Section 2

Introduction to Traffic Monitoring

**SECTION 2**

**CONTENTS**

*Section* *Page*

**CHAPTER 1---INTRODUCTION 2-1**

**CHAPTER 2---VARIABILITY IN THE TRAFFIC STREAM 2-4**

Introduction 2-4

Time-of-Day Variation 2-4

Day-of-Week Variation 2-7

Seasonal Variation 2-8

Directional Variation 2-10

Geographic Variation 2-11

**CHAPTER 3---DATA COLLECTION DESIGN ACCOUNTING FOR\
VARIABILITY 2-13**

Integration of Data Collection Efforts 2-14

Continuous Counts 2-15

Automatic Traffic Recorders 2-16

Statewide Continuous Vehicle Classification Sites 2-17

LTPP Sites 2-20

WIM Scales at Enforcement Sites 2-21

Statewide Weigh-in-Motion Sites 2-21

Traffic Operations Data 2-22

Site Selection for Continuous Counters 2-22

Short Duration Counts 2-24

Short Count Program Design 2-25

Coverage Count Programs 2-26

The HPMS Sample 2-27

Other Statistical Samples 2-28

Other Special Needs Counts 2-28

**CHAPTER 4---FACTORING TRAFFIC COUNTS 2-30**

The Creation of Factor Groups 2-31

How to Create Factor Groups 2-34

Cluster Analysis 2-35

Geographic/Functional Classification of Roads Factor Groups 2-36

Same Road Application of Factors 2-38

Combining Techniques 2-39

Alternatives to Factoring 2-40

Types of Factors 2-40

Computation of Factors 2-43

Computing AADT 2-43

Computing the Denominator for Monthly Factors 2-45

Computing the Denominator for Weekly Factors 2-46

Frequently Asked Questions---Factoring 2-46

**CHAPTER 5---COORDINATING COUNT PROGRAMS 2-49**

Why Coordinate Programs 2-49

Who to Coordinate With and What Data Can Be Obtained 2-49

How to Make Coordination Happen 2-51

Issues to Remember 2-52

**APPENDIX 2-A---ITS AND TRAFFIC MONITORING 2-54**

What Can ITS Data Do for You? 2-55

Current ITS Conditions 2-56

An Approach to Creating ITS Data Storage Systems 2-57

Creation of the Data Mine 2-57

Technical and Institutional Issues for the Data Mine 2-58

Data to Be Stored 2-58

Quality Control Functions 2-61

Communications 2-62

Costs 2-63

Agency Responsibilities 2-64

Iterative Development Process 2-65

**APPENDIX 2-B---SEASONAL GROUP DEVELOPMENT USING\
CLUSTER ANALYSIS 2-67**

**REFERENCES 2-74**

**FIGURES**

*Figure* *Page*

2-2-1 Basic Time of Day Patterns 2-6

2-2-2 Weekday/Weekend Truck Percentages 2-6

2-2-3 Typical Day-of-Week Traffic Patterns 2-7

2-2-4 Typical Monthly Volume Patterns 2-9

**TABLES**

*Table* *Page*

2-3-1 Types of Data Provided By Different Types of Data Collection
Devices 2-14

2-4-1 Effects of Alternative Current Year Factoring Procedures on AADT\
Estimates 2-42

2-B-1 Cluster Analysis Monthly ADT 2-68

2-B-2 Monthly Factors 2-69

2-B-3 Ward\'s Minimum Variance Cluster Analysis 2-70

2-B-4 Cluster Analysis Dendogram 2-71

2-B-5 Cluster Analysis Example 2-73

Section 2\
INTRODUCTION to Traffic Monitoring

Chapter 1\
Introduction

While a wide variety of measures can be collected to describe traffic,
this report concentrates on the collection of traffic volume, vehicle
classification, and truck weight data. No specific account is taken of
vehicle speed monitoring, vehicle occupancy, accident and incident
information, or a variety of other traffic measures that describe the
performance of the roadway system.

This report presents a data collection framework that highway agencies
can refine in order to implement a complete, cost-effective vehicle
volume and weight monitoring program that meets the needs of local,
State and federal traffic data users. To provide necessary background,
this report describes the characteristics of the traffic stream that
must be accounted for when collecting, manipulating, and reporting
traffic information. Understanding and accounting for the variations
that are present in the traffic stream is necessary if unbiased
estimates of travel are to be developed and reported.

Even though the basic traffic stream characteristics are similar
throughout the nation, each State's traffic collection and reporting
needs are somewhat different. In addition, each State has a different
set of political, organizational, and functional constraints. As a
result, there is no single "traffic data collection plan" that
adequately meets all States' needs. Instead, each State highway agency
tends to create plans that fit its own unique circumstances. As a
result, traffic monitoring programs differ substantially from State to
State.

The TMG is designed to help States improve their traffic monitoring
programs and ensure that data is available to meet the needs. Each
highway agency should carefully consider the Highway Performance
Monitoring System (HPMS) reporting requirements, as these data are used
to produce the statewide estimates of total vehicle miles of travel
(VMT) used for the apportionment of Federal-Aid funds. The HPMS sample
does not cover all roadway sections and the HPMS volume data may be
insufficient to meet all the data needs of a highway agency. The highway
agency should develop a comprehensive data program to meet defined data
collection requirements.

Ensuring a sufficient number of traffic volume counts to cover the
system is not the only need. Vehicle classification data are extremely
useful for many types of analyses, including pavement design, air
quality, and maintenance, and most States are now collecting
considerably more classification data than were suggested under previous
guidelines. The need for vehicle classification data has outgrown the
size of many current programs.

Once the State has defined its basic coverage program including the HPMS
and other needs, it is possible to refine the "expanded" effort in order
to reduce the total data collection effort required. The data collection
sites can be refined in a number of ways, including the following:

• some short duration counts can be eliminated because other short
duration counts satisfy the same needs

• some short duration counts can be replaced by permanent counters that
are required for other purposes

• some volume counts can be replaced by vehicle classification counts,
since vehicle classification counts provide total volume measurements as
a by-product of the classification effort

• some classification counts (and consequently some volume counts) can
be replaced by truck weight data collection efforts, since truck weight
data collection results in both volume and volume by classification
estimates

• some data collection locations can be moved slightly to allow a single
data collection effort to meet multiple uses.

Examples of these refinements include the following:

• volume counts needed for the HPMS may be provided by a freeway
surveillance system, thus eliminating the need to physically count that
location with portable data collection equipment

• a count taken for the HPMS may be moved one mile to the south (without
changing the basic characteristics of the roadway being monitored) to
co-locate that count with a Long Term Pavement Performance (LTPP) test
site at which vehicle classification data are collected year round. The
HPMS volume count is then provided by the data already collected for
LTPP, reducing the number of portable volume counts needed

• a State wishes to place a new permanent vehicle classifier to monitor
truck volumes throughout the year. It decides to place that counter at
an existing HPMS sample site to meet both the HPMS and permanent counter
need.

By adding new count locations to meet user needs, integrating counting
programs to reduce the total number of counts taken (while taking
advantage of monitoring efforts put in place for a variety of reasons),
and enhancing/refining of data collection efforts to ensure that all
needs are met; a better traffic monitoring program and reduced data
collection costs will result.

To successfully integrate and refine data collection efforts, it is
important to understand:

• the variability present in traffic

• the types of data collection efforts needed to measure and account for
this variability

• the equipment technology available to collect these data

• the various traffic data collection programs that exist within a State

• the data needs and reporting requirements.

These issues are discussed in the remaining chapters of this section. In
addition, an appendix describes data collection for Intelligent
Transportation Systems projects and how these data can be integrated
within the structure of the statewide traffic monitoring program.

Chapter 2\
Variability in The Traffic Stream

Traffic varies over time. This is an obvious statement, but only
recently has the availability of modern technology allowed States to
collect enough data to begin to understand just how traffic varies over
time (Wright et al, 1997). Traffic varies over a number of different
time scales, including:

• time of day

• day of week

• season (month) of the year.

Traffic also varies from place to place. Not only do roads carry
different volumes of traffic, but the characteristics of the vehicles
using those roads change from facility to facility. One road with 5,000
vehicles per day may have hardly any truck traffic, while another road
with the same volume of vehicles may have 1,000 trucks per day mixed in
with 4,000 cars. Similarly, one road section may be traversed by 1,000
heavily loaded trucks per day while a nearby road is used by 1,000
partially loaded trucks.

It is necessary to understand and be able to monitor all of these
differences in travel to make correct decisions about the design,
operation, and maintenance of roadways. This chapter discusses the
variation present in the traffic stream. The next chapter discusses the
data collection necessary to monitor and account for the variation.

Introduction

One of the most significant differences between this version of the
Traffic Monitoring Guide and previous versions is the attempt to
directly account for differences in traffic variation by type of
vehicle. Research has shown that truck volumes vary over time and space
differently than car volumes (Hallenbeck et al 1997). In fact, these
variations can be quite different from one type of truck to another. In
addition, the characteristics of specific truck types, especially
vehicle weights, can change dramatically from time period to time period
and location to location, even within a given truck classification. It
is therefore important that each State develop mechanisms within their
statewide traffic monitoring program that measure these variations, so
that they can be accounted for within the data reporting and analysis
process.

Time-Of-Day Variation

Since the early development of roads, it has been known that the use of
a road changes during the course of the day. In most locations, traffic
volumes increase during the day and decrease at night. A 1997 study for
the Federal Highway Administration (Hallenbeck et al 1997) calculated
general time of data distributions by vehicle type, based on traffic
data collected for the Long Term Pavement Performance study. This study
found the following facts:

Most truck travel falls into one of two basic time-of-day patterns. Most
passenger car travel also falls into one of two time-of-day patterns,
but these patterns are different than those of trucks. These four
patterns are illustrated in Figure 2-2-1.

Cars tend to follow either the traditional two-humped urban commute
pattern or the single-hump pattern commonly seen in rural areas, where
traffic volumes continue to grow throughout the day until they begin to
taper off in the evening. Trucks also have a single-hump pattern.
However, the truck pattern differs from the rural car pattern in that it
peaks in the early morning (many trucks are used to make deliveries
early in the morning to help prepare businesses for the coming work day)
and tapers off gradually, until early afternoon, when it declines
quickly. In addition, some types of trucks follow a very different
time-of-day pattern. These trucks, usually involved in hauling freight
long distances, travel constantly throughout the day.

The traffic at any given site comprises some combination of these types
of movements. In addition, at any specific location, time-of-day
patterns differ significantly as a result of local trip generation
patterns that differ from the "norm. " For example, Las Vegas, Nevada,
generates an "abnormal" amount of traffic during the night, because that
city is very active late at night. Local patterns also have a
significant effect on the directional time-of-day pattern for any given
road.

Because the volumes of cars and trucks often are very different, the
effect of these different time-of-day patterns on summary statistics
such as "percent trucks" and "total volume" can be unexpected. Often, in
daylight hours car volumes are so high in comparison to truck volumes
that the car travel pattern dominates, and the percentage of trucks is
very low. However, at night on that same roadway, car volumes may
decrease significantly while through-truck movements continue, so that
the truck percentage increases considerably, and total volume declines
less that the car pattern would predict. Figure 2-2-2 shows how typical
values of truck percentages change during the day for urban and rural
settings on both weekdays and weekends.

Because these changes can be so significant, it is important to account
for them in the design and execution of the traffic monitoring program,
as well as in the computation and reporting of summary statistics.

![](media/image1.wmf){width="6.059722222222222in"
height="4.0055555555555555in"}Figure 2-2-1\
Basic Time of Day Patterns

![](media/image2.wmf){width="5.809722222222222in"
height="3.8402777777777777in"}**Figure 2-2-2\
Weekday/Weekend Truck Percentages**

Day-Of-Week Variation

Time-of-day patterns are not the only way in which car and truck
patterns differ. Day-of-week patterns also differ, in large part because
of the use of cars for a variety of non-business related traffic,
whereas, for the most part, trucks travel only when business needs
require it.

![](media/image3.wmf){width="5.995833333333334in" height="4.1in"}As with
time-of-day patterns, day-of-week patterns for cars fall into one of two
basic patterns as shown in Figure 2-2-3. In the first pattern
(traditional urban), volumes are fairly constant during weekdays and
then decline slightly on the weekends, with Sunday volumes usually being
lower than Saturday volumes. This pattern also exists on many rural
roads. The other pattern, usually found in rural areas that contain
recreational travel, shows constant weekday volumes followed by an
increase in traffic on the weekends.

Figure 2-2-3\
Typical Day-of-Week Traffic Patterns

Trucks also have two patterns, both driven by the needs of businesses.
Most trucks follow an exaggerated version of the "traditional urban" car
pattern. That is, weekday truck volumes are fairly constant, but on
weekends, truck volumes decline considerably more than car volumes
(unlike cars, the decline in truck travel caused by lower weekend
business activity is usually not balanced by an increase in truck travel
for other purposes). However, as with the time-of-day pattern, long-haul
"through" trucks often show a very different day-of-week pattern. Since
long-haul trucks are not concerned with the "business day" (they travel
as often as driver schedules allow), they travel equally on all seven
days of the week. Thus, roads with high percentages of through-truck
traffic often maintain high truck volumes during the weekends, even
though the local truck traffic declines.[^1] This pattern is "visible"
in truck volume counts only when through-truck traffic is a high
percentage of total truck volume. More commonly what happens is that
weekend truck volumes do not drop as precipitously as they do at sites
where little through-truck traffic exists.

These significant changes in traffic volumes during the course of the
week have several effects on the traffic monitoring program. Most
importantly, the monitoring program needs to collect data that allow a
State to describe these variations. Second, the monitoring program must
allow this knowledge to be shared with the users of the traffic data and
applied to individual locations.

Without these two steps, many of the analyses performed with traffic
monitoring data will be inaccurate. Pavement designers need to account
for reductions in truck traffic on the weekends if they are to
accurately predict annual loading rates. Likewise, accident rate
comparisons for different vehicle classifications are not realistic
unless these differences are accounted for in estimates of
vehicle-miles-travel by class.

Seasonal Variation

Further complicating the analysis of temporal variation in traffic
patterns is the fact that both car and truck traffic change over the
course of the year. Seasonal changes in total volume have been tracked
for many years with permanent counters, traditionally called Automatic
Traffic Recorders (ATRs). Total volume patterns from these devices show
a variety of patterns, including common patterns such as the "flat
urban" and "rural summer peak" shown in Figure 2-2-4. The figure is
abstracted from the report "Vehicle Volume Distributions by
Classification" included in the refereces.

![](media/image4.png){width="5.997222222222222in"
height="4.101388888888889in"}Figure 2-2-4\
Typical Monthly Volume Patterns

Most States track four or more seasonal patterns, and they base the
patterns being followed on some combination of functional classification
of roadway and geographic location. Geography and functional
classification are used as readily available surrogate measures that
describe roads that follow that basic pattern. Geographic stratification
is particularly important when different parts of a State experience
very different travel behavior. For example, travel in areas that
experience heavy recreational movements follow different travel patterns
than those in areas without such movements. Even in urban areas where
travel is more constant year round, cities with heavy recreational
activity have different patterns than cities in the same State without
heavy recreational movements.

Not surprisingly, truck traffic has seasonal patterns that are different
than automobile patterns. Some truck movements (often defined by
specific types of trucks operating in specific corridors or regions) are
stable throughout the year. Other truck movements are highly seasonal,
for example in agricultural areas. It has even been shown that the
weights carried by some trucks vary by season. This is particularly true
in States where seasonal load restrictions are placed on roads and where
weight limits are increased during some winter months.

As with day-of-week patterns, tracking of seasonal changes in volumes is
necessary to calculate adjustments needed for various analyses. If
annual statistics are needed for an analysis, it is necessary to adjust
a short duration traffic volume count taken in mid-August to account for
the fact that August traffic differs from the average annual condition.
Exceptions exist such as Phoenix, where the influx of winter visitors
causes August to be a lower volume month relative to the average
condition.

