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Appendix F
Travel Demand and Ridership Forecasting
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-1
F TRAVEL DEMAND AND RIDERSHIP FORECASTING
As a part of WHRTAS, travel demand modeling was utilized to estimate the potential ridership of transit options serving SWF and other West-of-Hudson markets, including commuters, airport employees, and air passengers who would be attracted to the frequency, travel time improvements and convenience of an enhanced transit service. This appendix provides the general overview of the models considered, the applied methodology for the commuter, airport and employee models as well as a summary of the Stated Preference survey and the transit ridership results for the airport and commuter markets.
F1 Ridership Forecasting Models
Ridership forecasts were an important element used to differentiate between alternatives and assess key goals of the screening process such as reduced travel time and improvement in mode share. Ridership forecast were also used to derive estimation of fleet requirements, operating costs, revenue potential, parking requirements, and change in mode share.
Travel patterns for three distinct segments of travelers were evaluated during this study – commuters, air passengers, and airport employees. Forecasting the future use of transit service for all three segments required three different models.
Commuter model, which forecasts commute trips in the twenty-eight (28) county New York Metropolitan Transportation Council (NYMTC)1 region (see Figure F-1);
Airport passenger model, which forecasts passenger trips to and from SWF Airport based on assumptions provided by the Port Authority. The model considers travel to seven airports in the region (see Figure F-2); and
Airport employee model, which forecasts trips by SWF airport employees.
1 Note that the WHRTAS Phase I analysis used the region’s officially adopted transportation plans including the ARC project; the WHRTAS Phase II effort will re-evaluate the recommended alternatives based on updated transportation network assumptions
Figure F-1 28 County NYMTC Region
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-2 Alternatives Analysis Phase I Screening Report
Note: Stewart International Airport (SWF); Westchester County Airport (HPN); Long Island MacArthur Airport (ISP); Newark Liberty International Airport (EWR); La Guardia Airport (LGA); John F Kennedy International Airport (JFK)
Figure F-2 Airports Modeled in the Air Passenger Model
F2 Overview of Models The model selected for forecasting commuter travel patterns was the New York Best Practice Model (BPM), which is the adopted model of the New York Metropolitan Transportation Council (NYMTC), the Metropolitan Planning Organization for the New York counties in the metropolitan area. The BPM was selected based on an evaluation and comparison of available models in the region. The models evaluated were:
Regional Transit Forecasting Model (RTFM) developed by the MTA) North Jersey Regional Transportation Model – Enhanced (NJRTM-E) New Jersey Transit Demand Forecasting Model (NJTDFM) developed by the NJTPA and NJ
Transit). Various elements of these models were compared to determine the most appropriate travel demand forecasting tool to forecast commuter travel on the WHRTAS. The BPM was determined to be the most suitable for the project since it incorporated in great detail the geographic areas relevant to the study markets, it included denser highway and transit network definition and the project team had the ability to calibrate the model more proficiently compared to the other models2. The BPM is a state-of-the-art model that uses land use forecasts and programmed transportation projects adopted by the local Metropolitan Planning Organization, to forecast future urban commuter travel of all
2 West of Hudson Regional Transit Access Study, Comparison of Models, Technical Memorandum Version 2, September 10, 2008.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-3
travelers in the 28 county region, considering each household’s size, auto ownership, age distribution and trip making characteristics. The BPM evaluates both the highway and transit options that are proposed for the study area.
The Air Passenger Model consists of two major elements – an aiport choice model and a mode choice model. The model for air travelers will use the same demographic data and transportation networks that the regional model uses, supplemented with data collected in the New York Metropolitan Area Air Service Demand Study. The Air Passenger Model is discussed in section F.3.1.
The Airport Employee Model was developed based on 2000 Census Journey-to-Work data for the regional airports. It is primarily a mode choice model for employees and is discusssed further in section F.4.
Socio-economic and demographic forecasts, is an input to both the BPM and the air passenger models. The BPM contains highway and transit networks with detailed information on roadway classifications, number of lanes, transit route information, fares, and related data.
Although the three models are independent, the air passenger and airport employee models both use as input the skims (travel characteristics such as travel time and cost) generated by the BPM.. Figure F-3 illustrates the relationship between the three models used in this study. One of the key inputs to the air passenger model is annual enplanements. The airport employee model was developed primarily based on Census journey-to-work data and data provided by the Port Authority.
Figure F-3 Travel Demand Component Models
The BPM produces origin-destination, mode share, and transit ridership results for the commuter travel market. For the airport market, the airport and airport employee models also produce origin-destination and mode share, but the transit ridership is estimated by aggregating data for geographic markets referred to as “districts”.
BPM
Airport Model
Employee Model
Input •NYMTC Socio-Economic Data– Employment, Population, Income Distribution
•Highway and Transit Network Input •NYMTC Socio-Economic Data
•Enplanement Data•OthersSkims (Transportation system
characteristics) Travel Time & Cost
Skims
Output
• Commuter Ridership
Output•Airport Choice•O&D and Mode
Input•Census Journey to work data
CommuterModel
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-4 Alternatives Analysis Phase I Screening Report
F3 Districts
There are two distinct definitions for “district” used throughout this study:
1. A nine-district region used to analyze and report air passenger model results. The districts encompass the entire 28-county BPM region (Figure F-4).
2. A seventeen-district region comprising geographic areas surrounding various routes were defined for the alternatives analyzed. (Figure F-5).
