Appendix F Travel Demand and Ridership Forecasting...

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


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.



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



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



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



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



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



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.



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.



Figure F-7 Comparison of Bus Mode Share

Figure F-8 Comparison of Commuter Rail Mode Share

Bus Share Target Vs. WHRTAS



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



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.



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



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.



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.



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



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).



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.



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.



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.



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



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



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



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Appendix F Travel Demand and Ridership Forecasting...

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