Recent research has shown that seasonal monitoring and adjustment must
be done separately for trucks and cars (Hallenbeck et al 1997). Truck
volume patterns can vary considerably from car volume patterns. Roads
that carry significant volumes of through-trucks tend to have very
different seasonal patterns than roads that carry predominately local
freight traffic. Roads that carry large volumes of recreational traffic
often do not experience similarly large increases in truck traffic, but
do often experience major increases in the number of recreational
vehicles which share many characteristics with trucks.

Thus, it is highly recommended that States monitor and account for
seasonal variation in truck traffic directly, and that these procedures
be independent of the procedures used to account for variations in car
volume.

Directional Variation

Not all variation is temporal. Most roads exhibit differences in flow by
direction. The traditional urban commute involves a heavy inbound
movement in the morning and an outbound movement in the afternoon. On
many suburban roads, this directional behavior has disappeared, replaced
by heavy peak movements in both directions in both peak periods. When
these directional movements are combined, the time-of-day pattern shown
in Figure 2-2-1 still holds, but when looked at separately, new
time-of-day patterns become apparent.

In areas with high recreational traffic flows, directional movements
change the day-of-week traffic patterns as much as the time-of-day
patterns. Travelers often arrive in the area starting late Thursday
night and depart on Sunday.

Truck volumes and characteristics can also change by direction. One
"classic" example of directional differences in trucks is the movement
of loaded trucks in one direction along a road, with a return movement
of empty trucks. This is often the case in regions where mineral
resources are extracted. Volumes by vehicle classification can also
change from one direction to another, for example when loaded logging
trucks (classified as 5-axle tractor semi-trailers) move in one
direction, and unloaded logging trucks (which carry the trailer dollies
on the tractor and are classified as 3-axle single units) move in the
other.

Tracking these directional movements as part of the statewide monitoring
program is important not only for planning, design, and operation of
existing roadways, but as an important supplement to the knowledge base
needed to estimate the impacts that new development will generate in
previously undeveloped, rural lands.

Geographic Variation

The last type of variation discussed in this chapter is that caused by
locational differences in roadways. This type of differentiation is
taken for granted for traffic volumes. Some roads simply carry more
vehicles than others. This concept is readily expanded to encompass the
notion discussed above, that many of the basic traffic volume patterns
are geographically affected (California ski areas have different travel
patterns than California beach highways). It is important to extend
these concepts even further to recognize that truck travel also varies
from route to route and region to region. It is just as important to
realize that differences in truck travel can occur irrespective of
differences in automobile traffic.

One of the growing areas of interest in traffic monitoring is the
creation of truck flow maps and/or tonnage maps. These maps, analogous
to traffic flow maps, show where truck and freight movements are
heaviest. This is important for:

• prioritizing maintenance and roadway improvement funding

• instituting of geometric and pavement design and maintenance
guidelines that account for expected traffic

• studying the effects of regulatory changes in freight and good
movements (such as the abandonment of existing freight rail lines).

When these truck flow maps are developed, they often reveal that truck
routes exist, irrespective of the traffic flow and/or the functional
classification of the roads involved. Trucks use specific routes because
those roads lead from the trucks' origin to their destination, and the
route has (one hopes) sufficient geometric capacity to accommodate those
trucks. Truck drivers do not select routes because they are designated
as a "rural principal arterials." They select them because they are
convenient for their trip.

In fact, functional classification is a very poor predictor of truck
volume or percentage. As an example, Interstates that serve major
through movements (even in urban areas) tend to have high truck volumes,
but Interstates that do not service major freight movements tend to have
very low truck volumes.

Because truck flows (both truck volumes and weights) play such an
important (and growing) role in highway engineering functions, it is
vital that States collect truck volume data that describe the geographic
changes that exist. Which roads carry large freight movements? Which
roads carry large truck volumes, even if those volumes are a small
percentage of total traffic volume? And which roads restrict or carry
light volumes of freight?

Chapter 3\
Data Collection Design\
Accounting For Variability

The variability described above must be measured and accounted for in
the data collection and reporting program a State designs and
implements. The data collection program must also identify changes in
these traffic patterns as they occur over time. In some cases, observed
changes will indicate that the State needs to refine its data monitoring
process to better estimate traffic conditions on its roads. (For
example, a State may discover that it needs to refine its continuous
count and factoring programs in order to account for traffic patterns
that it had not previously known about.)

In general, to monitor traffic at the statewide level the recommended
data collection plan consists of:

• a modest number of permanent, continuously operating, data collection
sites, and

• a large number of short duration data collection efforts.

The permanent data collection sites provide knowledge of seasonal and
day-of-week trends. The summarization of the continuously collected data
allows the development of adjustment factors needed to convert short
count data (data collected for one or two days) into estimates of
"annual average" or "design" conditions. Continuous count summaries also
provide very precise measurements of changes in travel volumes and
characteristics at a limited number of locations.

The short duration counts provide the geographic coverage needed to
understand traffic characteristics on individual roadways, as well as on
specific segments of those roadways. Traffic volumes tend to vary
dramatically from one location to another. Because permanent counters
are expensive to install, operate, and maintain, short duration counts
are needed on roads throughout the State to provide accurate
measurements of traffic conditions on individual roadway sections. These
short duration counts are then adjusted to represent annual or design
conditions given the patterns measured at the continuous count
locations.

Determining where to place continuous counters and how to use the
available continuous count data to create reliable short count
adjustments are two of the hardest tasks in creating an effective
statewide traffic monitoring program. General guidelines for developing
and/or modifying this process are included later in this chapter.
Specific examples are presented in chapters 3, 4, and 5.

Integration of Data Collection Efforts

A well-designed data collection program takes advantage of the fact that
sophisticated traffic monitoring equipment can often provide more than
one type of data at a time. For example, permanently installed sensors
and electronics at a WIM site can be used for continuous vehicle
classification and volume data collection even when weight data are not
collected. Thus, a continuously operating WIM scale can serve three
purposes, reducing the need to place and operate additional data
collection devices. This ability to simultaneously collect all three
types of traditional traffic monitoring data is called "nesting" traffic
counts.

**Table 2-3-1**

**Types of Data Provided By Different Types of Data Collection Devices**

  ----------------- ----------------- ----------------- -----------------
  **Type of Data    **WIM Scale**     **Vehicle         **Volume
  Provided**                          Classifier**      Counter**

  **Axle and/or     **X**                               
  Gross Vehicle                                         
  Weight**                                              

  **Volumes By Type **X**             **X**             
  of Vehicle**                                          

  **Volume of       **X**             **X**             **X**
  Vehicles**                                            
  ----------------- ----------------- ----------------- -----------------

When used appropriately, this ability to "nest" data collection
activities allows a State to either reduce the number of continuous data
collection sites it operates or increase the number of data available
for monitoring traffic patterns. However, "nesting" is not restricted to
traditional classification and weight data collection. A variety of
traffic monitoring activities, including vehicle speed monitoring,
traffic management activities, toll collection devices, and incident
detection sensors, can provide traffic volume information.

A well-designed, efficient traffic monitoring program also takes
advantage of traffic data collected by other agencies within the State.
For example, truck weights and volumes may be monitored at the State's
borders by the agency in charge of collecting or enforcing the
collection of truck fuel taxes. Within the State highway agency, the
research office may collect truck weight data as part of specific
research projects, while the planning section may collect vehicle
weights to meet truck size and weight data needs.

By obtaining, summarizing, and distributing these data, it is possible
to increase the availability of traffic monitoring information while
decreasing the total cost of monitoring traffic. For example, the weight
data collected from the research and enforcement sources mentioned above
may be able to supplement or replace truck weight data collected by the
planning agency. However, it is important to realize that data collected
for specific purposes must be obtained and used with care, as that
purpose may bias the data in some fashion. For example, a truck scale
placed just upstream of a weight enforcement scale (in order to improve
the efficiency of weight enforcement officers) may not produce vehicle
weights that are representative of the weights found on other roads in
the State. The data at this location may be biased if illegally loaded
trucks are able to by-pass the enforcement scale. However, even if they
are slightly biased, the enforcement scale data do give an excellent
measure of the vehicle weights on the road leading to that scale (useful
for any pavement rehabilitation project on that roadway section), as
well as an excellent measure of the seasonal changes in volumes and
weights associated with roads affected by that enforcement site.

Another example of how integration can assist a statewide traffic
monitoring effort is that many local jurisdictions (counties and cities)
are installing and operating permanent traffic counters (both
volume-only counters and vehicle classification counters). Data from
these devices can be used to supplement the permanent counters operated
by the State highway agency. They provide additional information on
seasonal travel patterns in areas where monitoring those patterns is
important.

Often, different groups within a State highway agency monitor traffic
for their own purposes. These data collection efforts can include
everything from counts for specific project purposes (data collected for
pavement or geometric design), to special studies that respond to
legislative requirements or policy concerns (the tracking of HOV lane
usage), to fully staffed traffic surveillance centers created to help
manage traffic on road sections of major importance (mountain passes,
major tunnels and bridges).

In many cases, obtaining these data (or summaries) from others' data
collection efforts can significantly reduce the data collection burden
and/or inexpensively provide data to users who would not otherwise have
them. The Intelligent Transportation Systems (ITS) efforts under way in
many States offer a potential bonanza in traffic monitoring data. It is
up to the highway agency personnel to make sure that these data are
captured and put to use. Appendix A describes the steps needed to make
this happen.

Continuous Counts

Most States have established continuous count programs. The original
intent of most continuous monitoring efforts was to understand seasonal,
day-of-week, and time-of-day traffic volume patterns to help improve the
accuracy of traffic estimates used in a variety of analyses. As data
collection equipment has improved and traffic data needs have changed
over time, continuous traffic data collection programs have evolved.
Many continuous collection efforts now produce data that are not
routinely used for these traditional purposes. Instead, for example,
continuous data can be used as input for traffic management systems and
other operational purposes. In many cases, continuously collected data
are not even saved but are used in real time and then discarded. **A
State that recognizes that these data collection efforts exist and is
able to cost-effectively capture, summarize, and use these data can
significantly improve the quality of its traffic monitoring information
at relatively low marginal cost.**

The most common continuous traffic monitoring data collection programs
in use today include the following:

• automatic traffic recorders (ATRs)

• automatic, continuous vehicle classifiers used to supplement the ATR
program (often abbreviated AVC or CVC)

• continuously operating weigh-in-motion (WIM) scales placed to monitor
statewide trends in vehicle weights

• continuous vehicle classifiers or WIM scales used to provide load
information to the Long Term Pavement Performance (LTPP) study of the
Strategic Highway Research Program (SHRP)

• continuously operating WIM scales used to identify trucks that need to
be weighed statically at an enforcement scale

• volume and speed monitoring stations that provide facility performance
data to traffic management systems.

The subsections below describe the basic intent and functioning of each
of the above programs and how data from each program can fit into a
statewide traffic monitoring program. Additional continuous count
programs exist in some States. Each of these programs is designed to
meet other needs and can produce data useful to a statewide traffic
monitoring program.

Automatic Traffic Recorders

When most traffic data collection professionals think about "continuous
data collection," they think of automatic traffic recorders (ATR). These
devices (most incorporating inductance loop detectors) have been used
for many years to monitor traffic at specific locations and to produce
the factors applied to short duration traffic volume counts in order to
estimate annual average traffic volume conditions.

ATR data are commonly stored on site as hourly volumes by lane and are
downloaded periodically (daily, weekly, or monthly) to a central
location. At the central location, the data are checked for quality,
summarized, and stored for later use. The summary and raw values are
then made available to data users within the Department. Among the
summary volume statistics that are routinely reported are the following:

• annual average daily traffic at the site (AADT)

• annual average weekday traffic at the site (AAWDT)

• seasonal adjustment factors

• day-of-week adjustment factors

• 30^th^ highest annual hourly volume as a fraction of AADT

• 100^th^ highest annual hourly volume as a fraction of AADT

• lane distribution factors

• growth trends at that location.

Data from multiple ATRs are usually averaged to compute "representative"
factors, which are then used to adjust short-term count data from a
variety of locations in order to convert those short duration counts
into estimates of "annual," "design," or "average" conditions. The
grouping process is described in Chapter 4.

ATRs are placed at locations throughout the State for a variety of
reasons. In many cases, ATR locations are selected to measure specific
trends. This is often the case where it is important to monitor a given
traffic movement with a high degree of accuracy (for example, on a road
of particular importance), or where a specific location provides an
accurate measure of traffic activity for a larger, well defined group of
roads (e.g., the one road leading into a major recreational area).

Some ATR sites exist because the State has historically monitored trends
at specific locations. The reasons those locations were initially
selected may or may not be currently known. In addition, the reasons
some of those sites were initially selected may no longer be true or
applicable, but the fact that a long history of data exist at these
locations provides a reason for the continuing efforts to collect data
at those locations. (That is, the long-term trend information is
valuable in its own right, regardless of the other purposes that site
may no longer serve.)

Many other ATR locations are selected semi-randomly, as part of an
effort to monitor general travel trends within specific categories of
roads. For example, there may be interest in monitoring traffic trends
on rural Interstates that travel east/west across the State.
Consequently, a specific location from the road sections that fit within
that criterion may be selected randomly. A second example might use much
less restrictive criteria, such as some combination of the functional
classification of the road, the geographic location of the roadway
(e.g., the northeast part of the State), or the availability of power
and/or telecommunications access to locate the counter so that
sufficient numbers of sites are within a given factor group.

Statewide Continuous Vehicle Classification Sites

Many States have begun to expand their continuous count programs to
include continuous vehicle classifiers as a result of the development of
affordable equipment that can perform this task and from a growing
understanding of the importance of truck volume and load information.
The results of many traffic analyses are more dependent on truck volumes
than they are on total traffic volumes. For example, the depth of a
pavement design is primarily affected by the number and weight of heavy
vehicles (and particularly their axle weights) using that road section
(given soil and weather conditions) and is virtually unaffected by the
total number of vehicles crossing that section.

Given the importance of truck information, the need for continuous
vehicle classifiers becomes clear with the realization that truck
traffic often follows different seasonal and day-of-week trends than do
total volumes, which tend to be dominated by automobile traffic. If
truck movement patterns are to be understood and accounted for in the
traffic monitoring and data analysis efforts, then monitoring volumes by
vehicle classification becomes necessary.

Continuously operating vehicle classifiers allow the monitoring of
changes in truck volumes and changes in vehicle fleet mix (the
percentage of travel by specific vehicle types) to be tracked over time.
Other important analyses supported by continuous classification
equipment include the following:

• the size of seasonal commodity movements (e.g., how many truck trips
are generated on roads in agricultural areas during the harvest season)

• the seasonal fluctuations in truck travel on roads not significantly
affected by well defined seasonal commodity movements

• trends in annual truck volumes on specific roadways (nationally, truck
travel has grown faster than car travel)

• day-of-week traffic patterns for trucks as opposed to cars

• the lane distribution patterns of trucks.

Continuously operating vehicle classifiers use a variety of
technologies. The two most common are axle classifiers and length
classifiers. Axle classifiers use a combination of sensors[^2] to record
the number of vehicles in various categories. The vehicle classification
categories are defined by the number and location of axles for each
vehicle. Length classifiers usually use dual inductance loops to measure
the total length of passing vehicles, which is then used to classify
each passing vehicle.

The FHWA standard classification scheme requires the use of axle-based
classifiers to categorize vehicles into 13 classes. However, to be
useful within the context of a statewide traffic monitoring program, not
all continuous classifiers need to be capable of reporting vehicle
volumes in these categories. For some analyses three or four vehicle
categories are sufficient. In some locations the use of 13 vehicle
categories makes volumes within particular vehicle categories so low
that the volume estimates become statistically unreliable. It is
appropriate in both of these cases to aggregate vehicle categories in
order to produce reliable, useful volume estimates by vehicle class.
(See Section 4 for an explanation of alternative vehicle classification
schemes, their relation to the FHWA 13-category classification scheme,
and the application of these alternative schemes and data collection
devices within a statewide traffic monitoring program.)

