Land Use and Travel Demand Forecasting

1
Land Use and Travel Demand Forecasting

• CE 451/551
• Sources National Highway Institute Workshop on
  Travel Demand Forecasting Chapter 4, Meyer and
  Millers Urban Transportation Planning, Ch. 6,
  and Dr. Paul Hanley, University of Iowa
• See also INCORPORATING LAND USE AND
  ACCESSIBILITY VARIABLES IN TRAVEL DEMAND MODELS,
  by Chuck Pervis

2
Pop quiz

• Define the word sensitive as it relates to
  models and policies

3
Objectives

• Describe land use estimates and how they are used
  in the context of TDF
• Identify factors influencing household and
  employment location
• Identify methodologies for forecasting the
  location of households and employment
• Describe sub-allocation

4
Terminology

• Demographic data and projections
• Socioeconomic data and projections
• Land use estimates
• Population
• Households
• Employment
• Sub-allocation

5
Key Concepts

• What is land use?
• In travel forecasting terms, land use refers to
• Number, type and location of households
• Number, type and location of employment
• Additional socioeconomic data

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Why are we interested

• Land use estimates are a major input to the TDF
  process
• Bad estimates cannot be corrected later in the
  TDF process
• Land use forecasts must be reasonable for results
  of TDF and emissions estimates to be reasonable
• Land use policies and projects are seen
  increasingly as transportation solutions model
  must be sensitive

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What is the land use/ transportation relationship?
Not from NHI

• Effect of one on the other?
• Integrated? Why not?
• Different departments (e.g., energy problem
  policy solution)
• Typical Mode choice
• Typical Vehicle improvement
• What about land use change?
• Different analytical techniques
• Internal inconsistencies
• trip dist from LU model ? trip dist from TDF
  model
• Network used in LU model ? network in TDF model
• LU modeling not done much in USA 1970-1990s (but
  resurgence is underway)

9
Where does land use fit?
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Data sources

• Census
• CTPP
• State Employment Commission
• Market research listings
• Local area population and employment data
• Telephone directory
• Aerial photos
• State Dept. of Finance
• Local Planning departments

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Base year estimates

• For model calibration/base year, obtain most
  recent population, household and employment
  estimate
• Sub-allocate estimates to TAZs (if not already at
  this level)
• Output Household and employment estimates by TAZ
• Classify estimates by household size, income,
  employment type
• Use in the TDF process

12
Future year estimates

• Control totals
• Usually, land use forecasts assume a control
  total for the region or sub-region
• These forecasts are usually developed by a state
  economic development or employment agency
• In many states, regions must use these totals to
  constrain their forecasts
• See census state population forecasts here

13
Future year estimates

• Start with regional forecast from state
• Growth in population, households by income class,
  household size and employment by type
  sub-allocated to TAZs
• Issues
• Complicated technical steps
• Political involvement

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Population Forecast

• Ratio-trend method
• Cohort survival method (pop birth - death
  immigration- emigration)
• Economic-base method
• Constant or gradually declining compound growth
  rate
• Constant absolute rate change over a fixed period

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Employment Forecast

• Trend extrapolation
• Input-output analysis
• Judgment estimation

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Sub-allocation

• Forecasts, and sometimes base year estimates,
  need to be sub-allocated from regional and
  district totals to TAZs

17
What factors do you think are important location
considerations?

• for a commercial landuse?
• an industrial land use?
• a residential land use?

