# Transportation Economics/Decision Making

**Decision Making** is the process by which one alternative is selected
over another. Decision making generally occurs in the planning phases of
transportation projects, but last minute decision making has been shown
to occur, sometimes successfully. Several procedures for making
decisions have been outlined in effort to minimize inefficiencies or
redundancies. These are idealized (or normative) processes, and describe
how decisions might be made in an ideal world, and how they are
described in official documents. Real-world processes are not as
orderly.

**Applied systems analysis** is the use of rigorous methods to assist in
determining optimal plans, designs and solutions to large scale problems
through the application of analytical methods. Applied systems analysis
focuses upon the use of methods, concepts and relationships between
problems and the range of techniques available. Any problem can have
multiple solutions. The optimal solution will depend upon technical
feasibility (engineering) and costs and valuation (economics). Applied
systems analysis is an attempt to move away from the engineering
practice of design detail and to integrate feasible engineering
solutions with desirable economic solutions. The systems designer faces
the same problem as the economist, \"efficient resource allocation\" for
a given objective function.

Systems analysis emerged during World War II, especially with the
deployment of radar in a coordinated way. It spread to other fields such
as fighter tactics, mission planning and weapons evaluation. Ultimately
the use of mathematical techniques in such problems came to be known as
operations research, while other statistical and econometric techniques
are being applied. Optimization applies to cases where data is
under-determined (fewer observations than dependent variables) and
statistics where data is over-determined (more observations than
dependent variables). After World War II, techniques spread to
universities. Systems analysis saw further mathematical development and
application to a broad variety of problems.

It has been said of Systems Analysis, that it is:

-   \"A coordinated set of procedures which addresses the fundamental
    issues of design and management: that of specifying how men, money
    and materials should be combined to achieve a higher purpose\" - De
    Neufville
-   \"\... primarily a methodology, a philosophical approach to solving
    problems for and for planning innovative advances\" - Baker
-   \"Professionals who endeavor to analyze systematically the choices
    available to public and private agencies in making changes in the
    transportation system and services in a particular region\" -
    Manheim
-   \"Systems analysis is not easy to write about: brief, one sentence
    definitions frequently are trivial\" - Thomas

The most prominent decision-making process to emerge from systems
analysis is **rational
planning**, which will be
discussed next, followed by some critiques and alternatives.

![](TE-Systems-RationalPlanningAndDecisionMaking.png "TE-Systems-RationalPlanningAndDecisionMaking.png")

*How does one (rationally) decide what to do?*

The figure identifies three layers of abstraction. The first layer (top
row) describes the high level process, which we can summarize in six
steps. A second layer details many of the components of the first layer.
A third layer, identified by the blue box, \"abstract into model or
framework\" depends on the problem at hand

## Video

! Decision Making

## Overview data

The first step is observational, review and gather data about the system
under consideration. An understanding of the world around is required,
including specifying the system.

The problem (defined in the next step) lies within a larger system, that
comprises

1.  Objectives - measure the effectiveness or performance
2.  Environment - things which affect the system but are not affected by
    it
3.  Resources - factor inputs to do the work
4.  Components - set of activities or tasks of the system
5.  Management - sets goals, allocates resources and exercises control
    over components
6.  Model of how variables in 1-5 relate to each other

the detailed objectives are identified in the following step, and the
detailed model for analysis of the problem is specified in the step
after that.

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For instance in the case of intercity transportation in California, data
about existing demand conditions, existing supply conditions, future
demand expectations, and proposed changes to supply would be important
inputs. Changes in technology and environmental conditions are important
considerations for long-term projects. We would also want to know the
certainty of the forecasts, not just a central tendency, and the
potential for alternative scenarios which may be widely different.

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## Define the problem

The second step is to *define the problem* more narrowly, in a sense to
*identify needs*.

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Rather than an amorphous issue (intercity transportation), we might be
interested in a more detailed question, e.g. how to serve existing and
future demands between two cities (say metropolitan Los Angeles and San
Francisco). The problem might be that demand is expected to grow and
outstrip supply.

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## Formulate goal

The third step is to formulate a goal. For major transportation
projects, or projects with intense community interest, this may involve
the public. For instance

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*To serve future passenger demand between Los Angeles and San Francisco,
quickly, safely, cleanly, and inexpensively.*

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The goal will need to be testable, the process below \"formulate goal\"
in the flowchart suggests this process in more detail.

The first aspect is to operationalize the goal. We need to measure the
adverbs in the goal (e.g. how do we measure \"quickly\", \"safely\",
\"cleanly\", or \"inexpensively\"). Some are straight-forward.
\"Quickly\" is a measure of travel time or speed. But it needs to
account for both the access and egress time, the waiting time, and the
travel time, and these may not be weighted the same.

