# Acoustics/Noise from Cooling Fans

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![](Acoustics_noise_from_cooling_fans.JPG "Acoustics_noise_from_cooling_fans.JPG")

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## Proposal

As electric/electronic devices get smaller and functional, the noise of
cooling device becomes important. This page will explain the origins of
noise generation from small axial cooling fans used in electronic goods
like desktop/laptop computers. The source of fan noises includes
aerodynamic noise as well as operating sound of the fan itself. This
page will be focused on the aerodynamic noise generation mechanisms.

## Introduction

If one opens a desktop computer, they may find three (or more) fans. For
example, a fan is typically found on the heat sink of the CPU, in the
back panel of the power supply unit, on the case ventilation hole, on
the graphics card, and even on the motherboard chipset if it is a recent
one. Computer noise which annoys many people is mostly due to cooling
fans, if the hard drive(s) is fairly quiet. When Intel Pentium
processors were first introduced, there was no need to have a fan on the
CPU, however, contemporary CPUs cannot function even for several seconds
without a cooling fan. As CPU densities increase, the heat transfer for
nominal operation requires increased airflow, which causes more and more
noise. The type of fans commonly used in desktop computers are axial
fans, and centrifugal blowers in laptop computers. Several fan types are
shown here (pdf
format). Different
fan types have different characteristics of noise generation and
performance. The axial flow fan is mainly considered in this page.

## Noise Generation Mechanisms

The figure below shows a typical noise spectrum of a 120 **mm** diameter
electronic device cooling fan. One microphone was used at a point 1
**m** from the upstream side of the fan. The fan has 7 blades, 4 struts
for motor mounting and operates at 13V. Certain amount of load is
applied. The blue plot is background noise of anechoic chamber, and the
green one is sound loudness spectrum when the fan is running.

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![](Noisespectrum.gif "Noisespectrum.gif")

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(\*BPF = Blade Passing Frequency)\
Each noise elements shown in this figure is caused by one or more of
following generation mechanisms.

### Blade Thickness Noise - Monopole (But very weak)

Blade thickness noise is generated by volume displacement of fluid. A
fan blade has its thickness and volume. As the rotor rotates, the volume
of each blade displaces fluid volume, then they consequently fluctuate
pressure of near field, and noise is generated. This noise is tonal at
the running frequency and generally very weak for cooling fans, because
their RPM is relatively low. Therefore, thickness of fan blades hardly
affects to electronic cooling fan noise.\
(This kind of noise can become severe for high speed turbomachines like
helicopter rotors.)

### Tonal Noise by Aerodynamic Forces - Dipole

#### Uniform Inlet Flow (Negligible)

The sound generation due to uniform and steady aerodynamic force has a
characteristic very similar to the blade thickness noise. It is very
weak for low speed fans, and depends on fan RPM. Since some steady blade
forces are necessary for a fan to do its duty even in an ideal
condition, this kind of noise is impossible to avoid. It is known that
this noise can be reduced by increasing the number of blades.

#### Non-uniform Inlet Flow

Non-uniform (still steady) inlet flow causes non-uniform aerodynamic
forces on blades as their angular positions change. This generates noise
at blade passing frequency and its harmonics. It is one of the major
noise sources of electronic cooling fans.

#### Rotor-Casing interaction

If the fan blades are very close to a structure which is not symmetric,
unsteady interaction forces to blades are generated. Then the fan
experiences a similar running condition as lying in non-uniform flow
field. See Acoustics/Rotor Stator
Interactions for
details.

#### Impulsive Noise (Negligible)

This noise is caused by the interaction between a blade and
blade-tip-vortex of the preceding blade, and not severe for cooling
fans.

#### Noise from Stall

#### Rotating Stall

:   Click here "wikilink") to read the definition and
    an aerodynamic description of **stall**.

The noise due to stall is a complex phenomenon that occurs at low flow
rates. For some reason, if flow is locally disturbed, it can cause stall
on one of the blades. As a result, the upstream passage on this blade is
partially blocked. Therefore, the mean flow is diverted away from this
passage. This causes increasing of the angle of attack on the closest
blade at the upstream side of the originally stalled blade, the flow is
again stalled there. On the other hand, the other side of the first
blade is un-stalled because of reduction of flow angle.

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![](Stall.gif "Stall.gif")

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repeatedly, the stall cell turns around the blades at about 30\~50% of
the running frequency, and the direction is opposite to the blades. This
series of phenomenon causes unsteady blade forces, and consequently
generates noise and vibrations.

#### Non-uniform Rotor Geometry

Asymmetry of rotor causes noise at the rotating frequency and its
harmonics (not blade passing frequency obviously), even when the inlet
flow is uniform and steady.

#### Unsteady Flow Field

Unsteady flow causes random forces on the blades. It spreads the
discrete spectrum noises and makes them continuous. In case of
low-frequency variation, the spread continuous spectral noise is around
rotating frequency, and narrowband noise is generated. The stochastic
velocity fluctuations of inlet flow generates broadband noise spectrum.
The generation of random noise components is covered by the following
sections.

### Random Noise by Unsteady Aerodynamic Forces

#### Turbulent Boundary Layer

Even in the steady and uniform inlet flow, there exist random force
fluctuations on the blades. That is from turbulent blade boundary layer.
Some noise is generated for this reason, but dominant noise is produced
by the boundary layer passing the blade trailing edge. The blade
trailing edges scatter the non-propagating near-field pressure into a
propagatable sound field.

#### Incident Turbulent

Velocity fluctuations of the intake flow with a stochastic time history
generate random forces on blades, and a broadband spectrum noise.

