer pays cost, luggage, air party size, non-residence trip origin, household income, flights/year from Logan) Description This model was developed by the Central Transportation Planning Staff (CTPS) in Boston using a 1993 air passenger survey per- formed at Boston Logan International Airport. 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 two-level NL model, with separate second-level 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 multinomial logit (MNL) models and omit the long-term parking alternatives, but add a hotel shuttle mode. This model includes a rail access mode, the Massachusetts Bay Transportation Authority (MBTA) regional rail transit system, 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 treats rental car use as an indepen- dent decision and excludes it from the mode choice decision process. Explanatory Variables Independent variables include both in-vehicle and out-of-vehicle travel time, automobile access time to the public modes, the num- ber of transfers, travel costs, and dummy variables for the type of trip origin (residence or not), the amount of luggage, air party size, number of air trips in past year, and whether an employer was pay- ing travel expenses. Not all variables are included in all models, and various combinations of the independent variables were estimated. For some model variations, separate travel cost coefficients were estimated for low-income and high-income travelers or for those for whom their travel costs were paid by their employer. However, the definition of low-income and high-income travelers was not included in the model documentation. Travel times were measured in minutes and costs in dollars, based on 1993 rates. Model Coefficients Tables D3 to D6 show the estimated model coefficients for the four market segment models. Values in parentheses are the t- statistics of the estimates. With a few exceptions, most of the esti- mated coefficients are statistically significant at the 95% level or better. The t-statistics for the ASCs for the non-resident non-business model (Table D6) are as reported in the model documentation, but appear to be incorrect. They are identical to those shown for the non-resident business model (Table D5), which would be surpris- ing, and three have incorrect signs (t-statistics are generally re- ported with the same sign as the coefficient), suggesting that the wrong values were reported in the model documentation. As can be seen from Tables D3 to D6, separate travel time and cost coefficients were estimated from groups of modes. This has the effect of giving different implied values of travel time for different modes, as shown in Table D7. Whereas it can be expected that trav- elers choosing different modes will on average tend to have differ- ent values of time (e.g., travelers choosing a taxi will tend to have a higher value of time than those using the MBTA) that is an entirely different issue from assuming that a given traveler will have a different implied value of travel time when considering alternative modes (as implied by the models).

110 Travel Time Coefficients Travel Cost Coefficients Dummy Variable Coefficients Mode Const. Tree Coeff. IVTT OVTT Auto Access No. of Transfers Self- Pay Low Income Self- Pay High Income Empl. Pays Non- Resident Origin Luggage >2 Bags >2 Flights in Year Party Size >1 MBTA Rail 0.926 (2.9) (–4.7) –0.027 –0.092 (–8.1) –0.150 (–0.9) –0.232 (–2.9) –0.232 –0.232 –1.805 (–5.2) Scheduled Bus/Limo 3.799 (4.4) –0.027 –0.027 –0.027 –0.092 –0.150 –0.232 –0.232 –0.232 Logan Express 2.781 (5.1) –0.027 –0.027 –0.092 –0.150 –0.232 –0.232 –0.232 Water Shuttle –0.213 (–0.0) –0.027 –0.027 –0.092 –0.150 –0.232 –0.232 –0.232 Door-to-Door Nest –0.401 (–0.4) 0.470 (3.2) Taxi –0.957 (0.3) –0.057 (–1.7) –0.057 –0.093 (–4.6) –0.073 (–4.1) –0.073 1.118 (2.2) Limousine –0.057 –0.057 –0.093 –0.073 –0.073 2.452 (4.3) Automobile Nest 0.631 (4.7) Long-term park on airport 0.115 (1.4) –0.036 (–1.8) –0.066 (–1.0) –0.259 (–6.5) –0.118 (–5.4) –0.118 1.139 (4.0) Long-term park off airport –0.075 (0.1) –0.036 –0.066 –0.259 –0.118 –0.118 1.139 Short-term park at airport –0.074 (–3.6) –0.066 –0.259 –0.118 –0.118 –1.153 (–3.5) Drop off 0.604 (3.4) –0.074 –0.066 –0.259 –0.118 –0.118 –1.153 1.109 (4.1) Note: t-statistics shown in parentheses (omitted for repeated values). IVTT = in-vehicle travel time; OVTT = out-of-vehicle travel time. TABLE D4 BOSTON LOGAN RESIDENT NON-BUSINESS MODEL COEFFICIENTS Travel Time Coefficients Travel Cost Coefficients Dummy Variable Coefficients Mode Const. Tree Coeff. IVTT OVTT Auto Access Self- Pay Low Income Self- Pay High Income Empl. Pays Non- Resident Origin Empl. Pays Luggage >2 bags >6 Flights in Year MBTA Rail 1.471 ( 1.7) 0.034 ( 4.9) 0.072 ( 5.7) 0.080 ( 0.8) 0.080 0.080 1.175 ( 2.2) Scheduled Bus/Limo 0.437 (0.8) 0.034 0.034 0.034 0.072 0.080 0.080 0.080 Logan Express 0.126 0.290 (0.4) 0.034 0.034 0.072 0.080 0.080 0.080 Water Shuttle 2.851 ( 2.6) 0.034 0.034 0.072 0.080 0.080 0.080 Door-to- Door Nest 0.361 (2.9) –0.503 ( 2.5) 1.337 (4.3) Taxi 1.279 ( 3.4) 0.173 ( 2.0) 0.173 0.295 ( 2.2) 0.101 ( 7.5) 0.101 Limousine 0.173 0.173 0.295 0.101 0.101 Automobile Nest ( 0.9) 0.72 (5.6) Long-term park on airport 0.897 (2.4) 0.036 ( 2.2) 0.171 ( 2.9) 0.370 ( 3.4) 0.193 ( 6.1) 0.102 ( 6.1) 0.850 (3.7) Long-term park off airport 0.527 (0.8) 0.036 0.171 0.370 0.193 0.102 0.850 Short- term park at airport 1.491 ( 4.0) 0.070 ( 3.8) 0.171 0.370 0.193 0.102 0.794 ( 2.6) Drop off 0.070 0.171 0.370 0.193 0.102 0.794 Note: t-statistics shown in parentheses (omitted for repeated values). IVTT = in-vehicle travel time; OVTT = out-of-vehicle travel time. TABLE D3 BOSTON LOGAN RESIDENT BUSINESS MODEL COEFFICIENTS

111 Travel Time Coefficients Travel Cost Coefficients Dummy Coefficients Mode Const. IVTT OVTT Auto Acces s No. of Transfers Self- Pay Low Incom e Self-Pay High Incom e Em pl. Pays Non- Resident Origin Luggage >2 Bags Party Size >1 MBTA Rail 1.855 (–3.7) 0.022 (–4.2) 0.022 –0.039 (–4.3) –0.286 (–1.8) –0.091 (–7.9) –0.091 0.058 (–6.9) –0.508 (–1.9) Scheduled Bus/Limo 1.564 (–3.8) 0.022 0.022 –0.039 –0.286 –0.091 –0.091 0.058 Logan Express 2.856 (–4.7) 0.022 0.022 –0.039 –0.286 –0.091 –0.091 0.058 Water Shuttle 1.620 (–4.8) 0.022 0.022 –0.039 –0.286 –0.091 –0.091 0.058 Taxi 0.039 (–4.3) 0.039 0.091 –0.091 0.058 Lim ousine 0.275 (–1.4) 0.039 0.039 –0.091 –0.091 0.058 Hotel Shuttle 2.187 (–11.4) 0.039 0.039 Short-Term Park at Airport 1.586 (–2.1) 0.039 0.152 (–2.4) –0.058 (–6.9) –0.058 0.058 –2.105 (–9.6) Drop Off 0.376 (–1.2) 0.039 0.152 –0.058 –0.058 0.058 –2.105 0.377 (1.6) Note: t-statistics shown in parentheses (omitted for repeated values). IVTT = in-vehicle travel time; OVTT = out-of-vehicle travel time. TABLE D5 BOSTON LOGAN NON-RESIDENT BUSINESS MODEL COEFFICIENTS Travel Time Coefficients Travel Cost Coefficients Dummy Coefficients Mode Const. IVTT OVTT Auto Acces s No. of Transfers Self- Pay Low- Incom e Self- Pay High- Incom e Em pl. Pays Non- Resident Origin Luggage >2 Bags Party Size >1 MBTA Rail –1.066 (–3.7) –0.013 (–2.5) –0.013 (–2.5) –0.013 (–2.2) –0.213 (–1.2) –0.091 (–7.9) –0.091 –0.058 (–6.9) –0.508 (–1.9) Scheduled Bus/Limo 0.155 (–3.8) –0.013 –0.013 –0.013 –0.213 –0.091 –0.091 –0.058 Logan Express –2.020 (–4.7) –0.013 –0.013 –0.013 –0.213 –0.091 –0.091 –0.058 Water Shuttle –2.352 (–4.8) –0.013 –0.013 –0.013 –0.213 –0.091 –0.091 –0.058 Taxi –0.013 (–2.2) –0.013 (–2.2) –0.091 –0.091 –0.058 Lim ousine 0.812 (–1.4) –0.013 –0.013 –0.091 –0.091 –0.058 Hotel Shuttle –0.021 (–11.4) –0.013 –0.013 Short-Term Park at Airport –0.229 (–2.1) –0.013 –0.152 (–2.4) –0.058 (–6.9) –0.058 –0.058 –2.105 (–9.6) Drop Off 0.376 (–1.2) –0.013 –0.152 –0.058 –0.058 –0.058 –2.105 0.377 (1.6) Note: t-statistics shown in parentheses (omitted for repeated values). IVTT = in-vehicle travel time; OVTT = out-of-vehicle travel time. TABLE D6 BOSTON LOGAN NON-RESIDENT NON-BUSINESS MODEL COEFFICIENTS

It makes no sense that given travelers will value their time at one amount when considering a high-priced mode and a different amount when considering a less expensive, but more time-consuming mode. Because the CTPS modelers were able to obtain a statistically sig- nificant difference in the model coefficients for different modes sug- gests that this is a result of specification problems with the models or problems with the model estimation data. In particular, the omission of any air party size information in the utility functions for most modes would ignore the distinction between costs that are incurred on a per person basis from those costs that are incurred once per air party. Similarly, the use of the same travel cost coefficient for all air parties irrespective of income is likely to lead to differences in the estimated coefficients for modes with widely different costs. Given these problems with the data and the conceptual difficulty with having different implied values of time for different modes, there is no reason to expect any particular relationship between the implied values of time for different market segments or different in- come levels. However, the implied values of time for higher income travelers or those for whom their employer is paying their travel costs are generally higher than those for lower income travelers, as could be expected. Similarly, for non-resident travelers the implied values of time for business travelers are higher than the corre- sponding values of time for non-business travelers. Although this is also true for some modes for resident travelers, business travelers have a lower implied value of time than non-business travelers for automobile users paying their own travel expenses. The implied value of the ASCs, expressed as equivalent min- utes of in-vehicle time where a positive value indicates that the mode has a relative perceived disutility that would be offset by re- ducing the travel time by that amount, show no obvious pattern and 112 no consistent relationship across the different market segments. For some market segments a given mode is significantly more at- tractive than another mode, whereas for other market segments the reverse is true. It is quite likely that these values are so distorted by the model specification problems that they have no intrinsic interpretation. Model Fit The documentation on the model provided no information on the overall fit of the model. Model Application The model has been used as part of the regional transportation plan- ning process for the Boston metropolitan area to forecast vehicle trips to and from Boston Logan International Airport, as well as for specific planning studies by the Massachusetts Port Authority (the operator of Logan Airport), including an evaluation of a people- mover link between the MBTA Airport Station and an assessment of strategies to address an anticipated future parking shortfall in the on-airport parking facilities. Integration with Regional Planning Process The direct output of the model consists of the number of air passen- gers in 1993 air passenger survey using each mode by TAZ. These air passenger flows are then expanded to correspond to the desired Param eter Resident Business Resident Non- Business Non-Resident Business Non-Resident Non-Business Travel Tim e ($/hour) In-vehicle Shared-ride m odes a Self-pay/e mp loyer pays 26 7 15/23 9/13 Taxi/limousine Low-incom e 35 37 26 9 High-incom e/employer pays 103 47 26/40 9/13 Auto park Low-incom e 6 8 N/A N/A High-incom e/employer pays 11/21 18 N/A N/A Auto drop or park short-ter m Low-incom e 11 17 40 13 High-incom e/employer pays 22/41 38 40 13 Auto access (shared-ride m odes) Self-pay/e mp loyer pays 54 24 26/40 9/13 Constants (m inutes of IVT) b MBTA 43 –34 84 82 Scheduled bus/lim o –13 –141 71 –12 Logan Express 4 –103 130 155 Water Shuttle 84 8 74 181 Taxi 7 24 –– –– Lim ousine –– 7 7 –62 Hotel shuttle N/A N/A 56 2 Automobile Park long-ter m on airport –17 –3 N/A N/A Park long-ter m off airport –7 2 N/A N/A Park short-ter m at airport 25 –– 41 –29 Drop off 4 –8 –10 18 a MBTA, scheduled bus/limo, Logan Express, Water Shuttle. b Equivalent minutes of in-vehicle time. N/A = mode is not available for this market segment; –– = no alternative-specific constant estimated for this mode; IVT = in-vehicle travel. TABLE D7 IMPLIED VALUES OF BOSTON LOGAN MODEL COEFFICIENTS