A State may operate different types of vehicle classifiers in different
locations. For example, in urban freeway environments, dual loop
detectors are used to monitor vehicle speeds as part of freeway
management systems. These detectors can provide measurements of total
truck volumes by vehicle length category. At the same time, the State
may operate a series of axle-sensor-based vehicle classifiers in rural
areas to track changes in commodity movements. Both of these data sets
add considerably to the knowledge of the movement of freight and goods
on State roadways. However, care and skill are required when data from
both of these sources are combined.

As with ATRs, the location (and initial purpose) of the continuously
operating classifiers can be chosen for one of several reasons. The
location of some classifiers is based on the need for truck data on
specific roads or for specific commodity movements. Other continuous
classifier locations are based on the need to collect data for specific
pavement sections (see LTPP Sites below), while others are selected to
meet statewide monitoring needs, such as the creation of truck factor
groups. Finally, in some cases, such as the urban freeway example sited
above, truck volume information is a serendipitous extra that results
from data collection performed for very different purposes.

In all of these cases, the continuous classification data can and should
be used by the State to meet a variety of needs. However, not all of
these data are of the same quality, and the fact that some of these
sites exist for purposes other than "traditional" statewide monitoring
means that some caution must be used when the data from these sites are
used for "statewide" analyses. For example, if, because of a freeway
management system, 20 locations in one urban area provide truck data and
only five other urban locations in the state have continuous truck
counts, averaging seasonal patterns from the available data to produce a
"statewide urban adjustment factor" will result in an estimate that is
heavily biased toward the traffic patterns in that one urban area.

It is important to note that vehicle classifiers also provide the same
data as ATRs. That is, by simply combining all vehicle categories, a
continuous classifier provides continuous total volume estimates. Thus,
a classifier can [replace]{.underline} a conventional ATR location while
providing more beneficial data. Consequently, when States periodically
replace or update existing ATRs, they should consider upgrading them to
continuous vehicle classifiers. In addition, where classifiers are
placed independently from the ATR system, the State should look to
supplement its existing ATR system with data obtained from the
continuous vehicle classifiers. That is, volume data from the
classifiers may be used to add information to the computation of volume
factors estimated with ATR data.

LTPP Sites

One specific set of continuous WIM and/or AVC site locations was created
to meet the data needs of the Long Term Pavement Performance project.
LTPP is a national research project studying the causes of pavement
deterioration and the effects of different pavement and maintenance
designs. As part of this research project, States were requested to
collect continuous vehicle classification and WIM data at specific LTPP
test sites. This data collection effort was intended to accurately
measure the traffic loads that are being applied to particular pavement
sections. It is very important to LTPP that the changes in loading
patterns occurring over the course of a year be measured and included in
the loading estimates used in the LTPP analyses.

As a result of this program, many States are now operating permanent
vehicle classifiers and/or WIM scales at a number of LTPP test sites.
The problem with data collected for LTPP is that States have little
flexibility in choosing the location of the sites at which LTPP vehicle
classification data are collected. Many of these data collection sites
are not where the State would prefer to collect vehicle classification
or WIM data as part of a statewide traffic monitoring effort. However,
when properly installed, calibrated, and maintained, these sites can
contribute valuable data. Because relatively little is known about the
variation in truck loads over time on different types of roads in
different parts of most States, data from the LTPP sites can add
significantly to the understanding of how truck loads change over time.
They are particularly useful in helping to assess the types of variation
that occur in truck weights during the year and across different roads
within the State.

Most States are only beginning to understand the movement of trucks on
highways during different times of the year. Data obtained from LTPP
sites add considerably to that understanding, even when those sites are
not located at "traditional" ATR sites. Weight data are necessary for
converting truck volumes into the axle load estimates needed as an input
to pavement design and maintenance procedures. In addition to truck and
axle weight information, continuously operating WIM scales are also
capable of providing the same data as continuous vehicle classifiers and
ATRs. That is, a WIM scale counts as well as weighs all of the vehicles
crossing the scale. Thus, it can serve as one more site for monitoring
volume trends for both cars and trucks.

Coordinating the LTPP data with the statewide monitoring effort is often
made more difficult by the fact that in a number of States, LTPP
equipment is operated by "research office" personnel rather than
"statewide traffic monitoring" personnel. As a result, the LTPP data are
often not included in the statewide traffic database. Because these are
"research sites," the data are simply collected and shipped to LTPP.
They are not summarized and added to the traffic database commonly
available to all users. This prevents their being used in a vast number
of analyses, including helping define new factor groups specifically
aimed at truck volumes.

WIM Scales at Enforcement Sites

Other sources of continuous WIM data are 24-hour port-of-entry
operations and other WIM scales that operate continuously upstream of
static enforcement scales. At these sites, all trucks are weighed,
paperwork may be inspected, and safety checks may be conducted. Many
enforcement sites now use WIM scales to sort potentially over-weight
trucks from trucks carrying loads that are lower than the legal limit.
This sorting function speeds up the enforcement process by reducing the
number of trucks that must be statically weighed. These same data, if
stored, can be used for many other purposes. However, because they are
collected in conjunction with enforcement activity, the State must be
careful to ensure that these data are not biased measures of actual
truck weights. (That is, because truckers are aware that enforcement is
taking place, many trucks that are illegally loaded will avoid these
scales. Because illegally loaded trucks avoid enforcement scales when
possible, the data collected are often not representative of the
"complete" trucking population. The data may underestimate the number of
very heavily loaded vehicles.) Enforcement site evasion is not a problem
for all sites. For example, in many western States, there are few or no
by-pass routes around port of entry scales. Thus, the scale collects a
true measure of the truck and axle weights passing through. States
should be aware of both the potential for bias and the potential
knowledge that can be gained from WIM scales at enforcement sites before
such data are either discarded or routinely used.

Statewide Weigh-in-Motion Sites

Many States operate WIM scales that are related to neither LTPP nor
enforcement as part of their statewide monitoring program. These sites
are selected semi-randomly to be representative of specific parts of the
State's highway network. (WIM sites cannot be selected in a purely
random fashion because WIM equipment only works accurately[^3] on level
ground, with good pavement, and with little or no roadway curvature.
This eliminates many potential roadway segments from consideration for
truck weight data collection locations.) In addition, because WIM
equipment is fairly expensive to purchase, install, maintain, and
operate, many States cannot afford a large number of these sites. Thus,
the "semi-random" locations tend to be heavily oriented toward strategic
locations that provide data of high value to the States rather than
completely random selections aimed at ensuring statistical purity. These
sites can be particularly useful in describing the variability of the
"most important" truck routes in the State.

Where no better data exist, these "important" sites, along with unbiased
data from LTPP and enforcement locations, must serve as the basis for
understanding the weights of an area's truck population. Even when these
data collection efforts are imperfect, highway agency personnel must
understand that the availability of data and an understanding of their
limitations allow much more informed decision-making than a total lack
of information.

Traffic Operations Data

Many States have installed sensors to collect real-time surveillance
data for traffic management purposes. Much of this equipment can be
classified as part of the ITS deployment.

Because many new sensors are being marketed as a result of ITS, and
because each ITS traffic management system tends to incorporate slightly
different traffic performance inputs, it is difficult to generalize the
types of traffic data that can be available from ITS traffic management
systems. However, most traffic management systems provide estimates of
vehicle volumes and speeds, and some provide simplified vehicle
classification information. ITS management systems tend to operate
year-round, and thus, many traffic management systems can be viewed as
equivalent to ATRs or continuous vehicle classifiers.

Unfortunately, traffic management system data have traditionally not
been used effectively for general traffic monitoring purposes. In some
cases, the data are collected in "real time" (e.g., 20-second intervals)
and then discarded. In other cases the data are stored but are not
reported or made available in a useable form to other data users. Often
the data are collected by an operations group within the State highway
agency but not reported to the traffic monitoring office.

As traffic congestion grows and States turn to increasingly
sophisticated traffic management systems to ease the effects of that
congestion, the availability of traffic surveillance data from these
systems will expand. Collecting and using traffic surveillance data
provides a considerable resource that can be used to describe the
traffic volume, the nature of the traffic, and in many cases the
performance of that traffic (the frequency of congestion, peak periods,
the effect of incidents, etc.).

These same data allow the highway agency to produce traditional
engineering statistics traditionally obtained from ATRs such as seasonal
factors, day-of-week adjustment factors, peak hour factors, and AADT. In
fact, these systems can replace the need for "stand alone" ATRs in urban
areas that contain traffic management systems. The keys to using these
data are in the development of data storage, retrieval, and aggregation
software and hardware systems that make the data available to users
outside of the operations community.

Site Selection For Continuous Counters

Most States (and some local jurisdictions) have already placed many
continuous counters. Because these counters are expensive to move, the
primary issue for continuous counters is not where to locate them but
how best to use the data that come from these counters to develop short
count adjustment factors. This use of permanent counters for factor
development is discussed in Chapter 4.

The recommended procedure for designing the continuous count portion of
the statewide monitoring program is given below.[^4] The procedure is
designed to meet as many needs as possible, given limited data
collection resources. It recognizes that funding and data requirements
tend to come from two different sources: statewide monitoring sources
and project specific sources. Statewide monitoring funding is intended
to meet general data collection needs. Project specific funding is
intended to meet the needs of individual projects that value certain
data items highly enough to fund their collection.

The objective of the proposed procedure is to use, whenever possible,
project specific funds to meet both project specific and statewide
monitoring needs. Under the proposed plan, data from project counts
should be used wherever possible to meet the general statewide data
collection needs. Statewide needs that are not met by the special
project data collection should then be met with statewide funding
dollars.

A summary of the recommended steps for selecting continuous count
locations is presented below:

• Determine the "statewide" objectives for the continuous count program
(including the number and distribution of count locations to develop
seasonal and day-of-week factors, statewide trend reports, preparation
of reports that use permanent recorder data, etc.).

• Determine what continuous data collection is needed for specific
projects (LTPP, other special studies) and what continuous data
collection exists or is planned for operational purposes (traffic
management, weight enforcement).

• Determine the available funding (including both "traditional" funds
and funds from outside divisions that support continuous counter
operation that can serve statewide purposes).

• Prioritize the "specific" project locations.

• Place counters at the "specific" project locations for which funding
exists. (Note that the funding available for this step may only be a
small fraction of the available funding for continuous counters, or it
may be the vast majority of funding available.)

• Determine how those data collection efforts can help meet "statewide"
needs. (For example, can those count locations be used for factor
creation?)

• Determine the number of additional continuous count locations needed
to meet statewide needs (using the existing and desired "specific" count
locations as much as possible).

• Prioritize these remaining "statewide needs" locations.

• Allocate counters to these "statewide needs" locations on the basis of
their priority and the available funding.

• If funding remains after statewide needs have been met, place
additional continuous counters at the "specific" project sites for which
counters are currently not allocated.

This process allows a State to prioritize its expenditure of resources
for permanent counters. It also ensures the ability to use individual
count locations for as many purposes as possible. While this process
will not solve problems that occur because insufficient funds are
available to locate and operate permanent data recorders, it will help
to define the size of the budget shortfall and provide a basis for
estimating the impact of a "partial" continuous count program.

Short Duration Counts[^5]

The short duration count program, like the continuous count program,
consists of a number of inter-related data collection efforts. The basic
purpose of the short duration count program is to provide up-to-date
traffic data for a wide geographic coverage of roadway segments. These
data define how specific roadway segments are being used, which in turn
determines how highway agencies design, maintain, and manage those
segments.

This section describes the basic process a State should use to construct
its short count program. More details on this subject, organized by type
of data collected (volume, classification, or weight) are included in
Sections 3, 4 and 5.

Unlike permanent counts that normally occur in the same location year
after year, a State's short count program is largely revised each year.
Some locations are counted frequently (every year or every two or three
years), and others are only counted occasionally. Roadway sections that
are of major interest (locations of pavement design projects, corridors
for major investment studies, etc.) are counted often, while other
roadway sections with little activity may be uncounted for many years.

Short duration data collection counts that provide geographic coverage
can be part of a general statewide monitoring effort or can come from
focused, site specific studies or "project counts." General statewide
monitoring programs collect data that are intended to meet a wide range
of potential uses. These programs are pro-active data collection
efforts. That is, they are intended to provide the "average user" with
data when that user needs it. This means that the highway agency
maintains a database of information (updated with the short count data)
that defines in general terms how each roadway section is being used.
Because States cannot afford to collect all needed data on all roadway
segments, this "pro-active" approach is normally limited to collecting
sufficient data to meet routine data needs. Where more extensive data
needs exist, special count efforts, designed to collect exactly the data
required, are undertaken.

These "project counts" often entail multiple counts within a fairly
short stretch of roadway (providing much more information than is needed
for "routine" tasks) and often include more detail (vehicle class and/or
turning movements versus a simple volume count) than the statewide
program provides. For example, the statewide counting program may
provide a user with a volume count at milepost 167. A pavement design
project for that road between mileposts 159 and 169 might require five
vehicle classification counts within that 10-mile section to allow more
accurate calculation of pavement depth for each of the soil conditions
within those 10 miles of road.

An efficient statewide traffic monitoring program stores and makes use
of these project counts. In the above case, the volume data needed for
the general statewide program can be obtained from the vehicle
classification counts performed for the project. Thus, coordination of
these two types of short duration counting programs results in a net
reduction in traffic counting efforts, without a decrease in the
quantity or quality of data available to the data user.

The difficulty with short duration traffic counts is that they only
describe the traffic conditions present when the data were collected.
Depending on when data collection takes place, the data may or may not
represent "average," "normal," or "design" conditions for that road
section. This is when factors (obtained from the permanent count data)
are used. Factors allow adjustment of "raw" traffic data to represent
the desired traffic condition, usually AADT, AAWDT, or some other design
value.

In addition to factors for day-of-week and seasonal adjustment, many
short duration counts require other types of adjustments. For example,
many short duration counters measure the number of axles that pass by,
not the volume of vehicles. To estimate vehicle volume, an axle
correction factor (the average number of axles per vehicle) for that
road must be applied to that measurement.

The steps needed to adjust short duration counts to obtain AADT and
other statistics are discussed in the next chapter.

Short Count Program Design

The basic design process recommended for short count programs involves
defining and overlaying the different short duration counting
requirements and programs. In this manner, it is possible to see where
data are needed and which data collection needs overlap. Where overlap
occurs, a single data collection effort can often be used to meet
numerous needs. Where separate counts are still needed, a reduction in
staff and travel time costs can be achieved by combining the data
collection activities for these distinct needs. (That is, when the data
collection staff sets "project" counts, they can also set "general
coverage" counts in that same vicinity, thus having to travel to that
general location only once.)

When two or more data collection requirements exist for a given
location, collecting the data for the most precise need usually
satisfies all other data collection needs. For example, one program
requires volume data while another requires volume by classification at
the same location. Collecting the classification data can meet both
needs. In other cases, data from one or more locations can often be
substituted for data from a nearby location. This type of coordination
increases the efficiency of the traffic monitoring program.

In general, the highway agency will address the following data
collection needs within the short duration count program:

• counts taken to provide system coverage

• counts taken to meet the HPMS needs

• counts for special needs studies.

Statistical sample locations should be selected prior to the special
study needs because the statistical sample can suffer from bias, with a
consequent loss of accuracy, when data collection locations are not
randomly selected.

Coverage Count Programs

Coverage counts are needed to ensure that adequate geographic coverage
exists for all roads under the jurisdiction of the State highway
authority. In simple terms, "coverage counts" are data collection
efforts that are undertaken to ensure that "at least some" data exist
for all roads maintained by the agency. How much data should be
collected to provide "adequate geographic coverage" is a function of
each agency's policy perspective. Some State highway agencies consider
"adequate" a week-long count every seven years with data recorded for
every hour of each day. Others consider "adequate" a 24-hour count every
year, with no hourly records.

