FTA PPT Template

FTA PPT Template

Simplified Trips-on-Project Software 15 th presented at the TRB Conference on Planning Applications Atlantic City, NJ May 17, 2015 Agenda 1. STOPS and the FTA Capital Grant Investment Program 2. How STOPS works 3. How to work STOPS (abbreviated version) 4. How to work STOPS (detailed version) 5. Experiences of early STOPS adopters 2 1. STOPS AND THE FTA CAPITAL INVESTMENT GRANT

PROGRAM Trips-on-Project forecasting Topics FTA motivations Options for project sponsors Implications for FTA reviews Availability of STOPS and tech support Plans for upgrades and extensions 4 FTA motivations Streamlining Project-evaluation measures Mobility trips on project (total, transit-dependent) Environment change in auto vehicle-miles traveled Cost effectiveness project cost per trip on project Travel forecasting FTA to provide a simplified method

Simplified method to be good enough Reductions in level of effort (sponsors and FTA) 5 FTA motivations (continued) Resulting design standards for STOPS Focus on the purpose: trips on major-capital projects STOPS Urban fixed-guideways: BRT, streetcar, LR, CR, HR Use readily-available inputs Do not require any primary data collection Rely on public, standardized data sources where possible Keep it simplified for users Provide a graphical user interface Limit the number of switches, levers, and dials

Make it reasonably accurate Calibrate with data on many existing systems/lines Adjust to local conditions Operate on Windows-based computer with no additional software requirements 6 Options for project sponsors Project sponsors may prepare forecasts with: Augmented with Regional travel models Incremental models STOPS special market models, as needed

Sponsors may provide to FTA forecasts Augmented with from: Regional travel models only Incremental travel models only STOPS only Any combination of the above special market models, as needed 7 Implications for FTA reviews FTA review of formally submitted forecasts Source of forecast

Transit rider survey data Properties of the travel model Validation vs. current riders Plausibility of forecasts Regional model Incremental model STOPS Substantial scrutiny Modest scrutiny Limited scrutiny Note that these reviews pertain

to formally submitted forecasts. They do not reflect any technical assistance that FTA may have provided to sponsors during the development of forecasting methods or forecasts. 8 FTA reviews of STOPS-based forecasts fall 2014 Procedure Triage review Consistency of STOPS implementation with current recommendations Plausibility of predictions of key ridership characteristics and trips on the project Decision on detailed review Detailed review Potential sources of uncertainties

STOPS rerun(s) to test possible implications of uncertainties Decision on further work with the project sponsor 9 FTA reviews of STOPS-based forecasts fall 2014 Outcomes for the class of 2014 (7 proposed projects using STOPS) Triage reviews: 7 Detailed reviews, reruns:4

Discussion with sponsor: 3 Work with sponsor: 2 Revised final predictions:3 (one +, one , and one ) Effort Two months; FTA staff and contractor Deadline imposed by schedule for annual report to congress For 2015, timelines for early submittal of ridershiprelated materials Prepared with locally developed forecasting methods Prepared with STOPS 10 Availability of STOPS and technical support Download from the FTA website http://www.fta.dot.gov/grants/ 15682_15620.html Software and User Guide Sample application Census data including the 2000 CTPP

Request help (after good-faith local effort) FTA contact information on STOPS page 11 2. HOW STOPS WORKS Overview 2.0 How STOPS works 2.1 2.2 2.3 2.4 An overview Evolution of STOPS Some details of STOPS v1.50 Tests against national ridership experience

13 2.1 AN OVERVIEW General characteristics Modified 4 step trip-based model Highway impedances and SE inputs from regional models maintained by MPOs Transit paths and impedances directly from transit schedules in GTFS format Trip generation and distribution replaced by CTPP seed matrix used to develop person trip tables Standard nested logit mode choice model Automatic calibration User-specified region-wide unlinked transit trips Transit shares by attraction district from CTPP Optionally, observed transit boardings by station group 15 STOPS components

16 Key characteristics Person trips CTPP replaces trip generation and distribution (for ALL trip purposes) Focus on transit-candidate person trips No special markets Transit GTFS replaces coded abstract transit network Schedule-based path-builder replaces traditional path-builders Highway No internal representation of the highway network No assignment, loadings, or loaded conditions One model to describe transit behavior in dozens of metro areas and differences in actual ridership before and after project implementation in some areas 17 2.2 EVOLUTION OF STOPS

Evolving understanding of realities Initial vision: Single purpose: predict trips on the project ARRF with variable access-mode-specific catchment areas No applications where transit-ready models already exist Small projects in smaller metro areas Transparency more important than processing efficiency No requirement for commercial software for travel forecasting, GIS 19 Evolving understanding of realities Evolved vision Priority FTA support for project-trip forecasts; help with other uses ARRF-like approach inadequate to the task of projectlevel forecasts