#### Vortex Shedding

For some reason, a vortex can separate from a blade. Then the
circulating flow around the blade starts to be changed. This causes
non-uniform forces on blades, and noises. A classical example for this
phenomenon is \'Karman vortex
street\'.
(some images and
animations.)
Vortex shedding mechanism can occur in a laminar boundary layer of low
speed fan and also in a turbulent boundary layer of high frequency fan.

#### Flow Separation

Flow separation causes stall explained above. This phenomenon can cause
random noise, which spreads all the discrete spectrum noises, and turns
the noise into broadband.

#### Tip Vortex

Since cooling fans are ducted axial flow machines, the annular gap
between the blade tips and the casing is important parameter for noise
generation. While rotating, there is another flow through the annular
gap due to pressure difference between upstream and downstream of fan.
Because of this flow, tip vortex is generated through the gap, and
broadband noise increases as the annular gap gets bigger.

## Installation Effects

Once a fan is installed, even though the fan is well designed
acoustically, unexpected noise problem can come up. It is called as
installation effects, and two types are applicable to cooling fans.

### Effect of Inlet Flow Conditions

A structure that affects the inlet flow of a fan causes installation
effects. For example Hoppe & Neise \[3\] showed that with and without a
bellmouth nozzle at the inlet flange of 500**mm** fan can change the
noise power by 50**dB** (This application is for much larger and noisier
fan though).

### Acoustic Loading Effect

This effect is shown on duct system applications. Some high performance
graphic cards apply duct system for direct exhaustion.\
The sound power generated by a fan is not only a function of its
impeller speed and operating condition, but also depends on the acoustic
impedances of the duct systems connected to its inlet and outlet.
Therefore, fan and duct system should be matched not only for
aerodynamic noise reasons but also because of acoustic considerations.

## Closing Comment

Noise reduction of cooling fans has some restrictions:\
1. Active noise control is not economically effective. 80mm cooling fans
are only 5\~10 US dollars. It is only applicable for high-end electronic
products.\
2. Restricting certain aerodynamic phenomenon for noise reduction can
cause serious performance reduction of the fan. Increasing RPM of the
fan is of course much more dominant factor for noise.\
Different stories of fan noise are introduced at some of the linked
sites below like active RPM control or noise comparison of various
bearings used in fans. If blade passing noise is dominant, a muffler
would be beneficial.

## Links to Interesting Sites about Fan Noise

Some practical issue of PC noise are presented at the following sites.\
Cooling Fan Noise Comparison - Sleeve Bearing vs. Ball Bearing (pdf
format)\
Brief explanation of fan noise origins and noise reduction
suggestions\
Effect of sweep angle
comparison\
Comparisons of noise from various 80mm
fans\
Noise reduction of a specific desktop
case\
Noise reduction of another specific desktop
case\
Informal study for noise from CPU cooling
fan\
Informal study for noise from PC case fans\
Active fan speed optimizators for minimum noise from desktop
computers

-   Some general fan noise reduction
    techniques
-   Brüel & Kjær - acoustic testing device company. Various
    applications are presented in \"applications\"
    tap
-   \"Fan Noise Solutions\" by cpemma
-   How To Build A Computer/Quiet
    PC

## References

\[1\] Neise, W., and Michel, U., \"Aerodynamic Noise of Turbomachines\"\
\[2\] Anderson, J., \"Fundamentals of Aerodynamics\", 3rd edition, 2001,
McGrawHill\
\[3\] Hoppe, G., and Neise, W., \"Vergleich verschiedener
Gerauschmessnerfahren fur Ventilatoren. Forschungsbericht FLT 3/1/31/87,
Forschungsvereinigung fur Luft- und Trocknungstechnik e. V.,
Frankfurt/Main, Germany


# Acoustics/Piezoelectric Transducers

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# Introduction

Piezoelectricity from the Greek word \"piezo\" means pressure
electricity. Certain crystalline substances generate electric charges
under mechanical stress and conversely experience a mechanical strain in
the presence of an electric field. The piezoelectric effect describes a
situation where the transducing material senses input mechanical
vibrations and produces a charge at the frequency of the vibration. An
AC voltage causes the piezoelectric material to vibrate in an
oscillatory fashion at the same frequency as the input current.

Quartz is the best known single crystal material with piezoelectric
properties. Strong piezoelectric effects can be induced in materials
with an ABO3, Perovskite crystalline structure. \'A\' denotes a large
divalent metal ion such as lead and \'B\' denotes a smaller tetravalent
ion such as titanium or zirconium.

For any crystal to exhibit the piezoelectric effect, its structure must
have no center of symmetry. Either a tensile or compressive stress
applied to the crystal alters the separation between positive and
negative charge sights in the cell causing a net polarization at the
surface of the crystal. The polarization varies directly with the
applied stress and is direction dependent so that compressive and
tensile stresses will result in electric fields of opposite voltages.

# Vibrations & Displacements

Piezoelectric ceramics have non-centrosymmetric unit cells below the
Curie temperature and centrosymmetric unit cells above the Curie
temperature. Non-centrosymmetric structures provide a net electric
dipole moment. The dipoles are randomly oriented until a strong DC
electric field is applied causing permanent polarization and thus
piezoelectric properties.

A polarized ceramic may be subjected to stress causing the crystal
lattice to distort changing the total dipole moment of the ceramic. The
change in dipole moment due to an applied stress causes a net electric
field which varies linearly with stress.

# Dynamic Performance

The dynamic performance of a piezoelectric material relates to how it
behaves under alternating stresses near the mechanical resonance. The
parallel combination of C2 with L1, C1, and R1 in the equivalent circuit
below control the transducers reactance which is a function of
frequency.