Similarly, the spacing between counts along a roadway is also subject to
agency discretion. The primary objective is to count frequently enough
so that the traffic volume estimate available for a given highway
segment accurately portrays the traffic on that segment of roadway.
Generally, roadway "segments" are treated homogenously with respect to
traffic (that is, traffic volumes are the same for the entire roadway
segment.) For a limited access highway, this is true between
interchanges. However, it is also true for all practical engineering
purposes for a rural road where access and egress along a 10-mile
segment is limited to a few driveways and low volume, local access
roads.

**The TMG recommends, as a general rule, that each roadway segment be
counted at least once every six years**. This ensures that reasonable
traffic volume data are available, and that roadways are accurately
classified within the proper HPMS volume groups when State highway
agencies compute statewide VDT as part of their required federal
reporting. Careful definition of roadway segments can significantly
reduce the number of counts needed to cover all highways within an
agency's jurisdiction, while still providing the accurate volume data
required for planning and engineering purposes. Similarly, careful
coordination within and between agencies can greatly reduce the number
of counts that must be taken.

Finally, not all count locations should be counted on a six-year basis.
Some count locations need to be counted more often. Other roads have
such stable traffic volumes, that counts can be performed even less
frequently. Without knowing how data will be used (and the sensitivity
of specific analyses to variability and error in the traffic data
inputs), it is not possible to define "adequate geographic coverage",
other than for the HPMS (which meets specified national objectives).
Therefore, each agency must make this determination itself, given
available funding for data collection, the extent of the State
controlled highway system, and the uses for which the data are intended.

In general, roadway sections that experience high rates of growth
require more frequent data collection than those that do not experience
growth. Therefore, roads near growing urban centers and expanding
recreational sites tend to need more frequent counting than roads in
predominately rural areas where volumes have changed little in the last
ten years. Counting roads frequently in volatile areas allows the
highway agency to respond with confidence to questions from the public
about road use (a common concern in high growth areas), while also
ensuring that up-to-date statistics are available for the roadway
design, maintenance, and repair work that is common in high growth
areas. These frequent data collection efforts also limit the use of
average growth factors in areas where volatile change occurs.

High growth areas (if not necessarily roads with high volume growth) can
usually be selected on the basis of knowledge of the highway system and
available information on the construction of new travel generators, new
highway construction projects, requirements for highway maintenance,
applications for building permits, and changes in population statistics.
This information can best be gathered by communicating frequently with
agency staff familiar with the economic activity of each region within a
State.

The HPMS Sample

The basic statewide traffic data collection coverage program includes
the collection of volume and classification data for the HPMS. The HPMS
is a combination of complete coverage (universe) for the NHS and other
principal arterials, and a structured sample of roadway sections for the
remaining functional systems excluding the rural minor collectors and
local. The HPMS has specific requirements on the collection of traffic
data covering all systems. A primary goal of the HPMS traffic data
collection effort is to provide a statistically valid estimate of total
annual vehicle distance traveled (VDT). The traffic volume data reported
to the HPMS are used for a number of important analyses, including the
apportionment of Federal-Aid funds to the States.

The HPMS submittal includes both volume and classification information.
The HPMS sample selection process (completed many years ago by the
States) indicates the location of the HPMS samples. The sample locations
are adjusted periodically to account for changes in the road systems.
Detailed information on the HPMS requirements can be found in the latest
version of the HPMS Field Manual.

Other Statistical Samples

In addition to the HPMS, many States (and local highway agencies)
develop and collect traffic volume data as part of statistical studies.
These studies produce specific summary statistics within a given range
of reliability. Examples include:

• VDT estimates for roads within specific State boundaries (for example
at the county level)

• VDT estimates needed to meet data collection requirements for specific
State laws (such as growth management or air pollution control efforts)

• VDT in different jurisdictions within a State (used to distribute
State highway funds)

• the effectiveness of new traffic management plans and actions

• the changes in traffic conditions that result from new construction or
changes in roadway operation.

Count locations and data collection requirements (volume, class, weight,
length of count) for these studies are determined as part of the traffic
program plan. As a result, limited flexibility is usually available in
determining when and where these counts will take place. Therefore,
determining these count requirements (including the location, timing,
and type of counts to be taken) should occur early in the planning
process.

Where these sample counts are required for the same roadway sections
covered by the HPMS sample, a reduction in counting is possible. In
almost all cases, one count can be used to meet all needs. Note that
there usually is flexibility in exactly where and when counts are taken
for the HPMS and other studies. Agencies responsible for traffic
monitoring should try to take advantage of this flexibility to combine
count efforts whenever possible.

Other Special Needs Counts

Statistical samples are the most efficient ways to estimate population
means and totals. However, many traffic data uses require statistics
other than population means and totals. Random sampling is often an
inefficient mechanism for meeting highly specific traffic data needs.

One problem with random sampling is that data may be needed on road
sections that are not part of the sample. For example, uncounted roadway
sections outside the sample are not a concern for the HPMS because the
sample expansion process expands the sample in the statewide VDT
computation. However, if a pavement design will be developed for an
uncounted roadway section, a statewide average or VDT total is not a
good substitute for a traffic count specific to that road segment.

Consequently, States collect data at locations that are not part of the
HPMS or any other existing State-specific sampling study. The key to
making a program more effective is to limit the number of these "extra"
counts to a minimum to save resources for other tasks. This can be done
by ensuring that data collected are used for as many purposes as
possible, so that new data are not collected whenever an existing count
can provide that same information.

Additional "extra" counts generally are required to meet
project-specific studies and other hard to anticipate needs. Project
counts are undertaken to meet the needs of a given study (for example a
pavement rehabilitation design or a specific research project). They
cover a range of data collection subjects and are usually paid for by
project funds.[^6]

Project counts must often be performed at very specific locations. They
have traditionally been performed on relatively short notice and often
collect data at a greater level of detail than typically is required for
the HPMS and coverage counts. Project counts are done on short notice
because funding for data collection for major projects is often not
available until after a project has been selected for construction, and
insufficient time exists by that date to schedule the project counts
within the regular counting program. However, where it is possible to
include project counts within the regular count program's schedule,
significant improvements in staff utilization can be achieved.

Scheduling of project counts is difficult because funding for many
project count efforts is not available early enough in the design cycle
to meet the scheduling needs of the data collection group. However, many
project count locations can be anticipated by examining the highway
agency's priority project list. These lists tend to detail and
prioritize road projects that need to be funded soon. They normally
include sections with poor pavement that require repair or
rehabilitation, locations with high accident rates, sections that
experience heavy congestion, and roadways with other significant
deficiencies. While priority lists are rarely equivalent to the final
project selection list, high priority projects are commonly selected (if
not this year then next year), analyzed, and examined (for alternative
designs, to develop cost estimates, and to properly prioritize the
project). Making sure that up-to-date, accurate traffic data are
available for these analyses helps make the traffic database useful and
relevant to the State's data users and increases the support for
maintenance and improvements to that database.

Chapter 4\
Factoring Traffic Counts

Short duration traffic counts only measure the traffic conditions when
the counts are taken. To use these data to estimate "average" conditions
or to develop traditional engineering inputs, adjustments must be made
to account for variability in the traffic stream. In most cases, these
adjustments (factors) are developed from data collected at continuously
operated data collection sites. This chapter discusses the basic
procedures for creating and applying these factors.

As discussed earlier in this section, common necessary adjustments
include the following:

• time-of-day adjustments for counts that consist of less than 24
consecutive hours (the TMG recommends 48-hour counting periods)

• day-of-week adjustments for counts that do not measure traffic
conditions for all days of the week

• seasonal adjustments for counts that do not cover periods long enough
to account for variation from month to month or season to season

• axle correction adjustments for axle counts (such as counts taken with
a single road tube sensor) that do not directly convert the number of
axle pulses into vehicle counts by vehicle classification.

Many papers have been written on this subject, including the reports
referenced earlier and additional reports referenced here (Weinblatt
1995; Wright et al 1997; Ferlis et al 1980; Cambridge Systematics 1994;
Cohen and Margiotta 1992). Many efforts have described the need to
adjust short duration counts. All of these reports conclude that
seasonal adjustment is needed to reduce the significant temporal bias
introduced by short duration traffic counts.

Not all experts agree on the best method for calculating and applying
adjustment factors. However, work by Wright and Hu has shown that many
of the most common methods for volume count adjusting produce comparable
results. Consequently, flexibility is needed because the definition of
"best" is often a function of issues such as the number of continuous
counters a State can afford to operate and the extent of the roadway
system for which factors must be developed and applied. The following
recommendations are offered:

**• factors must be applied to short counts**

**• factors should be developed to best utilize available data
collection resources**

**• factors should be developed separately for total volume and for
estimates of volume for individual truck classifications.**

The last of these recommendations stems from recent analysis of
continuous vehicle classification count data, which showed that truck
volume patterns tend to be considerably different from automobile
travel. These pattern differences include all major temporal variables:
time of day, day of week, and season of the year.

The Creation of Factor Groups

Before creating factor groups, it is important to understand what
factors are and how they are applied. Short duration counts normally do
not adequately represent "average" traffic conditions. Unfortunately,
the data, needed to convert each short duration count into an "average"
with accuracy and precision, require a continuous counter. There are
insufficient continuous counters to describe how traffic behaves at
every location. However, assuming that temporal characteristics affect
all roads and since continuous temporal data exist at several points
(the continuous counter sites) to describe the temporal variation, it is
possible to transfer knowledge by developing factoring mechanisms.
Factor groups are used to create temporal variation factors to
statistically convert short counts to annual averages.

The factoring process defines a set of roads as a "group." All roads
within that group are assumed to behave similarly. Then a sample of
locations on roads from within that group is taken and data are
collected. The mean condition for that sample is computed and that mean
value is used as the "best" measure of how all roads in the group
behave. If the sample of data collection sites is randomly selected and
moderately large, the distribution of that measure about the mean is a
good measure of how well that mean applies to road sections in the
group.

This whole process involves a number of assumptions. Limitations in the
reliability of those assumptions are the source of many of the errors in
"annual" and "design" traffic estimates. Different techniques used to
create and apply traffic correction factors allow the user to control or
limit the errors associated with any given one of these steps.
Unfortunately, none of the available techniques can control for all of
the limitations. Thus, selection of the "best" technique is usually a
function of the availability of data and knowledge of the roadway system
more than the application of a theoretically pure analysis process.

Each of the basic steps required in the factoring process is listed
below, along with the primary type of error that is associated with the
assumptions that are required to perform each step.

1\. [It is difficult to define groups of roads that "are similar with
respect to traffic variation," and the more "mathematically alike" the
factoring groups created from the data, the more difficult it is to
define the attributes that determine which roads belong to a given
group]{.underline}.

It is easy to define groups of roads with a high level of precision
based simply on variability. However, the groups that can be easily
defined based on variability usually do not have clear characteristics
to identify the group. For example, the category of roads rural
Interstate highways is very easy to define. The problem is that all
rural Interstates often do not have the same travel pattern. Interstates
that pass near major recreational areas have different travel
characteristics than Interstates that do not.

Trying to subdivide the category into rural Interstate highway segments
that are affected by recreational areas and those that are not places
the analyst in the difficult position of trying to guess at just what
point on the Interstate highway system the influence of the recreational
area disappears. However, if the "group" is not divided, then it
includes all the roads despite the difference in temporal patterns. This
makes the factor associated with this combined group less "precise."
Although the computed factor may be the perfect mean value for the
group, it is not a good factor for any one specific location on the
rural Interstate highway system.

Therefore, the creation of factor groups usually involves balancing the
need to easily define a group of roads against the desire to ensure that
all roads within a given group have similar travel patterns.

This same trade-off occurs in the type and magnitude of errors in the
factoring process. For groups that are easy to define but include wider
ranges of travel patterns within the group, errors occur because the
mean factor computed for the group may not be a good estimate of the
"correct" factor for a specific road segment. For groups that have very
"tight" factors but for which it is difficult to define the roads that
fit, the error occurs in defining which factor group a specific road
segment belongs to.

2\. [The grouping process is made more difficult and error prone because
the appropriate definition of a "group" changes depending on the
characteristic being measured]{.underline}. The best example of this is
the computation of factors for total volume versus the computation of
factors for individual types of truck classes. Trucks have different
travel patterns (time of day, day of week, and season of the year) than
cars. Consequently, factor groups that work extremely well for computing
and applying total volume adjustments (dominated by car volume patterns)
often do not work well for truck volume adjustments.

The variation in truck traffic (and truck percentages) from road to road
even for roads of the same basic functional classification and
geographic location can also make common "volume factor groups" based on
geography and functional classification very poor "groups" for the
computation and application of axle correction factors. Factor groups
for computing axle correction factors are driven primarily by the
presence of common vehicle mixes. But vehicle mix is not a value that
would be considered in forming a factor group intended to seasonally
adjust total volume counts.

In general, the "best factor groups" are those that can be readily
defined and at the same time contain similar traffic patterns. However,
it is extremely difficult to find road groups in most States that are
both readily defined and that have very similar traffic patterns. In
addition, the "best" factoring process usually means having at least two
sets of factor groups, one for total volume and one for truck volumes.

3\. The next source for error in the factor computation process is that
[it is very difficult to select a representative sample of roads from
which to collect data for calculating the mean values used as
factors]{.underline}. The best alternative for selecting these sites is
to first define the factor group and then perform a random selection of
data collection sites. Normally, neither of these events takes place.
Consequently, the "mean" value computed is often not the "true" mean
value for the group.

Data collection points are usually not "perfect" for two reasons. The
first is that permanent data collection site locations are often
selected for a number of reasons, only one of which is factor
computation. These reasons include:

• the need for data from a specific site (for example, an LTPP site)

• the desire to track trends over time at sites that have been
historically monitored

• the need for specific physical conditions (the availability of power
or communications lines, the need for smooth, flat pavement)

• the wish to meet a number of needs with a single data collection
station.

Second, because factor groups are often determined on the basis of data
from existing data collection sites, the actual site locations often
exist before the grouping process, and cost considerations tend to
prevent their being moved. Thus, the data drive the grouping process,
rather than the grouping process driving the selection of data
collection points.

Both of these factors increase the chance that the data sites do not
truly represent the road segments included in a given group. When
combined with the limited budget available for permanent data collection
sites and the fairly high cost of permanent data collection sites, this
limitation usually results in a "less than random" sample of sites
within a factor group.

This problem is exacerbated by the small number of stations that
normally exist within a given group. The cost of installing,
maintaining, and operating permanent data collection sites tends to
limit the amount of data available for computing "mean" factors. Thus
the presence of one "unusual" location within a group of counters can
have an overly large effect on the factors computed and applied to
individual road segments.

4\. [The last source of error discussed in this section occurs in the
computation of factors because the datasets used to compute those
factors are not complete]{.underline}. No data collection device is
perfect. Within any given State, a number of permanently operating data
collection devices will fail each year, and those failures will last for
anywhere from a few hours to several months. The holes produced in the
"continuous" data sets for these sites must be accounted for to compute
factors. In some cases, so few data are available that the site may not
be included in the factor computation at all.

A number of procedures, most notably the AASHTO process for computing
AADT (AASHTO 1992), have been designed to limit the effects of missing
data. However, because of the holes in the data, errors are introduced
into the factors being computed. In general, the more data that are
missing, the more error that may be associated with the mean factors
applied to any given location. The best way to decrease the chance of
these errors occurring is to monitor and repair permanent data
collection equipment as quickly as possible. It is also helpful to
maintain more than the minimum number of counters within any given
factor group, so that if one counter experiences a long data outage, the
data from that counter can be removed from the computation process
without adversely affecting the factor computation.

How To Create Factor Groups

Three alternative techniques for computing factor groups are discussed
below. Each of these techniques has strengths and weaknesses. In most
States, some combination of these techniques is used to compute and
apply traffic adjustment factors. In many cases, this combination of
approaches is probably better than following any one technique
exclusively.