Applications in metro areas with well-developed travel models Projects of all sizes in metro areas of all sizes Processing efficiency crucial for huge problem sizes Still no commercial software, but looking for a GTFS editor 20 Learning from early applications Unintended situations arose requiring additional error detection NHB components generated too many trips between and within geographically large or separate CBDs

Single departure time (7:30 am) resulted in: Volatile results for longer headway services Peak service for very long (e.g., commuter rail) trips occurred before assumed departure time Station-group factoring may have misled forecasts for new stations Processing too slow and capacity too limited for very large metro areas Large CTPP zones in some metro areas distorted forecasts, maybe by a lot 21

Updating the software Version Version Version Version 1.01 1.02 1.03 1.50 official release in September, 2013

official release FTA in February, 2014 various interim releases throughout 2014 official release on May 6, 2015 New non-home-based components Higher limits on number of zones and stations Revised path-builder Revised coefficients and nesting structure for mode choice model Revised national calibration Expanded options for local calibration against station-group counts Additional output tables consistent with CGI reporting instructions 22 Upgrades and extensions STOPS v2.01 (beta release anticipated winter, 2015-2016) Update to CTPP from the American Community Survey Portal for special-market trips from external methods Options for easier GTFS editing

Steps Incremental method based on local rider-survey data Several components from STOPS GTFS transit representation and schedule-based pathbuilder Growth factoring over time with population/employment projections Highway impedances from the regional travel model 23 2.3 SOME DETAILS OF STOPS V1.50 Details of STOPS v1.50 Components Highway components Transit network Travel demand and adaptations Mode choice Automatic adjustments Special markets 25

STOPS components 26 Highway components Highway impedances Zone-to-zone from the regional travel model Current year (and future years, if needed) Calculation of change in person-miles of highway travel (PMHT) For each year of interest PMHT(I,J) = [highway person-trips(i,j) x highway distance(i,j) + auto-access trips(i,j) x auto-access distance(i,s)] Where: I, i = production district and zone J, j = attraction district and zone s = stop used for park-ride or kiss-ride access = sum over all i,j within each I,J PHMT change(I,J)

PMHT(I,J) = Build PMHT(I,J) minus No-build27 STOPS components 28 Transit network General Transit Feed Specification (GTFS) Key attributes Current-year data on actual service! Vetted by transit agencies (with inputs from AVLs, operators, and riders)! No aggregation (headways) into coded networks with uncertain runtimes! Download (sometimes public; more often from transit agencies) Edits to represent: Current-year No-build (if needed) Current-year Build (including adjustments to current routes)

Future-year(s) No-build and Build (if considered) 29 Transit network Designation of stations Aggregation of stop-IDs (train platforms, bus bays, directional stops) Degree of vertical separation (for inclusion in impedances) Flags on new stations (that are part of the project) Counts (for use in local calibration) Designation of park-ride lots Location (that STOPS links to proximate stops) Type (that defines the catchment area) Cost (in minutes to represent parking price, shadow price, vertical separation, others) 30 Transit network (continued)

STOPS generation of access, egress, and transfer connections Access, egress: For each zone centroid and each eligible transit stop Walk: airline distance +10% at 3 mph to/from useful stops within 1.0 miles KnR: airline distance at 25 mph to any stop within 3.0 miles or a park-ride lot (using the park-ride rules); and PnR: airline distance at 25 mph to user-designated parkride lots within catchment distance specified for the lot (3, 6, 10, or 25 miles depending on the nature of the lot) Transfers: For any pair separated by <0.25 airline miles Stop-to-stop or PnR lot-to-stop 31 Transit path-building in STOPS GTF Path Paths and impedances Two time periods AM peak (one of six possible arrival times 8:00 and 9:00 am) Midday (one of six possible arrival times between 1:00 and

2:00 pm) Three access type Walk Kiss-Ride Park-Ride Three path types FG only Bus only FG-bus 32 Transit path-building in STOPS GTF Path (continued) Schedule-based path selection Motivated by required arrival time (no more than 5 minutes late) No headways; instead: Actual individual vehicle trips Actual vehicle-arrival times at stops (based on actual running times) Generalized cost (in weighted minutes) for selection of best path

In-vehicle time (weight = 1.0 minute per minute) Walk time (weight = 1.1 minutes per minute) PnR and KnR time (weight = 1.5 minutes per minute) Wait time (weight = 1.0 minutes per minute) Wait time Transfers: Actual waiting time between vehicle-1 arrival and vehicle-2 departure Minus any stop-stop walk time (to avoid double-counting those minutes) Arrival-time difference: absolute value of desired-minus-actual arrival times First wait: none (perfect scheduling of departure) Boarding (5.0 minutes each) to account for uncertainties and inconvenience 33 Transit path-building in STOPS GTF Path (continued)

Attributes skimmed from each best path Origin-zone and destination-zone Origin access mode Origin access time Transfer walk time For Destination egress (walk) time mode