## Equivalent Electric Circuit

## Frequency Response

The graph below shows the impedance of a piezoelectric transducer as a
function of frequency. The minimum value at fn corresponds to the
resonance while the maximum value at fm corresponds to anti-resonance.
^Superscript\ text*Italic\ text*^

# Resonant Devices

Non resonant devices may be modeled by a capacitor representing the
capacitance of the piezoelectric with an impedance modeling the
mechanically vibrating system as a shunt in the circuit. The impedance
may be modeled as a capacitor in the non resonant case which allows the
circuit to reduce to a single capacitor replacing the parallel
combination.

For resonant devices the impedance becomes a resistance or static
capacitance at resonance. This is an undesirable effect. In mechanically
driven systems this effect acts as a load on the transducer and
decreases the electrical output. In electrically driven systems this
effect shunts the driver requiring a larger input current. The adverse
effect of the static capacitance experienced at resonant operation may
be counteracted by using a shunt or series inductor resonating with the
static capacitance at the operating frequency.

# Applications

## Mechanical Measurement

Because of the dielectric leakage current of piezoelectrics they are
poorly suited for applications where force or pressure have a slow rate
of change. They are, however, very well suited for highly dynamic
measurements that might be needed in blast gauges and accelerometers.

## Ultrasonic

High intensity ultrasound applications utilize half wavelength
transducers with resonant frequencies between 18 kHz and 45 kHz. Large
blocks of transducer material is needed to generate high intensities
which is makes manufacturing difficult and is economically impractical.
Also, since half wavelength transducers have the highest stress
amplitude in the center the end sections act as inert masses. The end
sections are often replaced with metal plates possessing a much higher
mechanical quality factor giving the composite transducer a higher
mechanical quality factor than a single-piece transducer.

The overall electro-acoustic efficiency is:

`             Qm0 = unloaded mechanical quality factor`\
`             QE  = electric quality factor`\
`             QL  = quality factor due to the acoustic load alone`

The second term on the right hand side is the dielectric loss and the
third term is the mechanical loss.

Efficiency is maximized when:

then:

The maximum ultrasonic efficiency is described by:

Applications of ultrasonic transducers include:

` Welding of plastics`\
` Atomization of liquids`\
` Ultrasonic drilling`\
` Ultrasonic cleaning`\
` Ultrasonic foils in the paper machine wet end for more uniform fibre distribution`\
` Ultrasound`\
` Non-destructive testing`\
` etc.`

# More Information and Source of Information

MorganElectroCeramics

Ultra Technology


# Acoustics/Generation and Propagation of Thunder

**Thunder** is the sound made by lightning. Depending on the nature of
the lightning and distance of the listener, thunder can range from a
sharp, loud crack to a long, low rumble (brontide). The sudden increase
in pressure and temperature from lightning produces rapid expansion of
the air surrounding and within a bolt of lightning. In turn, this
expansion of air creates a sonic shock wave which produces the sound of
thunder, often referred to as a *clap*, *crack*, or *peal of thunder*.
The distance of the lightning can be calculated by the listener
depending on when the sound is heard vs. the vision of the lightning
strike.

!A lightning bolt.{width="350"}

## Etymology

The *d* in Modern English *thunder* (from earlier Old English *þunor*)
is epenthetic, and is now found as well in Modern Dutch *donder* (cp
Middle Dutch *donre*, and Old Norse *þorr*, Old Frisian *þuner*, Old
High German *donar* descended from Proto-Germanic \**þunraz*). In Latin
the term was *tonare* \"to thunder\". The name of the Germanic god Thor
comes from the Old Norse word for thunder.

***NOTE*:The text above is taken from the wikipedia
entry.**


# New Zealand History/Introduction

## Introduction to A Concise New Zealand History

This is a concise textbook on New Zealand history, designed so it can be
read by virtually anyone wanting to find out more about New Zealand
history.

The textbook covers the time span of human settlement in New Zealand. It
includes:

-   The discovery and colonisation of New Zealand by Polynesians.
-   Maori culture up to the year 1840.
-   Discovery of New Zealand by Europeans.
-   Early New Zealand economy and Missionaries in New Zealand.
-   The Treaty of Waitangi.
-   European colonisation, and conflict with the Maori people.
-   Colonial, Twentieth Century and Modern Government.
-   Important events in the twentieth century and recent times.

Find out how events in New Zealand\'s humble beginnings have shaped the
way the country is in the present day.

New Zealand


# New Zealand History/The Colonial Government

New

## The Colonial Government

!The Colonial New Zealand
flag
After New Zealand was annexed by Britain, it was initially set up as a
dependency of New South Wales. However, by 1841, New Zealand was made a
colony in its own right. As a colony, it inherited political practices
and institutions of government from the United Kingdom.

The United Kingdom Government started the first New Zealand Government
by appointing governors, being advised by appointed executive and
legislative councils.

In 1852, the British Parliament passed the New Zealand Constitution Act,
which provided for the elected House of Representatives and Legislative
Council. The General Assembly (the House and Council combined) first met
in 1854.

New Zealand was effectively self-governing in all domestic matters
except \'native policy\' by 1856. Control over native policy was passed
to the Colonial Government in the mid-1860s.

The first capital of the country was Russell, located in the Bay of
Islands, declared by Governor Hobson after New Zealand was formally
annexed. In September 1840, Hobson changed the capital to the shores of
the Waitematā Harbour where Auckland was founded. The seat of Government
was centralised in Wellington by 1865.