The three techniques discussed briefly below are

• cluster analysis

• geographic/functional assignment of roads to groups

• same road factor application.

In addition to this discussion, the cluster analysis technique is
illustrated in Appendix B. This technique is more complex than the other
two, and the example should help clarify the process for staff
unfamiliar with it.

Each of these techniques starts from existing permanent counter
data.[^7] Therefore, for each of the three basic techniques, the first
step is to compute the adjustment factors that will be used in the group
selection process (and that would be applied to short counts if just
that one counter's data were to be used) for each site for which data
are available.

As part of this first step, the analyst should learn about the quality
of the data produced by each counting device. This includes
understanding when each counter malfunctioned, how the malfunction was
detected, and how the data for that counter was edited to account for
the malfunction. The implementation of truth-in-data concepts, as
recommended by the 1992 AASHTO guidelines, will greatly enhance the ease
with which this task is undertaken, as well as help improve the
analytical results and establish objective data patterns.

Cluster Analysis

In the cluster analysis process, a statistical program applied to the
computed factors determines which stations are "most similar." The
statistical analysis program uses a least-squares minimum distance
algorithm to determine which sets[^8] of factors are most similar. The
stations that are most similar are then grouped together, and the
process is repeated to find the next closest group.

The output of the cluster program normally includes a sequential list
that indicates which counters have traffic adjustment patterns that are
most similar, and in what order each group is formed. For example,
cluster analysis software normally provides an output that indicates
that in the first step, the stations are broken into two groups. In the
second step, the stations are broken into three groups. In the third
step, the stations are broken into four groups. This continues until
there are as many groups as stations. An example cluster program output
is shown in Appendix B.

The analyst's major function is to determine at what point to stop the
analysis and apply the groups formed. As described above, the cluster
analysis program works in a "step-wise" fashion. That is, in the first
step, the closest two groups are formed. In each succeeding step, an
additional group is added. At the end of the clustering process, each
station is in a single group. It is left to the analyst to make the call
as to how many groups are sufficient and how to implement the groups.

Deciding where to stop the groupings is commonly determined in one of
two ways. The first way is to look at the mathematical distance between
the clusters formed. A number of different mathematical tests can be
used to produce these measures of "distance" or goodness of fit. It is
sometimes possible to find major changes in the distance between two
consecutive group formations. These breaks indicate that although this
is the next best fit, the group being formed is not very homogenous.
Thus, large changes in the distances between groups indicate that this
might be a logical stopping point for the cluster process. Cluster
programs usually provide a summary of the explanatory value of the
groups formed, which allows a determination of how much explanatory
value additional groups add.

The second common approach to ending the cluster process is to choose a
predetermined number of groups to be formed. For example, an analyst
might want to create no more than five factor groups. The question then
is, "what is the best grouping of locations that will give me five
groups?"

The next issue for the analyst is the need to define the group of roads
a given cluster of continuous counters actually represents. That is, how
should the group of continuous counters grouped together be defined
spatially? Which roads are included in a factor group that is
represented by a specific set of count locations? This "spatial
definition" is necessary to assign arbitrary roadway segments to the
newly created factor groups, since the analysts must understand the
rules for assigning short counts to the factor groups.

In some cases, the underlying pattern (and the assignment rules) behind
a factor group may be obvious. For example, roads with heavy
recreational movements are usually outliers in the cluster process. They
are included in groups because their traffic patterns are extreme and
usually quite different than other roads in the State. As a result, they
are often defined as separate "groups." Other groups, such as urban
counters, also tend to be distinguished fairly easily because urban
areas tend to have much flatter variation patterns than rural roads.

However, in some cases the definition of what roads fit within a cluster
group can be difficult to determine. If the definition of "factor
groups" is hard to determine, try plotting the locations to see whether
a geographic pattern emerges. The difficulty in assigning definable
characteristics to the resulting clusters of continuous counters is one
reason the cluster process is often modified by the use of secondary
procedures to develop the final factor groups.

The cluster process is used to objectively determine the pure variation
patterns that exist in the continuous counter database. The information
gained is then used by the analyst to define appropriate factor groups.
The major difficulty in developing factors groups lies not in the
assignment of the continuous counters to the groups, but rather in the
specification of definable characteristics to allow the objective
assignment of short counts to the seasonal factor groups. The final
factor group definition is often a combination of statistical analysis
and analyst knowledge and expertise.

Geographic/Functional Classification of Roads Factor Groups

Whereas the cluster analysis is driven by mathematics, the analytical
procedure described here is driven by professional knowledge. In this
process, the analyst allocates roads into alternative factor groups on
the basis of available knowledge about those traffic patterns. Available
knowledge is usually obtained from a combination of existing data
summaries and professional experience with traffic patterns. Prospective
groupings initially based on the expectations of the analysts are
compared with available continuous counter data. The initial groupings
are modified on the basis of the results of the analysis and the groups
finalized.

The initial factor groups selected by the State highway agency will
differ from State to State, but they tend to be based on a combination
of functional roadway classification and geographic location. Some
States have found that the non-Interstate rural functional classes 2, 6,
7, and 8 have similar travel patterns, and others have found that rural
principal and sometimes minor arterials have different patterns than the
lower rural functional classes of roads. The characterization of
roadways using functional class makes it easy to assign individual road
sections to factor groups and also allows the creation of factor groups
that are intuitively logical. For example, initial factor groups might
include:

• urban Interstates and expressways

• other urban roads

• rural Interstates

• other rural roads in the eastern portion of the state

• other rural roads in the western portion of the state

• recreation routes.

In this example, it is assumed that urban and rural roads experience
different travel patterns (normally the case). It also assumes that
Interstate highways behave differently than non-Interstates. This is
often the case because Interstate highways tend to carry considerably
more through traffic than non-Interstates. This example also assumes
that there is a difference in traffic patterns in the eastern and
western portions of the State.

Sub-State differences often occur when two (or more) parts of the State
have different traffic characteristics resulting from different levels
and types of economic activity (the data-driven cluster analysis would
have identified these differences). Finally, the example shows one (or
more} recreational patterns. These are roads with particular, unusual
traffic patterns. These patterns may or may not be strictly
recreational. Any road or geographic area with a known, unusual traffic
pattern may require separation from the standard factor groups.

Once the initial factor groups have been identified, continuous counter
data are examined for each group.[^9] For each factor and each factor
group, the mean factor for the group and the standard deviation of that
factor are computed. The standard deviation tells the analyst the size
of the expected error of the average group factor. It is assumed that
the continuous counters for which data are available are representative
of a random sample of roads from within that defined group. Given the
assumption, the errors should be roughly normally distributed about the
factor group mean.

If the standard deviation is too high (i.e., the error associated with
factors computed for that group of roads is too large), the definition
of roads that fit within that group may have to be changed. This can
mean the creation of new factor groups (for example, splitting "eastern
other rural roads" into principal arterials and lower functional classes
of roads in the eastern part of the State), or the redefinition of those
groups (for example, a county that was believed to fall within the
eastern group may be more closely associated with the western group).
Changing factor group definitions effectively moves continuous counters
from one factor group to another and allows the variation within a given
group to be decreased.

When the standard deviation of the various factors for a group is
examined, it should be remembered that not all factors are of equal
importance. For example, in many States, the majority of traffic
counting takes place from the middle of Spring to the middle of Fall.
Therefore, the factor group variation in January and December is less
important (because these factors may never be used) than the variation
in the key May through September time period, when most short duration
traffic counts are taken. Likewise, if the May to September factors for
two defined groups are almost identical, then combining the groups
should be considered since the effect on short counts is the same.

Plotting the factors for each continuous counter within a group is also
very useful. This allows the analyst to determine specific outliers
(continuous counters that don't really fit within the basic pattern that
is assumed to exist). Plotting the continuous counter data is also a
good first step for redefining the factor groups and for moving a
specific counter (and the road segments it represents) from one factor
group to another. For example, if a counter does not fit within a factor
group, having a plot of that counter's data will allow the analyst to
determine whether the data for that site contain potential errors that
could affect the grouping process, indicate the need to create a
recreational factor group, or provide the insight to place the stations
in another group.

The development and application of factor groups is not a perfect
process. It combines both data driven analysis, statistical expertise,
and knowledge of traffic conditions. The result should be a fairly
simple, easy to apply process that reduces the periodic bias in short
counts to produce reasonable annualized estimates of traffic. The main
objective is neither statistical purity nor complete subjectivity, but
rather an effective process that meets the needs of the users of traffic
information and is understood by both users and data providers.

Same Road Application of Factors

An alternative to either of the group factoring approaches described
above has been commonly used by many States for sites that are near or
on the same road as a continuous counter. This process assigns the
factor from a single continuous counter to all road segments within the
influence of that counter site. The boundary of that influence zone is
defined as a road junction that causes the nature of the traffic volume
to change significantly. This approach avoids two of the common errors
of the group factoring approaches, the application of a mean value that
does not accurately describe traffic variation on that given road
section, and the problem of associating a specific road section with a
vaguely defined factor group.

For this approach, the association of a factor to a given count is quite
easy. The short count in question must be taken on the same road as the
continuous counter. The factor from the continuous counter is then
applied to that count. The likelihood that the traffic variation at the
continuous counter is similar to that of the short count is very high.
The error associated with the computation and application of that count
tends to be small in comparison to that associated with the computation
and application of a group factor (Cambridge Systematics 1994 and 1995).

Difficulties in the application of this technique only occur when the
short duration count is not near the continuous counter. In such a case,
traffic patterns at the count location may be different than those found
at the continuous counter.

This approach requires a dense network of continuous counters and/or a
very small number of roads against which these "single use" factors are
applied. Without these two conditions, there are many roads that will
not be associated with any continuous counter and for which no factor
can be computed.

Applying factors from a single continuous counter to arbitrary counts on
"nearby" but different roads is not advised. Application of factors from
individual locations in this fashion creates considerable potential for
bias in the factoring process. Without the availability of multiple
counters to balance the variability from a single location, unusual
traffic (e.g., the diversion effects of nearby construction activity)
can have a ripple effect. These unusual patterns are then reflected in
the adjustments made to roads that are not affected by the unusual
events.

Combining Techniques

As noted at the beginning of this subsection, most States develop and
apply factors by using some combination of the above techniques. For
example, on road sections where continuous counters exist nearby,
factors from specific counters can be applied to short duration counts
on those roads. For all other road sections, group factors can be
computed and applied. Factor groups can be initially identified by
starting with the cluster analysis process followed by the use of common
sense and professional judgment. In this way minor adjustments can be
made to the cluster results in order to define the final factor groups
in such a way that they can be easily identified for factor application.
Groups can also be initially defined judgmentally and then confirmed
and/or modified by using cluster analysis.

Alternatives To Factoring

An alternative to factoring exists. This technique is not commonly used,
but it is appropriate where factor groups are not readily known and the
annual traffic estimate must be very accurate. Work done showed that for
volume counts by vehicle classification, it was possible to achieve
accurate annual estimates by conducting four week-long counts per year
at the same location (Hallenbeck and O'Brien 1994).

This approach may seem like data collection overkill, but it provides
sufficient data to overcome the primary sources of variation in the data
collection process. Taking week-long counts removes the day-of-week
variation. Counting at the same location four times at equally spaced
intervals removes the majority of seasonal bias.

Similarly, the use of control counting procedures or taking short counts
at the same site on different times of the year are commonly used by
States to address the special needs of recreational sites or high growth
areas.

Types of Factors

Different States have adopted different procedures for developing and
applying factors. Work by Weinblatt and Margiotta (Cambridge Systematics
and Science Applications International 1994) showed that a number of
different factoring techniques can result in reasonably similar levels
of accuracy when short duration counts are converted into estimates of
average annual conditions. The key is that each successful factoring
technique must account for all types of variation present in the
data.[^10]

The Weinblatt and Margiotta work tested seven factoring strategies for
adjusting short duration count data (see Table 2-4-1). They found
relatively similar results in terms of the reduction in bias and the
expected errors remaining.

As can be seen in Table 2-4-1, the primary difference among successful
factoring techniques is the level of aggregation that exists in each
factor and the definition of "seasonal." In some techniques, day-of-week
and seasonal adjustments are combined in a single factor. In other
techniques, these two components are treated as separate factors,
although both factors must be applied to a short duration count as part
of the factoring process.

For seasonal adjustments, some techniques use monthly factors, whereas
others use weekly factors. Both of these techniques can be successful.
Seasonality does not necessarily vary smoothly from month to month.
Consequently, some States find that weekly factors work better than
monthly adjustment factors. However, others find that the monthly
factors provide equally good annual adjustments and require considerably
less effort to compute and apply. However, if "weekly" factors will be
applied, it is very important to use "same year" factors because the
characteristics that affect week-to-week travel (such as which week
Easter falls on) change from year to year.

Table 2-4-1\
Effects of Alternative Current Year Factoring Procedures on AADT
Estimates

  --------------- ------------ ------------ -------------- ----------- -----------
                  Mean         Average      Percent of     Number of   Number of
                  Absolute     Percentage   Observations   Weekday     Weekday and
                  Percentage   of Error     with Error \>  Counts      Weekend
                  of Error                  20%            Required    Counts
                                                                       Required

  Unfactored      12.4%        -0.6%        18.2%                      

  Separate Month  7.5%         -0.5%        6.2%           17          19
  and Day-of-Week                                                      

  Combined Month  7.6%         0.4%         5.9%           12          24
  and Average                                                          
  Weekday                                                              

  Separate Week   7.5%         -0.9%        6.0%           57          59
  and Day-of-Week                                                      

  Combined Month  7.4%         -0.2%        5.8%           60          84
  and Day-of-Week                                                      

  Combined Week   7.3%         0.5%         5.1%           52          104
  and Average                                                          
  Weekday                                                              

  Specific Day    7.1%         0.2%         5.1%           261         365

  Specific Day    7.0%         0.3%         4.8%           261         365
  with                                                                 
  Noon-to-Noon                                                         
  Factors                                                              
  --------------- ------------ ------------ -------------- ----------- -----------

For day-of-week factors, some States use day-of-week adjustments for
each day. Others combine some weekdays (traditionally Tuesday to
Thursday or Monday to Thursday). Both techniques can produce acceptable
results if they are applied appropriately. Whether a factor that relies
on the average of all weekdays is appropriate is a function of the
traffic patterns. For example, in some prairie States, weekday travel on
Interstates is not constant, particularly for trucks. This is because
these roads are heavily influenced by through-traffic, and that traffic
can be generated several days' drive away. Consequently, volumes on some
weekdays fall in travel patterns that look more like weekend volumes
than weekday volumes. For a State with this type of traffic pattern,
individual day-of-week factors (i.e., a Monday factor, a Tuesday factor,
etc.) will be much more accurate than a single "weekday" factor. In
cases where through-travel is less sizable, or in urban areas where
traffic is much more constant, a single "weekday" adjustment is simpler
to maintain while being equally as accurate.

Finally, it is important to once again stress that these analyses need
to be performed separately for total volume factors and for factors that
are applied to volumes by vehicle classification. There is no question
that on many roads, trucks have very different day-of-week and seasonal
patterns than cars, and many types of trucks have different patterns
than other types of trucks. States need to be aware of these differences
and to treat their factoring procedures accordingly.

Computation of Factors[^11]

Once a State has selected the types of factors it plans to use, it must
select the mathematics for computing those factors. There are two basic
steps in computing the factors to be used: computing the numerator and
the denominator. The numerator is assumed to be AADT. The
denominator[^12] is dependent on the factoring approach taken.

Computing AADT

Wright, Hu, et al (1997), provide an excellent discussion of alternative
algorithms for calculating AADT for continuous count locations. There
are two basic procedures. These two procedures are:

• a simple average of all days

• an average of averages (the AASHTO method).