113 period of analysis (e.g., average weekday or annual demand) using a survey expansion factor that relates the number of air passengers in the survey sample to the O&D passenger traffic at the airport for the period of analysis (accounting for forecast traffic growth for future years where necessary). Air passenger flows are then converted into vehicle trips for exclusive ride modes using the number of people in each travel party reported in each survey record. The resulting passenger and vehicle trip tables are incorporated in the CTPS regional highway and transit assignment models. The following five trip tables are created for each of the four weekday time periods (a.m. peak, midday, p.m. peak, and night time): • MBTA passenger trips • Scheduled bus and limousine passenger trips • Logan Express passenger trips • Water Shuttle passenger trips • Highway vehicle trips. The highway vehicle trips include automobile access and egress trips to and from the MBTA origin or destination stations, the scheduled bus and limousine stops, and the Logan Express termi- nals for those air passengers using private vehicles to access those services, as well as taxi, exclusive-use limousine, and rental car trips. The calculation of vehicle trips includes two-way trips for pas- sengers dropped off or picked up at the airport, MBTA stations, bus stops, or Logan Express terminals. Documentation Harrington, I.E., J. McClennen, E. Pereira and C.-Y. Wang, Summary of People Mover Study Passenger Mode Choice Models, draft memoran- dum, Central Transportation Planning Staff, Boston, Mass., May 17, 1996. Harrington, I.E., The Logan Airport Passenger Ground Access Mode Choice Model, draft memorandum, Central Transportation Planning Staff, Boston, Mass., Feb. 28, 2003. D3 CHICAGO AIRPORT EXPRESS RIDERSHIP FORECASTING STUDY Summary Airport Chicago O’Hare International Airport; Chicago Midway Airport Model Developer Resource Systems Group, Inc. Date Developed 2004 Market Addressed Air passengers Model Type Combined revealed preference and stated preference data Model Structure Nested logit Survey Data Used 2003 Chicago Air Traveler Stated Prefer- ence Survey Airport Profile O’Hare Midway Total annual passengers (2005): 73.4 16.8 million million Percentage O&D: 45% 73% Ground access mode split (2003 O-D survey): Private vehicle—drop off 22% 27% Private vehicle—parked 15% 22% Rental car 12% 13% Taxi 18% 15% Limousine 14% 10% Hotel/airport van 9% 4% CTA train 4% 6% Other 5% 4% Market Segmentation Residents—Business trips Residents—Non-business trips Non-residents—Business trips Non-residents—Non-business trips Explanatory Variables Total travel time (weighted access, trans- fer, and waiting time) Travel cost Availability of baggage check-in at down- town terminal Use of intermediate station on Airport Express rail link Description The Chicago Transit Authority (CTA) in partnership with the city of Chicago has been pursuing the feasibility of establishing an express train service between downtown Chicago and O’Hare International and Midway Airports (PB Consult 2006). The pro- posed service would utilize the tracks of the existing Blue Line of the CTA rapid transit system between downtown Chicago and O’Hare Airport and the existing CTA Orange Line tracks between downtown Chicago and Midway Airport, although some additional passing sections could be provided to reduce travel times. In 2003, the Chicago Department of Transportation (CDOT) retained Resource Systems Group, Inc. (RSG) and Wilbur Smith Associates (WSA) to undertake a ridership and revenue forecasting study of the proposed Airport Express train service (WSA 2004). The study examined the potential use of the Airport Express by air travelers with ground origins in a study area that extended approximately 9 miles north and south of the planned Airport Express terminal in downtown Chicago, as shown in Figure D2. To identify the pattern of air passenger trip ends, travel party characteristics, and existing access mode use, and to understand how airport travelers might change their access mode choice if the Airport Express train was available, two surveys of air travelers were under- taken by Resource Systems Group, Inc., at O’Hare and Midway Airports in September 2003, an O-D survey and a stated preference survey (RSG 2004). Both surveys were undertaken in the secure area of the airport terminals after passengers had cleared security screen- ing. The O-D survey intercepted 6,789 air travelers, of whom 3,348 were originating passengers and completed the survey. The O-D sur- vey results were used to develop a profile of existing originating air passenger characteristics, including 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 travelers in the two airports that a screening question had identified as having a ground access trip origin in the study area. Respondents were asked about details of their trip to the airport, including their trip origin, mode used, and travel times and costs, as well as their trip purpose and whether they were residents of the Chicago region. They were then asked to complete eight stated preference choice experiments in which they were presented with a choice between three modes: the mode they had just used, the Airport Express train, and a third mode. The characteristics of the Airport Express and third mode were varied in the experiments and the respondents were asked which option they would have chosen for their current trip had these been available. The experiments varied the travel time on the main mode and service headway, the access mode used, the access and egress time, travel cost, and availability of baggage check-in at the Airport Express terminal.

The results of the stated preference survey were used to esti- mate a mode choice model that defined nine airport access modes, as follows: • Private vehicle parked at the airport for the duration of the air trip (drive and park) • Dropped off at airport by private vehicle (dropped off) • Rental car • Taxi • Other private mode • Airport Express train • Airport Bus • CTA train • Other public mode. The “Other private mode” included limousine, hotel/motel cour- tesy shuttle, and shared-ride airport van service, whereas the “Other public mode” included PACE bus, METRA train service, regional bus and charter bus. This grouping of modes has the effect of com- bining modes with very different service characteristics, particu- larly limousine, shared-ride van, and hotel/motel courtesy shuttle. Similarly, charter bus is not usually a feasible option for most air travelers, and those using this mode (such as tour groups) typically have the mode choice decision made for them, in contrast to users of regional bus and rail services. The Airport Bus alternative is not currently available, but was included in the stated preference exper- iments as a proposed new service from downtown Chicago as an alternative to the Airport Express train. The mode choice model used a NL form with a somewhat dif- ferent nest structure for travelers on business and non-business trips, as shown in Figure D3. Generally, modes are grouped into two 114 nests, one for private modes and one for public modes, where taxi and rental car were considered private modes. The nest structure differed between business and non-business trips in the treatment of the Airport Express train. In the business trip model structure, the Airport Express was treated as a separate alternative at the same level as the private and public mode nests, whereas in the non- business trip model it was considered one of the public modes in the public mode nest. In both cases the model defined a lower level nest of five access sub-modes below the Airport Express mode. These included a free van service in addition to walk, taxi, drive and park, and CTA tran- sit. The free van alternative was assumed to only be available in certain downtown zones, principally those with a concentration of hotels along Michigan Avenue, whereas the walk alternative was restricted to zones within about a one-half mile of the downtown terminal. The drive and park access alternative was excluded from the available access modes at the downtown terminal, but was assumed to be available at intermediate stations where these were included in the Airport Express service scenarios. Explanatory Variables The utility functions for each airport access mode included two con- tinuous variables in addition to ASCs: total travel time and travel cost. The estimated coefficients for each of these two variables within a given market segment were constrained to have the same value for each mode. Traveler income was not explicitly included in the model but travelers were divided into two income categories on the basis of household income and separate travel cost coefficients estimated for each category, where low-income travelers were de- fined as those with a household income under $100,000 and high- income travelers were defined as those with a household income of $100,000 or more. 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 dif- ferent disutility of access, transfer, and waiting time (as applicable) for each mode. Egress time at the airport was considered to have the same disutility per minute as in-vehicle time on the primary mode. The other travel time components were assumed to have the same weight with different values of this weight for business and non- business trips. These values were determined by iteratively adjust- ing the weights in steps of 0.25 to find the value that gave the best overall model estimation result. This turned out to be 1.25 for business trips and 1.50 for non-business trips. It is surprising that travelers on business trips appear to be slightly less sensitive to access and waiting time compared with in-vehicle time than travel- ers on non-business trips. In addition to the two continuous variables, a dummy variable for the availability of baggage check-in at the downtown terminal was included in the Airport Express and Airport Bus modes. A sec- ond dummy variable was included in the Airport Express mode for those passengers boarding at an intermediate station. Estimation of the mode choice model coefficients used the travel times and costs reported by the survey respondents for the mode they had actually used and the values for the alternative modes pro- vided in the stated preference experiments. Model Coefficients Separate model coefficients were estimated for business and non- business travelers. Separate model coefficients were not estimated Midway O'Hare Block 37 O'Hare Blue Line Mid wa y O ran ge Lin e 0 2 4 6 Miles Airport Express Study Area Figure 1: Proposed Airport Express FIGURE D2 Proposed Airport Express routes and study area. (Source: WSA 2004.)

115 for residents of the Chicago region and non-residents, although the available modes for these two market segments were different, as noted earlier. The estimated model coefficients for travel time and the dummy variables were constrained to be the same for O’Hare and Midway airports, whereas separate travel cost coefficients were estimated for each airport. The ASCs for taxi and Airport Bus, as well as those for the access modes to the Airport Express train, were constrained to be the same for both airports, whereas those for the other modes were allowed to vary by airport. 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 O-D survey data by ad- justing several of the ASCs until the model predicted the observed mode shares from the study area. The initial estimated model coefficients for each trip purpose and each airport are shown in Table D8 (RSG 2004). The final calibrated model coefficients for each of the market segments at both airports are shown in Tables D9 and D10 (WSA 2004). It appears that further changes were made in all coefficient values between the estimated values reported in the results of the model estimation (RSG 2004) and the results of the model calibration (WSA 2004). The implied values corresponding to the calibrated model coefficients are shown in Tables D11 and D12. Because the implied values depend on the value of the travel cost coefficient, which varies between low- income and high-income respondents, two implied values are given for each coefficient, corresponding to the two income groups. The implied values of travel time appear reasonable and broadly consistent with values of time for air passenger travel in other mod- els. The values for business trips are higher than for non-business trips as expected, although the value for low-income respondents on business trips through O’Hare Airport appears surprisingly low com- pared with the value for high-income respondents at a little over one- third the value. Given the income category split at a household income of $100,000 per year and the general distribution of house- hold incomes in the population at large, one might expect a ratio of about 2.0, as observed for non-business trips at both airports. Simi- larly, the value for low-income respondents on business trips through Midway Airport appears surprisingly close to that for high-income respondents. The latter may be partly explained by the location of Midway Airport to the south of the downtown and the dominance of low-fare carriers at the airport. Higher income individuals are more likely to live in the more affluent suburbs to the north and northwest of the downtown. These individuals would find O’Hare Airport more convenient and may be less concerned about using a low-fare carrier, resulting in a different income distribution for business travelers using O’Hare and Midway Airports. However, if this is the explana- tion, it does not appear to apply to non-business trips, where the implied value of travel time for high-income travelers using O’Hare is slightly lower than for those using Midway. 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 D3 Chicago Airport Express mode choice model nesting structure. (Source: WSA 2004.)

These differences in the implied value of travel time across the market segments are troubling, because they directly affect the trade-off between travel times and cost, which is central to the eval- uation of a new service that offers shorter travel times for a pre- mium fare. Given the obvious importance of household income in explaining traveler behavior, both from the perspective of common sense and as demonstrated by the model estimation results, the classification of travelers into only two income categories must necessarily only provide a very approximate representation of the role of income in access mode choice. Although better than ignor- ing traveler income completely, it is likely to significantly reduce the ability of the model to explain observed mode choice behavior and could adversely affect predictions of the likely future use of new modes. The implied values of the two dummy variables present some in- teresting implications. As might be expected, business travelers (who typically have less baggage than non-business travelers and may only have carry-on bags) appear to value the availability of downtown baggage check-in at about half the value of non-business travelers. However, the implied value for non-business travelers of between about $7 and $18, depending on income, is surprisingly high. With 116 proposed fares on the Airport Express train of only $10 per passenger, this suggests that the provision of downtown check-in would have a similar effect in ridership to making the service free, at least for non- business travelers. Those travelers using an intermediate station appear to find the Airport Express service significantly less attractive than those using the downtown terminal, particularly travelers on busi- ness trips, who would require a fare difference of somewhere between $9 and $26 to make the attractiveness of the service similar to that of the downtown terminal, other things being equal. The upper end of this range is of course significantly higher than the planned fare and it is unclear why this should be so if the stated preference experiments had correctly reflected the access times and costs to both the downtown terminal and intermediate stations. One possible explanation is that the access options were different between the downtown terminal and the intermediate station, with the drive and park access option only available at the intermediate station. It is noteworthy that the ASCs for drive and park access are of the opposite sign to the intermediate station coefficients and somewhat larger (slightly larger in the case of business trips and about twice as large in the case of non-business trips). If these ASC values were overestimated, as discussed further later, the estimated Coefficient OíHare Business O’Hare Non-Business Midway Business Midway Non-Business Continuous Variables Total travel time (minutes) –0.098 (–4.5) –0.080 (–5.0) –0.098 (–4.5) –0.080 (–5.0) Travel cost—low income ($) –0.183 (–4.4) –0.212 (–5.0) –0.116 (–4.1) –0.239 (–4.9) Travel cost—high income ($) –0.069 (–3.8) –0.096 (–4.0) –0.056 (–3.5) –0.117 (–3.8) Dummy Variables Downtown baggage check 0.566 (2.1) 1.788 (3.9) 0.566 (2.1) 1.788 (3.9) Use of intermediate station –1.640 (–3.3) –0.723 (–2.1) –1.640 (–3.3) –0.723 (–2.1) Constants Private vehicle parked for trip –0.789 (–1.3) –1.270 (–1.7) –0.209 (–0.4) 1.453 (2.0) Private vehicle dropped off –3.269 (–3.9) –3.236 (–3.7) –0.351 (–0.6) –1.482 (–2.2) Rental car –2.911 (–2.7) 0.825 (0.9) 2.001 (2.6) 0 Taxi –2.730 (–4.3) –3.623 (–4.1) –2.730 (–4.3) –3.623 (–4.1) Other private mode –0.588 (–1.3) –0.605 (–0.8) –1.488 (–2.8) –1.964 (–1.5) Airport Bus –7.354 (–4.7) –4.669 (–4.8) –7.354 (–4.7) –7.087 (–4.9) CTA train –0.545 (–1.5) –0.943 (–2.5) 0.002 (0.0) 0.267 (0.5) Other public mode –4.426 (–2.5) 0.028 (0.0) –1.279 (–0.7) –0.524 (–0.7) Airport Express Access Modes Free van 0.683 (2.1) –0.025 (–0.1) 0.683 (2.1) –0.025 (–0.1) Walk –0.573 (–1.7) –1.100 (–2.3) –0.573 (–1.7) –1.100 (–2.3) Drive & park 2.261 (2.3) 2.431 (3.1) 2.261 (2.3) 2.431 (3.1) CTA bus or train –1.019 (–2.9) –0.943 (–2.5) –1.019 (–2.9) –0.943 (–2.5) Nest Coefficients Private mode nest 0.630 (8.0) 0.374 (5.5) 0.630 (8.0) 0.374 (5.5) Public mode nest 0.485 (5.5) 0.747 (10.0) 0.485 (5.5) 0.747 (10.0) TABLE D8 CHICAGO AIRPORT EXPRESS MODEL ESTIMATED COEFFICIENTS