18
Location choice considerations

• Commercial/industrial
• Transportation access to labor, customers,
  suppliers and shipping
• Cost and tax of site
• Local zoning regulation
• Residential
• Housing stock and cost
• Quality of schools, shopping, parks and
  entertainment
• Transportation access to work, shopping, and
  other activities

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Forecasting techniques

• Qualitative methods
• Delphi or expert panel
• Committees (policy, technical)
• Comprehensive plans
• Quantitative methods
• Logit models (draws on random utility theory and
  discrete choice analysis as their theoretical
  base)
• Input output models (e.g. Implan, Rims-II)
• Gravity models (derived from Lowry model)

similar to, but not the same as the trip
distribution gravity model
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35 largest MPOs

• Twelve MPOs are using DRAM-EMPAL models
• Five MPOs are using their own models (POLIS,
  PLUM, and three local models)
• One MPO is in the process creating its own model
• Two MPOs use the Delphi (exchange of expert
  opinion) Technique

Source Review of Land Use Models Theory and
Application, Kazem Oryani, URS Greiner, Inc. and
Britton Harris, University of Pennsylvania
http//ntl.bts.gov/lib/7000/7500/7505/789761.pdf
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Qualitative method Delphi

• Systematic way to utilize expert opinion
• Broad range of experts
• Begins with questionnaire
• Anonymous participation with statistical group
  response
• More than one round of questions and answers
• Moderator facilitates discussion
• Advantages
• Time and cost savings
• Diversity of experts
• Limitations not able to reflect complex real
  world interactions

Click here for e-version of book
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Land Use Models
23
Accessibility
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The Lowry Model

• Pittsburg 1964
• Growth is a function of the basic sector
• employment that meets non-local demand
• Location not critical (for export)
• exogenous (must be given)
• Retail sector (services)
• Endogenous
• Multiple of basic sector
• Residential sector (function of basic and retail)
  also endogenous
• Location of retail and residential a gravity
  function of distance to jobs in the basic sector

source , Rodrigue, J-P et al. (2006) The
Geography of Transport Systems, Hofstra
University, Department of Economics Geography,
http//people.hofstra.edu/geotrans/eng/ch6en/meth6
en/ch6m2en.html
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Lowry Model (cont.)
Economic forecast
Location of industrial employment
Friction of space
Location of industrial employees residences
Location of service employment
Friction of space
Location of service employees residences
source , Rodrigue, J-P et al. (2006) The
Geography of Transport Systems, Hofstra
University, Department of Economics Geography,
http//people.hofstra.edu/geotrans/eng/ch6en/meth6
en/ch6m2en.html
26
Lowry Model (cont.)
source , Rodrigue, J-P et al. (2006) The
Geography of Transport Systems, Hofstra
University, Department of Economics Geography,
http//people.hofstra.edu/geotrans/eng/ch6en/meth6
en/ch6m2en.html
27
Lowry Model Steps

1. The spatial distribution of basic employment is
   assumed as given.
2. The location of the basic workers is determined
   according to a location-probability matrix,
   itself the result of a least friction of distance
   function.
3. Calculation of the residential sector per zone
   according to the population per worker
   multiplier.
4. Calculation of the number of non-basic workers
   per zone to service the population. This is the
   result of a non-basic worker per capita
   multiplier.
5. The location of non-basic workers is determined
   according to a location-probability matrix.
6. Revision of the total population according to the
   population per worker multiplier.
7. Calculation of the total number of workers and
   the total population. This is the summation of
   the basic and non-basic employment and of the
   basic and non-basic related population.
8. The above processes (4 to 7) are repeated until a
   convergence is reached, that is an optimization
   of the equation system of the model following a
   set of constrains such as density.

source , Rodrigue, J-P et al. (2006) The
Geography of Transport Systems, Hofstra
University, Department of Economics Geography,
http//people.hofstra.edu/geotrans/eng/ch6en/meth6
en/ch6m2en.html
28
Lowry Equations (singly constrained)
source , Rodrigue, J-P et al. (2006) The
Geography of Transport Systems, Hofstra
University, Department of Economics Geography,
http//people.hofstra.edu/geotrans/eng/ch6en/meth6
en/ch6m2en.html
29
Lowry variable definitions

• Tij Interaction from residential zone i to work
  zone j (work-related travel).
• Sij Interaction from residential zone i to
  service zone j (service-related travel).
• Pi Total population of a zone i.
• Ei, EBi and ESi Total employment, employment in
  the basic (B) and service (S) sectors for zone i.
  