The two district definitions were developed for different purposes. The nine-district region was developed for the entire BPM region (28-counties) to understand air passenger travel trends in the region. They were not specific to the project study area.
The seventeen-district region was developed specfically for the WHRTAS in order to maintain consistency with the various bus and rail routes that were tested. The districts cover areas that were thought to contribute to the riderhip of a particular transit route. These districts were also used to estimate travel time benefits and mode shares across alternatives.
Figure F-4 Nine-district Region
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-5
Figure F-5 Seventeen-district Region
For example, the Hackensack district was comprised of Traffic Analysis Zones (TAZs) within a 5 mile radius around Anderson Street Station in Hackensack, NJ, which was the area considered to have the most potential for impact around that station.
The GW district, which represents the George Washington Bridge bus depot, was initially developed by selecting TAZs within a three mile radius of the depot. Due to the presence of a subway line that connects to the depot, the district was extended further south to 96th street, along the westside.
Due to model constraints, airport transit ridership estimates were derived utlizing the district to district method. All (transit) trips between one district and another in the No Build alternative were compared to all (transit) trips between the same distrcts in the Build alternative. The difference in total ridership was assumed to be atributed to the improvements of the Build alternative. This method was also used to compare mode share and travel time savings.
Figure F-6 illustrates the application of the district method. In the case of trips between the Stewart District (defined as 6-10 miles around SWF) and the Manhattan Central Buisness District (CBD), (defined as south of 59th street) all transit trips in the WHRTAS No Build alternative were compared to all transit trips in the Build alternatives. The difference in ridership was therefore attributable to the Build alternatives’ features.
For example, for trips from SWF to Manhattan, (or from Stewart District to Manhattan CBD), airport transit users were likely to use in the No Build alternative, the Beacon shuttle to connect to the Hudson Line service. However, in the Build alternatives these airport users could use either the Hudson line or the new Port Jervis line alternative. Any increase in total ridership between the WHRTAS No Build and Build
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-6 Alternatives Analysis Phase I Screening Report
alternatives for those districts indicates that the network and service features of the Build alternative are actually what contributes to the ridership increase.
Figure F-6 Example Illustrating District Usage
F4 Best Practice Model
Regional Travel Demand Modeling Methodology
The BPM represents a break from traditional modeling procedures. Since the 1950s, travel forecasting has typically relied on variations of the “four-step” process to forecast future urban travel based on characteristics of the land uses and transportation network. These are:
Trip Generation (Production and Attraction) – determining where trips are produced, and to where trips are attracted. This is usually based on land use and demographic data for each zone.
Trip Distribution – matching each trip origin with a trip destination. This process results in the "trip table", a matrix of trips between zones.
Modal Choice – the estimation of how many of those trips will use automobiles, buses, trains and other modes. This results in a trip table for each mode.
Assignment – how those trips are routed through the transportation network, resulting in vehicle volume estimates for each roadway or passenger volumes on each transit route in the network.
The BPM differs in at least two major respects from those traditional models; it uses “microsimulation”, and it is a “journey-based” model. Instead of considering the aggregate trips at the zone level prior to trip
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-7
assignment, "micro-simulation" individually simulates every trip in each household in the region. With 9 million households in the New York region in 1996, and an estimated 25 million daily paired journeys, this was not possible until recent advances in computing power. Based on a household survey conducted in 1997 and 1998, the model creates a list of households, each with certain characteristics - size, employed persons, students, income, and auto ownership.
Instead of treating each trip individually, the BPM generates "journeys" from these households, linked trips that may include several stops. For example, a journey may include driving to work, then leaving on the way home from work, stopping off to shop, and then picking up a child. This single “journey” would be represented as four separate unrelated trips in traditional models. The advantage of a journey-based model is that the locations of intermediate stops can be based on the location of work and the location of home. Moreover, each household's journey affects the others. Thus, if in a one-car household one member uses the car for a trip, then all other household members will not be able to drive and will have to use transit, a taxi, or other modes for their trips.
These processes create a set of trip tables by several modes. Once the trip tables are in place, highway and transit assignments in BPM basically follow the same procedures as traditional four-step models.
Overview of Ridership Forecasting Methodology
The modeled region consists of 28 counties in the New York Metropolitan Area, including 14 counties in northern New Jersey and two counties in southwestern Connecticut (Figure 4-18). The counties are divided into 3,586 internal zones and 111 external stations (i.e., points where vehicles from outside the model area enter the model network). In Manhattan and other dense areas, the zones are typically equivalent to census tracts, and in some places are subdivisions of tracts.
Key Inputs
The major elements of the model include socioeconomic data by model zone (including forecasts for various years in the future), current and future highway networks, and current and future transit networks. The networks can then be modified to assess the impacts of transportation improvements.
Socioeconomic and Demographic Forecasts
The study utilized Socioeconomic and Demographic forecasts developed by NYMTC for each traffic analysis zone in the NYMTC region. These forecasts also included data for New Jersey, Connecticut, Orange and Dutchess counties. The NYMTC approved County level forecasts, of June, 2009 was the latest update available at the time the WHRTAS analysis was performed.
A summary of existing population and employment data with Year 2035 forecasts is illustrated in Chapter 1, Table 1-1. These forecasts show higher than average future population growth in Orange County and the Mid-Hudson region compared to population growth in the larger NYMTC region. Future employment growth is comparable to the NYMTC region.