Coefficient Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Continuous Variables Total travel time (minutes) –0.092 –0.091 –0.092 –0.091 Travel cost—low income ($) –0.166 –0.220 –0.166 –0.220 Travel cost—high income ($) –0.060 –0.099 –0.060 –0.099 Dummy Variables Downtown baggage check 0.499 1.714 0.499 1.714 Use of intermediate station –1.553 –1.051 –1.553 –1.051 Constants Private vehicle parked for trip 5.690 7.981 N/A N/A Private vehicle dropped off –1.433 –2.311 –1.022 0.925 Rental car N/A N/A 4.081 2.159 Taxi 2.750 3.658 6.620 7.017 Other private mode 3.147 3.767 6.461 6.515 Airport Bus –6.981 –5.556 –6.981 –5.556 CTA train –0.639 –2.320 –0.639 –2.320 Other public mode 3.989 1.702 5.582 4.788 Airport Express Access Modes Free van 0.467 –0.092 0.467 –0.092 Walk –0.620 –1.728 –0.620 –1.728 Drive and park 1.795 2.380 1.795 2.380 CTA bus or train –0.963 –1.133 –0.963 –1.133 Nest Coefficients Private mode nest 0.647 0.335 0.647 0.335 Public mode nest 0.523 0.775 0.523 0.775 N/A = not applicable. TABLE D9 CHICAGO AIRPORT EXPRESS MODEL CALIBRATED COEFFICIENTS: O’HARE AIRPORT Coefficient Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Continuous Variables Total travel tim e (m inutes) –0.092 –0.091 –0.092 –0.091 Travel cost—low incom e ($) –0.088 –0.254 –0.088 –0.254 Travel cost—high incom e ($) –0.067 –0.096 –0.067 –0.096 Du mmy Variables Downtown baggage check 0.499 1.714 0.499 1.714 Use of interm ediate station –1.553 –1.051 –1.553 –1.051 Constants Private vehicle parked for trip 2.195 5.695 N/A N/A Private vehicle dropped off –2.767 0.189 0.157 0.335 Rental car N/A N/A 3.349 1.899 Taxi 0.625 3.532 5.588 5.849 Other private m ode 0.492 8.708 5.003 5.548 Airport Bus –6.981 –8.768 –6.981 –8.768 CTA train –0.150 –0.182 –0.150 –0.182 Other public m ode –2.242 –5.778 –2.506 –7.400 Airport Express Access Modes Free van 0.467 –0.092 0.467 –0.092 Walk –0.620 –1.728 –0.620 –1.728 Drive and park 1.795 2.380 1.795 2.380 CTA bus or train –0.963 –1.133 –0.963 –1.133 Nest Coefficients Private m ode nest 0.647 0.335 0.647 0.335 Public m ode nest 0.523 0.775 0.523 0.775 N/A = not applicable. TABLE D10 CHICAGO AIRPORT EXPRESS MODEL CALIBRATED COEFFICIENTS: MIDWAY AIRPORT

118 Param eter Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Modal Constants ($) O’Hare Airport Private vehicle parked for trip 34/ 95 36/81 N/A N/A Private vehicle dropped off –9/–24 –11/–23 –6/–17 4/9 Rental car N/A N/A 25/68 10/22 Taxi 17/46 17/37 40/110 32/71 Other private m ode 19/52 17/38 39/108 30/66 Airport Bus –42/–116 –25/–66 –42/–116 –25/–66 CTA train –4/–11 –11/–23 –4/–11 –11/–23 Other public m ode 24/66 8/17 34/93 22/48 Midway Airport Private vehicle parked for trip 25/33 22/59 N/A N/A Private vehicle dropped off –31/–41 1/2 2/2 1/3 Rental car N/A N/A 38/50 7/20 Taxi 7/9 14/37 64/83 23/61 Other private m ode 6/7 34/91 57/75 22/58 Airport Bus –79/–104 –35/–91 –79/–104 –35/–91 CTA train –2/–2 –1/–2 –2/–2 –1/–2 Other public m ode –25/–33 –23/–60 –28/–37 –29/–77 Notes: The two values shown for each coefficient are for low-income and high-income respondents, respectively. The table shows the implied values of the alternative-specific constants relative to the Airport Express train (i.e., the perceived utility of the mode relative to the train if travel times and costs are equal). N/A = not applicable. TABLE D12 IMPLIED MODAL CONSTANT VALUES FOR THE CHICAGO AIRPORT EXPRESS MODEL Parameter O’Hare Business O’Hare Non-Business Midway Business Midway Non-Business Travel Time ($/hour) Total travel time 33/92 25/55 63/82 22/57 Dummy Variables ($) Downtown baggage check 3/8 8/17 6/8 7/18 Use of intermediate station –9/–26 –5/–11 –18/–23 –4/–11 Constants ($) Airport Express access modes Free van 3/8 0/–1 5/7 –0.5/–1 Walk –4/–10 –10/–17 –7/–9 –7/–18 Drive and park 11/30 11/24 20/27 9/25 CTA bus or train –6/–16 –5/–11 –11/–14 –4/–12 Notes: The two values shown for each coefficient are for low-income and high-income respondents, respectively. The implied values of the dummy variables show the contribution of the attribute to the perceived utility of the Airport Express train alternative. The table shows the implied values of the alternative-specific constants for the Airport Express train access modes relative to taxi (i.e., the perceived utility of the mode relative to taxi if travel times and costs are equal). TABLE D11 IMPLIED VALUES OF CHICAGO AIRPORT EXPRESS MODEL COEFFICIENTS

119 value of the coefficient for the intermediate station dummy variable would attempt to correct for this. Interpreting the implied values of the ASCs for the access modes to the Airport Express train requires some caution owing to the way that the associated costs and travel times were included in the stated preference experiments. Each experiment presented an access time for the Airport Express option to the respondent and in- dicated the associated access mode. The access times were derived from the trip origin that the respondent had indicated; however, it is unclear from the model documentation whether these access times varied by the access mode indicated. It is also unclear from the model documentation how the associated access costs were presented to the respondent, if indeed they were. The example sce- nario shown in the documentation for the stated preference survey (RSA 2004) gives the access time to the downtown terminal by CTA bus or train but makes no mention of the fare involved. In any event, the respondents were only presented with one access option for each Airport Express scenario and could not choose between other options to access the service. Given the need to vary several different parameters over the course of only eight stated preference experiments, it is doubtful that the experiments provided much op- portunity to examine tradeoffs between time and cost of different access modes as distinct from those of the primary modes. Thus, it is likely that most respondents focused on the access time shown rather than the other attributes of the access modes. This could explain some of the rather surprising implied values. It is counterintuitive that a free van service would be viewed as more attractive than a taxi if the taxi fare was already accounted for, given that the taxi provides a direct service with no waiting and does not have to be shared with other riders. It is not unreasonable that walking would have a higher disutility than the same time spent rid- ing a taxi, although the travel time calculations apparently already include a weighting for this. The relatively high implied value of the ASCs for drive and park mentioned earlier, in the range of $9 to $30, could reflect that this access mode would have a much shorter travel time than access by CTA bus or train, whereas the higher cost involved may not have been fully apparent to the respondents. The model documentation does not provide any screen shots of stated preference scenarios involving the intermediate station or access options other than CTA bus or train; therefore, it is unclear how much information respondents had about the cost of different access alternatives. The implied values for many of the ASCs for the primary ac- cess modes shown in Table D11 appear generally to have the ex- pected sign, but many of the values are surprisingly high. It is un- clear why the option of “other public mode” is viewed as inherently more attractive than the Airport Express in the case of O’Hare Airport, by an amount equivalent to a cost difference as high as $93 (in the case of non-resident business trips by high- income respondents), but inherently less attractive than Airport Express in the case of Midway Airport by an amount equivalent to a cost difference of $77 (in the case of non-resident non-business trips by high-income respondents). Similarly, it is unclear why Airport Bus would be viewed as inherently less attractive than the Airport Express by an amount equivalent to a cost difference that varies between $35 and $104. If travelers really valued the attri- butes of rail travel over bus travel by amounts of this magnitude, the challenge of financing rail transit systems would have been solved long ago. These issues are a significant cause for concern because of the relative magnitude of the ASC values compared with the travel times and costs. Not only does this mean that the model predictions are rel- atively insensitive to changes in travel time and cost, but that errors in the estimation and calibration of the ASC values can easily swamp the effect of differences in travel times and costs between modes. Model Fit The model documentation presents t-statistics for each of the model coefficients, although it does not provide an overall measure of the fit of the model to the estimation data. Although the model calibra- tion process ensured that the predicted mode shares from the study area corresponded to the observed mode shares, the model docu- mentation does not discuss how different the predicted mode share using the estimated coefficients was from that given by the O-D sur- vey data, nor how much the mode share using the calibrated model coefficients differed from the observed mode share at the level of each TAZ. Although differences are inevitable at the TAZ level, be- cause the model is not a perfect predictor of individual behavior and the O-D data itself will contain some error at the zonal level owing to sample size issues, the issue of concern is whether these differ- ences exhibit any geographical bias that could affect the predictions of the use of the Airport Express train. The t-statistics for travel time and cost coefficients are all highly significant. The dummy variable coefficient for downtown baggage check is highly significant for non-business travelers and just statis- tically significant at the 95% confidence level for business travelers. The dummy variable coefficient for boarding at an intermediate sta- tion shows the reverse pattern, being highly significant for business travelers and just statistically significant at the 95% confidence level for non-business travelers. The statistical significance of the esti- mated ASCs varies, with several having t-statistics less than 1.0 and a few having t-statistics close to zero. However, it could be quite ap- propriate for an ASC to have a value close to zero, giving a very low t-statistic. What is of more concern is the absence of any consistent pattern in the relative values of the ASCs for each mode between the two airports and for each trip purpose. Model Application The calibrated mode choice model for each market segment was applied to forecast ridership and revenue for the Airport Express train for various service scenarios for two future years, 2009 and 2020. These service assumptions included two different fare levels, several different travel time assumptions for the service to O’Hare Airport (the travel time to Midway Airport was not varied), two different growth rates for off-peak highway travel time, and whether or not a free downtown shuttle or baggage check at the downtown terminal would be provided (WSA 2004). To apply the mode choice models it was necessary to calculate travel times and costs on each mode from each TAZ. This issue did not arise in the model estimation, because the values were either provided by the respondents for their existing choice or generated hypothetically for the other options in the stated preference experi- ments. Highway travel times were obtained from highway network skims of the Chicago Area Transportation Study, whereas transit travel times were obtained from similar skims from a CDOT/WSA transit network model developed for the CDOT Mid-City Transit- way Study (WSA 2004). Other travel cost and time assumptions were made for various components of the airport access time and cost by each mode. Because highway travel times vary by time of day, separate highway travel times were calculated for peak and off- peak conditions and the models applied separately to each time period, with the results weighted by the proportion of airport access and egress travel that occurs during each period based on the profile of flight arrivals and departures.

Because the air passenger trip tables that were developed for the study did not include detailed air party characteristics, but only counts of passengers from each TAZ by market segment, the model application was based on average air party size and average air trip duration for each market segment in calculating travel costs, as well as the average percentage of rental cars that were not needed for other purposes than travel to and from the airport. Because logit choice models tend to exhibit strong nonlinearity with respect to the values of the explanatory variables, it is unclear what effect this might have on the results. Modes such as the Airport Express train become less attractive to passengers traveling in larger parties, because the cost increases in proportion to the party size in contrast to modes such as taxi, limousine, or the use of private vehicles. The average air party size used in the analysis was based on the O-D sur- vey results and varied by trip purpose between 1.57 and 1.96. Thus, the mode choice analysis included no single-person parties, even though these are by far the largest percentage of air travel parties (because the average air party size reflects a small number of quite large parties). Similarly, the average number of days away by Chicago area residents varied between 3.8 and 5.5, depending on the airport and trip purpose. The mode choice behavior of someone who is away for a period from 4 to 6 days is likely to be very dif- ferent from someone who is only away for one or two days on the one hand or someone who is away for several weeks on the other, owing to the increasing cost of airport parking as the trip duration increases. Whether these effects cancel each other out, giving rea- sonable estimates of the likely use of the Airport Express train or result in biased estimates of ridership is unclear. Documentation PB Consult, Inc., in association with Mercer Management Consulting, Inc., Parsons Brinckerhoff Quade & Douglas, Inc., and Velma Butler & Com- pany, Ltd., Express Airport Train Service: Business Plan, Prepared for Chicago Transit Authority, Chicago, Ill., Final Report, Sep. 22, 2006. Resource Systems Group, Inc., O’Hare and Midway Airport Express Train Ridership Forecasting Study: Chicago Air Traveler Stated Preference Survey Report, Prepared for Chicago Department of Transportation, White River Junction, Vt., Jan. 2004. Wilbur Smith Associates, in association with Resource Systems Group, Inc., Airport Express Ridership and Revenue Forecast, Prepared for Chicago Department of Transportation, Chicago, Ill., 2004. D4 MIAMI INTERMODAL CENTER TRAVEL DEMAND FORECAST STUDY Summary Airport Miami International Airport Model Developer Gannett Fleming, Inc. with KPMG Peat Marwick Date Developed 1995 Market Addressed Air passengers Model Type Revealed preference data Model Structure Nested logit Survey Data Used 1991 Miami International Airport Air Passenger Survey Airport Profile Total annual passengers (2005): 30.2 million Percentage O&D: 55% Ground access mode split (1991 survey): Private vehicle—drop off 45% Private vehicle—parked 13% Rental car 28% Taxi 6% Limousine 2% Shared-ride van 3% 120 Public transit <1% Hotel van 3% Market Segmentation Residents—Business trips Residents—Non-business trips Non-residents—Business trips Non-residents—Non-business trips Explanatory Variables Travel cost In-vehicle travel time Waiting time plus terminal time (if any) Description The Miami Intermodal Center (MIC) is being planned as major transportation interchange facility located immediately to the east of the Miami International Airport (MIA), as shown in Figure D4. The project is intended to provide an integrated terminal for several intercity transportation services, including Amtrak, Tri-Rail com- muter rail, Greyhound buses, and future High Speed Rail and East–West Corridor rail lines, as well as Metrobus and Metrorail services (FHWA and Florida DOT 1997). An automated people mover (the MIC/MIA Connector) will link the MIC to the airport. Provision has also been made in the planning for a future Air- port/Seaport Connector rail link. The MIC will also accommodate growth of MIA by providing expanded airport landside facilities, including rental car facilities and long-term parking facilities. As part of the planning for the MIC, a travel demand forecast was prepared in 1995 (ICF Kaiser Engineers 1995). This 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. The Newark model was calibrated as a simple binomial logit choice model of the use of tran- sit versus non-transit modes and was subsequently converted into a nested model by adding the choice between rail and bus to the transit nest. 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 include the following modes: • Drop off by private vehicle (termed auto–kiss and ride) • Private vehicle parked for the air trip duration (termed auto–park and ride) • Rental car • Taxi • Limousine • Premium transit • Local transit • Shared-ride van (termed Super Shuttle) • Hotel courtesy shuttle (termed hotel van). The first five modes were grouped into a nest called Non-Group modes and the other four modes were grouped into a nest called Group modes. The resulting structure of the model is shown in Figure D5. The model adopted the four market segments used in the Newark model: • Resident business trips • Resident non-business trips • Non-resident business trips • Non-resident non-business trips. Air passenger trip tables were developed for each market seg- ment based on an air passenger survey of 3,002 respondents con- ducted at MIA by Landrum & Brown in 1990. This survey had coded the trip origins of survey respondents using a system of 19 zones. The number of air passenger trip origins in each zone was weighted