**Provincial Governments in New Zealand** !The boundaries of the former
New Zealand
provinces
From 1841 until 1876, provinces had their own provincial governments.
Originally, there were only three provinces, set up by the Royal
Charter:

-   New Ulster (North Island north of Patea River)
-   New Munster (North Island south of Patea River, plus the South
    Island)
-   New Leinster (Stewart Island)

In 1846, the provinces were reformed. The New Leinster province was
removed, and the two remaining provinces were enlarged and separated
from the Colonial Government. The reformed provinces were:

-   New Ulster (All of North Island)
-   New Munster (The South Island plus Stewart Island)

The provinces were reformed yet again by the New Zealand Constitution
Act 1852. In this constitution, the old provinces of New Ulster and New
Munster were abolished and six new provinces were set up:

-   Auckland
-   New Plymouth
-   Wellington
-   Nelson
-   Canterbury
-   Otago

Each province had its own legislature that elected its own Speaker and
Superintendent. Any male 21 years or older that owned freehold property
worth £50 a year could vote. Elections were held every four years.

Four new provinces were introduced between November 1858 and December
1873. Hawkes Bay broke away from Wellington, Marlborough from Nelson,
Westland from Canterbury, and Southland from Otago.

Not long after they had begun, provincial governments were a matter for
political debate in the General Assembly. Eventually, under the
premiership of Harry Atkinson, the Colonial Government passed the
Abolition of Provinces Act 1876, which wiped out the provincial
governments, replacing them with regions. Provinces finally ceased to
exist on the 1st of January 1877.

Twentieth Century


# New Zealand History/Famous New Zealanders

## Famous New Zealanders

!Sir Edmund Hillary in Poland,
2004.

**Edmund Hillary**

On the 29th of May 1953, New Zealander Edmund Hillary became the first
person to reach the summit of Mount Everest with Nepalese climber
Tenzing Norgay (the summit at the time was 29,028 feet above sea level).
He was knighted by Queen Elizabeth II on his return. Sir Edmund Hillary
was famous after news spread he had reached the summit, but he didn\'t
finish at Mount Everest. He led the New Zealand section of the
Trans-Antarctic expedition from 1955 to 1958.

In the 1960s, he returned to Nepal to build clinics, hospitals and
schools for the Nepalese people. He also convinced their government to
pass laws to protect their forests and the area around Mount Everest.

In the 1970s, several books were published by Hillary about his journey
up Mount Everest.

Edmund Hillary is one of the most famous New Zealanders, and appears on
the New Zealand five dollar note.

He died on the 11th of January 2008.

**Ernest Rutherford**

Ernest Rutherford was a nuclear physicist who became known as the
\'father\' of nuclear physics. He pioneered the Bohr model of the atom
through his discovery of Rutherford scattering off the atomic nucleus
with his Geiger-Marsden experiment (gold foil experiment).

He was born in Brightwater, New Zealand, but lived in England for a
number of years.

He received the Commonwealth Order of Merit, the Nobel Prize in
Chemistry in 1908, and was a member of the Privy Council of the United
Kingdom and the Royal Society.

Rutherford appears on the New Zealand one hundred dollar note.

Twentieth Century


# Speech-Language Pathology/Stuttering/Print version

# Core Stuttering Behaviors

# Incidence and Prevalence of Stuttering

# Development of Childhood Stuttering

# Neurology of Stuttering

# Genetics of Stuttering

# Physiology, Psychology, and Personality of Stutterers

# Belief-Related Changes in Stuttering

# Stress-Related Changes in Stuttering

# Measurement of Stuttering

# Other Fluency Disorders

# Research I\'d Like to See

# Choosing a Speech-Language Pathologist

# Why Do Stutterers Avoid Speech Therapy?

# Stuttering Therapies for Pre-School Children

# Stuttering Therapies for School-Age Children

# Stuttering Therapies for Teenagers

# Stuttering Therapies for Mentally Retarded Individuals

# Fluency Shaping Stuttering Therapy

# Stuttering Modification Therapy

# Treating Speech-Related Fears and Anxieties

# Personal Construct Therapy: You Always Have Choices

# Treating Psychological Issues

# Improving Self-Awareness of Stuttering Behaviors

# Anti-Stuttering Devices

# Anti-Stuttering Medications

# Alternative Medicine Therapies for Stuttering

# How We Treat Stuttering

# What Worked for Me

# Practice Word Lists

# You\'re Not Alone: Join a Support Group

# Famous People Who Stutter

# Stuttering and Employment

# How to Handle Telephone Calls

# Public Perceptions of Stutterers

# How the Media Presents Stuttering

# Cultural and Ethnic Differences in Stuttering

# High School Science Projects

# Acting and Theater

# Spouses of People Who Stutter

# Stuttering in the Military

# Advice for Listeners

# Stuttering in Movies and Television

# My Life in Stuttering

# Recommended Books and Periodicals


# 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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</div>
```
## 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

```{=html}
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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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</div>
```
### 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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</div>
```
## 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.

```{=html}
</div>
```
## 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.


# Transportation Economics/Modeling

*All forecasts are wrong; some forecasts are more wrong than others.* -
anonymous

**Modeling** is a means for representing reality in an abstracted way.
Your mental models are your **world view**: your outlook on life, and
the world. The world view is your *internal model of how the world
works*; it is employed every time you make a prediction: what do you
expect, what is a surprise. The expression "Where you stand depends on
where you sit" epitomizes this idea. Your world view is shaped by your
experience and your position.

When modeling, the issue of **Point of View** should be considered. It
must be clear who (and what) the results are for. If you are modeling
for personal pleasure, it will naturally reflect your own worldview, but
if you are working for an employer or client, their point of view must
also be considered, if the inputs or results deviate significantly from
their worldview, they may adapt their worldview, but more likely will
dismiss the model.

Modeling can be conducted both for subjective **advocacy** and for
objective **analysis**. The same methods may be employed in either, and
the ethical modeler will produce the same results in either case, but
they may be used differently.