Highway Network
The BPM highway network is derived from several networks that predated development of the model, among them NYMTC’s Interim Analysis Model (IAM) highway network, NJDOT’s TRANPLAN network, and ConnDOT’s network for New Haven County. There are about 40,000 highway links and an additional 13,000 links connecting the network to zones. All Interstate highways, state and US numbered routes, and parkways and most local arterials and collectors are coded in GIS format. Data is attached to each
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-8 Alternatives Analysis Phase I Screening Report
link describing roadway characteristics, such as number of lanes, access control, signal density and unconstrained speeds. Not every street is coded.
Certain highway lane configurations may vary by period and change during the day. This condition can be simulated by the BPM. This is significant, because for this study, it enables appropriate modeling of the reversible lane on the Tappan Zee Bridge. Auto and truck tolls can be specified separately.
All tolls and automobile operating costs have been updated to 2005 values. Future networks contain programmed improvements from the Transportation Improvement Program (TIP).
Transit Network
The BPM transit network is derived mainly from the Metropolitan Transportation Authority’s (MTA) Regional Travel Forecast Model (RTFM), and from the conversion of NJ Transit’s networks from MinUTP software into TransCAD. All commuter rail, subway, bus, and ferry routes in the region have been coded with routes, fares, schedules, and transfer locations. There are a total of 3,300 transit routes.
Transit Fares
For rail service, the commuter fare was assumed to be the equivalent of a one-way monthly rail pass fare. All other transit services assumed a flat fare for each service type. All fares were based on the equivalent of 2005 dollars. Air Passengers were assumed to pay the same fare as commuters.
Discounts on transfer fares can be represented in a relatively coarse manner – for example all Beeline bus riders can be charged a small surcharge to board any MTA bus route (or allowed to transfer for free). However a discount between specific bus routes and a commuter rail station cannot be represented.
Transit fares were adjusted to 2005 levels based on based on updated information from Metro-North and NJT, and expressed in 2005 dollars. Station-to-station fare matrices for Metro-North and NJT were updated accordingly.
Model Calibration and Validation
Transportation planning models are, by their nature, approximations of the actual travel behavior in the region. They are a means of estimating existing travel that can then be used to forecast future travel. Their success in estimating existing travel is determined by a process known as calibration, whereby the model components are adjusted until the estimated travel matches the actual travel well enough to be used as a forecasting tool. Even in the best of circumstances, it does not match perfectly, due to the many variables and complexities. Adjusting too much also could lessen the model’s responsiveness to change, hampering its applicability for future scenarios.
The BPM was developed by NYMTC during the period from 1996 and 2002. It was initially calibrated to the 1997 and 1998 Home Interview Survey conducted by NYMTC, which was factored back to a baseline year of 1996. This initial calibration effort is documented by NYMTC in its General Final Report: New York Best Practice Model (January 30, 2005). The major concerns in the NYMTC calibration process were the magnitude and modal distribution of travel to Manhattan, and the magnitude of travel crossing the Hudson River screenline.
As a result, the complexities of the West of Hudson Corridor were not fully accounted for in this version of the BPM. Therefore a calibration exercise was performed on an updated 2008 version of BPM released by NYMTC. The model was recalibrated to 2005 conditions to remain consistent with the NYMTC update.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-9
Major components of the recalibration effort included network coding to match 2005 conditions on the ground. Subsequently, model adjustment factors affecting trip distribution and mode choice were used to better match target calibration values. Those calibration targets included journey-to-work data from the census, as well as known ridership counts on transit lines, and total vehicles crossing major screenlines. This calibration is the basis for the model being used for WHRTAS.
After the model was calibrated for use in the WHRTAS corridor, further validation checks were made of the model’s ability to replicate current transit and commuter rail ridership levels in the corridor, and to replicate highway volumes on Hudson River crossings. In general, the re-calibrated BPM performed satisfactorily and was considered sufficient for use in evaluating the relative performance of future alternatives/options.
The operating assumptions of these alternatives are detailed in Appendix C and the transit ridership results in Chapter F.6.1. Figure F-7 compares bus mode share in specific markets reported in the model compared to observed Census journey-to work data. Figure F-8 illustrates the level of calibration attained for commuter rail mode share in key markets. Figure F-9 illustrates the level of calibration attained for auto mode share in key markets. Figures F-7 through F-9 illustrate that the project calibration results are close to the observed data in the relevant markets.
Model Application
Ridership results were forecast using the BPM for each Level 2 alternative group (Groups 1A, 1B, 2, 4, and 5). Within each alternative group individual alignment and service plan options were run only where the unique characteristics would likely have a significant impact on ridership. Refer to Chapter 4 for a physical description of each alternative.
The year 2035 No Build transportation network was assumed to include the infrastructure as described in Chapter 4. The travel demand and ridership forecasting includes an update of the socio-economic and demographic forecasts to the year 2035 using data provided by NYMTC, as summarized in Chapter 1.
A combination of the increase in population, employment, and more significantly the direct connection to Manhattan via ARC was expected to increase transit mode share between Orange County and Manhattan from 47 percent today to 65 percent in the year 2035 No Build. The transit mode share between Orange County and other New York City (NYC) destinations is expected to increase from 23 percent today to 31 percent in the year 2035 No Build.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-10 Alternatives Analysis Phase I Screening Report
Figure F-7 Comparison of Bus Mode Share
Figure F-8 Comparison of Commuter Rail Mode Share
Bus Share Target Vs. WHRTAS
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-11
Figure F-9 Comparison of Auto Mode Share
F5 Airport Modeling Methodology
The BPM does not model air passenger travel and therefore a separate approach for producing forecasts
of travel to and from SWF was needed. Air passengers have distinctly different mode choice preference
functions and decision contexts compared to most commuters and non-work travelers.