121 to represent the number of originating average weekday passengers in 1991 and doubled to account for arriving passengers. These air passenger trips were then assigned to the much larger number of re- gional (TAZs) in the regional travel model using a simple prorating process based on the type of trip origin given by the air passenger survey. Trips with home origins were allocated on the basis of pop- ulation, trips with a workplace origin were allocated on the basis of employment, trips from a hotel or motel were allocated on the basis of the number of hotels and motels in the zone, and trips with other types of trip origin were allocated on the basis of population. Air pas- senger trips from external zones in the regional model were allocated on the basis of the total internal–external trips for the external TAZ in the regional model. Highway and transit travel times and highway distances from each TAZ to the airport or MIC were obtained from the travel time skim tables in the regional travel model. Transit travel times were di- vided into in-vehicle time and waiting time from the transit network skim tables. Constant values for waiting and other out-of-vehicle times were assumed for other modes. Transit fares were assumed constant at $1.25. Private vehicle operating costs were assumed to be 27.5 cents per mile and parking costs were assumed to be $3 for drop-off trips and $18 for vehicles parked for the duration of the air trip. Taxi costs were estimated from the highway distance and meter flag drop and rate per mile, with a flat fare for airport area hotels and the seaport. Limousine and shared-ride van fares varied by trip ori- gin zone. The model documentation does not discuss how the size of FIGURE D4 Miami intermodal center location. (Source: ICF Kaiser 1995.) Miami International Airport (MIA) Person Trips Non-Group Group Local Transit Premium Transit Rental Car Auto P/R Auto K/R Limo Taxi Super Shuttle Hotel Van FIGURE D5 Miami air passenger ground access mode choice model structure. (Source: ICF Kaiser 1995.)

the air party was addressed in calculating travel costs, if at all. The cost assumptions appear to have been made on the basis of the travel cost for each air party. If these costs were then applied in the model on the basis of each air passenger, this would significantly overstate the cost of some modes for travel parties with more than one air pas- senger. Similarly, the same cost for parking at the airport for the duration of the air trip appears to have been used for each passenger, irrespective of the actual trip duration. This would tend to overstate the cost of this alternative for passengers on shorter trips and under- state the cost for those on longer trips. Peak highway travel times were assumed for all air passengers, irrespective of their actual flight time. This would tend to overstate the travel times for highway-based modes during most of the day and to the extent that transit travel times are less subject to peak period congestion would bias the model in favor of transit use. All modes were assumed to be available to each market segment, including parking a private vehicle for the duration of the trip, rental car, and hotel/motel courtesy shuttle. Because visitors do not have the option of parking a car at the airport during their trip (if they had access to a car during their visit they would not leave it at the air- port), and residents typically do not rent cars to access the airport or start their trip to the airport from a hotel or motel that provides a courtesy shuttle, it is likely that the estimated ASCs for these modes are biased by the need to explain the low mode share by those mar- ket segments for which certain modes are not a viable option. Explanatory Variables 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 walking from the parking lot to the airport terminal or returning a rental car), and travel cost. The same three variables are used for each mode, although the cal- 122 culation of the variable values differs for each mode. There is no consideration given to the effect of income differences in the model. Therefore, all respondents are assumed (in effect) to have the same value of travel time. Model Coefficients The values for the coefficients for the continuous variables were not estimated from the air passenger survey data, but rather adopted (or “borrowed” to use the expression in the model documentation) from the values estimated for Newark International Airport in the original model. There is no discussion in the model documentation about whether any adjustments were made to reflect changes in income levels and price values during the period of time that had elapsed be- cause the Newark model was estimated or there were differences in income levels between Miami and the New York/New Jersey region. The same coefficient values were used for all four market segments, implicitly implying the same value of time for business and non- business travelers and for residents of the Miami region and visitors. Values for the ASCs were estimated from the air passenger sur- vey data to fit the model predictions of mode use to the observed data. Estimated values were obtained for each mode, although usual practice is to set the ASC for one mode to zero and express the other ASCs relative to that. Because the premium transit service did not exist at the time of the air passenger survey, the values for that mode were based on the values of the ASCs for other modes and the abil- ity of the model to predict transit ridership levels typically observed in other U.S. cities with premium transit services similar to that en- visioned for Miami. The assumed and estimated values of the model coefficients are shown in Table D13 and the implied values of the coefficients are shown in Table D14. The values of the ASCs have been adjusted so that the ASC for drop off by private vehicle is zero. These values Coefficient Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Variables In-vehicle tim e (m inutes) –0.06383 –0.06383 –0.06383 –0.06383 Waiting/term inal time (min) –0.16077 –0.16077 –0.16077 –0.16077 Cost (cents) –0.00049 –0.00049 –0.00049 –0.00049 Constants Private vehicle—dropped off 1.67737 2.82737 2.22737 2.22737 Private vehicle parked for trip 2.22737 2.22737 0.89737 1.37737 Rental car 2.71737 4.41737 6.05737 6.07737 Taxi 0.47737 1.47737 2.76237 1.63737 Lim ousine –4.27263 2.69737 3.80737 2.28737 Prem ium transit –0.7 0.8 –0.2 2.8 Local transit –5.9 –3.7 –8.2 –4.2 Shared-ride van –6.2 –6.3 –7.66 –6.86 Hotel courtesy shuttle –8.0 –7.0 –5.5 4.0 Nest Coefficients Non-group m odes nest 0.3 0.3 0.3 0.3 Group m ode nest 0.3 0.3 0.3 0.3 TABLE D13 MIAMI INTERMODAL CENTER TRAVEL DEMAND MODEL COEFFICIENTS

123 vary across the four market segments, reflecting difference in mode use across the segments. The implied value of in-vehicle time of $78/h appears reasonable for air passenger travel, particularly because it applies to both busi- ness and non-business travel. The implied value of waiting and ter- minal time is about two and one-half times the value of in-vehicle time, which is somewhat higher than is commonly found in urban travel models but not unreasonable, particularly given the time- sensitive nature of airport access travel, where an unexpected delay could result in a missed flight. The implied values of ASCs vary widely, with several implausibly large. The large negative values for the hotel courtesy shuttle are most likely an artifact of this mode being available to all air passengers, rather than those starting their access trip from hotels that provide a courtesy shuttle service to the airport. However, the large negative implied values for the shared-ride van is surprising, because the assumed fares were based on the prevailing fare structure for SuperShuttle and travel times are similar to other highway modes. The travel time assumptions did not consider the time involved in any circuitry to pickup or drop off other passengers or schedule delay resulting from the frequency with which pickups can be scheduled. However, the implied ASC values are equivalent to more than 2 h of travel time, which is far greater than the time in- volved in waiting for a pickup or picking up other passengers. Although the difference in ASC between premium transit and local transit is in the expected direction, the magnitude of the dif- ference, an implied value of between about $90 and $160, seems unrealistically high. The model documentation does not clearly define premium transit, but presumably this mode is used to distin- guish between Metrorail and local bus service. Although air travel- ers may well prefer rail transit to bus service in situations in which the travel times on either mode are the same, it seems unlikely that this preference would be strong enough that the mode shares of the two services would be equal if travel times were the same, but the Metrorail fare was $160 more than the bus fare. The implications of the large ASC values is that the continuous variables are not explaining the observed mode shares very well at all and the ASCs are having to adjust the model to enable it to pre- dict the observed mode shares. This in turn suggests that the model is not going to do a very good job of predicting the use of new or improved modes, because changes in the values of the continuous variables will have a relatively small effect on the model predictions. The values of the nest coefficients were presumably also taken from the Newark model. It is unclear that these would still be valid if the number of alternatives in each nest is increased to the extent that was done. Model Fit The model documentation provides no information on the statistical significance of the estimated values of the ASCs or the overall fit of the model. Model Application The mode choice model was used to predict ground access mode use to MIA in 2020 and resulting peak-hour passenger trips using the MIC/MIA Connector. The 2020 air passenger trip tables were ob- tained by factoring up the 1991 trip tables by the forecast growth in total airport traffic and the assumed proportion of locally originating air passengers in 2020 provided by MIA staff. The peak-hour demand on the MIC/MIA Connector was assumed to occur on a Friday after- noon between 3:30 and 4:30 p.m. and the air passenger component was assumed to comprise 14% of the average weekday air passengers other than cruise ship passengers. The forecast average weekday air passenger traffic was reduced to allow for cruise ship passengers using the Airport/Seaport Connector who would not use the MIC, with the balance of the cruise ship passengers using the MIC. It was assumed that 13% of the cruise ship passengers on an average Friday using the MIC would do so in the peak-hour, together with 20% of the average weekday employees at the airport estimated to use the MIC. Documentation ICF Kaiser Engineers, Inc., with Gannett Fleming, Inc., and KPMG Peat Marwick, Miami Intermodal Center: Travel Demand Forecast Report, Prepared for Florida Department of Transportation and Federal High- way Administration, Miami, Fla., Aug. 1995. Parameter Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Travel Time ($/hour) In-vehicle time 78 78 78 78 Waiting/terminal time 197 197 197 197 Constants ($) Private vehicle—dropped off — — — — Private vehicle parked for trip 11 –12 –27 –17 Rental car 21 32 78 79 Taxi –25 –28 11 –12 Limousine –121 –3 32 1 Premium transit –49 –41 –50 12 Local transit –155 –133 –213 –131 Shared-ride van –161 –166 –202 –185 Hotel courtesy shuttle –198 –201 –158 36 TABLE D14 IMPLIED VALUES OF MIAMI INTERMODAL CENTER TRAVEL MODEL COEFFICIENTS

U.S. Federal Highway Administration and Florida Department of Transporta- tion, Miami Intermodal Center Final Environmental Impact Statement, Report FHWA-FLA-EIS-95-01-F, Tallahassee, Fla., Dec. 23, 1997. D5 OAKLAND INTERNATIONAL AIRPORT BART CONNECTOR STUDY Summary Airport Oakland International Airport Model Developer CCS Planning and Engineering, Inc. Date Developed 2001 Market Addressed Air passengers Model Type Revealed preference data Model Structure Multinomial logit Survey Data Used 1995 Metropolitan Transportation Commission Air Passenger Survey; 1999 Survey of AirBART Passengers Airport Profile Total annual passengers (2005): 14.1 million Percentage O&D: 95% Ground access mode split (2002 MTC Air Passenger Survey): Private vehicle—drop off 42% Private vehicle—parked 21% Rental car 15% Taxi 3% Limousine 2% Hotel courtesy shuttle 2% Shared-ride van 3% Scheduled airport bus 3% Shuttle bus from BART 8% Public transit bus 1% Other <1% Market Segmentation Residents—Business trips Residents—Personal trips Visitors—Business trips Visitors—Personal trips Explanatory Variables Travel time (private vehicles) Travel time (rail transit) Travel time (bus transit) Walk distance Wait time Travel cost Household income Description Oakland International Airport currently operates a shuttle bus link called AirBART between the airport and the Coliseum station of the Bay Area Rapid Transit (BART) system, located about 2.5 miles from the airport. Since the 1970s, a series of studies have been undertaken by the Port of Oakland (the operator of Oakland Inter- national Airport), BART, and other agencies to explore the feasi- bility of an automated people-mover connection between the airport and the Coliseum BART station (BART–Oakland International Air- port Connector . . . 2002, Executive Summary). The most recent of these efforts commenced in 1999 with a public scoping meeting for the preparation of an Environmental Impact Report/Environmental Impact Statement (EIR/EIS) for a planned Oakland Airport Con- nector. The Draft EIR/EIS was distributed in August 2001 and the Final EIR/EIS was approved in March 2002. As part of the analy- sis for the EIR/EIS, an airport access mode choice model was de- veloped and applied to generate ridership projections for the Con- nector (BART–Oakland International Airport Connector . . . 2002, Appendix B: Transit Ridership Procedures and Inputs). 124 The planned Connector would include two stops between the airport and the Coliseum BART station and would reduce the cur- rent AirBART travel time of about 13 min to about 6 min. It was assumed that service frequency would also improve from the cur- rent AirBART frequency of every 10 min to a 3.2-min headway. The goal of the mode choice analysis was to evaluate the effect of this service improvement on ridership on the Connector compared with the AirBART service, as well as to examine an alternative im- proved bus service. The mode choice model addressed both air passenger trips and airport employee trips. The employee trips were treated as a sepa- rate market segment and this aspect of the model is discussed fur- ther later. The general form of the model is a MNL model with air passenger trips divided into four market segments as follows: • Resident business trips • Resident personal trips • Visitor business trips • Visitor personal trips. Data on the air party characteristics for each market segment were obtained from the 1995 Air Passenger Survey performed for the Metropolitan Transportation Commission (MTC) at the three Bay Area airports, including Oakland International Airport (Franz 1996). This information was supplemented by surveys of AirBART passengers performed by CCS Planning and Engineering, Inc., in December 1999 and May 2000 as part of the study. The mode choice analysis assigns airport trips among the following eight modes: • Private vehicle (termed private auto) • Rental car • Scheduled airport bus (termed scheduled shuttle bus) • Public transit • Shared-ride van (termed door-to-door shuttle) • Hotel courtesy shuttle • Taxi and limousine • Other. Public transit included the use of BART through the AirBART shuttle (or the Connector in the future), as well as local transit bus service directly to the airport. However, these two modes were not separately identified in the model but rather the transit alternative for travelers with trip origins in zones near the airport was as- sumed to be local bus, whereas the transit alternative for those from more distant zones was assumed to be BART. The model did not distinguish between resident air passengers who were dropped off by private vehicle and those who parked at the airport for the duration of their air trip. Rather, all resident air passengers using private vehicles were assumed to park at the airport during their trip, whereas all visitors using private vehicles were assumed to be dropped off. The use of “Other” modes was not explicitly included in the model, but rather the use of those modes was assumed to remain constant from the mode share observed in the 1995 Air Passenger Survey. The perceived operating cost of private vehicles was assumed to be 15 cents per mile in 1999 dollars, whereas airport parking costs were calculated assuming a parking rate of $8 per day. However, the parking cost was not calculated separately for each air party, but rather the average trip duration and average air party size for each market segment was used to calculate a fixed parking cost for each segment. The highway travel time for visitors dropped off at the airport (or picked up on their arrival) was increased by 50% to rep- resent the inconvenience for the drivers dropping off or picking up air passengers. Appropriate costs and times were assigned to the other modes based on published fares and schedules and assumed walking distances and waiting times.