First wait time plus arrival-time difference choice Transfer wait time Number of boardings In-vehicle time (fixed guideway and bus, separately) Indicator of travel on the project For transit Path details (for up to four transit boardings) trip loading Boarding and alighting stops and Mode (GTFS type) summaries GTFS trip_id 34 Transit path-building in STOPS GTF Path (continued) Sample path report 35

Transit-trip loading Immediately after mode choice for each production-attraction pair Have: transit trips by Auto ownership Trip purpose Origin access mode Path type Accumulate: across each transit boarding, stratified by transit-trip characteristics Daily total of boardings on each route by origin mode of access Daily total of boardings at each station by station mode of access Station-station flows for project-related stations

36 STOPS components 37 Travel demand Topics CTPP Demographics Adaptations HBW HBO NHB Cloning zones Growth factoring

Mode choice Reporting 38 CTPP (2000, for now) Workers by: Residence location and workplace location, and Household auto ownership, and Usual main mode to work User selections to control CTPP data acquisition by STOPS Geography: the single type of geography that STOPS will use Tracts good, because tracts are defined everywhere Block groups better, because they are smaller but used only where selected by MPO/Census Bureau TAZs best, because designed for forecasting but used only where selected by MPO State(s), up to three

MPO, if geography is TAZs or block groups 39 Demographics GIS layer that includes: MPOs TAZ boundaries in latitude/longitude coordinates Must be consistent for all MPO-sourced files Demographics for every year of interest Highway time/distance file with current year, three horizon years Do not have to be consistent with boundaries of census zones For up to five years (2000, current year, three horizon years) Population in each TAZ Employment in each TAZ 40

Sample demographic file 41 Adaptations Why the context demands adaptability The kinds of adaptations made on the demand-side of STOPS 42 Adaptations (continued) Transit survey (in calibration cities) CTPP worker flows by i, j, mode Transit supply by i, j, path type

Person trips that are transit candidates Non-work mode shares Existing transit trips using each transit service (and ultimately the project Transit paths What we know What we must invent What we need Adaptations: Trip rates Trip rates: purpose-specific trips per CTPP worker(i,j)

Derived from NCHRP Report 716 (Quick Response #2), 1997 HBW trips Constant trip rate t(a) for each auto-ownership class Calibration parameter C for all auto-ownership classes Calibrated so that Normalized CTPP transit share x [C x t(a) x CTPP workers(a)] = linked HBW transit trips in rider-survey datasets Where the CTPP transit share is normalized to match: User-provided HBW linked trips on transit, or User-provided unlinked trips (assuming 40% HBW and 1.4 boards/linked trip) HBO and NHB trips Computations similar to HBW trips but: Scaled to shares of travel by purpose reported in NCHRP 716 Decayed with increasing distance Calibrated with same approach as HBW trips Transit.candidate.trips(i,j,purp) = workers(i,j) x trip.rate(purp) 44 Adaptations: Trip rates (continued)

Thoughts on CTPP flows representing HBO travel (in the cloud) Observations / hypotheses HBO is largest fraction of total person trips but not the largest fraction of transit trips HBW and HBO transit trips appear to have similar patterns but with shorter HBO trip lengths Same economic drivers (work force and employment) produce and attract both kinds of travel Implementation within STOPS HBO trips start with CTPP JTW flows (like HBW trips) HBO-specific trip rates HBO trips drop off more quickly with distance than do HBW trips 45 Adaptations: Trip rates (continued) Thoughts on CTPP flows representing NHB travel (in the cloud)

Observations / hypotheses Workers holding jobs in a neighborhood are attracted to economic activities located in places similar to the residents living in that neighborhood NHB transit trips have a shorter average trip length than HBW trips Implementation in broad strokes within STOPS NHB person trip flows from a zone are scaled by (total CTPP attractions in zone) / (total CTPP productions in zone) NHB-specific trip rates NHB trips drop off more quickly with distance than do HBW trips 46 Adaptations: Trip rates (continued) Trip rates applied to CTPP worker flows Transit-candidate trips(i,j) per CTPP worker(i,j) 0 car 1 car

2+ cars HBW 1.32 1.44 1.56 HBO 1.78 5.20 5.60 NHB 0.54 2.79

3.00 Sources HBW NCHRP 716 HBW trip rates Adjusted to match on-board survey trips while holding CTPP transit shares HBO = HBW rate x (4.0) (the ratio of NHTS HBO/HBW fractions of all travel) x 0.37 for 0-car households only, to avoid inflating mode choice Ks NHB = HBW rate x (2.1) (the ratio of NHTS NHB/HBW fractions of all travel) 47

Adaptations: Trip rates (continued) Non-work Decay Multiplier versus Distance 4.50 4.00 3.50 3.00 Decay Multiplier 2.50 NCHRP 365 target model decay 2.00 1.50 1.00 0.50 0

5 10 15 20 25 30 35 40 45 Distance from i to j (miles) 48 Adaptations:

Growth factoring between years Basic approach within STOPS Compute Y2000 home-end and work-end zone trip ends from existing CTPP JTW Estimate forecast year zone trip ends based on increase in zone population (home-end) and employment (work-end) IPF CTPP to match future trip ends 49 Adaptations: Growth factoring between years (continued) User-selected options for estimating tripend growth District level: Compute district level population and employment growth ratio and apply to all zones in district Zone level: Compute 2000 CTPP home trip-ends to population ratio

Apply ratio to forecast year zone population to compute future home trip ends Repeat for work trip ends using employment as basis for growth 50 Adaptations: Cloning zones for growth factoring Problems can occur in high-growth areas Sparse residential areas in 2000 that are well populated in forecast year Sparse employment areas in 2000 have many jobs in forecast year Solution User assigns one or more clone zones for each high-growth zone STOPS Applies trip patterns (e.g., destinations) from clone zones to the high-growth zone Factors trip patterns to maintain trip- ends consistent with forecast-year population and employment Considerations Clone zones must be nearby so that its trip patterns are reasonable for

the high-growth zone Clone zones must (in aggregate) have development types in 2000 similar to expected development in the high-growth zone for horizon year 51 Mode choice Nested logit model Discrete choices Auto, non-motorized Transit (walk, knr, pnr) x (bus only, fixed-guideway only, bus-and-fixed-guideway) Segmentation Trip purposes: home-based work, home-based other, non-home-based Car ownership: 0 car, 1 car, 2+ cars Times of day: peak (for HBW trips), mid-day for non-work trips Parameters Nesting coefficients: 0.7 for auto/walk 0.7 for transit access mode choice 0.7 x fixed guideway visibility (0.1 visibility 1.0) for path type choice

Coefficients on travel times and transit number of transfers Static constants: Auto, by trip purpose and household auto-ownership Transit, by trip purpose, access mode, and household auto-ownership Path-type constants Local-calibration constants for auto, by attraction district and auto-ownership 52 Mode choice tree structure All-mode person trips Non-transit Transit =0.7 Auto

=0.7 Walk Walk =0.7*VisVis Key: Vis=Guideway visibility factor FGO=Fixed guideway only FGB=Fixed guideway+bus Bus= Bus only FGO FGB PNR KNR =0.7*VisVis

=0.7*VisVis FGO Bus FGO FGB FGB Bus Bus 53 Mode choice (continued) Coefficients In-vehicle minutes: -0.030 (x 0.8 for fixedguideway time)

Walk minutes: 1.0 x C(in-vehicle time) First-wait minutes: 1.0 x C(in-vehicle time) Transfer-wait minutes: 1.0 x C(in-vehicle time) Number of transfers:5 minutes per transfer 54 Mode choice (continued) Adjustments Park-ride circuity Walk circuity

Short FG time on FG/bus path Very long walk PnR/KnR short transit IVTT Auto penalty = f(emp density) 55 Mode choice: static constants 0 car 1 car 2+ cars K auto in minutes added to auto utility K kiss-ride in minutes added to kiss-ride utility K park-ride in minutes added

to park-ride utility HBW HBO NHB 0.00 -1.58 -0.36 0.00 -47.26 -76.76 0.00 -24.84 -56.19 0 car 1 car

2+ cars HBW 93.62 60.64 69.08 HBO 89.76 69.01 55.73 NHB 74.50

55.73 73.34 0 car 1 car 2+ cars HBW 102.20 19.56 19.09 HBO 106.88

47.69 50.77 NHB 96.61 38.93 47.46 56 Mode choice: path-type constants Path-type constants in minutes of in-vehicle time Added as penalties to transit utility expressions Walk Kiss-ride Park-ride Bus/FG

Bus only Bus/FG Bus only Bus/FG Bus only 0 car 0 0 0 0 22.5 22.5 1 car 0 15 7.5 15 22.5 22.5

2+ cars 11.25 22.5 11.25 22.5 22.5 22.5 57 Automatic adjustments with local data Mode choice Constants derived from CTPP attraction shares, by autos owned Specific to trip attractions in user-defined districts Employed conventionally in utility expressions Total unlinked transit trips Single, fine-tuning factor System-wide trips for all trip purposes Adjusts trips from mode-choice and transit paths to match userprovided target

Boardings by station-group (SG) IPF of SG-to-SG flows Depending on method, improves agreement with actual SG counts for existing stations 58 Special markets No explicit treatment of special markets Students Out-of-town visitors International border crossings Special events (baseball!) Airport access trips by air passengers Access to other inter-city terminals Inter-city travel Until STOPS v2.0 is available Where special market trips are sizable and relevant to the project Calibrate STOPS with special market trips removed from calibration counts Use local special market procedures to estimate project ridership Manually report sum of STOPS and local special market models