• dij Euclidean distance between zone i and j (in
  km).
• Alpha population over basic employment
  multiplier.
• Beta service employment over population
  multiplier.
• Lambda Friction factor for residential
  interactions.
• Micron Friction factor for services
  interactions.
• WTTRij and WTTSij Willingness to travel for
  Residential (R) or Services (S) between zone i
  and j.
• LPRij and LPSij Locational probability for
  Residential (R) or Services (S) between zone i
  and j.

Prof. Rodriques Operational Lowry Model
Spreadsheet
source , Rodrigue, J-P et al. (2006) The
Geography of Transport Systems, Hofstra
University, Department of Economics Geography,
http//people.hofstra.edu/geotrans/eng/ch6en/meth6
en/ch6m2en.html
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Lowry limitations

• Static model evolution of transport system not
  considered
• Service jobs are the big changes these days
• No freight
• More recent variants do these things (see pages
  to follow)

source , Rodrigue, J-P et al. (2006) The
Geography of Transport Systems, Hofstra
University, Department of Economics Geography,
http//people.hofstra.edu/geotrans/eng/ch6en/meth6
en/ch6m2en.html
32
DRAM/EMPAL

• Derived from the Lowry Gravity Model
• Two main components
• Disaggregated Residential Allocation Model (DRAM)
• Employment Allocation Model (EMPAL)
• Allows interactions between land use and
  transportation
• Forecasts household location based on employment
  and work trips between zones
• Inputs
• employment, households, vacant land, land
  developed, regional employmentgthouseholds matrix,
  zonal trip impedance matrix
• Most widely used model in the US
• has been applied in Atlanta, Chicago, Dallas,
  Houston, Los Angeles, Sacramento, Boston,
  Detroit, Kansas City, Phoenix, San Antonio,
  Seattle, and Orlando.

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Integrated Land Use and Transportation Package
Trip Generation
Household location
DRAM
Trip Distribution
Employment location
EMPAL
Modal Split
Economic forecast
Traffic Assignment
Friction of space
source , Rodrigue, J-P et al. (2006) The
Geography of Transport Systems, Hofstra
University, Department of Economics Geography,
http//people.hofstra.edu/geotrans/eng/ch6en/meth6
en/ch6m2en.html
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METROPILUS (DRAM/EMPAL/LANDCON)

• DRAM/EMPAL can be used to produce
• employment location forecasts that reflect
  changes in the location of households
• household location forecasts that reflect changes
  in the location of employers.
• LANCON (for LANd CONsumption)
• Input calculated demands for residential and
  employment uses by zone
• estimates the change in each land use category
• 100-300 zones (each can contain 6,000 - 10,000
  people)
• Four - eight household types specified by income
  and place of residence
• Four - eight employment types, located by place
  of employment
• purchased as part of a comprehensive consulting
  package
• See Literature Review for Urban Growth modeling
  and Environmental Impact Analysis

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Index

• Gravity-type
• introduced by Criterion in 1994
• functions as a GIS-based neighborhood to regional
  scenario builder
• Cost
• Reference Criterion Planners (software home page)

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MEPLAN

• Derived from Lowry model, focus on housing market
  (bid-rent) and LOGIT type
• Integrated software package (demand and supply of
  both land use and transportation)
• Compares the effects of changes in various public
  policies
• Three main functions
• determines effects of transport on the choices of
  location by residents, employers, developers, and
  others
• determines how land use and economic activity
  create the demand for transport
• projects and evaluates the many impacts that
  planning decisions will have on land use and
  transport
• Applied in
• Cambridge and Stevenage, U.K. Santiago, Chile
  Sao Paulo, Brazil Tehran, Iran Bilbao, Spain
  Helsinki, Finland Tokyo, Japan

