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Choice Modeling in Transportation


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Title: Choice Modeling in Transportation

(No Transcript)
SMILE! Youre on Traffic Light Camera Applying
Stated Choice Modelingin Transportation
  • W. Douglass Shaw (presenter)
  • Dept. of Agricultural Economics and Recreation,
    Parks and Tourism Sciences
  • April 14, 2008

  • The graduate students in this semesters Ag.
    Econ. 695 (Frontiers in Natural Resource and
    Environmental Economics) and RPTS 616 (Economics
    of Tourism and Recreation)
  • Especially Lindsey Higgins (Ag. Econ.), Liam
    Carr (Geography)
  • Conversations on CM with Bill Breffle (and 2 of
    his slides), Barbara Kanninen, Edward Morey, Mary

My Main Contributionto Economics?
  • Probably
  • We (Pete Feather and I) showed that people can
    have an opportunity cost of their time that
    exceeds their wage rate (see Economic Inquiry
    2000 J. of Environmental Economics and
    Management 1999)
  • Are there any applications to transportation of

Links to Transportation?
  • Somebody apparently thought so
  • Peter is now the Chief of the Fuel Economy
    Division at the United States Department of
  • He is an environmental and natural resource

Outline / Preview
  • Hope I could present some statistical results
    from the graduate seminar class project on
    choice modeling
  • Not quite ready, but Ill show you what we have
    so far.
  • So, this talk is an overview of stated choice
    modeling method and how it can be applied to some
    transportation issues.
  • What are experimental/economic choice models and
    how can these be used to model transportation-rela
    ted preferences and behaviors?

Audience Knowledge?
  • How many here today know about stated choice
    models as a tool that can be used to evaluate
    transportation-related preferences?
  • Some big transportation names of people who
    have done this kind of modeling include C. Bhat,
    Dan McFadden, Moshe Ben Akiva, David Hensher, S.
    Lerman, Jordan Louviere, Charles Manski, Kenneth
  • Not sure any of these people are exclusively
    transportation researchers per se.
  • Lots of SCM papers published recently in the
    journals Transportation, Transportation Research,
    Transport Policy, Journal of Transportation
    Economics and Policy, etc.

A Little Technical Stuff
  • Choice modeling is similar to Conjoint analysis
  • Stated Preferences/choices/rankings can be used
    (so can data from actual or real choices)
  • Most use discrete choice analysis
  • Designs vary from
  • Paired Designs (Choose A or Choose B)
  • Multiple Choice Designs (Choose one from A,B,C)
  • Rank these routes (more often done in conjoint)

Discrete Choice Econometrics
  • As there are typically few choices, the error
    terms are not continuously/normally distributed
    rather, they relate to discrete distributes
  • The old standard is to use the extreme value
    distribution leading to the logit or multinomial
  • The new standard is to use the mixed or random
    parameters logit, or perhaps, a panel (fixed or
    random effects) logit model

  • Laboratory experiments are designed to control
    for every aspect that influences the outcome
  • Choice experiments seek the same level of control
  • Unlike using revealed preference data (e.g. data
    on your actual trips) the researcher here
    constructs every aspect of a choice alternative
    in a stated choice model (SCM)
  • Choices can be made in a computer laboratory

Another Advantage of SCMs
  • Suppose you want to market a new product or
  • The idea is a plan, as in a planned new
    transportation route or alternative
  • Define the attributes of the new route and
    develop an SCM
  • In Transportation New airline, bus route
    service new airplane configuration (more leg
    room) the new Texas highway toll roads new HOV
    lanes congestion taxes, new parking facilities
    new sidewalks new traffic lights remove traffic
    lights rotaries (new Beaver Creek, Colorado),
    etc, etc

Essentials of Experimental Design
  • The Alternatives
  • The Attributes of the alternatives
  • The Levels of the attributes
  • How are these designed so as to elicit the most
    information possible without increasing the
    complexity such that individuals cannot perform
    the experiment?

A Little More Jargon
  • A profile is a single alternative that is
    described by the levels of each attribute
  • A choice set is a set of alternatives (two or
    more) presented to the individual, e.g. A versus
    B, or A versus B versus C, where each letter is a
    profile and the combination is a choice set
  • Researcher has to first figure out how many
    profiles are needed, then how many choice sets

The Alternatives
  • Does a person look at two at once?
  • Or three?
  • Or four (or more)?