Where special markets are modest or not relevant to the project Run STOPS with total ridership used for calibration but review for distortions 59 2.4 TESTS AGAINST NATIONAL RIDERSHIP EXPERIENCE National calibration Approach single version of STOPS applied to: A national collection of transit systems/projects With both static (single time point) and dynamic (before/ after) cases Employing full local data and automated adjustments Through many (many!) iterations and model adjustments Observations on residuals versus rider-survey data in previous trials Hypotheses on behaviors not yet captured Revised relationships, new variables, and/or updated parameters New application to full set of urban areas

No case-specific or type-specific factors or rules Repeat until plausible explanation of behavior yields an introduced to match the data acceptable fit of the data More influence from the dynamic-calibration cases 61 National calibration

(continued) Evaluation measures Static cases Linked transit trips by purpose, autos owned, access mode, path type Geographic distribution of transit travel patterns Unlinked trips by fixed-guideway mode (light rail, commuter rail, etc.) Boardings by station group (before station-group adjustments) Before-and-after cases Change in linked transit trips Trips on projects 62 National calibration (continued) Some problems that led to STOPS revisions

Overestimates for close-in urban commuter rail stations Overestimates for underground and elevated stations Poorly represented PnR capture areas Incorrect transfer rates Heavy-handed influence of station-group factors on new stations Missing ridership at stations serving universities Struggles with NHB trips Inappropriateness of conventional parameters for path choice and mode choice in a setting where schedulebased wait times are available from GTFS data 63 Calibration data Systems with rider survey data Metro area Comm. rail Atlanta Heavy rail

Light rail Streetcar BRT 1 Total 1 Charlotte * 1 1 Denver * 1 1

Phoenix 1 1 3 San Diego 1 2 Salt Lake City * 1 1 Subtotal

2 1 6 0 1 3 1 10 *Vis Indicates survey data on ridership both before and after recent project openings 64 Calibration data Systems with count data only

Metro area Chicago Comm. rail Heavy rail 1 1 Houston Light rail Streetcar 1 1 1

Minneapolis * 1 Nashville * 1 1 2 1 1 1 San Jose 1 1

3 1 1 Seattle 1 1 1 Norfolk * SE Florida Total 2 Kansas City

Portland * BRT 1 1 2 1 St. Louis 1 3 1 Tacoma 1

1 1 Subtotal 6 2 7 3 1 19 Total 8

3 13 3 2 29 *Vis Indicates count data on ridership both before and after recent project openings 65 National calibration: results 120,000 STOPS Estimated Daily Ridership 100,000

80,000 60,000 Before-After Static Model=Actual 40,000 20,000 0 0 5 10 15

20 25 30 35 Actual Daily Fixed Guideway/BRT Ridership (Excluding Special Markets) 66 Observations from calibration Details matter Effective district and station group definitions Accurate count data Accounting for special markets Dont be misled by the previous slide. Forecasting is still an uncertain business and that uncertainty must be communicated to decision-makers.

Uncertainties include: The characteristics of the project itself Background assumptions for population, employment, Differentiation from competing transit systems The accuracy of the forecasting tools 67 Recap STOPS is Data-driven adaptation of conventional tripbased model Demand from CTPP Transit supply from GTFS schedule data Calibrated with national information on project ridership Adjusted to match local conditions using actual ridership experience More than a sketch planning model STOPS still requires careful attention to detail to generate reasonable forecasts

68 3. HOW TO WORK STOPS (ABBREVIATED VERSION) Steps to develop STOPS forecasts Implementation Testing and Core Adjustment Station Group Calibration and Review Preparation of Forecasts

Making forecast-ready 70 Implementation Set geographic context Geography type Geographic modeling area Assemble data CTPP Regional transit schedules Transit ridership counts Prepare other inputs Population and employment forecasts Highway travel times

Station file District definitions 71 Making forecast-ready Start simple Current year demographics Existing transit system Required input data (CTPP, GTFS, station file, district file, total unlinked trips) Count data (existing fixed-guideway stations) Station-group calibration approach set to 01-No Group Calibration Make adjustments Beginning with error correction Ending with fine-tuning

District definitions Station groups Calibration methodology Station penalties 72 Preparation of forecasts Code project service plan Edit existing GTFS to represent project Engage schedule-writers to use their scheduling software Represent growth in demographics Review change in forecasted population and employment Identify places where simple growth factoring unlikely to succeed Clone zones where necessary Prepare forecast-year files Confirm that STOPS properly represents growth in travel demand Confirm plausibility of forecasted project ridership Remember FTAs message from the past 25 years:

Use the forecasting results to tell a coherent story about the role of the project in the regional transit and transportation system 73 4. HOW TO WORK STOPS (ABBREVIATED VERSION) Topics Steps to develop STOPS forecasts Implementation Making forecast-ready Preparation of forecasts Output reports and graphics Uses for STOPS forecasts QC of ridership forecast prepared with other method(s) Support of request to FTA 75 Steps to develop STOPS forecasts