The second step is identifying the decision criteria. Each adverb may
have a certain value, but it might be that an alternative has not merely
have the most points in one area, but establish at least minimum
satisfactory points in all areas. So a very fast mode must meet a
specific safety test, and going faster does not necessarily mean it can
also be more dangerous (despite what a rational economist might think
about trade-offs).

The third is to weight those criteria. E.g. how important is speed vs.
safety? This is in many ways a value question, though economics can try
to value each of these aspects in monetary form, enabling
Evaluation. For
instance, many Negative
externalities
have been monetized, giving a value of time in delay, a value of
pollution damages, and a value of life.

## Generate alternatives

*Examining, evaluating, and recommending alternatives* is often the job
of professionals, engineers, planners, and economists. Final selection
is generally the job of elected or appointed officials for important
projects.

There are several sub-problems here, the first is to *generate
alternatives*. This may require significant creativity. Within major
alternatives, there may be many sub-alternatives, e.g. the main
alternative may be mode of travel, the sub-alternatives may be different
alignments. For network problems there may be many combinations of
alternative alignments. If the analyst is lucky, these are *separable
problems*, that is, the choice of one sub-alignment is independent of
the choice of alternative sub-alignments.

1.  Algorithms-systematic search over available alternatives
    1.  Analytical
    2.  Exact numerical
    3.  Heuristic numerical
2.  Generate alternatives selectively, evaluate subjectively
    1.  Fatal flaw analysis
    2.  Simple rating schemes
    3.  Delphi exercises
3.  Generate alternatives judgmentally, evaluate scientifically using
    system model

A critical issue is how many alternatives to consider. In principle, an
infinite number of more or less similar alternatives may be generated,
not all are practical, and some may be minor variations. In practice a
stopping rule to consider a reasonable number of alternatives is used.
Major exemplars of the alternatives may be used, with fine-tuning
awaiting a later step after the first set of alternatives is analyzed.
The process may be iterative, winnowing down alternatives and detailing
alternatives as more information is gained throughout the analysis.

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Several major alternatives may be suggested, expand highways, expand air
travel, or construct new high-speed rail line, along with a no-build
alternative.

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## Abstract into model or framework

\"All Models are Wrong, Some Models are Less Wrong than
Others\"---Anonymous

\"All Models are Wrong, Some Models are Useful\"---George Box [^1]

The term **Model** refers here to a *mathematical representation of a
system*, while a **Framework** is a *qualitative organizing principle
for analyzing a system*. The terms are sometimes used interchangeably.

### Framework Example: Porter's Diamond of Advantage

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! thumb\|right\|250px\| Michael Porter\'s Diamond of
Advantage

To illustrate the idea of a framework, consider Porter\'s *Diamond of
Advantage*

Michael Porter proposes four key determinants of competitiveness, which
he calls the \"Diamond of Advantage,\" based on cases from around the
world:

1.  factor conditions, such as a specialized labor pool, specialized
    infrastructure and sometimes selective disadvantages that drive
    innovation;
2.  home demand, or local customers who push companies to innovate,
    especially if their tastes or needs anticipate global demand;
3.  related and supporting industries, specifically internationally
    competitive local supplier industries, creating a high quality,
    supportive business infrastructure, and spurring innovation and
    spin-off industries; and
4.  industry strategy/rivalry, involving both intense local rivalry
    among area industries that is more motivating than foreign
    competition and as well as a local \"culture\" which influences
    individual industries\' attitudes toward innovation and competition.

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### Model Example: The Four-Step Urban Transportation Planning System

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Within the rational planning framework, transportation forecasts have
traditionally followed the sequential four-step model or urban
transportation planning (UTP) procedure, first implemented on mainframe
computers in the 1950s at the Detroit Area Transportation Study and
Chicago Area Transportation Study (CATS).

Land use forecasting sets the stage for the process. Typically,
forecasts are made for the region as a whole, e.g., of population
growth. Such forecasts provide control totals for the local land use
analysis. Typically, the region is divided into zones and by trend or
regression analysis, the population and employment are determined for
each.

The four steps of the classical urban transportation planning system
model are:

-   Trip
    generation
    determines the frequency of origins or destinations of trips in each
    zone by trip purpose, as a function of land uses and household
    demographics, and other socio-economic factors.
-   Destination
    choice
    matches origins with destinations, often using a gravity model
    function, equivalent to an entropy maximizing model. Older models
    include the fratar model.
-   Mode choice
    computes the proportion of trips between each origin and destination
    that use a particular transportation mode. This model is often of
    the logit form, developed by Nobel Prize winner Daniel McFadden.
-   Route
    choice
    allocates trips between an origin and destination by a particular
    mode to a route. Often (for highway route assignment) Wardrop\'s
    principle of user equilibrium is applied, wherein each traveler
    chooses the shortest (travel time) path, subject to every other
    driver doing the same. The difficulty is that travel times are a
    function of demand, while demand is a function of travel time.

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See **Modeling**
for a deeper discussion of modeling questions.