The changes in access to SWF being planned could result in significant shifts in the way air passengers
use the airport. New and improved transit service would provide more direct links between the airport and
the Mid-Hudson region, New York City, and the wider metropolitan area air travel market. With
substantially increased air service and improved ground accessibility, SWF could potentially provide a
viable travel alternative for air passengers who currently use the other large New York region airports.
One of the key elements of WHRTAS is to develop forecasts of the effects of both air service and ground
access changes on the SWF air passenger market and, ultimately, on air passenger ridership on a new
transit service.
The following summarizes the air passenger model3 and the stated preference survey
4 as presented in
separate reports. The air passenger model was designed to forecast the magnitude, by origin location,
airport and mode, of airport ground access trips by air passengers in the New York metropolitan region.
The Air Passenger Market in the New York Region
The air passenger model considered trips to the six major airports located within the district region
covered by the BPM, (John F. Kennedy (JFK), LaGuardia (LGA), Newark International (EWR), Stewart
3 West of Hudson Regional Transit Access Study – Air Passenger Model Documentation – March 2010
4 West of Hudson Regional Transit Access Study – Stated Preference Survey Report – April 2009
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-12 Alternatives Analysis Phase I Screening Report
International (SWF), White Plains (HPN), and Long Island‐Macarthur (ISP)). It also considered trips to
airports located outside the BPM district regions including Bradley International Airport (BDL),
Philadelphia International Airport (PHL), Albany Internal Airport (ALB), Lehigh Valley International (ABE),
Atlantic City International (ACY), and Trenton Mercer (TTN). However, the airport choice and airport
access mode choice models considered only the six airports within the BPM region.
The existing air service, in terms of the total number of flights and the mix of destinations served by those
flights, varies among the six airports included in the air passenger model. The daily domestic and
international air service at the six airports, taken from the Official Airline Guide (OAG) for November 2008,
is shown in Table F-1. The table shows that EWR and JFK each offer 33%, of the flights from the region,
LGA offers 29%, with the remaining 5% of flights split between the three smaller airports. The large
airports each serve a different mix of markets. JFK offers the majority of the transcontinental and
international flights from the region. LGA’s service is concentrated on closer destinations, offering many
of the flights from the region to the Mid Atlantic, the Southeast, the Upper Midwest, and the Lower
Midwest. EWR has a similar level of service to LGA to many of the domestic destinations while its
international service is closer to JFK. The three smaller airports concentrate on service to the South East
and also serve other destinations on the east coast and in the Upper Midwest.
Table F-1 Daily Scheduled Flights from New York Region Airports
Airport Domestic International Total
EWR 435 123 558
HPN 47 3 50
ISP 31 0 31
JFK 385 176 561
LGA 453 31 484
SWF 10 0 10
All Airports 1,361 334 1,695
In 2005, according to data from the Federal Aviation Administration (FAA), there were 51 million
enplanements at the six airports included in the air passenger model. Table F-2 shows the 2005
enplanements by airport, connection rates for Newark, LaGuardia, and JFK provided by the Port Authority
(the three small airports were assumed to have zero connection rates), and resulting originating
enplanements. The originating enplanements account for all of the air trips that began at one of the six
airports included in the air passenger model. Included are air trips with ground access trips that began
outside of the model region.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-13
Table F-2 2005 Annual Enplanements at New York Region Airports
Airport Enplanements Connection
Rate
Originating
Enplanements
JFK 20,260,359 0.245 15,296,571
EWR 16,444,959 0.280 11,840,370
LGA 13,014,314 0.098 11,738,911
ISP 1,055,832 0.000 1,055,832
HPN 462,256 0.000 462,256
SWF 199,741 0.000 199,741
All Airports 51,437,461 0.211 40,593,682
The spatial distribution of air passenger ground access trips with origins inside the model region was
derived using the 2005 FAA Regional Air Service Demand Study (FAA Survey). In total there were 38.9
million air passenger ground access trips with origins inside the model region and destination at the six
airports in the model region. Table F-3 shows the distribution of trip origins by nine districts within the
model region. Of the 38.9 million trips, 10.7 million trips originate west of the Hudson River, 4 million trips
originate east of the Hudson River and north of the Bronx, and 24.1 million trips originate in Manhattan,
the Bronx, and on Long Island. The choice of airport for air travelers from a particular origin is a function
of both the accessibility of the airport (measurable in terms of distance, travel time, travel cost and other
variables by various access modes) and the air service offered from the airport.