## Why Model?

There are a variety of reasons to model. Modeling helps

-   gain insight into complex situations by understanding simpler
    situations resembling them
-   optimize the use of resources in building or maintaining systems
-   operate system, particularly by testing alternative operational
    scenarios
-   educate and provide experience for model-builders
-   provide a platform for testing contending ideas and use in
    negotiations.

Particular applications in transportation include:

-   Forecasting traffic
-   Testing scenarios (alternative land uses, networks, policies)
-   Planning projects/corridor studies
-   Regulating land use: Growth management/public facility adequacy
-   Managing complexity, when eyeballs are insufficient, (different
    people have different intuitions)
-   Understanding travel behavior
-   Influencing decisions
-   Estimating traffic in the absence of data

## Developing Models

As an engineer, economist, or planner you may be given a model to use.
But that model was not spontaneously generated, it came from other
engineers, economists, or planners who undertook a series of steps to
translate raw data into a tool that could be used to generate useful
information.

The first step, specification, tells us what depends on what (the number
of trips leaving a zone may depend on the number of households). The
estimation step tells us how strong these relationships mathematically
are, (each household generates two trips in the peak hour).
Implementation takes those relationships and puts them into a computer
program. Calibration looks at the output of the computer program and
compares it to other data, if they don\'t match exactly, adjustments to
the models may be made. Validation compares the results to data at
another point in time. Finally the model is applied to look at a project
(e.g. how much traffic will use Granary Road after Washington Avenue is
closed to traffic).

-   Specification:
    -   $y=f(X)\,\!$
-   Estimation:
    -   $y=mX+b\,\!$; m=1, b=2
-   Implementation
    -   If Z \> W, Then $y=mX+b\,\!$
-   Calibration
    -   $y_{predicted}+k=y_{observed}\,\!$
-   Validation
    -   $y_{predicted1990}+k=y_{observed1990}\,\!$
-   Application

## Specification

When building a model system, numerous decisions must be made. These are
discussed below:

### Types of Models

There are numerous types of models, a short list is below. Each has
different applicability, multiple methods may be used in pursuit of the
same question, sometimes they are complementary, and sometimes
competitive techniques.

-   Network analysis
-   Linear Programming
-   Nonlinear Programming
-   Simulation
-   Deterministic queuing
-   Probabilistic queuing
-   Regression
-   Neural Nets
-   Genetic Algorithm
-   Cost/ Benefit Analysis
-   Life-cycle costing
-   System Dynamics
-   Control Theory
-   Difference Equations
-   Differential Equations
-   Probabilistic Risk Assessment
-   Supply/Demand Equilibrium
-   Game Theory
-   Statistical Decision Theory
-   Markov Models
-   Cellular Automata
-   Etc.

### Model Trade-offs

Building a model requires trading-off time and resource constraints. One
could always be more detailed, more accurate, or more comprehensive if
resources were not constrained. However, the following must also be
considered.

-   Money,
-   Data,
-   Computation,
-   Labor,
-   Ease of Use,
-   Convincing (e.g. Graphic Displays),
-   Extendable,
-   Evidence of Model Benefits,
-   Measuring Model Success

### Organization of Model System

-   Hierarchy of Models
-   Centralized vs. Decentralized (Optimization (Global) vs. Agent,
    Local Optimization)

### Time

-   Time Frame
-   Static vs. Dynamic
-   Real Time vs. Offline
-   Short Term vs. Long Term (Partial vs. General Equilibrium)
-   Proactive vs. Reactive (Predictive vs. Responsive)

### Space

-   Scale/Detail
-   Spatial Extent
-   Boundaries (Boundary Effects)
-   Macroscopic vs. Microscopic (Zones, Flows vs. Individuals, Vehicles)

### Process

-   Stochastic vs. Deterministic
-   Linear vs. Nonlinear
-   Continuous vs. Discrete
-   Numerical Simulation vs. Closed Form Solution
-   Equilibrium vs. Disequilibrium

### Type

-   Behavioral vs. Aggregate Model
-   Physical vs. Mathematical Models

## Solution Techniques

When solving the model, the system as a whole must be understood.
Several questions arise:

-   Does the solution exist?
-   Is the solution unique?
-   Is the solution feasible?

Solution techniques often trade-off accuracy vs. speed. Some solution
techniques may only guarantee a local optima, while others (such as
brute force techniques) can guarantee a global optimum, but may be much
slower.

## "Four-Step" Urban Transportation Planning Models

!A green transport
hierarchy
We want to answer a number of related questions (who, what, where, when,
why, and how):

-   Who is traveling or what is being shipped?
-   Where are the origin and destination of those trips, shipments?
-   When do those trips begin and end (how long do they take, how far
    away are the trip ends)?
-   Why are the trips being made, what is their purpose?
-   How are the trips getting there, what routes or modes are they
    taking?

If we know the answers to those questions, we want to know what are the
answers to those questions a function of?

-   Cost: Money, Time spent on the trip,
-   Cost: Money and Time of alternatives.
-   Benefit (utility) of trip (e.g. the activity at the destination)
-   Benefit of alternatives

The reason for this is to understand what will happen under various
circumstances:

-   How much "induced demand" will be generated if a roadway is
    expanded?
-   How many passengers will be lost if bus services are cut back?
-   How many people will be "priced off" if tolls are implemented?
-   How much traffic will a new development generate?
-   How much demand will I lose if I raise shipping costs to my
    customers?

In short, for urban passenger travel, we are trying to predict the
number of trips by:

-   Origin Activity,
-   Destination Activity,
-   Origin Zone,
-   Destination Zone,
-   Mode,
-   Time of Day, and
-   Route.

