Transcript Summit in the Wasatch

Summit in the Wasatch
John Britting
Wasatch Front Regional Council
Context – Fall 2001
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First light rail line was very successful
Local transit priority was commuter rail
Transit dedicated sales tax increase was passed
 Bold promises made re: CRT
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Initial forecasts from corridor study were high
(~35,000) and made FTA very uncomfortable
Lawsuits over analytical methods
My boss was handing over the reigns to me
Step 1: Engage FTA
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Hired PB Consult to upgrade models
Brought in FHWA/FTA for a focused peer
review of models in January, 2002
Opened up the black box and welcomed
feedback
Short-term and longer term priorities for model
improvements were developed
Synopsis: make every step better in short-term
Priorities – FTA Perspective
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Estimate/calibrate & validate NL mode choice
models using local data
Pay close attention to path-building
Identify CW/MD/NT markets
Incorporate segmentation into distribution
Consider handling unique markets differently
(e.g. college trips and airport trips)
Thorough validation of network speeds
Step 2: Progress as of 6/03
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Borrow NL mode choice models and calibrate
using local data (collected on-board data)
Little improvement to path-building
Identified CW/MD/NT markets
Did not incorporate segmentation into distribution
Used data on student addresses and an airport
users survey to developed unique trip tables
Thorough validation of network speeds
Step 3: Continue Dialog
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Questions remained about forecasts
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High non-work benefits
Significant reverse commute
Questions remained about validation
Summarized rail trips by # transfers and attraction
district for each trip purpose and auto segment
(example next slide)
Agreed on a range for forecasts and helped them tell
the story with some confidence
CRT was given a “recommended”
Step 4: Identify Illogical Benefits
HBW CRT Trips
LU Geography
0 Car HH
1
2
3
4
5
0
16
8
0
0
1
Transfers
1
126
77
0
16
19
2
207
97
0
96
121
Step 5: Progress as of 6/04
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Estimated/calibrated & validated NL mode choice
models using local data
Transit path-building informed by on-board and model
estimation (consistent weights; 95% of on-board
records had access; reasonable xfer rates)
Identified CW/MD/NT markets
0-car HBW distribution w/composite impedance
Used data on student addresses and an airport users
survey to developed unique trip tables
Thorough validation of auto speeds; model relating
transit speeds to auto speeds
Most Important Step - Validate
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Trips by purpose, market, period and mode
Boardings by route and route group
District-level trip tables
Results from on-board assignment
% person trips with a transit path, by market
% transit trips for each market by mode and transfers
% walk-rail that are walk-bus-rail
Expressed constants/parameters in terms of IVT
Threat 1 – naïve calibration/validation
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Extensive documentation
Asked FTA what they wanted to see
Built a calibration routine into models (in TP+)
and several scripts to automate reporting of key
indicators
Threat 2 – Incorrect Travel Markets
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Estimated, calibrated and validated a bestpractice auto ownership model
0-car HBW distribution
College and Airport trip tables
Threat 3 – Odd Properties
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FTA reviewed and commented on model
estimation results, model functional form and
implementation
Expressed all parameters and coefficients in
terms of equivalent in-vehicle minutes
Threat 4 – Transit Path Builder
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Overhaul of walk/drive support link generation
models
Used initial estimation results to refine pathbuilding weights
Built MC estimation file 2-3 times
Assigned transit on-board
Quality of O-D data
 Assure access where trips occurred
 Refine path-building parameters
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Threat 5 – Inaccurate Speeds
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Performed extensive auto speed data collection
Used data to calibrate and validate auto
assignment
Built model to forecast transit speeds from auto
speeds; based on facility type and auto speed –
model does a very good job
How we’ve used Summit
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Network quality control – does the distribution
of benefits seem reasonable?
Tell the story of who benefits
LRP - benefits per capita analysis
Identify symptoms of quirks in the models
Concerns with Summit Process
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A lot of work for me
Not sure FTA has staff to go through similar
process across the country
How do you get a baseline approved?
The cap has to be flexible
Old-school program
Summit proves models are not perfect, which
we all should already understand
Case for Higher Cap
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Differences in transit utility may reasonably exceed the
cap of 45 minutes
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Travel time differences (IVT and OVT)
Transfer differences
Unobserved attributes (i.e. constants)
This assumes you minimize or eliminate
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Coverage differences
Service level differences
Access differences
Recommendations
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FTA should ask for specific items to be documented
Process to justify cap increases
Process for defining range of key indicators
Modelers, not PM’s, need to open up to FTA early and
often
Work with Citilabs, Caliper, etc., to improve Summit
Recognize inherent imperfections and overcome most
critical
Start process early
Final Comment
Our commuter rail forecast went down from
35,000 boardings to 26,000 and our forecast is
much better.