Table F-3 2005 Observed Airport Access Trip Origins by District and Airport
District5 JFK EWR LGA ISP HPN SWF Total
Bronx 359,496 29,079 410,141 5,125 2,521 0 806,363
E. Central NJ 335,530 3,106,148 105,307 4,100 840 182 3,522,107
E. New York 841,821 177,117 800,884 8,201 219,782 65,188 2,112,992
Long Island* 4,877,168 269,640 3,264,504 955,374 2,942 363 9,369,990
Manhattan 6,434,985 1,921,846 5,511,968 61,505 6,724 363 13,937,391
N. New Jersey
380,467 2,699,044 349,174 5,125 3,362 4,540 3,441712
NE New** Jersey
365,488 2,400,325 230,012 5,125 420 363 3,001,734
SW Connecticut
931,695 124,246 670,637 3,075 198,350 1,816 1,929,818
W Hudson NY
161,773 309,293 177,358 2,050 14,708 75,175 740,358
Total 14,688,423 11,036,737 11,519,984 1,049,682 449,649 147,990 38,892,465
* Includes Queens and Brooklyn
** Includes Staten Island
Two sources of data describe the current mode shares for air passenger ground access trips to the
airports in the model region: a customer satisfaction survey that is administered at LGA, JFK, and EWR
and the FAA Survey, for which mode shares are available for all six airports (Table F-4). For the large
5 See figure F-4
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-14 Alternatives Analysis Phase I Screening Report
airports, taxi is the most widely used access mode, followed by drop off and then drive and park. Travel to
LGA in particular is dominated by taxi, while travel by personal auto modes (including rental car) is more
important at EWR. JFK has the highest transit access shares of any airport (with the FAA survey
suggesting particularly high transit shares). At the smaller airports, drop off and drive and park dominate
the access modes, with rental cars and taxi secondary modes. Shares for shared ride and transit are
relatively low.
Table F-4 FAA Survey Mode Share by Airport
Mode JFK EWR LGA ISP HPN SWF
Drive and Park 4.9% 14.4% 7.7% 16.4% 23.6% 24.8%
Drop Off 27.6% 41.0% 26.6% 51.0% 42.8% 57.0%
Rental Car 3.4% 8.7% 3.9% 19.0% 14.1% 14.9%
Taxi 39.2% 24.7% 51.8% 7.4% 17.4% 3.1%
Shared Ride 7.6% 6.7% 5.6% 2.9% 1.5% 0.1%
All Transit 17.4% 4.5% 4.4% 3.4% 0.7% 0.1%
Model Overview
The air passenger model was designed to forecast the number of airport ground access trips (by air
passengers) in the New York metropolitan region number, by origin location, airport and mode. The air
passenger model is comprised of an airport trip generation model, an air passenger airport choice model
and an air passenger access mode choice model. The air passenger model inputs included forecasts
produced by the Port Authority of originating enplanements for each airport. The air passenger model
does not forecast enplanements; instead, it uses enplanement forecasts produced by the Port Authority
for each airport and is calibrated to match those control totals.
Trip Generation Model
The airport trip generation model was designed to take socioeconomic data as an input and forecast the
quantity and location of air traveler trip origins. The airport trip generation model uses socioeconomic and
land use data and the traffic analysis zone (TAZ) structure from the BPM. The forecasts of trip origins are
scaled based on airport enplanements to satisfy control totals of regional annual originating
enplanements provided by the Port Authority. The model was estimated using survey data obtained
primarily from the FAA Survey and secondarily from the 2005 New England Regional Airport System Plan
(NERASP) survey; and from socioeconomic and land use data from the BPM.
For the airport trip generation models, the air passenger market was segmented into four passenger
types developed during stated preference model estimation: Resident Non-Business (RN), Resident
Business (RB), Non-Resident Non-Business (NN), and Non-Resident Business (NB).
The number of future year trips was estimated by inputting the future year socioeconomic data into the
regression models and then scaling the results to forecasts of future year annual originating enplanements
from origins inside the model region. The socioeconomic forecasts for 2035 were developed by NYMTC.
Originating enplanement forecasts for 2035 for the six airports in the BPM region were developed by the
Port Authority. Table F-5 summarizes the future year total originating enplanements at each airport by
internal attractions, and Table F-6 shows the breakdown of internal attractions by trip type.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-15
Table F-5 Future Year Originating Enplanements - Internal Attractions*
Airport 2035 Originating Enplanements
JFK 25,229,298
EWR 16,210,848
LGA 13,591,181
SWF 3,103,623
ISP 2,290,037
HPN 1,415,873
All Airports 61,840,860
* From within the NYMTC BPM Region.
Table F-6 Future Year Internal Attractions* by Trip Type
Airport
Resident Business
Non-Business
Business Non-
Business Business
JFK 9,874,571 2,408,181 9,416,605 3,529,941
EWR 5,866,968 3,312,061 4,185,699 2,846,120
LGA 4,904,203 2,304,975 3,844,895 2,537,108
SWF 1,397,491 464,683 1,006,040 235,409
ISP 887,837 147,600 1,033,200 221,400
HPN 435,348 285,821 431,378 263,326
All Airports 23,366,418 8,923,321 19,917,817 9,633,304
* From within the NYMTC BPM Region.
Airport Choice and Mode Choice Model
The approach selected for developing the distribution of ground access trips to SWF and the mode
choices for those ground access trips was a joint destination choice and mode choice model, which is a
form of choice model that allows many variables to be considered in the choice of airport and mode. The
WHRTAS Stated Preference survey discussed on page F-16 found that variables such as air ticket price,
number of connections during the air trip, access and parking costs, and transit level of service all
affected airport choice and mode choice. Analysis of the choices made by respondents to the FAA Survey
showed that level of air service to flight destinations (or geographical groupings of destinations) explains
airport choice more accurately than simpler measures of airport size such as total number of flights.