This is clearly a multidimensional problem.

In practice, the mechanism to do this is to employ a \"four-step\" urban
transportation planning model, each step will be detailed in subsequent
modules. These steps are executed in turn, though there may be feedback
between steps:

-   Trip Generation - How many trips $T_{i}$ or $T_{j}$ are entering or
    leaving zone $i$ or $j$
-   Trip Distribution or Destination Choice - How many trips $T_{ij}$are
    going from zone $i$ to zone $j$
-   Mode Choice - How many trips $T_{ijm}$ from $i$ to $j$ are using
    mode $m$
-   Route Choice - Which links are trips $T_{ijmr}$ from $i$ to $j$ by
    mode $m$ using route $r$

## Thought Questions

-   Is past behavior reflective of future behavior?
-   Can the future be predicted?
-   Is the future independent of decisions, or are prophecies
    self-fulfilling?
-   How do we know if forecasts were successful?
-   Against what standard are they to be judged?
-   What values are embedded in the planning process?
-   What happens when values change?

## Additional Problems

-   Homework
-   Additional
    Problems

## Key Terms

-   Rational Planning
-   Transportation planning model
-   Matrix, Full Matrix, Vector Matrix, Scalar Matrix
-   Trip table
-   Travel time matrix
-   Origin, Destination
-   Purpose
-   Network
-   Zone (Traffic Analysis Zone or Transportation Analysis Zone, or TAZ)
-   External station or external zone
-   Centroid
-   Node
-   Link
-   Turn
-   Route
-   Path
-   Mode

## Video

-   Models

## References


# Transportation Economics/Data

There are a variety of types of transportation data used in analysis.
Some are listed below:

-   Infrastructure Status
-   Traffic Counts
-   Travel Behavior Inventory
-   Land Use Inventory
-   Truck/Freight Demand
-   External/Internal Demand (by Vehicle Type)
-   Special Generators

## Revealed Preference

Household travel surveys which ask people what they actually did are a
type of *Revealed Preference* survey data that have been obtained by
direct observation of the choice that individuals make with respect to
travel behavior. *Travel Cost Analysis* uses the prices of market goods
to evaluate the value of goods that are not traded in the market.

*Hedonic Pricing* uses the market price of a traded good and measures of
its component attributes to calculate value. There are other methods to
attain Revealed Preference information, but surveys are the most common
in travel behavior.

### Travel Behavior Inventory

While the Cleveland Regional Area Traffic Study in 1927 was the first
metropolitan planning attempt sponsored by the US federal government,
the lack of comprehensive survey methods and standards at that time
precluded the systematic collection of information such as travel time,
origin and destination, and traffic counts. The first US travel surveys
appeared in urban areas after the Federal-Aid Highway Act of 1944
permitted the spending of federal funds on urban highways.[^1] A new
home- interview origin-destination survey method was developed in which
households were asked about the number of trips, purpose, mode choice,
origin and destination of the trips conducted on a daily basis. In 1944,
the US Bureau of Public Roads printed the Manual of Procedures for Home
Interview Traffic Studies.[^2] This new procedure was first implemented
in several small to mid-size areas. Highway engineers and urban planners
made use of the new data collected after the 1944 Highway Act extended
federally sponsored planning to travel surveys as well as traffic
counts, highway capacity studies, pavement condition studies and
cost-benefit analysis.

Attributes of a household travel survey, or travel behavior inventory
include:

-   Travel Diary of \~ 1% sample of population (all trips made on one
    day) every 10 years
-   Socioeconomic/demographic data of survey respondents
-   Collection methodology:
    -   Phone,
    -   Mail,
    -   In-Person at Home,
    -   In-Person at Work,
    -   Roadside

Many such surveys are available online at: Metropolitan Travel Survey
Archive

### Thought Question

What are the advantages and disadvantages of Revealed Preference
surveys?

## Stated Preference

In contrast with revealed preference, *Stated preference* is a group of
techniques used to calculate the utility functions of transport options
based on the response of an individual decision-maker to certain given
options. The options generally are based on descriptions of the
transport scenario or are constructed by the researcher

-   *Contingent valuation* is based on the assumption that the best way
    to find out the value that an individual places on something is
    known by asking.
-   *Compensating variation* is the compensating payment that leaves an
    individual as well off as before the economic change.
-   *Equivalent variation* for a benefit is the minimum amount of money
    one would have to be compensated to leave the person as well as they
    would be after the change.
-   *Conjoint analysis* refers to the application of the design of
    experiments to obtain the preferences of the individual, breaking
    the task into a list of choices or ratings that enable us to compute
    the relative importance of each of the attributes studied

### Thought Question

What are the advantages and disadvantages of Stated Preference surveys?

## Pavement conditions

*adapted from Xie, Feng and David Levinson (2008) The Use of Road
Infrastructure Data for Urban Transportation Planning: Issues and
Opportunities. Published in Infrastructure Reporting and Asset
Management Edited by Adjo Amekudzi and Sue McNeil. pp- 93-98. Publisher:
American Society of Civil Engineers, Reston, Virginia.*[^3]

Road infrastructure represents the supply side of an urban
transportation system. Pavement condition is a critical indicator to the
quality of road infrastructure in terms of providing a smooth and
reliable driving environment on roads. A series of indices have been
developed to evaluate the pavement conditions of road segments in their
respective jurisdictions: Pavement Condition Index (PCI) is scored as a
perfect roadway (100 points) minus point deductions for "distresses"
that are observed; Present Serviceability Rating (PSR) is measured as
vertical movement per unit horizontal movement (e.g. millimeters of
vertical displacement per meter of horizontal displacement) as one
drives along the road; (SR) Surface Rating is calculated by reviewing
images of the roadway based on the frequency and severity of defects;
Pavement Quality Index (PQI) is calculated using the PSR and SR to
evaluate the general condition of the road. A high PQI (up to 4.5) means
a road will most likely not need maintenance soon, whereas a low PQI
means it can be selected for maintenance.[^4]