125 Rental car costs were calculated on the basis of $50 per day fac- tored by the average trip duration and average air party size for each market segment. Apart from that this assigned the same cost to all air parties, irrespective of their actual trip duration, it also assumed that the full cost of renting the car was attributable to the airport access and egress trip. It is unclear from the model documentation whether this cost was divided between the access and egress trip or whether the costs and travel times for all modes were calculated on a round- trip basis (the same issue applies to parking costs at the airport). Highway and transit travel times and highway distances were obtained from the MTC regional travel demand network model. However, the model was not run using the regional travel demand model TAZ system, but rather a system of 25 larger zones. A repre- sentative TAZ was chosen for each of the analysis zones to obtain travel times and distances. Access times to transit for each zone were estimated based on the size of the zone and assuming walk access for the smaller zones and driving for the larger zones. Explanatory Variables 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. Obvi- ously, 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 automated people-mover as well as account- ing for 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 adjustment was not applied to business trips as it was considered that business travel decisions are unaffected by income level because business travelers are usually reimbursed for travel expenses. However, rather than using the ac- tual income level of each air party, the average income for all air passengers at Oakland International Airport found in the 1995 Air Passenger Survey appears to have been used. If this is the case, then this of course is a constant that applies to all air passengers and does not vary behavior by income level as implied by including this in the model formulation. Model Coefficients The model coefficients for the continuous variables were adopted directly from an earlier airport ground access mode choice model for 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 resulting values of the as- sumed and estimated coefficients are shown in Table D15. The cor- responding implied values of the coefficient values are shown in Table D16. The implied values of travel times are fairly low compared with values typically found in air passenger travel models. The model documentation notes that the average household income of the respondents to the 1995 MTC Air Passenger Survey was $75,000. Assuming 1.7 wage earners per household (about the current U.S. average)—that translates to an average wage rate of about $22/h. However, the values of time implied by the model coefficients for the continuous variables derive directly from the coefficients esti- mated by Harvey (1988), with those for personal travel factored up by the change in income levels between 1985 (the base year used by Harvey to estimate his model) and 1995 (the year of the MTC air passenger survey data used to estimate the current model). This also explains why the implied values of time for business trips are approximately the same as for personal trips (or even slightly lower) rather than higher as would be expected. Because the coeffi- cients for travel times and costs reflect the perceived value of time, Coefficient Resident Business Resident Personal Visitor Business Visitor Personal Variables Highway tim e (m inutes) –0.071 –0.044 –0.068 –0.039 Rail transit time (minutes) –0.053 –0.031 –0.050 –0.029 Bus transit time (minutes) –0.093 –0.051 –0.089 –0.045 Walk distance (m iles) –5.17 –3.28 –4.69 –2.94 Wait tim e (m inutes) –0.107 –0.077 –0.096 –0.071 Cost (cents) –0.00277 –1.04/ (HHINC) 1.5 –0.00256 –0.973/ (HHINC) 1.5 Constants Private vehicle — — — — Rental car –0.8 –4.2 0.7 –1.2 Scheduled airport bus –0.5 –1.4 0.0 –1.2 Public transit –1.5 –1.2 –1.0 –1.8 Shared-ride van 0.0 –0.9 1.0 –0.9 Hotel courtesy shuttle N/A N/A –3.2 –4.2 Taxi/limousine –0.2 –1.6 0.8 –0.8 Notes: HHINC = annual household income in thousands of dollars; N/A = mode is not available for this market segment. TABLE D15 OAKLAND AIRPORT CONNECTOR MODEL COEFFICIENTS

coefficient values estimated on data for one year cannot simply be applied to data for another year without adjustment for changes in real income levels (and hence the perceived value of time) between the two years. Given these problems with the values of the coefficients for the continuous variables, it follows that the estimated values for the ASCs are accounting for more than the differences in the perceived inherent attractiveness of the different modes. In addition, they have to correct for biases in the predicted mode use that result from the use of invalid coefficients for the continuous variables. Although the ASCs have been estimated relative to the private vehicle mode, expressing the implied values in this way would give misleading relative values of the coefficients across the different market seg- ments because of the assumption that the private vehicle mode rep- resents parking for the duration of the air trip in the case of resident trips but drop off in the case of visitor trips. Therefore, it would pro- vide a better comparison to express the implied values of the con- stants relative to taxi, which offers the same service characteristics across each market segment, as shown in Table D16. The values shown in Table D16 indicate the amount by which the cost of a mode would have to be greater than that for taxi (or less than if neg- ative) for travelers to consider the mode and taxi to be equally at- tractive if travel times were identical. In addition to reflecting the inherent relative attractiveness of the different modes and any biases resulting from invalid coefficients of the continuous variables, the ASCs also have to correct for incorrectly specified mode availability. The large negative values for the rental car constant for resident trips undoubtedly reflects that most residents already have a private vehicle available and therefore do not need to rent one. Similarly, the large negative value for the hotel courtesy shuttle for visitor trips reflects that this alternative is only available for those beginning their trip from a hotel near the airport that offers a courtesy shuttle. Visitors staying in hotels elsewhere in the region or with family or friends do not have this option available. Less obvi- 126 ously perhaps, the negative value for the constant for private vehicle (i.e., drop off) for visitor business trips reflects that those visitors stay- ing in a hotel or starting their trip to the airport from a business or sim- ilar location will generally not have access to someone who can take them to the airport by private vehicle. Residents on business trips and visitors appear to find scheduled airport bus and transit less attractive than taxi (after accounting for differences in travel time and cost), as might be expected, with transit significantly less attractive than scheduled airport bus. Somewhat surprisingly, residents on personal trips appear to find transit and scheduled airport bus more inherently attractive than taxi after accounting for differences in travel time and cost, al- though the effect is not very large. Shared-ride van appears to be inherently more attractive than scheduled airport bus, as might be expected owing to the door-to-door service. However, the dif- ference in the perceived value of the constant is not very large, equivalent to just two or three dollars. The relatively high implied value for the private vehicle constant for residents making personal trips could be a result of assuming that all residents using private vehicles park for the duration of their air trip. In practice many are dropped off and do not incur the parking cost assumed for this mode. Therefore, the constant will need to be large enough to off- set the assumed parking cost. Overall, whereas the implied values of the ASCs appear to be readily explainable from the way that the availability of the differ- ent modes has been assumed and the values of the service charac- teristics for each mode defined, it also appears likely that the result- ing model, although replicating the observed mode shares for the base condition, will not correctly reflect the effect on mode share of the service improvements provided by the Oakland Airport Con- nector. In particular, the low implied values of travel time will tend to underestimate the effect on the attractiveness of using BART of the reduced waiting and travel times involved in traveling between the Coliseum station and the airport. Parameter Resident Business Resident Personal Visitor Business Visitor Personal Travel Time ($/hour) Highway travel time 15 16 15 16 Rail transit time 11 12 11 12 Bus transit time 20 19 19 18 Walk time 56 61 55 59 Wait time 23 29 21 28 Constants (dollars) Private vehicle 0.7 10.0 –3.1 5.3 Rental car –13.0 –16.2 –0.4 –2.7 Scheduled airport bus –1.1 1.2 –3.1 –2.7 Public transit –4.7 2.5 –7.0 –6.7 Shared-ride van 0.7 4.4 0.8 –0.7 Hotel courtesy shuttle N/A N/A –15.6 –22.7 Taxi/limousine — — — — Notes: Implied values of personal trips calculated for an annual household income of $75,000 per year. Implied value of walk time based on a walking speed of 3 mph. N/A = mode is not available for this market segment. TABLE D16 IMPLIED VALUES OF OAKLAND AIRPORT CONNECTOR MODEL COEFFICIENTS

127 Model Fit The model documentation did not provide any information on the statistical significance of the estimated values of the model coeffi- cients or the overall fit of the model to the estimation data. Model Application The mode choice model was used to estimate transit ridership for two future years, 2005 and 2020, and three project alternatives: a continuation of the AirBART shuttle (no-project alternative), an improved shuttle bus alternative (termed Quality Bus), and the pro- posed automated people-mover (termed Automated Guideway Transit). Forecasts of future levels of air passenger traffic were interpolated from forecasts prepared as part of the Regional Airport System Plan and estimates of future airport employment levels were provided by the Port of Oakland. These forecasts were used to fac- tor up the air passenger and airport employee trip tables before applying the mode choice model. Airport Employee Mode Use The mode choice model included a market segment for airport em- ployees that considered only two modes, private vehicle and transit. The employee model used the same coefficient value for both high- way and transit travel time, including rail and bus. In addition to travel time, the utility functions included walking distance, waiting time, and cost. The coefficient values were not estimated from em- ployee survey data but were adapted from the MTC regional travel demand model for home-based work trips. The model documenta- tion does not state what assumptions were made about the cost for employee parking. The model coefficients and implied values are shown in Table D17. In this case, the implied value of the transit ASC is relative to private vehicle, because taxi was not considered in the employee mode choice model. The coefficient values for the continuous variables were taken directly from the MTC mode choice model (Purvis 1997, Table 5.1), with the walk time coefficient converted to distance assuming a walking speed of 3 mph. However, the MTC model includes seven modes and several other explanatory variables, including household income and vehicle ownership. Excluding these variables from the model would change values of the other coefficients, because the behavioral explanation that they provide would now have to be ac- counted for by the remaining variables. The magnitude of this effect would depend on the distribution of the employee characteristics and is unclear without more detailed analysis. Furthermore, the MTC mode choice model was also estimated using constant 1990 dollars for travel costs. Applying these model coefficients to costs in 1999 current dollars without making any adjustments for inflation would overstate the effect of cost in mode choice decisions. The ASC for transit use does not correspond to the MTC model coeffi- cients and appears to have been estimated to make the transit mode share match the observed data for airport employees. Although this will offset the effect of any bias in the model coefficients for the base year, the net effect on the model predictions for any future year is unclear. The implied values of travel time components correspond to the values in the MTC model (not surprisingly, because the coefficients are unchanged). The documentation of the MTC model (Purvis 1997) notes that the value of time for journey-to-work trips has typ- ically been found to be in the range of 25% to 50% of the wage rate. Unlike air passenger business travel costs, journey-to-work travel costs are not generally reimbursed by employers and so ignoring income in the utility function will fail to reflect the effect of income on mode choice. This becomes a particularly important issue if the model is to be used to forecast travel behavior in future years. With- out knowing what assumptions were made for employee parking costs it is difficult to assess the reasonableness of the value of the ASC for transit. Documentation Franz, J.D., 1995 Metropolitan Transportation Commission Airline Passen- ger Survey, Final Report, Prepared by J.D. Franz Research for the Met- ropolitan Transportation Commission, Oakland, Calif., Feb. 1996. Harvey, G., ACCESS: Models of Airport Access and Airport Choice for the San Francisco Bay Region—Version 1.2, Prepared for the Metropolitan Transportation Commission, Berkeley, Calif., Dec. 1988. Purvis, C.L., Travel Demand Models for the San Francisco Bay Area (BAYCAST-90): Technical Summary, Metropolitan Transportation Com- mission, Oakland, Calif., June 1997. U.S. Federal Transit Administration and San Francisco Bay Area Rapid Transit District, BART—Oakland International Airport Connector, Final Environmental Impact Report/Environmental Impact Statement, State Clearinghouse No. 99112009, Oakland, Calif., Mar. 2002. D6 PORTLAND INTERNATIONAL AIRPORT ALTERNATIVE MODE STUDY Summary Airport Portland International Airport Model Developer Cambridge Systematics, Inc. and Portland Metro Date Developed 1997–1998 Market Addressed Air passengers Model Type Combined revealed preference and stated preference data Model Structure Multinomial logit Survey Data Used Port of Portland Air Passenger Survey Airport Profile Total annual passengers (2005): 13.6 million Percentage O&D: 85% Ground access mode split (2006 Cus- tomer Satisfaction and Terminal User Survey): Parameter Model Coefficient Implied Value Variables $/hour Travel time (minutes) –0.02683 11 Walk distance (miles) –1.1552 24 Wait time (minutes) –0.0418 17 Cost (cents) –0.001468 Constants $ Private vehicle — — Public transit –2.0 –14 Notes: Coefficients derived from regional travel demand model. Implied value of walk time based on a walking speed of 3 mph. TABLE D17 OAKLAND AIRPORT CONNECTOR AIRPORT EMPLOYEE MODEL

Private vehicle—drop off 36% Private vehicle—parked 24% Rental car 19% Taxi 4% Limousine 2% Shared-ride van 4% Light rail transit 6% Hotel shuttle 4% Charter/tour bus 1% Market Segmentation Residents—Business trips Residents—Non-business trips Non-residents—Business trips Non-residents—Non-business trips Explanatory Variables Travel time (in-vehicle time, wait, on- airport time) Travel cost/ln(household income) Drop-off driver time (as cost at assumed value of time) Drop-off automobile operating cost (assumed) Description Soon after the Boston Logan model discussed earlier was devel- oped, a similar model was developed for Portland, Oregon, as part of a ground access study for Portland International Airport (PDX) that was jointly undertaken by the Port of Portland and Metro, the regional MPO, with the assistance of Cambridge Systematics, Inc. The primary purpose of the model was to forecast the potential rid- ership on a planned extension of the Portland MAX light rail system to the airport, as well as other ground access enhancements. An air passenger survey was performed at the airport that consisted of a re- vealed preference (RP) survey that examined air passengers’ actual mode use and a stated preference (SP) survey that was designed to determine travelers’ preferences for modes that were not then avail- able, namely light rail, express bus, and shared-ride door-to-door vans (termed shared-ride transit). An initial model estimation by Cambridge Systematics jointly es- timated four MNL models using both the RP and SP data, with dif- ferent modal alternative choice sets for residents and non-residents of the region and separate coefficients for business and non-business travelers (Bowman 1997; Cambridge Systematics 1998). These models were subsequently revised by the Metro staff to combine some of the choice alternatives and adjust the ASCs to recalibrate the models (Portland Metro 2001). The revised model coefficients re- ported by Metro staff also included one change to the cost coefficient for non-resident non-business trips. The final model included eight modes: private vehicle parked at the airport for the trip duration, drop off at the airport by private ve- hicle, rental car, taxi and limousine (combined), hotel shuttle, shared-ride van and scheduled bus, light rail, and express bus. The use of private automobile parked at the airport for the trip duration (termed auto park) was restricted to residents of the region, whereas the use of a rental car was restricted to non-residents of the region. In the case of the light rail and express bus alternatives it was assumed that travelers would be dropped off at the station or stop by a private vehicle. As part of the overall study of alternative airport access modes, a review of the experience of other U.S. airports with a range of air- port ground access strategies was undertaken (Coogan 1997). This report included statistics on the ground access mode shares of vari- ous airports that had implemented ground transportation services similar to those being considered for Portland, as well as a discus- sion of the operational experience of those airports with the ground 128 transportation services and the lessons that might be applicable to the Portland situation. Although there was no explicit comparison of the results of the mode choice analysis with the experience at other airports, this study nonetheless helped put the results of the mode choice modeling into a larger context and served to provide some assurance of the likely validity of the modeling results. Explanatory Variables The model included four explanatory variables: travel time, travel cost, household income, and the time and cost of the driver dropping off air passengers by private vehicle. Travel time combined in- vehicle time, waiting time, and any on-airport time (such as the time required to travel from a parking lot to the terminal). Travel costs were divided by the natural logarithm of the average household income for each origin zone. The direct costs of each mode (but not the operating costs and value of driver time of automobiles dropping off air passengers) were divided by the logarithm of the average household income for the trip origin zone (in thousands of dollars per year) for each market segment, determined from the air passenger survey. This gives values of time that vary with household income, as is to be expected, but that have a nonlinear relationship that increases at a declining rate at higher income levels. The use of the average household income for the zone resulted from the way that the model was applied, although this obviously fails to account for the effect of variation in household income across survey respondents from a given zone. For the drop off alternatives, including air passengers dropped off at the airport by private automobile (termed auto drop off), the time of the driver (termed the chauffeur in the model documenta- tion) was assigned a value of $20/h for business travelers and $10/h for non-business travelers according to the model documentation (tables giving the final model coefficients indicate that $20/h was used for all trip purposes; however, this is assumed to be a typo- graphic error). Automobile operating costs were assumed to be 12 cents per mile. Model Coefficients The final model coefficients are given in Tables D18 to D21 (the vari- ation in the number of decimal places of the coefficient estimates reflect the model documentation prepared by Portland Metro). Sepa- rate coefficients were estimated for the same four market segments as the Boston Logan model. In addition, separate ASCs were estimated for each mode for trips originating within the Portland metropolitan area (termed internal trips) and those originating outside the metro- politan area (termed external trips). Two different sets of model coef- ficients were estimated for each market segment. The first set (termed Model 1) assumed that the ASCs for the light rail and express bus modes would be the same as those for shared-ride van and RAZ bus (a scheduled bus service between the airport and downtown Portland lo- cations operated by RAZ Transportation, a Gray Line affiliate). The second set (termed Model 2) used the SP data to estimated separate ASCs for the light rail and express bus modes. The documentation on the initial model estimation by Cambridge Systematics provides t-statistics for the coefficient estimates, but the documentation of the final model does not. The model documentation does not explain why ASCs were not determined for taxi and limousine use for resident business trips from external origins or for shared-ride van and RAZ bus use for non-resident business trips from external zones, but were deter- mined for the other three market segments in each case. Indeed, it is