A future year flight schedule was developed using a blend of two approaches: the first simply scaled the
number of current flights at airports other than SWF to match projected growth (based on forecasts
provided by the Port Authority) at these airports. For SWF, a more complex approach was taken. The air
service scenario developed for 2035 for SWF assumes that the domestic air service from SWF matches
the average service from BWI, MHT and PVD, and adds international air service to Transatlantic,
South/Central American and Canadian destinations to match the forecast split between domestic and
international enplanements.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-16 Alternatives Analysis Phase I Screening Report
Model Validation
The base year model output was validated by comparing the model output to various observed
distributions from the FAA Survey and other data sources:
Trip length distribution by airport
The number of trips that cross the Hudson River in accessing an airport
Airport shares by county
Mode shares by airport
Mode shares by county
Mode shares by trip type
In general, the base year model output matched closely to the observed data. Future year inputs were
used to develop preliminary future year forecasts to validate model performance. Like the base year
model, the future year model was adjusted to match the originating enplanement control totals by trip
type. Thus, the overall future year airport distribution is consistent with future year enplanement
assumptions provided by the Port Authority.
Stated Preference Survey
The purpose of the Stated Preference (SP) survey was to determine the sensitivity of travelers to
changes in air service at SWF Airport as well as changes in the access options serving the airport. The
sensitivity estimates could then be used to develop an air passenger model for use in forecasting demand
for transit service to SWF under a variety of possible future development scenarios.
Two SP studies were conducted to research the access alternatives and air traveler markets specific to
two geographic regions. The first – the Manhattan Study – evaluated plans to provide rail or bus transit
access to SWF from Manhattan and locations south of SWF. The second, the Mid-Hudson Study,
evaluated plans to provide express or local bus transit access to SWF from locations throughout the Mid-
Hudson region west, north, and east of SWF.
Survey Approach
The SP survey approach for both studies employed a computer-assisted self-interview (CASI) technique.
Customized software was programmed to develop the survey in a web-based format, which could be
used for intercept administration on laptop computers and online administration to targeted audiences via
email distribution.
For the Manhattan Study, the survey was administered to air travelers departing from Newark-Liberty
International Airport (EWR), John F Kennedy International Airport (JFK) and LaGuardia Airport (LGA).
Sample flights were selected for administration that was representative – by carrier, destination, and time
of day – of the total set of flights departing from each airport during the survey period. Intercepting
passengers on these flights provided an efficient means for collecting a representative sample of air
travelers and for acquiring sufficient survey response from key travelers segments, including business
travelers, non-business travelers, residents, visitors, and international travelers.
Administration of the Mid-Hudson survey was conducted by two methods: 1) intercept administration at
SWF and 2) online administration via an online member panel. Intercept administration at SWF took place
during the week and on the weekend, obtaining responses from travelers on all flights departing from
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-17
SWF. Online administration of the Mid-Hudson survey targeted residents throughout the Mid-Hudson
region, acquiring responses from a broad geographic sample of potential future users of regional transit
services accessing SWF.
Survey Questionnaires
A unique survey questionnaire was developed for the Manhattan Study and the Mid-Hudson Study. Each
questionnaire consisted of five sections:
Airport choice – questions regarding a specific air trip and airport choice decisions
Access mode choice – questions regarding airport access for the trip and access mode
choice decisions
Stated preference – exercise in which respondents selected a preferred airport and access
mode from various alternatives
Debrief – questions regarding choices in the stated preference section and general
opinions/attitudes
Demographics – questions regarding individual and household characteristics
Survey Administration
Data collection for the Manhattan Study took place at Newark Liberty International Airport (EWR), John F.
Kennedy International Airport (JFK), and LaGuardia Airport (LGA) in December 2008. Survey data were
collected by intercepting air passengers at departure gates from a representative sample of flights, with a
total of 1008 respondents completing the survey.
Online administration of the Mid-Hudson survey was conducted to acquire responses from individuals
using one of the four airports (ALB, BDL, HPN, or SWF). Email invitations with an embedded link to the
survey were sent to previously screened residents, with a total of 400 surveys completed online between
December 2008 and January 2009.
Survey Results
The Manhattan and Mid-Hudson Study data sets were vetted to ensure consistent and credible
responses. Manhattan Study respondents were then grouped into four segments for analysis and
modeling based on:
The direction of travel (flying away from home/flying to return home)
The purpose of the air trip (business or non-business)
The Manhattan respondent’s primary access mode (the mode used for the greatest amount of time during
the access trip) also served as a key attribute throughout analysis. Nearly half of respondents (47%)
reported an access trip made by taxi or limo, and 14% reporting a transit access trip (Table F-7).
For analysis and modeling of the Mid-Hudson Study data, responses were also grouped into two
segments based on the purpose of the air trip (business or non-business).
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-18 Alternatives Analysis Phase I Screening Report
Table F-7 Segmentation and Primary Mode – Manhattan Study
Segmentation Count Percent
Resident business 172 20%
Resident non-business 259 29%
Non-resident business 154 18%
Non-resident non-business 294 33%
Total 879 100%
Primary Mode Count Percent
Drive and park 80 9%
Drive and drop off 182 21%
Taxi/limo 416 47%
Transit 120 14%
Other 81 9%
Total 879 100%
Among travelers departing from Stewart International Airport (SWF), Albany International Airport (ALB),
Bradley International Airport (BDL), or Westchester County Airport (HPN), the majority (82%) accessed
the airport by personal vehicle – 38% driving and parking and 44% driven and dropped off (Table F-8).
