W. Douglass Shaw (presenter)
Dept. of Agricultural Economics and
Recreation, Parks and Tourism
Sciences
April 14, 2008

The graduate students in this semester’s 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 Riddel

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 that…?

Somebody apparently thought so
Peter is now the Chief of the Fuel
Economy Division at the United States
Department of Transportation…
– He is an “environmental and natural resource” economist

Hope: I could present some statistical results from the graduate seminar class project on
“choice modeling”
– Not quite ready, but I’ll 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 transportationrelated preferences and behaviors?

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 Train
– 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.

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 (econometrics)
– Designs vary from:
Paired Designs (Choose A or Choose B)
Multiple Choice Designs (Choose one from A,B,C)
A B C
Rank these routes (more often done in conjoint)

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 logit
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 setting

Suppose you want to “market” a new product or idea?
– 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…

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 “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

Does a person look at two at once?
A B
A B
Or three?
Or four (or more)?
A B C
C
D

e.g.: What are the attributes of a commuting alternative that matter to people?
– Cost (money and time)
– Comfort
– Discretionary power (flexibility in choosing schedule)
– Reliability

What are the money prices?
– Range from “free” to calculations based on parking, toll roads, gasoline prices, mpg of the vehicle
– 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)

L levels, K attributes L K = possible “profiles”
Full factorial design considers all possible profiles
– Possible?
Example of Commuting Attributes (# of Levels)
– Price (five), Time (five), Comfort (three), Flexibility
(three), Reliability (three)
– 5 2 X 3 3 = 25 X 27 = 675

I didn’t probably get all the attributes or levels covered. If more…
Can you “cover” 1,000 profiles?
– No
So, what to do? That’s the art of design
– Fractional factorial design

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 frequencies
– Simply: attributes are purposefully uncorrelated
(makes it easier to identify variable X’s influence)

(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 efficiency:
D-optimal efficiency
The D-optimal criterion seeks to maximize the determinant of the Fisher information matrix
Max: D = 100{1/N|(X’X) -1 | 1/A
N = number of observations; A is number of attributes*levels in design; X’X is the information matrix
1.
Uninformed prior – all parameters equal zero
2.
A priori information based on pretest or other data
3.
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)

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

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

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

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)

Obs
7
8
9
5
6
3
4
1
2
14
15
16
10
11
12
13
8
8
8
8
8
4
4
4
4
12
12
12
8
12
12
12
Cameras Cost Location
50
75
75
100
100
50
100
75
50
75
75
50
100
100
50
75
Current
Current
Current
Current
High vol
Mixed
High Ped
High vol
High Ped
Mixed
Mixed
High Ped
High vol.
High Ped
High vol
Mixed
Speed
Decrease
Decrease
Current
Current
Decrease
Current
Decrease
Current
Current
Current
Decrease
Current
Current
Current
Decrease
Decrease

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, we’d get:
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

A
Cameras cost loc speed
8 100 HP decrease
9 50 HP current
4 50 current decrease
4 75 current decrease
4 75 current current
4 100 current current
8 75 mixed current
8 50 mixed current
B
Cameras cost loc speed
12 50 mixed current
4 75 HV decrease
4 75 mixed current
8 100 HP current
8 100 HP decrease
12 75 HV decrease
12 100 mixed decrease
8 50 HP decrease

http://geography.tamu.edu/cares_survey/
Aside on Internet surveys
– See Knowledge Networks Inc. (webcast of presentation on survey bias, April 24 th )
– 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 you’d 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 prefer.
Intersections
1 – S. Texas Ave. & Walton Dr. 2- Harvey Rd. & George
Bush Dr. E.
3 – Harvey Rd & Munson Ave. 4 – Wellborn Rd. & George
Bush Dr.

1. Alternative A Alternative B
4 Additional Cameras placed throughout CS
$75 fine/citation for running light
35 mph Speed limit
No additional cameras
$125 fine/Citation for running light
35 mph Speed limit
Which of these above do you prefer, A or B? (circle)
2. Alternative C
4 added cameras placed at high traffic volume intersections
$75 citation
40 mph speed limit
Alternative D
No additional cameras
$75 citation
40 mph speed limit
Which of these above do you prefer, C or D? (circle)

Cameras make it safer 1 strongly disagree (N = 7)
2 (2)
3 (13)
4 (6)
5 strongly agree (10)
Additional cameras will improve traffic safety
Additional cameras will improve pedestrian/bike safety
1 (7)
2 (3)
3 (9)
4 (10)
5 (9)
1 (6)
2 (2)
3 (14)
4 (10)
5 (6)
Cameras are primarily to get revenue for town
1 (6)
2 (6)
3 (13)
4 (3)
5 (10)

Preliminary Responses (N = 38, all drivers except 4 who take the bus)
1.1A 4 HP cameras, $100 fine, speed down by 5 mph
Number who chose this: 13
1.1B 8 mixed cameras, $50 fine, current
Chosen by 25 speed
1.2A 4 HP cameras, $50 fine, speed current
Chosen by 32
1.2B No cameras, $75 fine, speed down by
5 mph
Chosen by 6
1.3B No cameras, $75 fine, speed current
Chosen by 24
1.3A No cameras, $50 fine, speed down by
5 mph
Chosen by 14
1.4A No cameras, $75 fine, speed down by
5 mph
Chosen by 15
1.5A No cameras, $75 fine, speed current
Chosen by 24
1.6A No cameras, $100 fine,
Chosen by 14 speed current
4 more HP cameras, $100 fine, current
Chosen by
5 mph
23 speed
4 more HP cameras, $100 fine, speed down
Chosen by 14
8 more HV cameras, $75 fine, speed down
5
Chosen by 24
1.7A 4 mixed cameras, $75 fine, speed current
Chosen by 30
1.8 4 mixed cameras, $50 fine, speed current
Chosen by 24
8 more mixed cameras, $100 fine, speed down by 5 mph
Chosen by 8
4 more HP cameras, $50 fine, speed down by 5 Mph
Chosen by 14

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

Hurricane evacuation behavior and risk perceptions
– 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 increases…

Managed Lanes (less congestion, but pay a toll for this)
Katy Freeway
– Will people use it/the ML’s?
– What will they willing to pay in tolls?
– What is the value of ML’s?
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.

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)

Round I – mostly from B/CS – living here
“temporarily”
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 subjects

Tried two panel logit specifications ( η is normally distributed individual-specific component, T is # of observations per person i). Log likelihood (random effects):
L Y
 

)
 i
I  

1
[ t
T i

1
{ Y

A it
}ln[ ( X it
 
)]
 
Y it

A }ln[1
 
( X it
    

Variable Fixed Effects
Model
*** significant at the 1% level
Random Effects
Model
Risk Level:
None
Low
Medium
Net Income
Constant
Houston
New Orleans
1.09***
0.976***
0.696***
0.00011***
--
-.004
0.123
1.01***
0.940***
0.655***
0.0001***
-1.09***
-.009
0.114

Variable Fixed Effects
Model
*** significant at the 1% level,** 5%, * 10%
Random Effects
Model
Risk Level:
None
Low
Medium
Net Income
Constant
Houston
New Orleans
0.830***
0.945***
0.659**
0.00011***
--
-.831*
-.489*
.768**
0.939***
0.553*
0.0001***
-0.792**
-.777**
-.475*

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

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