Implementation Testing and Core Adjustment Station Group Calibration and Review Preparation of Forecasts Making forecast-ready 76 Implementation Geographic scope of the analysis Relevance

Only for mega-regions: scope may be some subset of the region For all other metro areas: scope should be the entire region Dimensions Geographic area Extent of the transit system Considerations Importance of travel markets to the project Focus of calibration on markets relevant to the project Processing time, file sizes, and (maybe) STOPS capacity Representation in STOPS Geographic area: flags on individual CTPP zones Transit system: inclusion of GTFS files from individual transit providers 77 Implementation

(continued) Data 2000 Census and CTPP files (from the FTA website) Boundary files for Census geography in the appropriate state(s) CTPP journey-to-work tabulations (selected tables from Parts I, II, and III) Boundary files for census blocks GTFS file(s) Current file for each relevant transit agency Publicly available: https://code.google.com/p/googletransitdatafeed/wiki/PublicFeeds http://www.gtfs-data-exchange.com Available only from the agency: make direct request to agency staff Not available: ask anyway; most scheduling software have GTFS as option Data from rider counts and surveys

Total boardings on the included system (required by FTA) Boardings at existing fixed-guideway stations (required by FTA) Boardings at bus stops in the corridor (good practice) System-wide total linked transit-trips by trip purpose (optional) 78 Implementation (continued) Data (continued) Files from the regional travel model Zone-level boundary file MPO adopted population and employment Years: 2000, current, and (if applicable) horizon year(s) Zone-to-zone highway impedances Peak-period time and distance Years: current and (if applicable) horizon year(s)

Requirements: Consistent zone numbers for both files Consistent set of 2000, current, and horizon forecasts Boundaries Definitions of employment Forecast series/vintage and/or methodology 79 Implementation (continued) Other input files Fixed-guideway stations and selected bus stops (manually coded)

Station name Latitude and longitude GTFS stop_id Station group Grade separation Boarding count Time penalties Definitions of districts (manually coded) Aggregations of census zones Uses within STOPS Calibration of attraction-district transit constants in mode choice For growth factoring CTPP flows with population & employment changes For reporting of the forecasts 80 Making forecast-ready Testing and core adjustments

Start simple Current year demographics Existing transit system Required input data (CTPP, GTFS, station file, district file, total unlinked trips) Count data (existing fixed-guideway stations) Station-group calibration approach set to 01-No Group Calibration Make adjustments beginning with error correction and ending with fine-tuning Station group adjustments and final review 81 Testing and core adjustments CTPP district calibration and total unlinked trips, no station group calibration If problems with getting unadjusted STOPS estimates to match observed regional unlinked trips or station group boardings, look for:

Mechanical errors Missing attribute information (time, cost, user preferences) that describe components of the transit system Special markets not represented in STOPS (and ought not match) Problems with observed data Repeat until confident that STOPS understands markets as well as possible before introducing station group boarding calibration 82 Testing: assessment of fit to the data Basic measures predicted versus actual: Total unadjusted system-wide unlinked trips Boardings by station-group Boardings on individual routes Advanced measures (if data are available) predicted vs. actual: District-to-district linked trips Station-group to station-group unlinked transit

trips 83 Local calibration: assessment of fit to the data Total unadjusted system-wide unlinked trips Regional calibration factor computed by STOPS target unlinked transit trips Factor = -------------------------------------raw unlinked transit trips Goal: modest calibration factor in the range of 0.7 to 1.3 Factors outside the range Not necessarily fatal but need to be investigated and understood Most likely cause is transfer rate that is different from assumption of 1.4 assumed by STOPS in calculation of linked trips from system-wide unlinked trips (when the user does not provide information on system-wide linked trips) Remedy: specify the number of system-wide linked HBW trips 84 Assessment of fit to the data (continued)

Table 2.04 reports results without any station-group calibration Regional calibration factor is between 0.7 and 1.3 85 Assessment of fit to the data (continued) Boardings by station-group Comparisons For existing fixed-guideway stations (required by FTA) For bus stops in a subarea (good practice) Goal is to have station group boardings* differ from goals by 40% or less so that when station group calibration is applied it will finetune results rather than make major adjustments Focus attention on: High-volume station groups

Station groups that will influence trips-on-project forecasts Factors outside of range, minimize risk to project forecasts Identify other factors affecting station use and represent with time penalties Include project stations and all nearby stations or stops in same group Plan on using station group calibration methods 6, 7, or 8 * Remember, at this stage, station-group calibration is set to option 01 - no calibration 86 Assessment of fit to the data (continued) Table 2.04 (continued) reports results without any station-group calibration Pre-stationcalibration group

boardings differ from goals by less than 30% 87 Assessment of fit to the data (continued) Boardings on individual routes Comparisons Table 10.01 predicted total boardings by route and access mode Compare predicted boardings for the existing system against actual boardings Goals For routes (in some cases, groups of routes) in the project corridor: 30 % For routes elsewhere: correct order of magnitude 88