In the first of these techniques, annual average daily traffic (AADT) is
computed as the simple average of all 365 days in a given year. When
days of data are missing, the denominator is simply reduced by the
number of missing days.

This approach has the advantage of being simple and easy to program. Its
drawbacks come from the fact that missing data can cause biases (and
thus inaccuracy) in the AADT value produced. In particular, blocks of
missing days of data (for example, data from June 15^th^ to July 15^th^
) can bias the annual values by removing data that have specific
characteristics. On a heavy summer recreational route, missing data from
June 15^th^ through July 15^th^ would likely result in an
underestimation of the true annual average daily traffic for that road.

When the simple average is used to compute average monthly traffic, the
missing data can bias the results when an unequal number of weekday or
weekend days are removed from the dataset. Because most ATRs have some
equipment "down time" during a year, and some miss considerable numbers
of days, AASHTO adopted a different approach for calculating AADT. The
AASHTO approach first computes average monthly days of the week. These
84 values (12 months by 7 days) are then averaged to yield the seven
average annual days of the week. These seven values are then averaged to
yield the AADT. This method explicitly accounts for missing data by
weighting each day of the week the same, and each month the same[^13],
regardless of how many days are actually present within that category.

The resulting two versions of AADT are very close to each other. The
study by Wright, Hu, et al., indicates that the differences are so small
as to be unimportant. The "simple average" method is certainly easier to
compute. However, where data are likely to be missing, the AASHTO method
will provide a more reliable and accurate value.

**The AASHTO method for computing AADT is recommended**. This is the
case because it allows factors to be computed accurately even when a
considerable number of data are missing from a year at a site, and
because it works accurately under a variety of data conditions (both
with and without missing data). On the other hand, the simple average
works accurately only when the data set is complete, or when little bias
is present in the missing data. Because a common method should be used
for all AADT computations, the AASHTO method is preferred.

Computing the Denominator[^14] for Monthly Factors

The numerator is AADT. The denominator depends on the procedure used.
For example, suppose a State chooses to compute and use an adjustment
factor that converts any weekday ADT for a given month into AADT. This
would convert monthly average weekday traffic to annual average daily
traffic. The first step is to define what a weekday is. This can be done
by determining the days on which data will be collected. If data will be
collected on the 5 days of the week (Monday to Friday), then the
denominator is the sum of all weekdays (Monday to Friday) divided by the
number of days of data present.

If no short count data will be collected on Fridays, then the
denominator should be the sum of all Mondays to Thursdays, divided by
the number of days of data present. The key is that the only days that
should be included in the computation of the denominator are the days
that will actually be included in the data collection effort. Using only
those days for which data will be collected (and then used in the
estimation of AADT) means that the factor computed applies directly to
the count against which it is being applied.

Following this same logic means that holiday traffic could be excluded
from the calculation of the denominator and thus the adjustment factor,
if no traffic volume data is collected on holidays. Few States collect
short duration count data on holidays, other than as part of an effort
to measure special holiday flows.

The definition of a "holiday" (only for the purpose of computing
adjustment factors) should thus be driven by [whether short count data
is collected on those days.]{.underline} For example, if no traffic data
will be collected on the Friday following the fourth of July (because
the traffic is so unusual), then this Friday should be excluded from the
denominator calculation. Note: holidays [are]{.underline} included in
the computation of AADT that is used in the factor calculation. The
definition of holiday periods can be difficult and changes from year to
year. Influence days are the days before and after a holiday where
traffic is greatly influenced by the holiday.

The next step in the computation of the denominator is to determine
whether simple averages will be used, or the "average of an average"
approach recommended by AASHTO for AADT computation. For monthly
averages (e.g., monthly average weekday traffic), both techniques are
reasonable. The same advantages and disadvantages apply at this level,
although for monthly averages, bias caused by missing days of data is
more easily introduced because the number of days used in the
calculation is smaller than when AADT is computed. In other words, the
effect of each missing day is accentuated. This is particularly true if
an unequal number of weekdays and weekend days are missing. In general,
whichever technique is used for AADT computation should be used for
MAWDT.

If the State chooses to compute an average monthly day-of-week factor
(i.e., combining the monthly variation and the individual day-of-week
variation), then the denominator is the simple average of available
daily volumes for that day of the week for that month.

Computing the Denominator for Weekly Factors

If the State decides to use a weekly factor, the denominator is simply
the average of the seven days for the appropriate week. (One of the
difficulties with this technique is how to handle missing data without
biasing the weekly adjustment.) Holidays in the weekly process are
either included or excluded, depending on how data to be used for AADT
estimation will be collected.

frequently asked questions---Factoring

When I compute a "weekday" factor, should I include Mondays in the
weekday? Should I include Fridays?

There is no definite answer. The decision must be made by each
organization. Traffic patterns vary from site to site. In most urban
cases, Monday traffic volumes are fairly similar to Tuesdays, Wednesday,
and Thursdays. Fridays, however, tend to have lower morning volumes and
slightly higher afternoon volumes than the other weekdays. In rural
recreational areas, Mondays, like Fridays, can have substantially
different volumes than the other weekdays. In other rural areas, Monday
volumes tend to be similar to Tuesday through Thursday volumes. The
procedures recommended in the TMG produce adequate estimates of AADT
regardless of whether these days are included or excluded.

Should I compute factors for days that run from midnight-to-midnight or
from noon-to-noon?

The answer to this question depends on the data against which analysts
will be factoring. If counts are routinely taken from noon to noon, then
computation of the factors using noon-to-noon "days" is appropriate. If
the "days" from short duration counts are always based on midnight start
times (that is, the earliest hours of the data collection period are
essentially discarded), then the "days" used in the factor computation
should be based on calendar days. Analysis has shown that the use of
either alternative has little impact on the AADT estimates.

Should I use data from this year, last year, or a combination of several
years to compute factors for short counts taken this year?

The best factoring results are obtained if the factors being applied are
for the same year as the short duration counts being factored. That is,
a short count taken in 2000 should be factored with ATR data from 2000.
This is done because significant events affecting the ratio of a short
duration count to annual travel this year (e.g., a big snow storm) are
accounted for in this year's ATR data. They were not present in last
year's ATR data.

The drawback to using current year data for the factors is that
computation must wait until the end of the year. States often wish to
use AADT estimates from short counts taken during the current year
before the end of the current year. One alternative is to create and use
a "temporary" factor until the calendar year is complete. This factor is
computed with the data from the previous 12 months. The "temporary"
factor would be used until the "final" factors are computed. This
"final" value would then be maintained as the annual estimate.

Another is to use more than one year of data to compute seasonal
factors. However, this technique does not account for annual conditions
that affect traffic when it is applied to short duration counts that are
from a different year.

Perhaps, the simplest solution is to use the available AADT figure until
a new one based on the current year factors is computed. **Factors based
on "current year" data are recommended**.

How do I assign short counts taken in "rural" areas that are affected by
urban traffic? Are they "urban" counts or "rural" counts?

There is no simple solution to this problem. These locations tend to
have unique day-of-week patterns that reflect typical urban patterns on
the weekdays, but rural patterns on weekends. Similarly, seasonal
variation tends to be partway between the flat pattern found in most
urban settings and the more varied "peak" patterns often found in rural
areas. This occurs with commuter routes where the urban pattern extends
outside the urban boundary. In most cases, analyst judgment is the
answer.

One alternative is to take longer short duration counts. A week-long
count will provide the data needed to account for the day-of-week
variation without factors. The factor application then only has to
adjust for the seasonal component. Another solution may be to install an
ATR for that route. Another may be to apply the appropriate factors
outside the group boundaries as a special case.

How many ATRs should be in a factor group?

There is no single answer to this question. Statistics and the desire to
have factors that yield annual AADT estimates with [+]{.underline}10
percent accuracy with 95 percent confidence tend to require a factor
group size of between 5 to 8 counters. A bare minimum of two counters is
required to compute a standard deviation of the average factors that
become the group factors. The standard deviation is used to estimate the
reliability of the group factors. Recreational or special groups often
have only a single continuous counter. Many States prefer to have
additional counters to compensate for downtime and missing data
problems.

Chapter 5\
Coordinating Count Programs

In the fiscal climate in which most State highway agencies operate, it
can be very difficult to collect enough data to meet the needs of all
its primary data users. One of the best mechanisms available for
stretching the available data collection budget is to get all groups
interested in traffic monitoring information to coordinate their data
collection efforts and share their resulting databases.

Why Coordinate Programs

By coordinating traffic monitoring efforts between divisions within a
State highway agency and with other roadway agencies, the following
advantages can be obtained:

• More data are available to users at relatively little increase in cost
(i.e., at only the cost of the coordination effort itself), since
additional data are not being collected. The data already being
collected are simply made more accessible.

• Duplication in the collection of traffic counts can be reduced or
eliminated, thus either reducing the total cost of data collection or
expanding the number of locations for which data are available for the
existing budget.

• Resources can be more efficiently distributed to take advantage of
each agency's capabilities and interests.

• Independent measures of traffic can be collected, allowing more
effective quality control. This improves the quality of the traffic
estimates provided to users.

• Expertise in traffic monitoring skills (equipment placement and
repair, data processing, data reporting, etc.) can be identified, so
that these human resources can be accessed quickly and efficiently when
they are needed, resulting in better trained staff in all agencies,
quicker problem resolution, and better, more reliable traffic counting
programs for all cooperating agencies.

Who To Coordinate With and What Data Can Be Obtained

Coordination of data collection activities and sharing of data resources
often need to take place within the State highway agency as well as
outside. It is quite common for multiple groups or divisions within a
State highway agency to collect traffic data. Yet, in many cases, many
of these data do not become available to other users within the agency.
For example, there are cases where the research program collects traffic
data (volumes, classification and weights) that are not included in the
agency's main traffic database and are therefore not available to other
users. Another common example occurs when traffic control systems
collect and store traffic volume and performance data, but those data
are not made available as part of the central traffic database for the
highway agency. Similarly, short duration data collection efforts are
often taken to meet specific project needs, including pavement design
inputs, traffic operations and control system improvements, or planning
and programming efforts. Often these counts are used for their special
project purposes and then discarded. Simply making sure these data are
incorporated into the highway agency's primary traffic database may
prevent a second data user from having to recount these same roadways.

Another excellent source of traffic monitoring information comes from
the other jurisdictions that operate roadways in the state. Local
jurisdictions (cities, counties, townships, etc.) almost always perform
some level of traffic monitoring on roads they control. These data can
provide two specific advantages to the State highway agency. First, they
provide coverage on roads that are not already covered by the SHA data
collection effort. Many of these counts are needed for the HPMS
submittal. In addition, access to these data can often serve a variety
of purposes. Larger jurisdictions often maintain sophisticated traffic
monitoring programs. These can include permanent counters and vehicle
classification counts. These data can be used to expand a State highway
agency's knowledge of seasonal and time of day variation in vehicle
movements.

In many cases, agencies that collect traffic monitoring data do not
realize that they are collecting data that have value to other agencies.
This is particularly true when the data are used for purposes other than
traditional highway monitoring. Common examples of this are the
following:

• commercial vehicle regulatory agencies that collect truck volume and
weight statistics (how many trucks are passing an enforcement site, what
those trucks weigh)

• environmental agencies that collect their own traffic counts as part
of air quality and/or pollutant emission studies

• toll authorities that collect volume and vehicle classification data
as part of their revenue collection process.

In each of these cases, these data collection efforts can supply data of
significant value to the State highway agency. Data from the commercial
vehicle regulatory agency can serve as excellent input for pavement
design and rehabilitation efforts on the road on which they are
collected. For example, if the data are collected extensively (e.g.,
they are collected throughout the year), these data can also be used to
describe seasonal and day-of-week patterns in trucking movements, even
though the presence of the enforcement activity will cause some bias in
the data being collected.[^15]

How to Make Coordination Happen

The difficulty with sharing data across agency divisions, as well as
between different agencies, is that it requires a conscious effort to
work outside of normal institutional communication channels. Thus, a
specific communications effort that crosses these boundaries must be
undertaken to learn about what data are being collected, determine how
(and if) those data can be of use, and the best mechanism for obtaining
those data.

This communications mechanism may start out as a specific, one-time
effort (for example, a consultant contract). However, it needs to become
an ongoing process. It does not need to be a large, ongoing activity. It
can be a small part of an existing communications effort. For example,
in many States "traffic management groups" consisting of traffic
engineers from neighboring jurisdictions meet periodically to discuss
all of the jurisdictional issues that affect the operation of road
networks that cross jurisdictional boundaries. Some of these meetings
include planned roadway improvements, coordination of traffic control
systems, coordination of incident and emergency response actions. Data
sharing opportunities can simply become one more item on the agenda of
these existing groups. The availability of data that result from
coordination and cooperation can then be broadcast to potential data
users through the same mechanisms used to describe other cooperative
jurisdictional efforts: newsletters, Web sites, announcements in
meetings, etc.

Taking advantage of existing multi-agency groups can significantly
improve the success of these data sharing efforts. For example, rural
and metropolitan planning organizations (RTPOs and MPOs) are required by
federal law to assist in the planning and programming of transportation
projects that affect multiple jurisdictions. In many cases, these
agencies already collect data from multiple agencies to support their
planning function. In some areas, MPOs directly perform data collection
under contract to individual agencies. In others, they simply use the
data collected by their member jurisdictions. In either case, they are a
logical agency to undertake the task of helping coordinate traffic data
collection activities and to help ensure that data from these efforts
become available to all potential users. Working with these agencies to
achieve effective, efficient data sharing can help ensure the success of
these efforts.

Issues to Remember

While coordination and sharing of traffic data have great potential to
benefit traffic monitoring, several key issues must be kept in mind if
the coordination efforts are to produce the anticipated benefits. These
issues include the following:

• Coordination is not free and does not happen automatically. It
requires a continuing effort and commitment from the parties.

• Continuing communication between the data collection groups is
necessary for the success of the on-going coordination effort.

• Efficient data transfer mechanisms need to be adopted.

• Shared traffic data must be carefully described to users so that they
can be used correctly.

One of the primary reasons that traffic data are not shared between the
groups that collect them (usually for a specific purpose) and the groups
that could productively use them (for some purpose other than what they
were originally collected for) is that the group that collects the data
has no incentive to take the extra step(s) needed to make those data
available to other users. Providing the necessary incentive must become
the job of the group in charge of statewide or regional traffic
monitoring.

In effect, the sharing of data between agencies or groups must be a
win/win situation. Each agency must see some benefit in making the extra
effort needed to share their data. In some cases, this means that one
agency must provide external incentives (for example, funding,
equipment, or staff time) to obtain data from another agency or group.
Supplementary funding may be also appropriate to enhance an existing
system so that it stores, summarizes, and reports data for later use
that are already being collected but not saved. This typically occurs
with older traffic control systems that collect but do not store data
from surveillance systems.

In some cases, no incentives are needed to support data sharing. All
that is needed is open communication to discuss common objectives and
define data needs. For example, a county may operate a permanent traffic
recorder. The State highway agency is probably not interested in the raw
data from that device. Instead they are interested in simple summaries
of data including AADT and AWDT values, and seasonal and day-of-week
adjustment factors.

Open lines of communication allow cooperating agencies to learn:

• what data are available

• what needs to be done to obtain those data

• in what formats those data can be readily supplied

• where cooperative efforts can be most beneficial

• what improvements can be made to the data sharing process.

One common problem that must be surmounted is the need for a mechanism
that allows easy transfer of both data and the location information that
indicates where the data were collected. Geographic information systems
(GIS) being adopted by many agencies allow for easier sharing of data.
While not all GIS are directly compatible, it is usually possible to
write conversion software that allows simple file transfers from one
system to another. Sharing of traffic data via GIS also encourages
different agencies to work toward making sure that their GIS are
reasonably compatible, which improves the sharing of other vital
transportation system related data.

