Discussion-piece #8

CTPP-based Aggregate Model

Federal Transit Administration

June 6, 2006

1.  [Motivation.]{.underline} Unlike conditions 25 years ago in the
    early days of the New Starts program, today there are many recently
    built rail lines and other transit guideways -- many in locations
    outside the very largest metropolitan areas where rail transit has
    been in continuous operation for nearly a century. As part of
    efforts to support quality control testing of forecasts for new
    proposals, FTA has sponsored research on the ability of completed
    projects to supply insights on ridership potential through simple,
    easily applied ways. This research has produced the Aggregate Rail
    Ridership Forecasting Model (yes, the AARF model) that uses data
    from the Census Transportation Planning Package (CTPP2000) to
    predict unlinked rail transit trips for light rail and commuter rail
    systems. This model is intended by FTA as a way for project sponsors
    to develop order-of-magnitude estimates of ridership for new rail
    lines in metropolitan areas where no existing fixed guideway transit
    facilities are present -- often called "new" New Starts. Forecasts
    from the model are not intended to replace carefully prepared
    forecasts from local travel models; rather, they provide another
    source of insights into the reasonableness of those local forecasts.
    Careful thought on differences between predictions from the AARF
    model and from local models can help inform the discussion of the
    reliability of the local estimates.

2.  [Overview.]{.underline} The AARF model has been calibrated against
    ridership on existing systems throughout the country that are
    generally similar to proposed "new" New Starts. Because these
    proposals are generally in growing cities without an extensive
    history of fixed guideway transit, the calibration excluded light
    rail systems in the very largest metropolitan areas and those that
    have been in operation for many decades. Similarly, the calibration
    has excluded commuter rail systems where they are part of a large
    network that has been in operation for many decades. The model
    applies a series of expected rail shares to the total work-travel
    flows (by any mode) found in CTPP2000 in the rail corridor. In
    calibration with rail systems that existed in 2000, the flow data
    represented development patterns and travel that may have been
    encouraged by the rail line itself. For proposed systems, the
    resulting model provides a ridership estimate based on "current"
    (year 2000) development and travel flows. Estimates of future-year
    ridership should either be adjusted to represent expected growth in
    the corridor or used as part of the calibration process to adjust
    conventional travel forecasting models.

3.  [CTPP data.]{.underline} The basic inputs to the model are the
    CTPP2000 work-flow data, disggregated by auto-ownership class and
    employment density at the work end. To identify the travel markets
    served by a rail line, the model uses a series of concentric buffers
    around each rail station. Workers traveling between residence and
    workplace locations that are [both]{.underline} within station
    buffers establish the overall markets from which rail riders are
    drawn. In essence, these data provide an estimate of the total
    market for a candidate rail line and the remainder of the model
    provides information on the typical number of rail trips generated
    by these flows.

4.  [Level-of-service data.]{.underline} For the LRT version of the
    model, the only necessary supplemental information is the
    directional route miles for the service. The model uses this
    variable to generate an alternate estimate of ridership based solely
    on route miles. For the commuter rail version, the model uses a
    broader set of characteristics:

-   Annual revenue vehicle-miles;

-   Annual revenue vehicle-hours (used with miles to generate average
    speed);

-   Average consist length;

-   Weighted operating days per year;

-   Directional route miles (used with preceding items to compute train
    miles per route mile);

-   Flag indicating whether the system connects or does not connect to a
    rail distributor (i.e., LRT or rapid transit line connecting the
    commuter rail station to downtown destinations)

5.  [Ridership data used for model calibration.]{.underline} Wherever
    possible, model calibration used Year 2000 ridership and rail
    stations (to define the rail corridors) to preserve consistency with
    Year 2000 CTPP data. In several cases, however, more detailed
    information (i.e., trips by purpose or mode of access) was known for
    survey years other than 2000. In those cases, calibration relied
    upon the ridership and station locations for the survey year rather
    than the Year 2000. In cities where the system was extended during
    2000 (or the system was not yet opened), ridership for another year
    was used so that the data unambiguously represents the stabilized
    volume associated with the system being modeled. Table 1 summarizes
    the selected data used to calibrate the LRT model. Table 2
    summarizes the commuter rail data.

    Table 1. Ridership Data Used for Weekday LRT Model Calibration

    ![](media/image1.wmf){width="6.995833333333334in"
    height="1.8458333333333334in"}

6.  [Calibration Approach.]{.underline} Data on a variety of station
    buffer distances, purpose segmentation, and access mode
    segmentations were tested and the models that generated the highest
    regression coefficients with generally explainable coefficients
    signs and magnitudes were selected as the final model. In the case
    of commuter rail, the wide range of service levels offered by the
    different systems had a significant impact on ridership. Two level
    of service variables (average speed and average weekday train miles
    per weekday directional route mile) were defined and demand was
    adjusted up or down by comparing the system-specific values to the
    nationwide average and an assumed elasticity of +0.3. Thus, systems
    with 10 percent faster average speeds would generate 3 percent more
    ridership. Systems with 100 percent more train miles per route mile
    (i.e., twice as much service) would generate 30 percent more
    ridership. Finally, based on one case (Seattle) where no connecting
    rail service was available to the CBD, a rail connection variable
    was tested. Similar level-of-service adjustments were not found to
    be necessary for the LRT model. A possible reason is that existing
    LRT systems are more alike in fundamental service levels than the
    different commuter rail systems.