Attributes Characterize Alternatives (the Choices)
  • e.g. What are the attributes of a commuting
    alternative that matter to people?
  • Cost (money and time)
  • Comfort
  • Discretionary power (flexibility in choosing
  • Reliability

Levels Determine Definition of the Alternative
  • What are the money prices?
  • Range from free to calculations based on
    parking, toll roads, gasoline prices, mpg of the
  • e.g. Local prices per trip are 0, 1, 2.50,
    5.00, 8.00
  • What are the times?
  • Range from few minutes to hours
  • e.g. Local commuting times per trip (including
    all parts of the trip) 5 min., 10 min, 20 min,
    30 min, 1 hour)
  • Comfort (low, medium, high) Reliability (very
    unreliable, sometimes unreliable, always reliable)

How Many Possibilities? Design
  • L levels, K attributes LK possible profiles
  • Full factorial design considers all possible
  • Possible?
  • Example of Commuting Attributes ( of Levels)
  • Price (five), Time (five), Comfort (three),
    Flexibility (three), Reliability (three)
  • 52 X 33 25 X 27 675

Quickly Exploding
  • I didnt probably get all the attributes or
    levels covered. If more
  • Can you cover 1,000 profiles?
  • No
  • So, what to do? Thats the art of design
  • Fractional factorial design

Key Design Components (Huber and Zwerina)
  • Level Balance Each level of each attribute
    should appear with equal frequency
  • Orthogonality mathematical independence to allow
    identification of parameters
  • Satisfied when joint occurrence of any 2 levels
    of different attributes appear in profiles with
    frequencies the product of their marginal
  • Simply attributes are purposefully uncorrelated
    (makes it easier to identify variable Xs

Key Design Components (cont.)
  • Minimal Overlap the probability that an
    attribute level repeats itself in a choices set
    is minimized
  • Utility Balance Balance the utility received
    Avoid dominance of choices, the probability of
    choosing each alternative should be fairly even

Quantitative measures of efficiencyD-optimal
  • The D-optimal criterion seeks to maximize the
    determinant of the Fisher information matrix
  • Max D 1001/N(XX)-11/A
  • N number of observations A is number of
    attributeslevels in design XX is the
    information matrix
  • Uninformed prior all parameters equal zero
  • A priori information based on pretest or other
  • Bayesian information hierarchically added

Conclusions about D-criterion
  • D-efficiency preferred when specification and
    design are both correct
  • D-efficiency with Bayesian info preferred when
    specification is incorrect but design is correct
  • Shifting preferred to D-efficiency when the
    specification is correct but the design is not
  • Most common
  • If design is correct, do not need a
    design-creating process!
  • Also known as cycling

Alternatives using D-criterion
  • Fractional design drawn multiple times, with
    D-criterion compared for each draw
  • Frequentist model averaging design evaluated
    over a distribution of parameter values and final
    design is a weighted average (uses partial info)

Class on Choice Modeling An Example Project
  • Agricultural Economics 695 (PhD seminar)
  • Recreation, Parks, and Tourism Sciences (616
    PhD course)
  • Assignment Design a choice modeling experiment
    that has something to do with transportation
    issue in Bryan/College Station

Students Decision
  • Identify the impact of CARES (Camera Advancing
    Red Light Enforcement Safety) on driver behavior,
    road and traffic safety, and pedestrian safety
  • Installation of red light cameras, coupled with
    75 citation for violations
  • Expansion of the program planned
  • No funding, so using convenience sample and
    internet survey

Student News Item
  • The Battalion (April 9, 2008) Nathan Ball
  • 3,318 citations as of April 1st, generating
    248,859 (four existing cameras)
  • Of existing fines, 659 mailed to College Station

  • Four attributes of cameras the location of
    the intersections for installation the cost of
    the fine the posted speed limit (mph)
  • Student in the class used SAS Optex procedure
  • 16 profiles (see next slide)

Original Profiles (Thanks Lindsey Higgins)
Corrected Profiles
Next Step
  • Suppose we want to create a pair of profiles to
    evaluate and ask the person to choose profile 1
    versus profile 2.
  • Does it matter which profiles are paired?
  • Yes
  • How do we match them?
  • Answer is complicated and there are many schemes
    that try to achieve an efficient design based on
    the four goals above.