As data sharing takes place, it is imperative that the new data made
available to users be adequately described so that they can be
appropriately used. For example, if the data being collected from a
local agency are simple ADT values that have not been adjusted to
represent AADT, these estimates must be described as ADTs, not AADTs.
The agency leading the data sharing effort should work with all groups
that collect data to determine the appropriate adjustment factors needed
to compute and report AADT, AWDT, and other summary statistics of
interest to users.

The issue of ADT values versus AADT estimates is a good example of the
last communication issue that needs to be addressed in this report. When
sharing data, it is necessary to ensure that each user understands what
the data they are about to use represent. A key to this task is adopting
a common set of terminology and procedures. In some States, a specific
guideline is adopted on how traffic data should be collected,
manipulated, and reported. In other States, jurisdictions have more
freedom in how they collect data, but "meta-data" must accompany each
data item reported, so that users have an accurate understanding of what
the data they have obtained represent. The users are then responsible
for ensuring that they use those data items correctly.

Efforts to create more standardization result in better data for the end
user. The report "AASHTO Guidelines for Traffic Data Programs" (1992)
provides an excellent reference for ensuring both the use of proper
terminology and the correct manipulation of collected traffic data.

Appendix 2-A\
ITS And Traffic Monitoring

One of the major emphases of the FHWA is the implementation of
Intelligent Transportation Systems (ITS). Described simply, ITS involve
the application of modern electronic and communication technologies to
the business of moving people and goods. Most ITS applications involve
the collection, analysis, and use of data obtained from sensors in the
field to make better operational decisions. For roadway operations, this
means the collection of volume, speed, lane occupancy, travel time, and
other facility performance data to revise facility control strategies
(changing traffic signal timing, detecting and responding to incidents)
and improve the overall productivity of the facility.

From a traffic monitoring standpoint, ITS have the potential to be a
substantial data resource. Because ITS tend to require current facility
performance information to carry out their operational tasks, many ITS
include the installation and operation of extensive surveillance
systems. Luckily, the same data collected to make operational decisions
can be used for a large number of other tasks within the transportation
field, including (but certainly not limited to):

• operations planning

• maintenance planning

• safety analysis

• facility performance monitoring

• policy analyses

• congestion monitoring

• systems planning

• environmental analysis.

The Archived Data User Service (ADUS) is the part of ITS that focuses on
re-use of ITS-generated data in other transportation activities. The
National ITS Architecture includes this user service in the form of an
Archived Data Management System (ADMS). This is a relatively new part of
ITS which is in various stages of development in different States and
regions. What ITS-generated data are archived and what form they are
available depend on the stage of ADUS implementation. While ITS is
oriented toward operations, ADUS provides an opportunity for those in
traffic monitoring to benefit as well.

This Appendix briefly describes what ITS data can do for a traffic
monitoring program and the steps necessary to access the data. The
following material highlights the types of data that can be obtained
from ITS, and the functions needed to obtain and make those data usable
for a number of important purposes.

The most important aspect of gaining access to ITS data is for planners
and other data users to be proactive in obtaining, interpreting, and
archiving data collected by ITS. This means becoming involved as early
as possible in the ITS system design process so that the data needs of
these users can be understood and incorporated (at least as far as
funding allows). Because ITS systems are heavily oriented toward the
operation of facilities, the "secondary" uses of data developed by the
systems are often unintentionally ignored. Data users must be proactive
to alert system designers of the potential uses of these data and to
ensure that data user needs are adequately expressed during the design
and development process.

What can ITS Data Do For You?

From a purely practical standpoint, data from ITS can often fill
significant holes in the traffic monitoring efforts of many State
highway agencies. ITS, particularly the advanced traffic management
systems (ATMS) that are designed to optimize the operation of heavily
used facilities, can often supply a wealth of information in just those
areas that are:

• the most important or that have the highest travel

• the hardest to count with traditional methods (because volumes are too
high to place traditional portable counters and costs are too high to
place permanent counters strictly for monitoring purposes).

\` In addition, ITS often provide a wealth of traffic performance
information beyond simple traffic volume measurements. Depending on the
traffic surveillance technologies that are used, ATMS can provide the
following:

• vehicle volumes

• vehicle volumes by various classifications

• vehicle and average speeds

• travel time measurements

• origin / destination patterns

• incident location, severity, type and duration

• a variety of more specialized data items.

These data items can be used alone or be combined with other data to
measure the performance of important roadways (Hallenbeck and Ishimaru
2000), determine the usage of those facilities, and determine the long
and short term effects of various transportation systems and travel
demand management actions.

Another advantage is that most ITS operate continuously. Thus, at a
minimum, many ATMS surveillance sites can serve as additional permanent
traffic counting stations. This allows the ITS data to produce
"traditional" computed quantities such as:

• day of week factors

• seasonal adjustment factors

• lane distribution values

• peak hour and peak period percentages

• design values for crowded urban facilities.

Consequently, ATMS (which often have surveillance locations at 1/2- to
1-mile spacings) can help reduce the need for both short duration and
continuous counts in urban areas while providing accurate traffic
measurements on important facilities. This can free data collection
resources while providing excellent data for a variety of important
analyses. It is worthwhile for agencies to examine the benefits that may
accrue by storing ITS data, as well as by creating access to the stored
data.

Consideration of data from ITS that deal with public transportation
services and commercial vehicle operations suggests how broad the
analyses are that ITS data can make possible. In addition to urban ATMS,
other ITS, particularly advanced traveler information systems (ATIS),
commercial vehicle operation systems (CVO), and advanced public
transportation systems (APTS), can provide extremely useful traffic
monitoring information. State highway agency personnel in charge of
traffic monitoring activities need to be aware of all of the ITS being
considered and/or constructed in their States to determine whether these
systems can provide useful monitoring information.

Current ITS Conditions

There are no "mature" ATMS in the United States. Most if not all ATMS
efforts are still in the design, development, and implementation stages.
Many of these systems have been designed with little input or
consideration toward the storage and use of data that are being
routinely collected to make operational decisions. However, because the
systems are still under construction and testing, data storage and
access can be incorporated into the design and development of many.
Modern computer technology even makes it possible to "add on" data
storage and data access to older systems, without changing the basic
operational software, by "eavesdropping" on data being transmitted from
the field to an operations computer and then sending a copy of the data
obtained to a computer specifically designed to provide data storage and
access.

While only one of many possible system designs, this \"add on\" design
fits well within the ITS National Systems Architecture and has the
advantage of ensuring an extra layer of physical security between the
computer providing data to outside users and the machine performing
operational tasks. In addition, with this design, changes can be made to
the data storage and access system without the operations computer being
\"touched.\" This may be important given the sensitivity of operations
personnel to the security of their hardware and software systems. This
design also allows upgrades to the data storage system to be made more
easily if usage of the data becomes so high that a larger, more powerful
computer is needed to hold and transmit the data.

An Approach To Creating ITS Data Storage Systems

There are three basic stages to constructing the systems necessary to
gain access to ITS data. These stages are as follows:

1\. acknowledging that the ITS data have value and that value can be
obtained with only marginal expenditures of funds

2\. initially designing the data storage and access system to meet user
and data provider needs

3\. repeating the system design, implementation, and refinement process
to allow the data storage and access system to grow to accommodate new
users and uses over time.

Creation of the Data Mine[^16]

The first step necessary to make use of ITS data is to acknowledge that
the ITS system primarily collects data for operational purposes and that
the storage of those data once they have been initially collected is
only a small additional cost that may produce very large benefits. That
is, if the primary reason for collecting data was to create a database,
the system would not be built because the cost of sensor installation
and operation would be too high to warrant the system. However, these
sensors and systems are being installed and operated because of the
[operational]{.underline} benefits that they provide. Once the sensor
and communications systems have been built, the marginal cost of adding
data storage and access functions (a "data mine") is relatively small,
and the worth of that "mine" far exceeds the cost of the database
function.

Once it has been acknowledged that the "mine" (the data) is of value and
that its value exceeds the cost of creating the "mine," it is possible
to determine the design and operation of the data mine.

Technical and Institutional Issues for the Data Mine

Once the decision has been made to build a data storage and access
facility, the following issues must be resolved:

• What data will be stored in the mine?

• What quality control functions will take place to ensure that the data
from the mine are accurate?

• How will the following communication tasks take place?

\- Telling possible users what data are available for use.

\- Describing to users what the data are and what they represent.

\- Describing to users how to physically get the data (i.e., providing a
set of protocols and physical links).

• Determining the costs to develop and operate the mine and how the
resources necessary for those operations will be made available.

• Determining who will do what with respect to the mine. (ITS often
require cooperation, coordination, and integration across jurisdictional
and institutional boundaries. Operation of the data mine may also cross
those boundaries.)

Note that each of these issues has both technical and institutional
aspects to it.

Data to Be Stored

The first task to is to determine what data will be stored in the data
mine. From a technology perspective this means analyzing which data are
being physically collected by the ITS surveillance systems, the
frequency of data collection, the storage required to maintain the data,
and the need for other data sources to make the collected data usable.
For example, GIS base files allow location codes to be correlated with
other data items. From an institutional perspective this means assessing
the benefits that can be potentially produced by providing access to
certain data items versus the potential for misuse of the data, along
with the sensitivity of some data items.

The institutional issues of whether some collected data should be stored
at all, and if they are stored, what access should be allowed to the
data (who and by what mechanism) are often far more difficult to resolve
and more important for the successful operation of the mine than the
technical issues of what data can be physically collected and stored.
Many ITS data items raise privacy and public policy concerns, requiring
careful consideration of their potential uses before they are stored.

Storing some collected data (for example, probe vehicle information from
cars that use toll tags) can present significant privacy problems.
Agencies often refuse to store records that contain vehicle ID
information. Because of laws that allow public access to public records,
agencies that record toll tag IDs might be required to provide them to
requesters creating a burden for the agency. Allowing outside access may
also discourage vehicle owners from using the tags in the first place,
reducing their effectiveness for their primary task, in this case high
speed toll collection.

Unfortunately, omitting some data can reduce the effectiveness of the
data mine. For example, removing the toll tag ID from a tag observation
record prevents those records from being used to calculate travel times
and information on vehicle O/D patterns, as well as a variety of other
facility performance measures.

There are two major alternatives for cases in which privacy concerns
limit the data that can be stored: the computation and storage of
secondary statistics (travel times, O/D patterns) before the tag ID
information is discarded; and the creation of a new record ID value that
cannot be tracked to a specific ID tag but that serves the same purposes
within the data mine.

The first alternative works well when the uses for the original data are
well known. It is also helpful to include the computation of new
statistics from the available raw data before the ID information is
discarded. However, it is not possible to use historical data to compute
these new statistics because the required ID data have already been
discarded.

The second alternative is more complicated and requires more data
storage, but it allows more creative use of the raw data. In this
technique, a table is created (each day or another given time period)
that matches a "real" ID with an "artificial" ID that will be stored in
the data mine. This allows all records for a real ID for a given time
period to be stored as an "artificial" ID. Once the conversion table has
been destroyed (at the end of the given period), the "real" vehicle IDs
cannot be traced from the "artificial" IDs stored in the database. It
also becomes impossible to track a tag from one period to another. (That
is, if the artificial IDs are reset every day, a given vehicle cannot be
tracked from one day to the next.) However, a given "artificial" tag can
be tracked throughout a given period within the data mine. This
alternative allows the vast majority of statistics desired from the
database to be computed while maintaining complete vehicle privacy.

Another issue that causes data to be omitted from a database is agency
sensitivity to the data. One example of this type of data is video
surveillance data. In many areas, accident scenes are not recorded with
traffic surveillance cameras. This is not because surveillance cameras
can't make such recordings but because the legal implications of having
these recordings (dealing with subpoenas, the possibility of the
recording being used against the agency in a liability case) outweigh
the advantages of storing those images.

In other cases, agencies might not want to store items that might be
useful for performance indicators (such as when incident responders are
notified of an incident, and when they report reaching the scene)
because of either their potential for misuse or because an agency does
not want the performance of a specific item monitored. (For example,
while the above incident response variables present the opportunity to
monitor response time, their use could encourage responders to drive
recklessly to an incident scene because they know that their jobs will
be reviewed in part on this criterion. Those same data may also not
accurately represent the true incident response time, in that many
responders do not report when they arrive at the scene until after they
have inspected the scene.)

The creation of the mine is further complicated by the fact that ITS
data may need to be obtained from, or stored in, more than one computer.
In many cases, more than one data mine can be created, each with a
different set of useful traffic monitoring variables. These mines may be
operated by different public agencies (often State highway agencies, but
sometimes cities and counties, transit authorities, or even regional
governments and metropolitan planning organizations). When multiple data
collection/surveillance systems exist, and/or when these systems involve
multiple agencies, a variety of both technical and institutional issues
appear. These issues include the following:

• Will the data be stored by the agency that initially collects them, or
will they be stored at a single location?

• Can the two sets of data be combined (either within the data mine or
outside of the mine)? This usually implies the need for compatible
location referencing systems.

• Who is in control of access to those data, the agency that initially
collects the data or the agency that operates the data mine (if they
aren't the same agency)?

In general, the data collected should be stored in the lowest level of
aggregation that can be affordably maintained. This allows the data to
be used for the widest possible number of analyses. It also allows the
data to be reviewed for quality assurance purposes. The benefits of
saving data at these low levels of aggregation must be balanced against
the storage requirements for those data, the cost of accessing and
maintaining those disaggregated values, and the abilities of users to
work with the disaggregated data. (One possible compromise between
saving disaggregated data and more highly aggregated data is to store
the most disaggregated data only on a sample basis, whereas more highly
aggregated levels of data are stored continuously.)

Most users do not need to see the data at their lowest levels of
aggregation, and therefore summarized data also need to be provided in
the data mine. This means that the mine must contain a data aggregation
process and in many cases one or more levels of aggregated data.
(Holding aggregated data speeds access to those data in comparison to
calculating aggregated values "on the fly" each time they are
requested.)

Including the aggregation process in the data mine is necessary because
many potential users of the data will not have the time, knowledge, or
tools necessary to correctly compute aggregated statistics from
disaggregated data.[^17] (For example, it is not possible to correctly
aggregate data unless the user understands how to treat missing data in
the aggregation process.)

In the end, because of the many uncertainties and concerns involved in
the creation of ITS data mines, some agencies have elected to build the
best system that the current technological and institutional constraints
allow them to build. At the same time, they acknowledge that the system
is still under construction and that changes to that system (in the data
kept, the procedures followed, the access provided) can be expected over
time as users become familiar with the operation and capabilities of the
mine and as participating agencies become familiar with the analyses
being performed.

Quality Control Functions

Once decisions on what data to store have been made, a process must be
designed and implemented to ensure that only valid data are made
available for use. This includes:

• creating procedures that determine that specific data are valid

• developing systems that handle the holes left when invalid data are
removed

• providing mechanisms that allow users to report "suspicious" data

• having resources available to investigate those "suspicious" data

• being able to periodically revise the quality assurance system.

One of the intentions and advantages of the "data mine" concept is that
the data can be made available to a variety of users. This is good in
that when the data can fulfill a large number of uses, support is
generated for the operation of the system. However, it is bad in that
many of the data users will not be familiar with the intricacies of the
data (or the data collection process), and therefore these users may not
be able to perform their own "sanity" checks and the other quality
assurance functions that are often performed by knowledgeable users as
part of their analyses.

Consequently, the data mine itself must contain the quality assurance
procedures that ensure that the data are accurate. Quality assurance
steps may include:

• tests of sensor output

• checks against historical values

• checks against expected ranges of values

• any number of other comparisons.

Included in the quality assurance function are the data aggregation
steps mentioned in the previous section. Handling missing and
questionable data in the aggregation process is a technically difficult
task that can have dramatic effects on the computation of aggregated
statistics. Thus, the quality assurance process must look at both the
individual data items and any aggregated statistics computed from the
base variables.