Advanced assessment of fit to the data With a reliable on-board survey dataset, tabulate: District-to-district linked trips by: Trip purpose Access mode Path type Autos owned With reliable data on station-to-station unlinked trips, tabulate: Station-group to station-group unlinked trips Compare against analogous tables from the STOPS report file District-to-district: Tables 15.01 through 350.01 Station-group to station-group: Table 3.01

Station-to-station: Tables 15.02 through 350.02 89 Core adjustments: find and fix mechanical errors Common mechanical errors Incorrect specification of geography type for CTPP data Missing or inconsistent zone-level population and employment Reliance on incorrect field in shape file to identify zone number Specified date not found in provided GTFS file(s) Missing stop_ids in the station file Absent park-ride file where park-ride lots exist or will exist Coordinates in some files not in latitude-longitude system Inconsistent geographic coverage among:

CTPP flows GTFS files Total unlinked and linked transit trips Boardings at stations and stops Changes to input files not captured in subsequent reapplication 90 Core adjustments: address other problems Appropriate actions depend on the nature of the problem No action Problems relatively minor in extent or magnitude Calibration discrepancies not likely to affect project ridership Broad adjustments Refine estimate of region-wide unlinked transit trips Add estimate of linked transit trips by purpose

Refine district definitions for CTPP transit-shares Specific adjustments Clone zones to improve grasp of growth patterns 2000current Refine station-groupings Represent additional local conditions 91 Core adjustments: address other problems (continued) Refine estimate of region-wide unlinked transit trips In some situations, the correct number of unlinked transit trips may not be apparent STOPS application considers only a portion of the metropolitan area GTFS provided for a subset of all transit operators Remedy: initial run with best first estimate but then evaluate

region-wide scaling, station boardings, route boardings to refine unlinked trip estimate to minimize all factoring Add estimate of linked transit trips by purpose When transfer rates substantially different from 1.4, STOPS may need a heavy adjustment factor to match region-wide unlinked trips Remedy: Provide linked trip totals so that STOPS does not have to estimate linked trips by purpose 92 Core adjustments: address other problems (continued) Refine district definitions for CTPP transit-shares Initial district level calibration may have missed key attraction locations: Specific high transit-share locations diluted inside a larger district Large, dispersed, low-transit areas co-mingled with denser,

more urban, moderate transit share areas Remedy: revise district system to reflect: Distinct geographic areas with similar urban form Areas with particularly high or low existing transit shares Avoid creating too many districts CTPP transit trip estimates may become too lumpy in lower-ridership areas Human reviewers unable to grasp tables larger than 20x20 93 Core adjustments: address other problems (continued) Clone zones to improve grasp of growth patterns 2000current Zones with large change in population and employment may have changes in trip patterns that are greater than simple growth in trip

ends Agrarian town suburban bedroom community Suburban bedroom community major employment activity center Employment center mixed-use urban community Remedy: clone zones to copy trip patterns from nearby areas that were similar in 2000 to what exists in the forecast year in the rapidly- 94 Core adjustments: address other problems (continued) Refine station/stop-groupings Have station/stop groups represent geographically contiguous areas organized around: Existing fixed guideway

Line (e.g., north commuter rail, west LRT) Area type along a line (e.g., CBD, CBD fringe, urban, suburban) Other corridors (including corridors with the project) Bus stops in project corridor by area type 95 Core adjustments: address other problems (continued) Application of specific calibration adjustments Add station/stop penalties to account for: Differences in fare policy Conditions that make some stations more or less accessible Public perceptions of individual operating

agencies/services Other large patterns in differences between observed and estimated ridership Penalties established through hypothesis, trial, and error. Hypotheses are essential-- they help define what adjustments are applied to new stations 96 Station group adjustments and final review Select appropriate method(s) for station group calibration factors 01 no calibration: STOPS default 06 static factors: STOPS computes factors for each i-j pair and uses identical factors for each scenario. 07 district path and access type constants (limited magnitude): STOPS computes production and attraction district constants with impact limited to 0.6 to 1.4 08 district path and access type constants (full adjustment): STOPS

computes production and attraction district constants with impact limited to 0.3 to 2.0 09 factors based on boarding and alighting station: full factoring to match existing counts that are applied to no-build and build scenarios based on station groups used in each scenario Calibration approaches are a new feature of STOPS and best practices are still emerging 97 Initial thoughts on calibration approach Method Advantages Disadvantages Application 01 no calibration No station group hammers that may distort results

