Transcript Modeling in the “Real World”

Modeling in the “Real World”
John Britting
Wasatch Front Regional Council
April 19, 2005
Introduction
• Forecasting manager for Salt Lake City metropolitan
planning organization
• MPOs maintain region’s short and long-term transportation
plans
• The “3 C’s”
• Responsible for developing and using models to
forecast future travel patterns
• Mathematical models representing current travel behavior are
used to forecast future travel behavior
• Analyze future alternatives, quantify benefits and costs
Quick Facts
• 2 MPOs
• 4 Counties
• 1300 Square
Miles
•1.8 million
people today
•2.7 million
people by 2030
Typical Analyses
1) Air Quality Conformity -NAAQS
2) System Performance (aggregate)
-VMT, VHT, Mode Share, etc.
3) Corridor-level Analyses
-Identify and compare options
4) Facility Performance
-V/C, Ridership, speed
The other 3 C’s
• Complexity
• Challenges (legal)
• Creativity
Advancing the modeling practice is not easy.
What is a Travel Model?
A systematic tool to forecast future travel.
One of many tools used in decision-making process.
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The 5 steps of modeling process (typically) are:
 1. Land Use Forecasting
 2. Trip Generation
 3. Trip Distribution
 4. Mode Split
 5. Trip Assignment
Model Inputs
Network of zones
and links
• 1300 zones contain
demographic data
(people/jobs)
• 20,000 links
describe
road/transit
infrastructure
(lanes, speed,
capacity, headway
etc.)
Networks
Trip Generation
Each zone produces and
attracts trips, based on the
amount and types of
activities within the TAZ.
LANDUSE DATA
TAZ
Population
1000
393
679
500
300
176
0
800
Modeling Steps
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Jobs
0
Trip Generation
Trip Distribution
Mode Choice
Trip Assignment
Trip Distribution
Trip Distribution
estimates the
number of trips
between zones
Modeling Steps
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Trip Generation
Trip Distribution
Mode Choice
Trip Assignment
Mode Choice
Mode Choice considers
travel time, auto
availability, and costs in
estimating the likelihood
of making trips by car,
train, bus, etc.
Modeling Steps
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Trip Generation
Trip Distribution
Mode Choice
Trip Assignment
Trip Assignment
Trip assignment estimates
which road or route should
be taken. Considers travel
time, congestion, speed,
distance, transit transfers,
etc.
Modeling Steps
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Trip Generation
Trip Distribution
Mode Choice
Trip Assignment
Trip-based Models
Limitations of Traditional Models
• Aggregate and Trip-based
• Poor accounting
• Assume similarity within zones
• Over-simplifies family dynamics and location choice
• No feedback to land-use forecasting process
• Land-use does not change with transportation
• Simplistic response to land-use
• No sensitivity to urban form (diversity, density,
design)
Tour-based Models
Difficult Emerging Questions
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Land-use affects
transportation decisions
Transportation affects
land-use growth
New technologies (e.g.
ITS, rail)
New policies (e.g. tolls,
taxes)
Introduction to UrbanSim
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Forecasts future land-use (households, jobs)
Effective means to incorporate city and county land-use
plans into regional transportation plans
State-of-the-art
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Defensible microeconomic theory
 Incorporates
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transportation accessibility
Locally calibrated
Tremendous interest across the U.S.
WFRC Interest
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Committed to exploring and discussing linkages
between land-use and transportation in LRTP
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Wasatch Choices visioning effort
Extensive staff time fine-tuning UrbanSim
database and model
Major technical questions have been answered
 Testing about to begin anew in visioning effort
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UrbanSim – Travel Model
Interactions
Households by
Income
Age of head
Size
Workers
Children
Employment by sector
Travel Models
UrbanSim
Accessibility
Highway Travel Times
Vehicle Ownership Probabilities
Linked Urban Markets
Services
Governments
Infrastructure
Land
Housing
Households
Developers
Labor
Floorspace
Businesses
Flow of consumption from supplier to consumer
Regulation or Pricing
Overview of Modeling system
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>30 models within local UrbanSim application
Land Value (by type of use)
 Real Estate Development (by type of use; intensity)
 Residential location (by type of household)
 Employment location (by type of industry)
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Key Variables in Models
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Land value
Vacant land (for developer models)
Accessibility measures (for example)
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Neighborhood traits (for example)
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Proximity to transportation facilities
Jobs/households within 30 minutes
Housing and employment within walking distance
Neighborhood mix (e.g. by income, by type of real estate)
Decision-maker’s characteristics (e.g. income, HH size,
sector)
Model Constraints
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Environmental features
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Regional Policies
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Steep slope
Wetlands/lakes/streams
Superfund
Urban growth boundary
Open Space
Local Land Policies
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Type of use
Allowable density of use
Observed
Predicted
Land Price Validation
Residential Location Validation
Observed Total
Observed %
Modeled Utility
Visioning
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Plans to test UrbanSim extensively over next 4-6
months
Plenty of opportunity for local review and
feedback
Relatively safe opportunity to vary land and
transportation policies and see what the model
says
Political Challenges
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Political issues can be more challenging than the
technical
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Inherent resistance to change
Committing to a tool like UrbanSim affects
entire planning realm (local/regional/state)
Implications for project development must be
well understood
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