  • Do we want choice A to be profile 1 and choice B
    to be profile 2? From the original profiles, wed
  • Choose between A (4 cameras, citation fine is
    50, cameras at current locations, speed is
    reduced) and B (4 cameras, citation fine is 74,
    cameras at current locations, speed is reduced)
  • Only thing that varies between A and B is the
    citation/fine amount

Final Choice Set - Each Gets 8
See Their Survey
  • http//
  • Aside on Internet surveys
  • See Knowledge Networks Inc. (webcast of
    presentation on survey bias, April 24th)
  • Wave of the future?

For each section read any instructions and each
question carefully before answering. Please do
not leave any answers blank. An answer of N/A
is provided for questions youd choose to not
answer. Thank you again for taking the time to
complete this survey. Current Residency College
Station Bryan Neither Prior to taking this
survey, of the four intersections with red light
cameras, how many can you confidently name or
locate? 0 1 2 3 4
The map shows the location of red light cameras
in the College Station CARES Program. The
cameras carry a 75 citation at intersections of
roads with a 40 mph speed limit. For the purposes
of this survey, these four cameras will remain in
use. For each question, you will be given two
alternatives for changing the CARES Program. The
alternatives may change the cost of the citation,
the speed limit, number of additional cameras,
and placing additional cameras at intersections
with high pedestrian traffic, high road volume,
or a mix of intersections throughout College
Station. Based on the information and your own
personal knowledge, select the alternative you
Example Choices
Few Preliminary Results N 38
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Initial Thought
  • Not everyone thinks cameras are good in safety
    may be revenue for town
  • They might focus on speed limit
  • Had hoped to have at least some more preliminary
    statistical results on this one
  • In the field giving surveys
  • No complete data set yet

Thoughts on CM Applications to Some Other Texas
Transportation Issues
  • Hurricane evacuation behavior and risk
  • What is the risk that a hurricane will hit?
  • Given this, will you evacuate? Perhaps add, how
    long before you do? (could add the risks of
    getting caught in a traffic jam, which are
    function of when you leave)
  • New routes through rural and other areas?
  • Biking v. Driving as the cost of gasoline

Application(UTCM project w/ Mark Burris)
  • Managed Lanes (less congestion, but pay a toll
    for this)
  • Katy Freeway
  • Will people use it/the MLs?
  • What will they willing to pay in tolls?
  • What is the value of MLs?

Managed Lanes (ML) offer travelers the option of
congestion free travel in corridors where the
general purpose lanes (GPL) are congested. To
ensure the MLs do not become congested (and often
to help pay for the construction of the lanes)
travelers have to pay a toll to use the MLs. This
toll varies by time of day or by congestion
level, increasing as demand for the lane
increases. Thus travelers have to make a
decision, often at the spur of the moment, on use.
If Time Allows Another Example
  • NSF Hurricane Project
  • Small Exploratory Grants Research (SGER) Program
  • Look at victims from Katrina/Rita who had
    relocated here or in Houston
  • Examine their location preferences for moving
    back or elsewhere using a choice model
  • Also look at their subjective perceptions of
    risk (just after the hurricanes in 2005, and over
    one year later)

Two Rounds
  • Round I mostly from B/CS living here
  • Round II from B/CS and from Houston (we lost
    many from Round I no one knows where they went)
  • Compare risks and behaviors in model of all

Empirical Approach
  • Tried two panel logit specifications (? is
    normally distributed individual-specific
    component, T is of observations per person i).
    Log likelihood (random effects)

Round I (N 72 508 responses)
Round II (N 45 206 responses)
Marginal WTP (One time)
  • High Risk to None
  • Round I 10,100
  • Round II 4,800
  • High Risk to Medium Risk
  • Round I 6,550
  • Round II 3,456

Results from Hurricane Study(in words)
  • Key
  • Risks matter higher risks, less likely to
    choose that location
  • Risks still matter to both groups, but matter
    less a year later
  • People do NOT want to all go back to New Orleans
  • Net income (income less housing costs) increases
    chance of picking a location
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