May not match observed station counts well 06 static calibration Good match to counts. Distortions are held constant, less likely to cause unpredictable changes in total linked or unlinked trips Station adjustments not transferred to similar stations on project unless zone already served by same station group 07 district (limited) Similar to conventional district level calibration, generates

system unlinked trips equal to input number, incremental linked and unlinked trips respond properly to changes in service May not match station group volumes well. District factors calibrated separately for each access mode and path type. Station adjustments not transferred to projects that change access or path type Early testing, cases where STOPS naturally matches counts, cases where unfactored results are to be analyzed May be best approach when model adjustments of under 40% are required

to match counts and when district station counts include facilities with same access and path type as those envisioned for project continued 98 Initial thoughts on calibration approach (contd) Method Advantages Disadvantages Application 08 district

(full) Same as Type 7 but may have better match to counts Same as Type 7 but with better calibration Similar to 7 but when higher levels of factoring required 09 full calibration Good match to counts. Adjustments developed for station groups applied to new stations with same groups. Factors may lead to illogical shifts in total unlinked or linked transit

trips Cases where project stations are related to existing station counts with little chance of changing station groups. 99 Preparation of forecasts A calibrated model is just the beginning of the forecasting process Coding project service plan Representing growth in demographics Confirming plausibility of forecasted project ridership 100 Coding transit alternatives Create new GTFS folder with

alternative: Engage schedule-writers to use existing tools to generate new project schedule; or Manually edit existing GTFS to represent project 101 Coding transit alternatives (continued) Some thoughts on a productive strategy Structure coding changes into groups. Examples Changes related to the project The project itself Direct competitors Feeder services Intersecting crosstown routes Distant parallel services Changes to the overall transit system required to represent

horizon year background services New fixed guideway services previously committed for implementation Expansion of bus services to newly developed areas (in vicinity of the project) Reconfiguration of service (e.g., grid system of routes to hub-and-spoke) Expansion of bus services to newly developed areas (not near project) 102 Coding transit alternatives (continued) Some thoughts on a productive strategy (continued) Build each alternative on the appropriate antecedent 11 Current Current Year Year Existing Existing 22

55 33 Current Current Year Year No-Build No-Build Current Current Build Build Horizon Horizon Year Year No-Build No-Build Horizon

Horizon Year Year Build Build 44 For each scenario, concentrate on coding a GTFS file set with an appropriate level of changes before coding the next scenario For new scenario Code highest priority change first Run STOPS and record changes in project ridership Code and run next highest priority changes. Repeat until expected changes in project ridership are small in comparison to the effort required to code the next round of changes For final run in each year, no-build and build must be consistent in terms of geographic coverage and service levels 103 Representing demographic growth Parallel to process used to represent

growth from 2000 to today (discussed earlier) Since growth to the future cannot be observed, extra attention required concerning plausibility of inputs and resulting trip growth in STOPS This needs to emphasize the need for another look at corridor growth and consideration of extra steps (cloning) that might be necessary to properly represent future travel 104 Confirming plausibility Everything that FTA has said for the last 25 years still pertains to forecasts generated by STOPS Use the forecasting results to tell a coherent story about the role of the project in the regional transit and transportation system

105 Output reports and graphics The information required to tell the story of the project comes from STOPS reports and graphical outputs STOPS generates 1,021 tables (and up to 14 sub-tables for each main table) with each run. The key is finding what you need. The report begins with an index of all tables that helps the user locate the information being sought 106 STOPS table index 107

Output reports and graphics (continued) Useful reports that help support the story of the project: District population and employment (Table 12.01) District-to-district person travel patterns Available for each scenario, trip purpose, auto ownership level (AllMode in index) Transit trip patterns Available for each scenario, trip purpose, auto ownership level, access mode, path type (Linked Trips in index) Transit volumes Station-station unlinked trips available for each scenario, trip purpose, auto ownership level, access mode, path type (Sta-Sta in index) Route level ridership (Table 10.01 and 10.02) Change in auto mode PMT (Table 8.01) 108

Output reports and graphics (continued) Change in transit service levels See GTFPath path report files in skims\ subdirectory Provides listing of selected path for each scenario, access mode, path type, and time period for zone-zone pairs where both the origin and destination zone are named in the district file STOPS can also display selected transit trip table information using linked GIS functions 109 Sample graphical output 110 Uses of STOPS

QC of ridership forecast prepared with other method(s) Comparison of two forecasts can provide more than twice the insights of either forecast separately Details matter in the comparison Travel markets Market shares Changes in transit level-of-service caused by the project Differences in outcomes should be explored: Data sources? Coding errors? Differences in model response/elasticity? Results lead to two useful outcomes: Better understanding of the projects contributions to 111 Uses of STOPS

(continued) Support of request to FTA for engineeringentry / funding All information for New Starts templates is in STOPS outputs No additional tabulations are needed to satisfy the reporting requirements in the results report Project sponsors provide to FTA a copy of the entire STOPS implementation (inputs and reports) Satisfying the reporting requirements Allowing FTA to test answers most questions that may arise without having to bug the sponsor about them Project sponsor still required to prepare the story of the mobility benefits of the project from the forecasts 112

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