Table F-8 Segmentation and Primary Mode – Mid-Hudson Study
Segmentation Count Percent
Business 212 35%
Non-business 393 65%
Total 605 100%
Primary Mode Count Percent
Drive and park 228 38%
Drive and drop off 264 44%
Taxi/limo 62 10%
Transit 3 0%
Other 51 8%
Total 605 100%
Model Estimation
Statistical analysis and discrete choice model estimation were carried out using the SP survey data
segmented in the Manhattan Study by residency (residents and visitors) and purpose (business or non-
business) and segmented for the Mid-Hudson Study by purpose (business and non-business). For
Manhattan Study respondents, sensitivity to the air and access trip costs were shown to vary by
residency and trip purpose, and within segments, to vary by household income. Sensitivity to air and
access trip travel times were also shown to vary by traveler segment, while rail was shown to be the
preferred transit mode for accessing SWF across all segments.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-19
For Mid-Hudson Study respondents, sensitivity to costs and travel times were demonstrated to be similar
across purpose segments, Sensitivity to air fare cost was lower for business travelers as was sensitivity to
taxi and transit travel times (modes that allow productive use of travel time), while sensitivity to access
cost was slightly lower for non-business travelers (a segment making less frequent air trips). Express bus
transit access to SWF was not shown to be preferred (with statistical significance) to local bus service.
F6 Airport Employee Model
Although the BPM can include employees in its model, the airport employees were modeled separately
due to their characteristics being different from other commuters. For example, airport employees work in
shifts and do not necessarily commute during regular peak periods. Projected employment at SWF was
provided by the Port Authority.
Table F-9 illustrates the 2000 mode share for the three airports used to estimate the airport employee
mode choice model, and mode choice at SWF, which was predominantly accessed by highway.
Table F-9 Mode Share in terms of Auto and Public Transit in Four Airports
Airport
Highway
(Drive alone,
Shared rides)
Transit
(Subway, Bus,
Rail, Ferry)
Total Reporting
Employee
LGA 80% 20% 3,783
JFK 82% 18% 32,025
EWR 74% 15% 20,663
SWF 100% 0% 1,346
Table F-10 presents the observed employee mode share by airport vs. forecasted mode share. In
general, the transit is slightly over estimated by 2% for each airport.
Table F-10 Mode Share by Airport: Observed vs. Forecast
Airport Observed Forecast
Auto Transit Auto Transit
JFK+EWR+LGA 83% 17% 81% 19%
JFK 82% 18% 80% 20%
EWR 85% 15% 83% 17%
LGA 77% 23% 76% 24%
Elasticity
The elasticity of the model6
was tested in terms of the change of transit share with respect to fare, in-
vehicle time (IVT) and out-of-vehicle time (OVT). Overall, the fare elasticity falls within a range of -0.11 to
-0.25, which is consistent with other studies regarding fare elasticity in New York City.
6 The elasticity reflects how an explanatory variable X could affect the change of Y. It is defined as percentage change of Y divided
by the percentage change of X.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-20 Alternatives Analysis Phase I Screening Report
F7 Transit Ridership Analysis and Results
The following section presents an overview of the transit ridership results for the commuter and airport
markets. The analysis to estimate ridership was performed using the NYMTC BPM in conjunction with an
Airport Passenger Model and an Airport Employee Model as described in previous sections. Given the
differences in the methodology approach between the models, the ridership forecasts for the commuter
market and airport market are presented separately.
The following alternatives were modeled:
a. The WHRTAS No-Build
b. Group 1 – All Direct Bus and BRT Alternatives that advanced from Level 1 screening (see
Section 4.1.1 and 4.1.2 for details)
c. Group 2 - Direct CRT Alternatives – Breunig Road and Aqueduct Balsam alignments7 (see
Section 4.1.3 for details)
d. Group 4 - Hybrid - Salisbury Mills-Cornwall – Bus in mixed traffic and the BRT option (see Section
4.1.4 for details)
e. Group 5 - Hybrid - Beacon Alternatives – Bus in mixed traffic (see Section 4.1.5 for details)
For each of the alternatives, information on the following key indicators were summarized: commuter
mode share for the AM peak period from the “Stewart District” (Traffic Analysis Zones selected in a radius
of 6 to 10 miles around SWF) to Manhattan Central Business District (CBD) (all of Manhattan south of
59th Street); daily commuter ridership; and daily air passenger ridership.
Assumptions:
Commuter Market:
1. The BPM forecasts transit ridership results for only the AM peak period (6AM -10 AM). In order to
obtain daily numbers, the AM ridership is multiplied by a factor of 2.86, provided by Metro-North.
The factor represents the difference between peak period and all day and assumes two-way
commuter ridership on the transit route.
Air Passenger Market:
1. The modeling process assumed that in 2035 SWF would handle 7 million8 passengers (3.5
million annual arrivals and the same number of annual departures, including connecting flights).
7 The Eastern Alignment and the Aqueduct Reed Alignment were not modeled. The ridership for these alignments were estimated
based on results observed for the Aqueduct Balsam and Breunig alignments respectively. 8 Port Authority supplied originating enplanement forecasts for 2035 for the six airports in the BPM region. Port Authority staff also
provided a 2035 forecast for SWF of 7 million annual passengers, equally split between arriving and departing travelers. Port Authority and the WHRTAS team also posited a future flight schedule of domestic and international destinations and airfare assumptions potentially capable of attracting residents and visitors otherwise likely to use the region's other airports. These assumptions and time frame go beyond projections of SWF activity cited elsewhere by Port Authority. However, they are consistent with WHRTAS goals and objectives and Port Authority's SWF redevelopment strategy, which both recognize the relationship between enhanced transit access for SWF and its potential to emerge as a significant alternative for air travelers in the wider metropolitan region. This "aspirational" forecast of SWF volumes provides a sufficient base of potential users for the transit alternatives to support a meaningful analysis and comparison of the performance of the airport transit "build" alternatives. This approach supports long-term planning for sustainable SWF development by measuring the potential value of a SWF transit link to the PJL even beyond the WHRTAS AA time horizon, especially given expressed concern over the potential loss of rights-of-way to ongoing development in the area.
