Fundamentals of Transportation/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.

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


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.

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.

Define the problem


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

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.

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

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

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.

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

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

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.

Model Example: The Four-Step Urban Transportation Planning System


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.

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:

"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."

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


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.

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:

Other strategies describe how organizations and political systems work:

Some do both:

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


  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.