These indices of pavement quality are basic measures for road
maintenance and preservation, for which each county develops its own
performance standards to evaluate pavement conditions and make decisions
on maintenance and preservation projects. Typically, pavement
preservation projects are prioritized based on PCI of road segments: the
lower the PCI, the higher the likelihood of selection. Taking Washington
County, Minnesota as an example,[^5] the county has determined that a
reasonable standard to maintain is an average PCI of 72. Thus any road
segment with its PCI below 72 has a chance to be selected for
preservation. Dakota County, Minnesota on the other hand, scores its
preservation projects according to the measure of PQI: a road segment
will be allocated 17 points (out of a possible 100) if its PQI falls
lower than 3.1.

The pavement data structure is incompatible with the link-node structure
of the planning road network used by the Metropolitan Council and other
planning agencies. Typically, the measures of PCI, PSR, and PQI are
stored in records indexed by "highway segment numbers" along each
highway route. Highway sections with the same highway segment number are
differentiated by their starting and ending stations. There is no exact
match between highway segments in the actual road network and links in
the planning network, as stationing is a position along the curved
centerline of a highway while a planning network is a simplified
structure consisting of only straight lines intersecting at points.
Historic pavement data is generally unavailable in electronic format,
although the information on pavement history such as pavement life and
the duration since last repaving are important to estimate the cost of a
preservation project, also affecting the decision whether a specific
project will get selected and how much funding will be allocated.

## Traffic flow

Traffic conditions reflect the travel demand loaded on a given road
infrastructure. Traffic flows on roads, together with road capacity, can
be used to calculate the volume/capacity (V/C) ratio, which is an
approximate indicator for the level of service of road infrastructure,
and is commonly adopted by the jurisdictions in their respective
decision making processes. The traffic flows on the planning road
network are predicted by the transportation planning model, but the
results have to be calibrated with actual traffic data.

Loop detectors are the primary technology currently employed in many US
metropolitan areas to collect actual traffic data. E.g. In the Twin
Cities of Minneapolis-St. Paul, about one thousand detector stations
have been buried on major highways, through which Mn/DOT\'s Traffic
Management Center collects, stores, and makes public traffic data every
30 seconds, including measured volume (flow) and occupancy, and
calculated speed data for each detector station. Although the estimates
of Annual Average Daily Traffic (AADT) for the planning road network are
readily available, loop detectors provide more accurate measures of
traffic volume, since they are collecting real-time data on a continuous
basis. It also allows for calibrating models to hourly rather than daily
conditions.

Due to the limited ability to convert raw data collected by loop
detectors, however, most forecasting models rely on AADT data. The raw
data are stored in a 30- second interval in binary codes. For planning
uses they have to be converted and aggregated into desired measures,
such as peak hour average, average for a particular month or a
particular season, etc., in a systematic way.

Another issue in integrating loop detector data into a planning road
network is to match the detector stations with the links in planning
networks. Similar to the problem encountered in translating pavement
data, detectors are located along major highways and mapped on the
actual geometry of the network, while the planning road network is a
simplified structure with only straight lines.

## Sampling Issues (Statistics)

-   Sample Size,
-   Population of Interest
-   Sampling Method,
-   Error:
    -   Measurement,
    -   Sampling,
    -   Computational,
    -   Specification,
    -   Transfer,
    -   Aggregation
-   Bias,
-   Oversampling
-   Extent of Collection
    -   Spatial
    -   Temporal
-   Span of Data
    -   Cross-section,
    -   Time Series, and
    -   Panel

## Metadata

*Adapted from Levinson, D. and Zofka, Ewa. (2006) "The Metropolitan
Travel Survey Archive: A Case Study in
Archiving" in*Travel
Survey Methods: Quality and Future Directions, Proceedings of 5th
International Conference on Travel Survey Methods*(Peter Stopher and
Cheryl Stecher, editors)*[^6]

Metadata allows data to function together. Simply put, metadata is
information about information -- labeling, cataloging and descriptive
information structured to permit data to be processed. Ryssevik and
Musgrave (1999)[^7] argue that high quality metadata standards are
essential as metadata is the launch pad for any resource discovery, maps
complex data, bridges the gap between data producers and consumers, and
links data with its resultant reports and scientific studies produced
about it. To meet the increasing needs for the proper data formats and
encoding standards, the World Wide Web Consortium (W3C) has developed
the generic Resource Description Framework (RDF) (W3C 2002). RDF treats
metadata more generally, providing a standard way to use Extended Markup
Language (XML) to "represent metadata in the form of statements about
properties and relationships of items" (W3C 2002).[^8] Resources can be
almost any type of file, including of course, travel surveys. RDF
delivers detailed and unified data description vocabulary.

Applying these tools specifically to databases, the Data Documentation
Initiative (DDI) for Document Type Definitions (DTD) applies metadata
standards used for documenting datasets. DDI was first developed by
European and North American data archives, libraries and official
statistics agencies. "The Data Documentation Initiative (DDI) is an
effort to establish an international XML-based standard for the content,
presentation, transport, and preservation of documentation for datasets
in the social and behavioral sciences" (Data Documentation Initiative
2004). As this international standardization effort gathers momentum it
is expected more and more datasets to be documented using DDI as the
primary metadata format. With DDI, searching data archives on the
Internet no longer depends on an archivist\'s skill at capturing the
information that is important to researchers. The standard of data
description provides sufficient detail sorted in a user-friendly manner.