129 Coefficient Model 1 Model 2 Variables Drop off cost ($) –0.0195 –0.0195 Travel time (minutes) –0.0176 –0.0176 Cost/ln(income) $/ln($K) –0.2185 –0.2185 Constants (auto park base) Internal trips Auto drop off 0.85 0.85 Taxi and lim ousine –1.162 –1.272 Van, RAZ bus, and hotel shuttle –0.988 –1.258 Light rail (auto drop off) –0.988 –1.258 Express bus (auto drop off) –0.988 –1.258 External trips N/A = not available. Auto drop off –0.85 –0.85 Taxi and limousine N/A N/A Van, RAZ bus, and hotel shuttle 2.312 0.742 Light rail (auto drop off) 2.312 0.742 Express bus (auto drop off) 2.312 0.742 Coefficient Model 1 Model 2 Variables Drop off cost ($) –0.0235 –0.0235 Travel time (minutes) –0.0264 –0.0264 Cost/ln(income) $/ln($K) –0.2170 –0.2170 Constants (auto park base) Internal trips Auto drop off –0.30 –0.30 Taxi and limousine –2.068 –1.538 Van, RAZ bus, and hotel shuttle –1.632 –1.362 Light rail (auto drop off) –1.632 –0.3654 Express bus (auto drop off) –1.632 –1.5281 External trips Auto drop off –0.80 –0.80 Taxi and limousine –2.188 –2.188 Van, RAZ bus, and hotel shuttle 2.368 –0.652 Light rail (auto drop off) 2.368 –2.3447 Express bus (auto drop off) 2.368 –3.8869 TABLE D18 PORTLAND GROUND ACCESS STUDY RESIDENT BUSINESS MODEL COEFFICIENTS TABLE D19 PORTLAND GROUND ACCESS STUDY RESIDENT NON-BUSINESS MODEL COEFFICIENTS Coefficient Model 1 Model 2 Variables Drop off cost ($) –0.0082 –0.0082 Travel time (minutes) –0.0073 –0.0073 Cost/ln(income) $/ln($K) –0.0913 –0.0913 Constants (rental car base) Internal trips Auto drop off –0.50 –0.50 Taxi and limousine –0.9135 –1.2335 Hotel shuttle –0.8865 –0.9965 Van and RAZ bus –0.9365 –1.3965 Light rail (auto drop off) –0.9365 –0.8009 Express bus (auto drop off) –0.9365 –0.9960 External trips Auto drop off –0.30 –0.30 Taxi and limousine –1.0635 –2.2135 Van and RAZ bus N/A N/A Light rail (auto drop off) –1.287 –1.4665 Express bus (auto drop off) –1.287 –2.4165 N/A = not available. Coefficient Model 1 Model 2 Variables Drop off cost ($) –0.0082 –0.0082 Travel time (minutes) –0.0092 –0.0092 Cost/ln(income) $/ln($K) –0.0716 –0.0716 Constants (rental car base) Internal trips Auto drop off 0.10 0.10 Taxi and limousine –1.754 –1.574 Hotel shuttle –0.246 –0.046 Van and RAZ bus –0.596 –0.956 Light rail (auto drop off) –0.596 –0.914 Express bus (auto drop off) –0.596 –0.935 External trips Auto drop off –0.50 –0.50 Taxi and limousine –1.304 –2.054 Van and RAZ bus –0.346 –1.206 Light rail (auto drop off) –0.346 –1.206 Express bus (auto drop off) –0.346 –0.6862 TABLE D20 PORTLAND GROUND ACCESS STUDY NON-RESIDENT BUSINESS MODEL COEFFICIENTS TABLE D21 PORTLAND GROUND ACCESS STUDY NON-RESIDENT NON-BUSINESS MODEL COEFFICIENTS

not clear why the RAZ bus was included as an option for external trips at all or why the hotel shuttle was considered as an option for resident trips. There are a number of counterintuitive or surprising values for the ASCs. Because the ASCs for taxi and limousine have a gener- ally higher disutility than auto drop off suggests that the perceived cost of taxi and limousine fares have been underestimated. Also, it is not clear why the perceived relative disutility of existing modes should change between Model 1 and Model 2 when the values for the light rail and express bus were adjusted using the SP data. The large positive value of the ASC for shared-ride van and RAZ bus for resident trips from external zones seems inconsistent with the val- ues for internal trips. The implied values of the model coefficients for Model 2 are shown in Table D22. Because the inclusion of household income in the cost term results in implied values of time that vary with aver- age household income, these values have been calculated for aver- age annual household incomes of $50,000 and $150,000. Although the resulting values of time seem consistent for resident and non- resident travelers for each trip purpose, this is a consequence of the way the model was estimated, and the lower value of time for busi- ness trips compared with non-business trips is counterintuitive. The relatively small change in the value of time between a zone with an average annual household income of $50,000 and one with an average annual household income of $150,000 per year is a con- sequence of the use of the logarithmic transform. For comparison with the implied values shown in Table D22, a household with one 130 worker and an annual income of $50,000 would have a wage rate of $25/h, whereas a household with two workers and an annual income of $150,000 would have an average wage rate of $37.50/h. There- fore, the implied values appear to be in the general range of the wage rate. The implied values of the ASCs, expressed as equivalent min- utes of travel time, appear implausibly large for many modes. For example, the relative disutility of most public modes for non-resi- dent trips compared with auto drop off, apart from any differences in cost and travel time, is equivalent to well over an hour of travel time and more than 3 h of travel time in the case of taxi or limousine use for non-business trips from internal zones, or taxi, limousine, or express bus use for business trips from external zones. The large dif- ferences in the auto drop off constant compared with auto park (for resident trips) and rental car (for non-resident trips) between busi- ness and non-business trips suggests that these constants are ac- counting for more than just the inherent differences in the comfort and convenience of the various modes. The ratio of the auto drop off cost coefficient to the cost coeffi- cient for all other costs suggests that the auto drop off costs (pri- marily the time of the driver) are valued at between about one-third and one-half of the other costs. This is not unreasonable, because some air travelers may consider being taken to the airport by others as essentially without cost to them. However, it is worth noting that the assumed values of time for the drivers (twice as high for busi- ness trips as for non-business trips) are inconsistent with the esti- mated values of time for the air passengers, which are about half again higher for non-business trips than business trips. Parameter Resident Business Resident Non- Business Non-Resident Business Non-Resident Non-Business Travel Time ($/hour) $50,000 avg. h/h income 19 29 19 30 $150,000 avg. h/h income 24 37 24 39 Auto Drop Off Cost Ratio $50,000 avg. h/h income 0.35 0.42 0.35 0.45 $150,000 avg. h/h income 0.45 0.54 0.45 0.57 Constants (minutes) Internal trips Auto drop off –48 11 68 –11 Taxi and limousine 72 58 169 171 Hotel shuttle 71 52 137 5 Van and RAZ bus 71 52 191 104 Light rail (auto drop off) 71 14 110 99 Express bus (auto drop off) 71 58 136 102 External trips Auto drop off 48 30 41 54 Taxi and limousine N/A 83 303 223 Van and RAZ bus –42 25 N/A 131 Light rail (auto drop off) –42 89 201 131 Express bus (auto drop off) –42 147 331 75 Notes: avg. h/h income = average household income. N/A = not available. TABLE D22 IMPLIED VALUES OF PORTLAND GROUND ACCESS STUDY COEFFICIENTS

131 Model Fit The initial Cambridge Systematics model estimation results include t-statistics for the coefficients for each variable and measures of overall goodness-of-fit of the model, including the final value of the log likelihood and the improvement over the log likelihood with zero coefficients or constants only. The overall improvement in the goodness-of-fit of the model from the inclusion of the continuous variables is not particularly large. Model Application As noted, the model was primarily developed to predict ridership on the planned extension of the Portland MAX light rail system to serve the airport, as well as to examine alternative ground access measures, including development of an express bus service and charging private vehicles a fee to drop off or pick up air passengers at the airport. The approach to applying the model follows the traditional four- step urban transportation planning approach, with the number of trips generated by the airport determined from the airport traffic forecasts. A trip distribution model (termed an origin location model in the documentation) calculates the number of air parties beginning their access trip in each zone. The mode choice model is then applied to these trips to calculate the number of vehicle and air passenger trips from each zone. Documentation Bowman, J.L., Portland PDX Airport Access Project Mode Choice Models, memorandum to Keith Lawton, Metro, Cambridge Systematics, Inc., July 28, 1997. Cambridge Systematics, Inc., Portland International Airport Alternative Mode Study, Prepared for the Port of Portland, Portland, Ore., Oct. 1998. Coogan, M.A., The Peer Airport Analysis Report, prepared for the Port of Portland, Apr. 9, 1997. Included as Appendix D to Cambridge System- atics, Inc., Portland International Airport Alternative Mode Study, Prepared for the Port of Portland, Portland, Ore., Oct. 1998. Portland Metro, PDX Ground Access Study Model Summary, Prepared by the Travel Forecasting Staff, Portland, Ore., undated (May 1998), revised June 2001. D7 SAN JOSÉ INTERNATIONAL AIRPORT MODEL Summary Airport Norman Y. Mineta San José International Airport Model Developer Dowling Associates, Inc. Date Developed 2002 Market Addressed Air passengers Model Type Combined revealed preference and stated preference data Model Structure Multinomial logit Survey Data Used 1995 Metropolitan Transportation Com- mission Air Passenger Survey; Supple- mentary stated preference survey at San José International Airport Airport Profile Total annual passengers (2005): 10.6 mil- lion Percentage O&D: 91% Ground access mode split (2002 MTC Air Passenger Survey): Private vehicle—drop off 49% Private vehicle—parked 17% Rental car 19% Taxi 7% Limousine 1% Hotel courtesy shuttle 2% Shared-ride van 2% Shuttle bus from train 1% Public transit bus <1% Other <1% Market Segmentation Residents—Business trips Residents—Personal trips Visitors—Business trips Visitors—Personal trips Explanatory Variables Travel time (private vehicles) Travel time (rail transit) Travel time (bus transit) Walk distance Wait time Travel cost Household income Description This model was developed by Dowling Associates to estimate the ridership on a planned automated people-mover (APM) to connect the airport to a nearby Santa Clara Valley Transportation Authority (VTA) light rail line (Dowling Associates 2002). The model was estimated using data from an air passenger survey performed at the airport for the Bay Area Metropolitan Transportation Commission (MTC) in 1995 (Franz 1996) and supplemented with the results of stated preference surveys that were conducted as part of the study to determine how air passenger mode choice might be influenced by the availability of the people-mover, as well as to overcome the problem that there were very few users of the light rail line in the 1995 survey sample. MNL models were estimated for the same four market seg- ments used in the Oakland Airport BART Connector Study: resident business trips, resident personal trips, visitor business trips, and vis- itor personal trips. Each market segment model included six modes: private vehicle, rental car, scheduled airport bus, shared-ride van (termed door-to-door shuttle), taxi, and public transit. In addition, the visitor segment models included hotel shuttle. The model also included an airport employee segment as dis- cussed further here. This only considered two modes: private vehi- cle and public transit. Because the primary purpose of the model was to estimate ridership on the planned APM, the model allowed up to four connecting transit routes and developed separate fare and travel times for each. Those routes involving the use of the APM to access the VTA light rail line were given a separate ASC from other transit routes to reflect the greater attractiveness of the APM based on the stated preference survey. Explanatory Variables Independent variables consisted of the automobile travel time, transit travel time by rail, transit travel time by bus, waiting time, walking distance, and cost. The cost variable for personal trips was divided by the annual household income raised to the power 1.5. Only one set of ASCs for private car was presented in the re- port, making no distinction between air parties being dropped off and those parking for the duration of the air trip. This resulted from a limitation in the 1995 air passenger survey, which also did not make this distinction. It was assumed in the model estimation that residents using private vehicles parked at the airport, whereas visitors were dropped off. The parking cost was included in the parking utility function for resident trips, whereas a “drop-off”

factor was included in the private vehicle utility function for visi- tor trips to account for the inconvenience for drivers dropping off air passengers (the details of this factor are not given in the report). It is possible to use the estimated model to predict the choice of resident air passengers being dropped off by including both modes in the model and assuming that the ASC is the same for both drop off and park. Model Coefficients The approach taken in estimating the model followed that used in the Oakland International Airport BART Connector Study with the model coefficients for the continuous variables adopted directly from an earlier airport ground access mode choice model for Bay Area developed by Harvey (1988). The values of the ASCs were then esti- mated to fit the model to the mode use data from the 1995 MTC Air Passenger Survey. The estimated model coefficients presented in the study report are shown in Table D23. As discussed earlier in the description of the Oakland Airport Connector study model, the use of coefficients from a model that was estimated on much earlier data, without any adjustments for changes in the implied values of time, introduces significant distor- tions in the model that are compounded when the model is used to predict future mode use. The implied values of the estimated coefficients are shown in Table D24. As with the Oakland Airport model, the implied values of the ASCs are expressed relative to taxi, because the private vehi- cle mode is different for residents and visitors. These implied val- ues are expressed in dollars and represent the difference in cost be- tween the mode and a taxi that would be required for travelers to be indifferent between use of the two modes if travel times were the same. Because the implied values for personal trips depend on the household income, the values have been calculated for a household 132 income of $55,000, which is stated in the study report to be the av- erage annual household income for potential transit users at San José International Airport based on data for Santa Clara County from the Association of Bay Area Governments (it is unclear what “potential transit users” means in this context or how the Associa- tion of Bay Area Governments could determine the household in- come of such users, but the value provides a reasonable point of comparison). The implied values of the various components of travel time are quite low by comparison with the values typically found in air pas- senger ground access mode choice models (and air travel models generally). However, because these implied times came directly from the coefficients estimated by Harvey (1988) using 1985 data, this is hardly surprising. Because the implied value of rail transit travel time is lower than travel time by private auto is counterintu- itive. Although the higher implied value for bus transit travel time is consistent with typical experience in urban travel models, the difference from travel time by private auto is surprisingly small, particularly for visitor personal trips. Similarly, the implied values of the ASCs are quite low compared with those typically found in air passenger ground access mode choice models and the differ- ences between the values for different modes are surprisingly small and in several cases intuitively unreasonable. For example, it makes no sense that the implied value of the ASC for transit or shared-ride van for resident business trips would be greater than that for taxi, which provides significantly greater comfort and con- venience. Similarly, it seems quite implausible that scheduled bus, transit, or shared-ride van would be viewed by visitors on business trips as more attractive than being dropped off at the airport by pri- vate automobile. What is most likely distorting the values of the estimated coef- ficients is a failure to control for the need for and availability of different modes for different air parties. Visitors who are not stay- ing with residents of the area may not have anyone who can take Coefficient Resident Business Resident Personal Visitor Business Visitor Personal Variables Auto time (minutes) –0.071 –0.044 –0.068 –0.039 Rail transit time (minutes) –0.053 –0.031 –0.050 –0.029 Bus transit time (minutes) –0.093 –0.051 –0.089 –0.045 Walk distance (miles) –5.17 –3.28 –4.69 –2.94 Wait time (minutes) –0.107 –0.077 –0.096 –0.071 Cost (cents) –0.00277 –1.04/ (HHINC)1.5 –0.00256 –0.973/ (HHINC)1.5 Constants Private vehicle — — — — Rental car –2.9 –4.1 3.9 1.0 Scheduled bus –2.3 –2.7 1.2 –0.8 Transit (does not use APM) –1.3 –2.0 0.9 –0.4 Transit (uses APM) –1.2 –1.8 0.8 –0.3 Shared-ride van –1.2 –1.4 0.6 –0.1 Hotel shuttle N/A N/A 0.0 –3.1 Taxi –1.4 –1.3 1.1 0.1 Notes: HHINC = annual household income in thousands of dollars; N/A = mode is not available for this market segment; APM = automated people-mover. TABLE D23 SAN JOSÉ INTERNATIONAL AIRPORT MODEL COEFFICIENTS