## Ascertain performance

This is either an output of the analytical model, or the result of
subjective judgment.

Sherden[^2] identifies a number of major techniques for technological
forecasting that can be used to ascertain expected performance of
particular technologies, but that can be used within a technology to
ascertain the performance of individual projects. These are listed in
the following box:

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\"Major techniques for technological forecasting [^3]

-   Delphi method: a brain-storming session with a panel of experts.
-   Nominal group process: a variant of the Delphi method with a group
    leader.
-   Case study method: an analysis of analogous developments in other
    technologies.
-   Trend analysis: the use of statistical analysis to extend past
    trends into the future.
-   S-curve: a form of trend analysis using an s-shaped curve to extend
    past trends into the future.
-   Correlation analysis: the projection of development of a new
    technology past developments in similar technologies.
-   Lead-user analysis: the analysis of leading-edge users of a new
    technology predict how the technology will develop.
-   Analytic hierarchy process: the projection of a new technology by
    analyzing a hierarchy of forces influencing its development.
-   Systems dynamics: the use of a detailed model to assess the dynamic
    relationships among the major forces influencing the development of
    the technology.
-   Cross-impact analysis: the analysis of potentially interrelated
    future events that may affect the future development of a
    technology.
-   Relevance trees: the breakdown of goals for a technology into more
    detailed goals and then assigning probabilities that the technology
    will achieve these detail goals.
-   Scenario writing: the development of alternative future views on how
    the new technology could be used.\"

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## Rate alternatives

The performance of each of the alternatives is compared across decision
criteria, and weighted depending on the importance of those criteria.
The alternative with the highest ranking would be identified, and this
information would be brought forward to decision-makers.

## Compute optimal decision

The analyst is generally not the decision maker. The actual influence of
the results of the analysis in actual decisions will depend on:

1.  Determinacy of evaluation
2.  Confidence in the results on the part of the decision maker
3.  Consistency of rating among alternatives

## Implement alternatives

A decision is made. A project is constructed or a program implemented.

## Evaluate outcome

*Evaluating outcomes* of a project includes comparing outcome against
goals, but also against predictions, so that forecasting procedures can
be improved. Analysis and implementation experience lead to revisions in
systems definition, and may affect the values that underlay that
definition. The output from this \"last\" step in is used as input to
earlier steps in subsequent analyses. See e.g. Parthasarathi, Pavithra
and David Levinson (2010) Post-Construction Evaluation of Trafﬁc
Forecast Accuracy. *Transport
Policy*

## Relationship to other models

We need a tool to \"Identify Needs\" and \"Evaluate Options\". This may
be the transportation forecasting
model.

## Problem PRT: Skyweb Express

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The Metropolitan Council of Governments (the region\'s main
transportation planning agency) is examining whether the Twin Cities
should build a new Personal Rapid Transit system in downtown
Minneapolis, and they have asked you to recommend how it should be
analyzed

1\. What kind of model should be used. Why?

2\. What data should be collected.

Form groups of 3 and take 15 minutes and think about what kinds of
models you want to run and what data you want to collect, what questions
you would ask, and how it should be collected. Each group should have a
note-taker, but all members of the group should be able to present
findings to the class.

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## Thought Questions

-   Is the \"rational planning\" process rational?
-   Compare and contrast the rational planning process with the
    scientific method?

## Some Issues with Rational Planning

Nevertheless, some issues remain with the rational planning model:

### Problems of incomplete information

-   Limited Computational Capacity
-   Limited Solution Generating Capacity
-   Limited input data
-   Cost of Analysis

### Problems of incompatible desires

-   Conflicting Goals
-   Conflicting Evaluation Criteria
-   Reliance on Experts (What about the People?)

## Alternative Planning Decision Making Paradigms: Are They Irrational?

No one really believes the rational planning process is a good
description of most decision making, as it is highly idealized.
Alternatives normative and positive paradigms to the rational planning
process include:

Several strategies normatively address the problems associated with
incomplete information:

-   Satisficing
-   Decomposition "wikilink")
    hierarchically into Strategy/Tactics/Operations.

Other strategies describe how organizations and political systems work:

-   Organizational Process
-   Political Bargaining

Some do both:

-   Incrementalism

The paper Montes de Oca, Norah and David Levinson (2006) Network
Expansion Decision-making in the Twin Cities.*Journal of the
Transportation Research Board: Transportation Research Record* #1981 pp
1-11 describes the
actual decision making process for road construction in the Twin Cities

## References

Transportation Economics}}


[^1]: Box, G.E.P., Robustness in the strategy of scientific model
    building, in Robustness in Statistics, R.L. Launer and G.N.
    Wilkinson, Editors. 1979, Academic Press: New York.

[^2]: Sherden, William (1998) The Fortune Sellers, Wiley.

[^3]: Figure 6.4, p. 167 Major techniques for technological forecasting,
    in Sherden, William (1998) The Fortune Sellers, Wiley.