    Table 2. Commuter Rail Data Used for Weekday Model Calibration

    ![](media/image2.wmf){width="6.990277777777778in"
    height="2.59375in"}

7.  [Final LRT Model.]{.underline} The final LRT Model is documented
    below. Table 3 and Figure 1 present the performance of the model.

> Weekday Unlinked
>
> Drive Access to Work
>
> Rail Trips = 0.030 \* CTPP PNR 6 -to-1 Mile JTW Flows (\<50K Den) +
>
> 0.202 \* CTPP PNR 6 -to-1 Mile JTW Flows (\>50K Den)
>
> Weekday Unlinked Other
>
> (Non-Drive Access to Work)
>
> Rail Trips = 0.395 \* CTPP 2 -to-1 Mile JTW Flows (\<50K Den) +
>
> 0.445 \* CTPP 2 -to-1 Mile JTW Flows (\>50K Den)
>
> Total Weekday Unlinked Rail Trips
>
> Rail Trips = Weekday Unlinked Drive Access to Work Rail Trips +
>
> Trips Weekday Unlinked Other Rail
>
> Where:

-   CTPP PNR 6-to-1 Mile JTW Flows (\<50K Den) is the total JTW flow for
    cases where home is within 6 miles of a rail station with Park-Ride
    facilities, work is within 1 mile of any rail station, and the
    worker density (from the CTPP) at the work end of the journey is
    less than 50,000 workers per square mile.

-   CTPP PNR 6-to-1 Mile JTW Flows (\>50K Den) is the total JTW flow for
    cases where home is within 6 miles of a rail station with Park-Ride
    facilities, work is within 1 mile of any rail station, and the
    worker density (from the CTPP) at the work end of the journey is
    greater than 50,000 workers per square mile.

-   CTPP 2-to-1 Mile JTW Flows (\<50K) is the total JTW flow for cases
    where home is within 2 miles of any rail station, work is within 1
    mile of any rail, and the worker density (from the CTPP) at the work
    end of the journey is less than 50,000 workers per square mile.

-   CTPP 2-to-1 Mile JTW Flows (\>50K) is the total JTW flow for cases
    where home is within 2 miles of any rail station, work is within 1
    mile of any rail, and the worker density (from the CTPP) at the work
    end of the journey is greater than 50,000 workers per square mile.

    Table 3. Comparison of Observed and Modeled LRT Ridership

    ![](media/image3.wmf){width="5.633333333333334in"
    height="2.6284722222222223in"}

    Figure 1. Comparison of Observed and Modeled LRT Ridership

    ![](media/image4.wmf){width="6.99375in"
    height="4.120138888888889in"}

8.  [Final Commuter Rail Model.]{.underline} The final Commuter Rail
    Model is documented below. Table 4 and Figure 2 present its
    performance.

> Commuter Rail Weekday
>
> Unlinked Trips = Nominal Ridership x Demand Adjustment Factor
>
> Where:
>
> Nominal Ridership = 0.069\*High Income CTPP Flows within 6 miles of a
> PNR station at the home end and 1 mile of any station at the work end
> of the trip +
>
> 0.041\*Medium Income CTPP Flows within 6 miles of a PNR station at the
> home end and 1 mile of any station at the work end of the trip +
>
> 0.151\*Low Income CTPP Flows within 2 miles of any station at the home
> end and 1 mile of any station at the work end of the trip
>
> Demand Adjustment Factor = (1+0.3\*Percent Deviation in Average System
> Speed) x
>
> (1+0.3\*Percent Deviation in Train Miles per Mile) x
>
> Rail Connection Index
>
> Where:
>
> Percent Deviation in
>
> Average System Speed = [System Average Speed-35.7 mph]{.underline}
>
> (System Average Speed+35.7)/2
>
> System Average
>
> Speed = Annual Revenue Vehicle Miles/Annual Revenue Vehicle Hours
>
> Percent Deviation in
>
> Train Miles per Mile = [Weekday Train Miles per Directional Route
> Mile-10.3]{.underline}
>
> (Weekday Train Miles per Directional Route Mile+10.3)/2
>
> Weekday Train Miles per
>
> Directional Route Mile = Annual Revenue Vehicle Miles/250/Average
> Train Length
>
> Rail Connection Index = 1.0 if commuter rail line connects to an urban
> rail line providing distribution to the CBD, otherwise 0.5
>
> High Income = Annual Household Income greater than or equal to
> \$60,000
>
> Medium Income = Annual Household Income greater than or equal to
> \$25,000 and less than \$60,000
>
> Low Income = Annual Household Income less than \$25,000

Table 4. Comparison of Observed and Modeled Commuter Rail Ridership

![](media/image5.wmf){width="3.3666666666666667in"
height="1.9416666666666667in"}

Figure 2. Comparison of Observed and Modeled Commuter Rail Ridership

![](media/image6.wmf){width="6.997222222222222in"
height="3.9006944444444445in"}

9.  [Implementation.]{.underline} FTA will conduct tests and prepare
    example applications of the AARF model during the remainder of 2006.
    Beginning in January 2007, FTA will ask sponsors of potential "new"
    New Starts projects seeking to enter preliminary engineering to
    generate AARF-model-based aggregate forecasts and compare those
    estimates with local forecasts. Discussions of differences between
    the aggregate forecasts and locally prepared forecasts will help to
    inform consideration of the forecasts. The purpose of the aggregate
    model is simply to add information to setting that often have little
    current information -- or ridership patterns -- to inform
    conventional models sufficiently to support reliable forecasts of
    fixed-guideway ridership.