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-21
This assumption was derived by the Port Authority for planning purposes as an ‘aspirational’
growth level.
2. Air passenger service at SWF assumed domestic air service similar to the average service at
Baltimore Washington International Airport (BWI), Manchester-Boston Regional Airport (MHT),
T.F. Green International Airport (PVD), and international air service to Transatlantic,
South/Central American and Canadian destinations to match the forecast split between domestic
and international enplanements.
3. The Air Passenger Model developed for this project produced annual air passenger trips. A factor
of 1/365 was used to convert from annual to average daily trips.
F7.1.1 Commuter Ridership9
Figure F-10 summarizes the highway, CRT, and bus travel mode shares for the AM peak period for trips
between the Stewart District and the Manhattan CBD for the WHRTAS No-Build and Build alternatives.
Travel mode share is the percentage each mode represents of the total estimated trips for each
alternative. Based on the proposed improvement to either commuter rail or bus services, all alternatives
succeed in increasing the mode share for their focused improvement. For example, the Direct Rail
Alternative has the highest mode share of commuter rail. Similarly bus and bus rapid transit is expected
to experience the greatest bus mode share increase, approximately 6 percent higher than the WHRTAS
No-Build Alternative.
Figure F-10 Commuter Mode Share (AM Peak Period) – Stewart District to Manhattan CBD
Figure F-11 summarizes the daily commuter ridership by alternative, which represents the total trips
expected for each alternative. The year 2035 No-Build daily commuter ridership is estimated at 18,800.
Almost all alternatives provide for increased capacity to serve trips as shown by an increase in the total
daily commuter ridership, with the exception of the Salisbury Mills and Beacon Shuttle Hybrid alternatives.
Under both the bus and BRT alternatives, the combined daily transit (bus and rail) ridership is estimated
9 Assumes implementation of ARC
2035 No Build
Bus BRT Direct
Rail SMC
Hybrid Beacon Hybrid
HWY 17% 15% 15% 13% 17% 16%
CR 63% 60% 58% 69% 63% 61%
Bus 21% 24% 27% 18% 20% 23%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Mo
de
Sh
are
Commuter Mode Share (AM Peak) - SWF District to Manhattan CBD
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-22 Alternatives Analysis Phase I Screening Report
to be 19,300 trips. The BRT alternative would generate 4,100 commuter trips compared to 3,400 trips for
the Bus Alternative. This indicates that in the Orange to Manhattan CBD transit market, both the Bus and
BRT alternatives would divert riders from the CRT service.
Note: In the No-Build – Bus service is assumed from the 17K park and ride to the Port Authority Bus Terminal. Rail service is assumed on the PJL.
Figure F-11 Daily Commuter Ridership by Alternative
F7.1.2 Airport Ridership
Figure F-12 present the mode share to SWF by airport users by alternative from the New York County
District. Based on the proposed improvement to either commuter rail or bus services, all alternatives
succeed in increasing the mode share for their focused improvement. However, both the CRT and bus
alternatives capture the highest mode share percentages compared to the WHRTAS No-Build Alternative.
Figure F-12 2035 Forecast Airport Users - Transit Mode Share –
New York District to SWF by Alternative
2035 No Build
CRT Base Build
bus SMC Hybrid
BRT Beacon Hybrid
Series1 12.3% 33.4% 34.2% 23.7% 24.1%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Mo
de
Sh
are
Transit Mode Share to SWF by Altenative
2035 No Build
Bus BRT Direct
Rail SMC
Hybrid Beacon Hybrid
Bus* 2,400 3,400 4,100 2,500 2,500 2,500
Rail** 16,400 15,900 15,200 20,400 16,400 16,000
Total 18,800 19,300 19,300 22,900 18,900 18,500
-
5,000
10,000
15,000
20,000
25,000 R
ide
rsh
ip
Daily Commuter Ridership by Alternative
NY County Transit Mode Share
to SWF by Alternative
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
Alternatives Analysis Phase I Screening Report F-23
Figure F-13 show the number of total daily trips of airport users by rail and bus (to and from SWF) by
alternative. Figure F-14 show the number of total daily trips of airport users by rail only (to and from SWF)
by alternative and by rail only. (For additional details see section 4.4.2.2)
Figure F-13 Airport Users Mode Share – New York District to SWF
Figure F-14 Airport Users Mode Share – New York District to SWF
2035 No Build
CRT Base Build
bus SMC Hybrid
BRT Beacon Hybrid
Series1 351 1445 1360 975 1045
0
200
400
600
800
1000
1200
1400
1600
Rid
ers
hip
Daily Airport Rideship to/from SWF
(Rail and Bus) by Altenative
2035 No Build
CRT Base Build
bus SMC
Hybrid BRT
Beacon Hybrid
Series1 320 1114 710 780 670
0
200
400
600
800
1000
1200
Rid
ers
hip
Daily Airport Rideship to/from SWF (Rail only) by Altenative
* Estimated
Daily Airport Ridership to/from SWF
(Rail and Bus) by Alternative
Daily Airport Ridership to/from SWF
(Rail only) by Alternative
Appendix F TRAVEL DEMAND AND RIDERSHIP FORECASTING
F-24 Alternatives Analysis Phase I Screening Report
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