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MEPLAN

• Suggested staff transport engineer, planner, and
  economist
• Cost
• References
• NCHRP Project 8-32, Task 3 (1998). Land Use
  Impacts of Transportation A Guidebook.
  Prepared By Parsons Brinckerhoff Quade
  Douglas, Inc., pp. 63-70.
• United States Environmental Protection Agency
  (2000). Projecting Land-Use Change
• A Summary of Models for Assessing the Effects of
  Community Growth and Change on Land-Use
  Patterns. Office of Research and Development,
  pp. 86-90.
• A review of the MEPLAN modelling framework from a
  perspective of urban economics

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MEPLAN Transportation / Land Use Model
Rents
Employment and Residences Locations and Trips
Non-Exporting Employment and Total Population
Trip Distribution
Exporting Industry Employment Forecast
Modal Choice
Traffic Assignment
Friction of Space
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MEPLAN
MEPLAN land use and transportation-interaction
models traditionally include models of
business-location choice. The mechanisms in the
model that allow for realistic aggregate
assignment of firms to zones, and the resulting
impact that firm location has on the entire
modeling system, are described. The models use a
form of logit function to allocate production
activities to zones. Interdependencies in the
Social Accounting Matrix, together with trip- and
land-cost data and usage rates, generate a
utility function for the attractiveness of
purchasing a given sector's output from a given
zone. The alternative specific constants and the
dispersion parameter are estimated in
calibration, based primarily on cost data,
trip-length distributions, and arrangement of
activity in a calibration year. Results from a
model of Sacramento show the various features and
strengths of the model, while pointing out some
weaknesses and potential pitfalls. The strengths
include the realistic representation of current
and continuing patterns, the close linkages
between different industry and household types,
and the market-based nature of the model. The
weaknesses include the aggregate nature of the
model and the possibility of difficulties in
establishing realistic, alternative, specific
constants. Source FIRM LOCATION IN THE
MEPLAN MODEL OF SACRAMENTO , Abraham, J E
Transportation Research Record No. 1685
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Internet Planning for Community, Energy, Economic
and Environmental Sustainability (PLACE3s)

• LOGIT-type
• ArcView (GIS) platform
• used by Sacramento Council of Governments (SACOG)
  
• Blueprint tool that uses PLACE3S in conjunction
  with the MEPLAN model and SACMET (Sacramentos
  travel demand model)
• can be run on the internet for no charge?
• Blueprint description
• Blueprint Power Point

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TRANUS

• LOGIT-type
• Integrated Land Use and Transport Planning System
• Simulates effects of projects and policies
  relating to location of activities, land use, and
  the transportation system
• Evaluates effects from economic,
• financial and environmental points of view
• applied in Baltimore, MD and Salem, OR
• Cost
• References
• United States Environmental Protection
• Agency (2000). Projecting Land-Use Change
• A Summary of Models for Assessing the Effects
• of Community Growth and Change on Land-Use
• Patterns. Office of Research and Development,
• pp. 117-124.
• developers website

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Family tree of the TRANUS system
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UrbanSIM

• LOGIT-type
• behavioral approach
• Treats urban development as the interaction
  between market behavior and governmental actions
• simulates
• where households and businesses choose to locate
• how governments place constraints on development
  in the form of land use plans and policies
• Cost free and downloadable from
  http//www.urbansim.org.
• still in developing stages
• experience in Honolulu, HI, Eugene-Springfield,
• OR, Salt Lake City, UT, and Seattle, WA.
• requires
• GIS tools
• statistical packages, like SAS or SPSS
• for calibration
• LOGIT estimation software package
• integration with a travel model to reflect
• transportation through accessibility measures
• contained in the model
• References

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What if?

• Lowry Gravity-type
• ESRI GIS based
• model changes that may result from different
  policy strategies
• References
• Klostermann, Richard. (1998) The What if?
  Collaborative Planning Support System.
  Environment and Planning, B Planning and Design,
  26 (1999) 393-408
• What if?, Inc

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Validation and Error Checking

• Document procedures and results
• Check output data against independent data
  (trends)
• Households
• Auto availability
• Income
• Total employment
• Special generators
• Illustrate output data