133 them to the airport and therefore either rent a car to meet their local transportation needs or use public modes. To explain these choices in a situation when the model has assumed that being dropped off by private vehicle is an option that is available, the values of the ASCs have to be increased. Similarly, residents of the region gen- erally have access to a private vehicle and therefore do not need to rent a car. Model Fit No goodness-of-fit statistics for the estimated coefficients or the overall model were provided in the report. Model Application The model was developed as part of planning studies for a proposed APM that will connect the passenger terminal at San José Interna- tional Airport to a nearby stop on the Santa Clara VTA light rail line that runs about a block to the east of the airport and connects in downtown San José with other lines on the light rail network serv- ing the Santa Clara Valley. A possible future extension of the APM to the west side of the airport would also serve the nearest station on the Caltrain commuter rail line that serves the Peninsula corridor between San José and San Francisco. Airport Employee Mode Use The mode choice model included a market segment for airport em- ployees that considered only two modes, private vehicle and transit, although separate ASCs were developed for transit trips using the APM and those without the APM being available. The employee model used the same coefficient value for both highway and transit travel time, including rail and bus. In addition to travel time, the utility functions included walking distance, waiting time, and cost. The coefficient values were not estimated from employee survey data, but were adapted from the Santa Clara County countywide travel model for home-based work trips. The employee model doc- umentation does not state what assumptions were made about the cost for employee parking. The model coefficients and implied values are shown in Table D25. In this case, the implied value of the transit ASC is relative to private vehicle, because a taxi was not considered in the employee mode choice model. The implied values of travel time are extremely low, which is most likely the result of the adoption of the model coefficients from Param eter Resident Business Resident Personal Visitor Business Visitor Personal Travel Time ($/hour) Auto time 15 10 15 10 Rail transit time 11 7 11 7 Bus transit time 20 12 19 11 Walk time 56 39 55 37 Wa it time 23 18 21 18 Constants (dollars) Private vehicle 5.1 5.1 –4.3 –0.4 Rental car –5.4 –11.0 10.9 3.8 Scheduled bus –3.3 –5.5 0.4 –3.8 Transit (does not use APM) 0.4 –2.8 –0.8 –2.1 Transit (uses APM) 0.7 –2.0 –1.2 –1.7 Shared-ride van 0.7 –0.4 –2.0 –0.8 Hotel shuttle N/A N/A –4.3 –13.4 Taxi — — — — Notes: Implied values of personal trips calculated for an annual household income of $55,000 per year. Implied value of walk time based on a walking speed of 3 mph. TABLE D24 IMPLIED VALUES OF SAN JOSÉ INTERNATIONAL AIRPORT MODEL COEFFICIENTS Parameter Model Coefficient Implied Value Variables $/hour Travel time (minutes) –0.02545 4.2 Walk distance (miles) –1.17 9.6 Wait time (minutes) –0.05854 9.6 Cost (cents) –0.00366 Constants $ Private vehicle — — Transit (does not use APM) –1.4 –3.8 Transit (uses APM) –1.3 –3.6 Notes: Coefficients derived from countywide travel demand model. Implied value of walk time based on a walking speed of 3 mph. TABLE D25 SAN JOSÉ INTERNATIONAL AIRPORT EMPLOYEE MODEL

the Santa Clara countywide travel demand model. Although these have similar values for travel time components (and walking dis- tance) to those for the MTC regional travel demand model dis- cussed in the description of the Oakland Airport Connector model earlier, the coefficient for cost is significantly greater, suggesting that the countywide model is based on travel costs in even earlier constant year dollars than the MTC model. Without adjusting for changes in prices and income, the direct use of these coefficients and their implied value of time will tend to overstate the attractive- ness of transit. Documentation Dowling Associates, Inc., San Jose International Airport Transit Connection Ridership, Final Report, Prepared for San Jose International Airport, Lea+Elliott and Walker Parking, Oakland, Calif., June 2002. Franz, J.D., 1995 Metropolitan Transportation Commission Airline Passenger Survey, Final Report, Prepared for the Metropolitan Trans- portation Commission, Oakland, Calif., by J.D. Franz Research, Feb. 1996. Harvey, G., ACCESS: Models of Airport Access and Airport Choice for the San Francisco Bay Region—Version 1.2, Prepared for the Metropolitan Transportation Commission, Berkeley, Calif., Dec. 1988. D8 TORONTO AIR RAIL LINK REVENUE AND RIDERSHIP STUDY Summary Airport Toronto Lester B. Pearson International Airport, Canada Model Developer Halcrow Group Limited Date Developed 2002 Market Addressed Air passengers Model Type Combined revealed preference and stated preference data Model Structure Binomial logit diversion Survey Data Used February 2002 Air Passenger Survey Airport Profile Total Annual Passengers (2005): 29.9 milliona Percentage O&D: 75%b Ground access mode split (2005 air pas- senger survey):c Private vehicle—drop off 45% Private vehicle—parked 13% Rental car 9% Taxi and limousine 24% Courtesy vehicles 6% Scheduled airport bus 2% Public transit 1% Source: Greater Toronto Airports Authority aPassenger Statistics as of June 30, 2007. bEstimate quoted in Halcrow Group (2002). cPersonal communication, Marc Turpin, 6/29/07. Market Segmentation Residents—Business trips Residents—Non-business trips Non-residents—Business trips Non-residents—Non-business trips Explanatory Variables In-vehicle travel time Service headway Travel cost Dummy variables: travel party size (accompanied/travel alone); checked baggage 134 Description In May 2003, Transport Canada issued a Request for Business Case for a public–private partnership to develop an Air Rail Link between Toronto Lester B. Pearson International Airport (LBPIA) and Toronto Union Station (Request for Business Case . . . 2003). Subse- quently, a private sector consortium, Union Pearson AirLink Group, was selected to finance, design, construct, and operate the rail link in association with eight public and three private stakeholders, includ- ing the Greater Toronto Airports Authority and several regional and national rail and transit agencies (Borges 2006). In preparation for the Request for Business Case, a revenue and ridership forecasting study was undertaken in 2002 (Halcrow Group 2002). This study included 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 interviewed 2,566 passengers of whom 1,927 (75%) were not connecting between flights. The sur- vey collected data on the air party characteristics, ground trip origin, and the ground access mode for the current trip. Some 807 respon- dents were identified as potential air rail link users, based on their ground trip origins, and completed a stated preference question- naire. The results of the survey were then used to estimate a set of mode choice models. The Greater Toronto region was divided into a system of 34 zones, with the 2 downtown Toronto zones further subdivided into 10 zones. Peak period highway and transit travel times between each of these zones and the airport and potential Air Rail Link sta- tions were extracted from the city of Toronto Greater Toronto Area transportation network model. Off-peak highway travel times were assumed to be 6% less than peak period travel times based on an analysis of the travel times reported in the stated pref- erence survey. Travel costs were largely derived from actual costs reported in the stated preference survey. Taxi fares for zones with no reported data were derived from a distance-based regression using the reported fares from zones for which there were data. Pri- vate vehicle fuel costs were calculated on the basis of 4.6 cents per kilometer. The mode choice model consists of a set of binomial logit mod- els (described in the model documentation as logistic, probabilistic, diversion models), each of which models the choice between an ex- isting mode and the planned rail link. If such models had been de- veloped for every existing mode, they could have been combined into a MNL model. However, air travelers using a hotel bus or rental car to access the airport were excluded from the diversion analysis, because users of the hotel bus were assumed to have a door-to-door service 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 considering the use of other modes. The use of the bi- nomial logit diversion models has another consequence, namely that the models only predict the diversion from existing modes to the rail link and do not allow for future shifts in use between exist- ing modes owing to changes in future values of travel time and cost for the different modes or changes in travel patterns in the Toronto market. Although the purpose of the analysis was to predict the rid- ership on the rail link, because this is calculated on the basis of the diversion from existing modes, any errors in the predictions of the future use of existing modes without the rail link will in turn affect the ridership projections for the rail link. The model documentation does not explicitly discuss the modes that were included in the analysis; however, based on the presenta- tion of the survey results it would appear that the analysis was based on four modes: private vehicle parked at the airport for the trip du-

135 ration (termed drive & park), drop off by private vehicle (termed driven in car), taxi/limousine, and transit/airport bus. In addition to estimating a formal mode choice model, the study included a benchmark comparison analysis that examined the mode share of existing airport rail links in 24 cities in the U.S., Europe, and Australia. This analysis developed cross-sectional regression relationships that expressed 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 airport from the central city, and the ratio of the rail travel time to the 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 resulting range of rail mode shares (which varied with the characteristic chosen) was compared with the results of the formal mode choice modeling process, to pro- vide a reality check on the modeling analysis. The details of this analysis are presented in the model documentation (Halcrow Group 2002). They are not presented here, because they are not directly relevant to the details of the mode choice model, but the results of the comparison are discussed below. Explanatory Variables The mode choice model relationships used 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 addi- tion, the utility function for the rail link included two dummy vari- ables: 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) in- tended to check any bags. These dummy variables were chosen based on survey results that indicated that air travelers who traveled to the airport alone or did not intend to check any bags were more likely to use the rail link. This seems reasonable, because larger travel parties or those with significant amounts of baggage are likely to find other modes more convenient or cost-effective. In particular, being accom- panied to the airport by well-wishers will often mean that someone is available to take the air party to the airport by private vehicle. The model structure did not directly consider the type of trip origin. However, the approach of developing separate diversion models for each existing access mode indirectly addressed some of these effects, because air passengers being dropped off by private vehicle would largely have begun their trip from a private residence, while those using taxi or airport bus would be more likely to have begun their trip from a hotel or place of business. The primary rea- son for including the type of trip origin in an airport access mode choice model is to account for the effect of this on available access modes (principally the availability of someone who can take the air party to the airport). By segmenting the mode choice analysis by the existing access mode, this effect is already considered in the data. However, this approach does have one drawback. Because the share of existing access modes in the absence of the rail link is required to perform the analysis, which is necessarily derived from existing conditions, the approach assumes that the distribution of air passen- ger trips across the different types of trip origin will remain un- changed in the future. The model utility functions also do not consider the household income of the air travelers. Because the rail link will offer a differ- ent combination of cost and travel time from existing modes, the attractiveness of this service to air travelers from a given geographic zone is likely to vary depending on their income. This in turn is likely to have a significant effect when considering the impact on revenue of changing fare levels. Model Coefficients The estimated model coefficients are shown in Table D26, with the associated implied values of the travel time components and dummy variables shown in Table D27. ASCs were initially included in the model utility functions; how- ever, these were found to not be statistically significant and were dropped from the model. This is surprising given the relatively sim- ple 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. The implied values of in-vehicle time (strictly the implied val- ues of reduced in-vehicle times) appear reasonable. The values for business trips are approximately twice those for non-business trips, which is not unreasonable, particularly given that income is not Coefficien t Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Continuous Variables In-vehicle time (minutes) –0.0494 (–4.2) –0.0665 (–7.6) –0.0435 (–2.8) –0.0462 (–3.9) Headway (minutes) –0.1180 (–4.5) –0.0665 (–3.7) –0.1678 (–7.0) –0.0692 (–3.1) Cost ($) –0.0557 (–5.9) –0.1382 (–8.6) –0.0368 (–7.3) –0.0820 (–4.5) Dummy Variables Accompanied to airport N/S –0.2736 (–2.3) N/S –0.7951 (–4.8) Checked baggage N/S –0.4284 (–2.8) N/S –0.5120 (–2.5) Number of cases 173 348 167 162 Notes: N/S = not statistically significant; t-statistics shown in parentheses. TABLE D26 TORONTO AIR RAIL LINK MODEL COEFFICIENTS