## References

-   Travel Survey Manual online

## Appendix: Typical Household Survey Questions

(source: Denver Regional Council of Governments 2001 )

1.  What is/verify home address
2.  Assigned survey day
3.  Is your residence a single-family home, duplex/ townhouse,
    apartment/condominium, mobile home, or other?
4.  How many people live in this household?
5.  How many overnight visitors from outside of the region stayed with
    you on your survey day?
6.  How many motor vehicles are available to your household?
7.  In total, how many telephone lines come into your home?
8.  How many of the lines are used for voice communication?
9.  Has telephone service in your home been continuous for the past 12
    months?
10. What was the combined income from all sources for all members of
    your household for 1996?

### Vehicle Questions

1.  Vehicle model year
2.  Vehicle make
3.  Vehicle model
4.  Body type
5.  Fuel type
6.  Who owns or leases this vehicle?
7.  Prior to the survey day, when was the last day it (the vehicle) was
    used?
8.  Odometer reading (mileage) at the start of the survey day
9.  Odometer reading (mileage) at the end of the survey day

### Person Questions

1.  Person\'s first name (used for identification purposes only during
    the survey; not saved on final data files)
2.  Relation to head of household
3.  Age
4.  Sex
5.  Licensed to drive?
6.  Student status (not a student, part time, full time)
7.  Grade level
8.  Employment status
9.  Primary job description (nurse, sales, teacher)
10. Primary employer\'s name
11. Primary employer\'s address
12. Primary Employer\'s business type (hospital, retail, etc.)
13. Does your primary employer offer flextime?
14. If flextime offered (primary employer), type of deviation allowed at
    start

of day

1.  If flextime offered (primary employer), type of deviation allowed at
    end of

day

1.  Number of other jobs or employers
2.  Do you have a transit pass?
3.  Monthly cost \[of transit pass\] to you
4.  Did you make trips on the survey day?
5.  If trips were made, did you use E-470 on the travel day?
6.  If trips were made, did you use the HOV lanes on the travel day?
7.  Did you work at your main job on the survey day?

### Environment Questions

Using a 1 to 10 scale, with 10 the best, how would you describe the
walking and bicycling environment around your:

1.  home
2.  work
3.  school
4.  Was the person interviewed by the surveyor?
5.  Based on responses and the survey, did the person appear to use the
    activity diary?

### Travel Diary Questions

1.  This place is my home, my regular workplace, or another place
2.  What kind of place is this (bank, grocery, park etc.)?
3.  Place address
4.  At what time did you arrive at this place?
5.  What did you do at this place (main activity)?
6.  What else did you do at this place (up to eight secondary
    activities)?
7.  Was this your last place for the 24-hour day?
8.  At what time did you leave this place to go to the next place?

### Travel Method

1.  Travel method used to make this trip and related travel details

### Auto Trip Questions

1.  Which household vehicle was used (if a household vehicle

was used)?

1.  Total number of people in the vehicle
2.  Total number of household members in the vehicle
3.  If more than one person was in the vehicle, "is this a formal

carpool/vanpool"?

1.  Were HOV lanes used on this trip?
2.  Was E-470 used for this trip?
3.  What was the parking cost paid at the end of this trip?
4.  What period is covered by the parking cost paid?
5.  Was the parking cost fully or partly reimbursed?
6.  What was the parking location (cross streets, lot name, if
    applicable, and city)

### Transit Trip Questions

The following four questions were asked if the travel method was transit

1.  What was the transit route number?
2.  What was your wait time for transit?
3.  What was the transit fare paid?
4.  How did you pay the transit fare?

The following four questions were asked if the travel method was walk or
bicycle

1.  What was the bicycle or walk time?
2.  Was a bike path used?
3.  Where did you store this vehicle?
4.  Was a walk path used?

## References

[^1]: Weiner, Edward, (1997) Urban Transportation Planning in the
    United States: An Historical
    Overview.
    U.S. Department of Transportation. Fifth Edition.

[^2]: US Department of Commerce (1944) Manual of Procedures for Home
    Interview Traffic Studies. US Bureau of Public Roads. Washington, DC

[^3]: Xie, Feng and David Levinson (2008) The Use of Road Infrastructure
    Data for Urban Transportation Planning: Issues and Opportunities.
    Published in Infrastructure Reporting and Asset Management Edited by
    Adjo Amekudzi and Sue McNeil. pp- 93-98. Publisher: American Society
    of Civil Engineers, Reston, Virginia

[^4]: Hammerand, J. (2006). "Rating Minnesota's Roads." The Minnesota
    Local Technical Assistance Program (LTAP): Technology Exchange,
    vol.24, No.1, 3

[^5]: Washington County Transportation Division (2004). "Washington
    County Annual Performance Report for 2004."
    \<<http://www.co.washington.mn.us/client_files/documents/adm/PerfMeas-2004//ADM->
    PM-04-Transportation.pdf\> (May 20, 2006)

[^6]: Levinson, D. and Zofka, Ewa. (2006) "The Metropolitan Travel
    Survey Archive: A Case Study in
    Archiving" in
    *Travel Survey Methods: Quality and Future Directions, Proceedings
    of 5th International Conference on Travel Survey Methods* (Peter
    Stopher and Cheryl Stecher, editors)

[^7]: Ryssevik J, Musgrave S. (1999) The Social Science Dream Machine:
    Resource discovery, analysis and delivery on the Web. Paper given at
    the IASSIST Conference. Toronto, May.

[^8]: World Wide Web Consortium (2002) Metadata Activity Statement