explicitly included in the model. Business travelers will tend to have higher incomes on average than non-business travelers. The implied values of in-vehicle time for non-residents are somewhat higher than for residents (33% higher for business trips and 17% higher for non-business trips). Because it can be assumed that a fairly high proportion of non-residents are from the United States, this is also not unreasonable. However, the implied values of waiting time appear surprisingly high except for resident non-business trips, for which waiting time is perceived as twice the inconvenience of in-vehicle time, which corresponds to typical experience in urban travel demand models. The implied value for non-resident non-business trips is three times the implied value of in-vehicle time, which is higher than would normally be expected. The implied values of waiting time for busi- ness trips are implausibly high, at almost five times the value of in-vehicle time for resident trips and almost eight times the value of in-vehicle time for non-resident trips. Because the non-resident business travelers already had a fairly high value of in-vehicle time, the resulting implied value of waiting time is more than $500/h. Although business travelers may be particularly averse to waiting, it would be surprising if they found waiting that onerous compared with travel time. Indeed, such a ratio implies that they would be willing to incur an additional hour of travel time to avoid a 10 minute wait, which makes no sense. This is not a trivial issue, because overstating the disutility of waiting time in the model will result in underestimating the demand for the rail link if headways are increased and conversely overesti- mating the demand if headways are reduced. This will overestimate the willingness of travelers, particularly business travelers, to pay a higher fare to have a shorter headway and could result in an operat- ing plan that runs too many trains at too high a fare. In contrast to the implied values of waiting time, the implied values of the disutility of the rail link for travelers who are accom- panied to the airport or have checked baggage are relatively low, varying between $2 and $10, and being higher for non-residents than residents. These values can be interpreted as the reduction in fare that would be needed to offset the disutility and result in the rail link being perceived to be as attractive as it is for those traveling alone with only carry-on bags. Thus, the fare reduction needed to offset the combined disutility of being accompanied to the airport and having checked bags is approximately $16 for non-residents and $5 for residents. This would represent a significant percentage of the proposed fare of $20. Model Fit The model documentation presents t-statistics for each of the explanatory variables, as shown in Table D26. However, there is no discussion of the overall fit of the model relationships to the survey data. The t-statistics for each of the variables included in the model utility functions are all statistically significant at the 95% confi- 136 dence level; highly so in the case of the cost terms and most of the travel time terms. Model Application The binomial logit diversion models were used to predict the rider- ship on the Air Rail Link for future years following the introduction of service under a range of different service scenarios. These in- cluded potential intermediate stations between Union Station and the airport, as well as different fare and frequency assumptions. The pre- dicted ridership on the Air Rail Link during the first two years after the start of service was adjusted to allow for an expected ramp-up in the diversion from other modes based on the experience following the start of service on the Heathrow Express rail link to London Heathrow Airport. The models were applied to a range of different service scenar- ios, including a base case with an intermediate stop at the Dundas Station of the Toronto Transit Commission Bloor–Danforth subway line, as well as nonstop service from Union Station to LBPIA and a reduced headway for both the nonstop and one-stop service with trains departing every 20 min rather than every 15 min. Other sensi- tivity tests were performed to explore the effect of changes in rail service travel times, changes in access times for travelers to reach the rail link stations or egress times from the airport station to the pas- senger terminal, changes in highway travel times, changes in the value of time for air passengers, and the introduction of another in- termediate station at a proposed entertainment complex located at the site of the Woodbine Racetrack adjacent to the planned rail route. To understand the potential uncertainty in the prediction of rider- ship and revenues over the first 30 years of the project, a risk analy- sis was performed that took into account the confidence in the mode choice model relationships as well as the predicted future values of the airport traffic, changes in market segments, trip end distribution, and transportation system variables. This analysis generated distrib- utions of predicted future levels of ridership and revenue that were used to present both 20% and 80% confidence values for both mea- sures. This recognized that private investors in the planned Air Rail Link would want to adopt a conservative view of the likely return on investment, whereas design of the necessary facilities would need to consider a more aggressive forecast of possible ridership. Comparison with Benchmarking Analysis A particularly interesting aspect of the Air Rail Link Revenue and Ridership study is the comparison of the predictions of the mode choice model and the findings of the benchmarking analysis. In spite of both the very broad scope of the benchmarking analysis and the technical concerns with the mode choice model discussed earlier, the results of both approaches gave surprisingly consistent results. The benchmarking analysis suggested that the likely mode share of originating air passenger traffic at LBPIA using the rail link Parameter Resident Business Resident Non-Business Non-Resident Business Non-Resident Non-Business Travel Time ($/hour) In-vehicle time 53 29 71 34 Waiting time 254 58 547 101 Dummy Variables ($) Accompanied to airport N/A 2 N/A 10 Checked baggage N/A 3 N/A 6 N/A = not applicable. TABLE D27 IMPLIED VALUES OF TORONTO AIR RAIL LINK MODEL COEFFICIENTS

137 might varying between about 7% and 14%, with a central estimate of 9.3%. The forecasts of ridership applying the mode choice model to the base case service scenario gave a mode share of 9.3% for 2001 traffic levels, rising to 9.7% for 2021 traffic levels. Documentation Borges, H., Air Rail Link: Pearson Airport–Union Station, presented to the Chartered Institute of Logistics and Transport, Transport Canada, Ottawa, ON, Apr. 11, 2006 Halcrow Group Limited with Cansult Ltd., Air Rail Link from Lester B. Pearson International Airport to Union Station: Revenue & Ridership Study, Report T8080-01-1213, Final Report, Prepared for Transport Canada, Ottawa, ON, May 2002. Request for Business Case: Air Rail Link from Toronto–Lester B. Pearson International Airport to Toronto Union Station, Transport Canada, Ottawa, ON, May 2003. D9 UNITED KINGDOM SERAS STUDY AIR PASSENGER SURFACE ACCESS MODEL Summary Airports London [Heathrow (LHR), area airports Gatwick (GTW), Stansted (STN), Luton (LTN)] Model Developer Halcrow Group Ltd. Date Developed 2002 Market Addressed Air passengers Model Type Revealed preference data Model Structure Nested logit Survey Data Used U.K. Civil Aviation Authority air passen- ger surveys 1992–1997 Airport Profile LHR GTW STN LTN Source: Total 66.8 31.8 21.7 8.9 United Kingdom annual m m m m Civil Aviation passengers Authority, CAA (2005): Passenger Survey Percentage 66% 84% 89% 94% Report 2005 O&D: m = million. Ground access mode split (2005): Private 34% 51% 48% 56% vehicle Rental 3% 2% 4% 3% car Taxi/ 26% 14% 9% 13% minicab London 13% — — — Under- ground Rail 11% 26% 25% 18% Bus/ 13% 7% 14% 10% coach Other <1% <1% 1% <1% Market Segmentation U.K. business passengers on domestic trips U.K. business passengers on interna- tional trips U.K. leisure passengers on domestic trips U.K. leisure passengers on international trips Non-U.K. passengers on business trips Non-U.K. passengers on leisure trips Explanatory Variables Travel time (in-vehicle time, walk access time) Waiting time Travel cost (fares, parking, automobile operating cost) Driving distance Interchange penalties Description As part of the SERAS study undertaken for the U.K. Department of Transport, Local Government and the Regions, a set of surface access models were developed that included an air passenger mode choice model, as well as an airport employee trip distribution model and an airport employee mode choice model. The structure of the air passenger mode choice model is stated to be the same as the Heathrow Surface Access Model (HSAM) developed by the MVA Consultancy for the British Airports Authority. This is a NL model that covers 12 defined ground access modes and has separate coefficients for 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 • Non-U.K. passengers on leisure trips. The 12 ground access modes consist of: • Drop off by private automobile (termed Kiss & Fly) • Private automobile parked at airport (termed Park & Fly) • Rental car (termed Hire Car) • Taxi • Local bus and intercity coach • London Underground • Coach links to British Rail stations (BR Coach) • Dedicated premium rail service (Heathrow Express) • New standard British Rail services • Alternative premium rail service • Charter coach (including hotel bus) • Inter-airport transfer coach. Although the term British Rail is used in the model documenta- tion, these services are now provided by private companies (e.g., Great Western Trains) and British Rail as such no longer exists. The Park & Fly mode was assumed to only be available to U.K. pas- sengers and was substituted by the Hire Car mode for non-U.K. passengers. The Heathrow Express is a dedicated non-stop service between London Paddington Station and Heathrow Airport. The al- ternative premium rail service was assumed to be a similar service from another London station, whereas the new standard British Rail service would provide direct rail service to the airport using conven- tional rail equipment with intermediate stops. The hotel bus service refers to a system of shuttle buses that serve local hotels near Heathrow Airport. However, the use of charter coach, hotel bus, and interairport transfer coach was not explicitly represented in the model, but instead the use of these services was determined inde- pendently and the resulting vehicle trips added to those determined using the mode choice model. Thus, the mode choice model for each market segment consisted of nine modes. The nesting structure of the model is shown in Figure D6. There are several levels of nest, particularly for the different rail modes. The utility functions for each mode use a generalized cost approach that considers the travel time and out-of-pocket costs (fares, park- ing, and private automobile operating costs), as well as time penal- ties for interchanges on public modes, and converts all costs to equivalent minutes of travel time. The utility function divides the

0.53079 0.14119 0.28186 0.20884 0.13157 0.07445 0.23525 0.09404 0.10991 0.23545 0.41689 0.13338 0.18127 0.40598 Rail Routes Coach Taxi Kiss & Fly Park & Fly Rail Routes Rail Routes Coach Coach Taxi Taxi Kiss & Fly Kiss & Fly Park & Fly Park & Fly Rail Routes Coach Taxi Kiss & Fly Park & Fly Segment 1—U.K. Business Domestic Mode Choice Structure Segment 3—U.K. Leisure Domestic Mode Choice Structure Segment 2—U.K. Business International Mode Choice Structure Segment 4—U.K. Leisure International Mode Choice Structure FIGURE D6 SERAS mode choice model nesting structure.

(0.001) 0.12421 0.19839 0.68082 (0.001) 0.11883 0.29196 Rail Routes Rail Routes Taxi Bus/Coach Kiss & Fly Hire Car Taxi Bus Kiss & Fly Hire Car Segment 5—Non-U.K. Business Mode Choice Structure Segment 6—Non-U.K. Leisure Mode Choice Structure All Rail Routes London Underground BR Coach Direct Service New Standard BR Premium Non -stop Heathrow Express Alternative Premium Service FIGURE D6 (Continued ).

generalized cost for the mode by the square root of the direct dri- ving distance to the airport. There are no calibration parameters as such, although different values of time are assumed for each mar- ket segment and different weights are applied to waiting time for some market segments. Different automobile operating costs (in pence per kilometer) are assumed for U.K. business and U.K. leisure passengers. Because the models are applied to estimates of air passenger trips that originate in each analysis zone, an average air party size and average trip duration are assumed for each mar- ket segment. One of the most questionable aspects of the SERAS model is the use of an average value of time for each market segment. Although this is a consequence of the use of aggregate trip generation data rather than applying the model to disaggregate air passenger survey data, it will tend to under-predict the use of public transport modes by lower-income travelers and over-predict their use by higher- income travelers. To the extent that higher- and lower-income trav- elers are not uniformly distributed geographically, this will result in biased estimates of public transport mode use from any given zone, and hence for any particular service. Another questionable feature of the SERAS model is the division of the computed generalized cost by the square root of the distance in computing the utilities. To the extent that the same distance is used in computing the utilities for each air party from a given origin zone, this simply scales the utility values, which implicitly assumes that the variance of the error term in the utility functions increases with distance from the airport, albeit at a declining rate. Although it is likely that the uncertainty in highway travel times increases with dis- tance from the airport, this is not true for out-of-pocket costs (such as public transport fares and parking costs) or for travel times on rail or intercity bus modes, which operate to a published schedule (whereas intercity buses may get delayed in traffic congestion, passengers are likely to base their mode choice decisions on the pub- lished schedule). Therefore, the effect of travel time uncertainty should play a greater role for private car, rental car, and taxi modes than for public transport modes. Another concern with this approach is that the scaling effect changes most rapidly at short distances. However, it is precisely at these distances that travel times are most predictable. What would therefore provide a better reflection of 140 uncertainty in travel times is an S-shaped distance function that is asymptotic to one at short distances and would only be applied to private car, rental car, and taxi modes. The utility functions for each mode are as follows: where Tm = in-vehicle time plus access walk time for mode m (minutes), D = direct driving distance to airport (kilometers), c = perceived private car fuel cost (pence/kilometer), v = value of travel time (pence per minute), g = air party size, p = parking rate (pence per day), Fm = fare for mode m (pence), Wm = wait time for mode m (minutes), X1 = number of cross-platform interchanges, I1 = number of full intra-modal interchanges, I2 = number of intermodal interchanges, α = weighting of wait time relative to in-vehicle time, τx = cross platform transfer penalty (minutes), τ1 = intra-modal interchange penalty (minutes), τ2 = intermodal interchange penalty (minutes), θ = direct rail constant (minutes). U T W v F D X I Irail x= + + + + + + rail rail rail 1 1 1 2 α τ τ τ1 2 θ U T W v F D Ibus bus bus bus 1 1= + + + α τ1 U T gv F D taxi car taxi 1 = + U T c gv D pd gv D P&F car = + + Parameters U.K. Business Domestic U.K. Business International U.K. Leisure Domestic U.K. Leisure International Non-U.K. Business Non-U.K. Leisure Value of Time (£/hour) 28.5 46.3 4.7 6.6 47.8 5.6 Vehicle Operating Cost (p/km) 9.40 9.40 8.14 8.14 n/a n/a Average Air Party Size 1.36 1.36 1.99 1.99 1.56 2.08 Average Trip Duration (days) 2.57 8.50 6.43 18.66 N/A N/A Wait Time Weighting Factor 1.0 1.0 1.0 1.9 1.0 1.35 Parking Adjustments 2.0 2.5 2.5 4.0 N/A N/A Interchange Penalty (min) Cross-platform 0.43 0.50 0.77 0.90 0.30 0.69 Intra-modal 2.13 2.52 3.86 4.48 1.48 3.45 Intermodal 2.48 2.52 3.86 5.40 1.48 4.19 HEX Constant (min) Central London 6.70 9.10 17.93 15.54 5.88 16.90 Outer London 3.20 4.09 5.10 7.84 2.99 9.41 Notes: N/A = not applicable; p/km = pence/kilometer; HEX = Heathrow Express. TABLE D28 SERAS MODE CHOICE MODEL PARAMETERS

141 Model Coefficients The values for the various model parameters that were used in the SERAS study are shown in Table D28. Air passenger value of time and vehicle operating costs are given in 1998 pence. Most of the pa- rameter values were adopted unchanged from the 1991 version of the Heathrow Surface Access Model. The values of time for business travelers appear reasonable, although those for leisure travelers appear surprisingly low (in 1998 the pound was worth approximately 1.66 dollars). The interchange penalties appear too low, particularly for cross-platform connections. In general, travelers will experience a wait of about half the headway of the outbound service at an interchange, in addition to any walking time involved. However, it is not clear from the documentation whether these penalties are in addition to any waiting time or are intended to account for it. The Heathrow Express constant (θ) reflects the higher quality of service relative to the London Underground. The difference in value between central and outer London presumably results from the need for a longer journey on the Underground to reach the Heathrow Express terminal at Paddington Station. How- ever, because the ride on the Heathrow Express is the same duration for all travelers, any measure of the higher utility of the Heathrow Express service should be a constant for all travelers. Because these interchange penalties and direct rail constants have been estimated from air passenger survey data, this suggests that the model estima- tion has underestimated the perceived disutility of the access journey to Paddington Station, possibly owing to underestimated interchange penalties (from most parts of London, reaching Paddington Station by Underground involves several changes of line or even changes of mode). Documentation Halcrow Group Ltd., SERAS Surface Access Modelling, Prepared for the Department of Transport, Local Government and the Regions, South East and East of England Regional Air Services Study, London, Eng- land, July 2002.