The quality assurance function also includes steps that prevent users
from accidentally misusing data (this is also part of the communications
process described in the next section) by correctly labeling data and
describing what they represent. It may also mean preventing some users
from accessing some data items stored within the mine, either because
the data have not been adequately checked for quality control purposes
or because those data are not appropriate for specific types of
analyses.

An example of "valid" data that should not be used by the "average" user
is traffic volume estimates from stop bar detectors at intersections.
Volume estimates from these detectors often underestimate the "true"
volume because of the physical design of the loops (which tend to be
long and thin, rather than square or circular) and the nature of the
traffic that crosses them (which tends to be closely spaced, stop and go
traffic, with multiple vehicles over the loop at any given time, as
opposed to free flow conditions and gaps between vehicles crossing
system loops). Yet stop bar volumes can be very useful for some traffic
operations analyses, even if they should not be used blindly for
something like VMT estimation.

Communicating what a specific variable stored in the mine represents is
often a difficult task. However, it is vital to the use of the mine, as
are several other communications issues.

Communications

Communication is the key to successful continued operation of the data
mine. If users and potential users are aware of the mine, can access the
data in the mine, and have the ability to slowly refine the operation of
the mine over time to meet their needs, the mine will be heavily used
and widely supported. This support is needed to maintain the revenue
stream that allows operation of the mine and, consequently, provides
access to the ITS data.

If adequate communications do not take place, history indicates that
funding for operation of the mine will disappear as departmental budgets
become tighter, and the data resource will cease to exist. As noted
above, a variety of communications needs must be met for successful
operation of the system. These include the following:

• telling possible users that the data exist so that they know that it
is available for use

• describing to users what the data (stored variables) are and what they
represent

• describing to users how to physically get the data (i.e., providing a
set of protocols and physical links).

The first of these tasks is an outreach effort that must take place both
within the organization that is building the ITS system and between
organizations in the region. The nature of an ITS data mine is that it
should be available for use by many groups (the State highway agency
planning office, the district engineering office, the regional MPO,
researchers at local and national universities, and private participants
in the local ITS systems). Those interested in building the data mine
must reach out to these groups to alert them of these data and to help
them use the data. This process takes time and effort but is necessary
to ensure support. It also must be a continuing process, since ITS will
change over time. Thus, the outreach effort must keep users informed of
changes, particularly with respect to the addition or subtraction of new
types of data, new surveillance system locations, and new mechanisms for
obtaining data.

Once potential users know that the ITS data resource exists, they have
to be taught about the data, including what data exist (as well as what
levels of aggregation are available), how those data are collected, what
they represent, and suggested ways to use them. The AASHTO standards of
"truth in data" (that is, labeling the data for what they are) must be
applied, and training materials to help new users get started, as well
as support systems to help users when they have problems, must be
provided. The support system should also be used to provide feedback on
system refinements.

Finally, the data mine must have protocols and physical links that allow
users to obtain the data with relative ease. These may include building
and giving away software that allows users to access the data mine, or
creating summary files (on CD-ROM or other media) that contain data
summaries that are useful to potential users. As technology changes and
as users provide feedback, these links (physical and logical) will
likely change over time. The communications protocols selected must be
capable of handling these changes, and the outreach mechanism used must
be capable of communicating them to data mine users.

Costs

Three issues must be addressed in examining the cost of the data mine:

• the cost of constructing the mine

• the cost of operating the mine

• the distribution of those costs among users.

Construction of the mine is the simplest issue, if only because it is
primarily a series of technical questions. To develop such a cost, the
available ITS data that will be included in the system must be
determined. Then the potential users of the system should be gathered,
and they and ITS data mine developers should look at the possibilities
for the mine itself. Answering the basic questions discussed briefly
above (What data will be collected? What types of data do they need?
What type of access do they need? What type of communications already
exist within the ITS functions of the region?) will allow a basic design
to be developed and costs estimated.

Operations costs are more difficult to deal with because many of these
costs are bound up in the operational ITS systems that are the basis for
the data mines. The data mine, as any database system, will require
staff and resources for routine operations and upkeep. Depending on the
system design, this may or may not be a significant cost. Similarly,
depending on how access to the mine is provided (CD-ROM versus on-line,
Internet style access), the communications costs for physically
accessing the data could range anywhere from fairly small to fairly
large.

Operations costs for bringing data into the system are likely to be
higher than the costs for providing access to the data once they are in
the mine, since on average, more data are expected to flow into the mine
than out of the mine. (The summary statistics that are pulled out will
be much smaller than the raw data that are put in.) However, as with the
data access costs, these costs will vary considerably, depending on the
design of the system. Very little communications cost is associated with
a system that requires only a wire from the operations computer to a
data server sitting next to it. A system that requires a fast Internet
connection to obtain large quantities of operations data from multiple,
remote, operations computers will have much higher costs.

Allocating these costs among users can be a difficult political
question. Costs can be absorbed by those who operate the ITS systems
supplying the data (since one of the major users of the stored data
should be the operators of those facilities being monitored), or they
can be split among the agencies that desire access to those data
sources. Finally, in some regions (where State and local laws allow it)
groups can be charged for accessing the data, and those fees can be used
to help offset the cost of operating the data mine. These decisions will
need to be made on a case by case basis, taking into account the
political and fiscal realities of the region in question.

Agency Responsibilities

Allocating agency responsibilities, like allocating costs, must be done
at the local level. Which agencies are willing and/or able to perform
the various tasks that must be accomplished to build, operate, and
maintain the data mine is a function of the structure of the ITS system
that supplies the data, as well as the political/organizational
structure of the agencies in the region.

In some areas of the country, the State highway agency will take on the
primary functions, either using its own staff or hiring contractors to
perform those tasks. In other parts of the country, the regional MPO
will take on these tasks as part of its regional coordination
responsibilities.

The primary requirements are that all participating agencies understand
their responsibilities, that they agree and commit to performing those
responsibilities, and that all of the important functions of the data
mine are accounted for within those responsibilities. It is not
important to specify whether public agency or contract staff perform
these tasks, only that specific functions take place, take place
correctly, and take place in a timely fashion.

It is also important that a structure be designed (usually as part of
the communications process discussed above) to allow problems to be
identified and solutions to those problems to be developed and
implemented. This may mean a formal committee structure (with
participating agencies), or a less formal structure, such as including
the subject of the data mine in ongoing, regional meetings on other
traffic issues, or including a "problem submittal" capability in the
communications medium participants used to access the data mine.

Iterative Development Process

One of the confounding problems with ITS is that the systems are so new
and different that as they come on-line, system users and operators need
to change the ITS operation, both to take advantage of previously
unavailable opportunities and to remove functions that prove to be
ineffective. Thus, ITS project implementations often require several
iterative loops, as feedback from early system development and
deployment is used to refine system design and operations.

In the same manner, the design, development, and use of an ITS data mine
will be iterative. As more people become aware of the possibilities of
ITS data, changes in access, data storage methods, and calculated
quantities will likely be necessary to more effectively deal with these
user needs. Similarly, changes in technology may cause new mechanisms to
become appropriate for obtaining and/or disseminating information. A
good illustration of this process is the growth of the Internet and the
resulting changes in how information is delivered and exchanged. Another
change is the constantly decreasing cost of computing power and data
storage. These changes make the storage and retrieval of data much
easier and less costly than in the past, and if this trend continues,
that may affect what data can be cost effectively stored in the data
mine.

Operation of the data mine may reveal needs to change the preliminary
quality control process (either because it is too restrictive or because
it allows user access to poor quality data). Organizational sensitivity
to data changes over time, both as initially unexpected uses of the data
surface and as demand for specific data items becomes apparent. Often,
restrictive data access policies are relaxed once sufficient safeguards
have been developed or as organizational sensitivities to data change.
Having outside entities access data may also change an organization's
philosophy of what data can or cannot be stored. All of these issues can
result in the need to change the basic structure of the data mine.

In the developmental phase of a data mine, it is important to realize
that some of these changes will probably take place and to simply plan
for these possibilities in the design process. Similarly (and as part of
the communications process), it is important for potential data users to
participate early in the design process so that their needs will be
considered.

At an absolute minimum, the mine must be allowed to grow over time. The
surveillance systems used by most ATMS systems are expected to grow
geographically and sometimes technologically (i.e., by adding new types
of surveillance sensors) over time. This growth must be accommodated in
the design of the mine. Changes in the scope of the available data must
be seamlessly handled and passed on to users. This will allow users to
take advantage of the growth as it occurs and will allow the benefits of
the expanding ITS efforts to be incorporated into the region's traffic
monitoring process.

aPPENDIX 2-b\
Seasonal Group Development uSING Cluster Analysis

The computer printout tables included in this appendix were produced by
the SAS (Statistical Analysis System) package on a microcomputer. For a
description of SAS procedures refer to the SAS User\'s Guides (SAS
Institute, Inc Ref. 1 and 2). The SAS statistical procedures are also
available for minicomputers or mainframes. Other statistical packages
can also be used to conduct the analysis.

Table 2-B-1 describes the continuous ATR data used in the example. The
first column presents the observation number (OBS), followed by station
number (STNUM), the monthly average daily traffic from January thorough
December (Ml to M12), the functional class (FUNC), the AADT, and the
coefficient of variation of the monthly values as a percentage (MCV). In
the table, the monthly traffic peaks are underlined.

Table 2-B-2 presents the monthly factors (F1 to F12) computed as the
ratio of MADT to AADT in the same format as Table 2-B-1, followed by the
functional class (FUNC), the average of the factors (MFAC), and the
coefficient of variation (CV). The cluster analysis is carried out using
the monthly factors, because using the monthly traffic values allows the
large volume differences between the stations to impact the cluster
formation and invalidate the analysis. As can be seen by examining the
variation coefficients from the two tables, the numbers have changed
somewhat (due to the data transformation) but the variation picture does
not change.

Table 2-B-3 shows statistical information produced by the cluster
program and used to evaluate the cluster formation. An understanding of
this page is helpful but not necessary to interpret the results of the
clustering. A complete explanation of the statistical terminology and
procedures is provided in the SAS User\'s Guide listed in the
references.

Table 2‑B‑4 presents a dendogram or graph of the cluster formation. An
understanding of this graph is necessary to select the clusters and an
explanation is provided in the SAS references. The station location
numbers (STNUM) are presented at the top. The semi-partial R‑squared
values gained during cluster formation are shown along the x‑axis. The
blank columns in the graph indicate the cluster breaks. In this example,
the first two clusters (separated by the highest blank column) consist
of the first 14 and last 6 stations. The third cluster break separated
station 14. The fourth separated stations 20 and 15 from the previous
group. The process continues until each station is in an individual
group at which point all of the variation is explained.

**Table 2-B-1: Cluster Analysis Monthly ADT**

**Cluster Analysis**

**Continuous ATR Data**

Monthly ADT

  --------- ----------- -------- -------- -------- -------- --------------------- --------------------- --------------------- --------------------- -------------------- --------------------- --------- --------- ---------- ---------- ---------
  **OBS**   **STNUM**   **M1**   **M2**   **M3**   **M4**   **M5**                **M6**                **M7**                **M8**                **M9**               **M10**               **M11**   **M12**   **FUNC**   **AADT**   **MCV**

  1         6           15333    17594    16111    16131    17668                 18311                 20981                 21460                 20809                [22114]{.underline}   17929     16867     1          18442      12.6

  2         9           32804    34095    36175    41362    47371                 49410                 [50445]{.underline}   50431                 42124                41530                 44345     38398     1          42374      14.7

  3         18          25424    26269    28001    30186    33693                 37683                 45575                 [46661]{.underline}   38521                36077                 31847     30643     1          34215      20.3

  4         20          11372    11627    13529    15827    18847                 22660                 [28528]{.underline}   19564                 15411                13354                 11978     14471     2          16431      31.4

  5         15          16480    19060    20797    24846    28779                 37099                 [48206]{.underline}   45510                 37253                28074                 20789     20824     2          28976      36.8

  6         5           3785     3188     4206     3147     4671                  4872                  4572                  4781                  [4835]{.underline}   4768                  4445      3772      6          4254       14.9

  7         2           2820     2902     2953     3359     4054                  4566                  [5990]{.underline}    5910                  4398                 4033                  3450      3059      6          3958       27.8

  8         14          1570     1778     1013     1070     2650                  2668                  [2768]{.underline}    2742                  2590                 2545                  2180      1975      7          2129       30.2

  9         26          43544    45043    45822    46704    47865                 49329                 [51554]{.underline}   45851                 47108                43581                 46240     49501     11         46845      5.1

  10        22          63980    66140    71135    75364    77367                 [77706]{.underline}   75087                 77275                 76569                76368                 73924     68590     11         73292      6.4

  11        60          34276    33817    37513    40193    43226                 45610                 46000                 [46528]{.underline}   46499                42912                 40973     39138     11         41390      11

  12        7           13230    13076    14694    16721    18969                 21338                 24895                 [26296]{.underline}   22159                19101                 17303     16024     11         18651      23.2

  13        8           49576    49554    54095    54992    56945                 59423                 57404                 [60159]{.underline}   57560                58489                 56035     55045     12         55773      6.1

  14        19          37879    37977    40989    41970    41753                 45023                 43756                 45391                 44822                [46168]{.underline}   43325     41780     12         42569      6.4

  15        16          20370    19204    21015    21657    22618                 24109                 24797                 [25618]{.underline}   25341                23777                 22923     22024     12         22788      8.9

  16        1           8067     8259     8846     9165     [10183]{.underline}   10155                 9466                  10026                 9851                 9745                  9413      9374      14         9379       7.4

  17        13          7244     7305     7848     8183     8589                  8765                  8570                  8885                  [9039]{.underline}   8895                  7724      8090      14         8261       7.6

  18        3           6574     6497     7175     7624     7629                  7936                  7600                  [8670]{.underline}    7909                 7686                  7561      7418      14         7523       7.8

  19        4           4494     5390     5531     6061     7021                  6157                  7739                  7728                  7653                 [7995]{.underline}    6619      5528      14         6493       17.6
  --------- ----------- -------- -------- -------- -------- --------------------- --------------------- --------------------- --------------------- -------------------- --------------------- --------- --------- ---------- ---------- ---------

**Table 2-B-2: Monthly Factors**

**Cluster Analysis**

**Continuous ATR Data**

Monthly Factors

  ----- ------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  OBS   STNUM   F1     F2     F3     F4     F5     F6     F7     F8     F9     F10    F11    F12    FUNC   MFAC   CV

  1     6       1.2    1.05   1.14   1.14   1.04   1.01   0.88   0.86   0.89   0.83   1.03   1.09   1      1.01   12.2

  2     9       1.29   1.24   1.17   1.02   0.89   0.86   0.84   0.84   1.01   1.02   0.96   1.1    1      1.02   15.2

  3     18      1.35   1.3    1.22   1.13   1.02   0.91   0.75   0.73   0.89   0.95   1.07   1.12   1      1.04   19.3

  4     20      1.44   1.41   1.21   1.04   0.87   0.73   0.58   0.84   1.07   1.23   1.37   1.14   2      1.08   25.9

  5     15      1.76   1.52   1.39   1.17   1.01   0.78   0.6    0.64   0.78   1.03   1.39   1.39   2      1.12   33.5

  6     5       1.12   1.33   1.01   1.35   0.91   0.87   0.93   0.89   0.88   0.89   0.96   1.13   6      1.02   16.9

  7     2       1.4    1.36   1.34   1.18   0.98   0.87   0.66   0.67   0.9    0.98   1.15   1.29   6      1.07   24.6

  8     14      1.36   1.2    2.1    1.99   0.8    0.8    0.77   0.78   0.82   0.84   0.98   1.08   7      1.13   41.7

  9     26      1.08   1.04   1.02   1      0.98   0.95   0.91   1.02   0.99   1.07   1.01   0.95   11     1      5.06

  10    22      1.15   1.11   1.03   0.97   0.95   0.94   0.98   0.95   0.96   0.96   0.99   1.07   11     1      6.84

  11    60      1.21   1.22   1.1    1.03   0.96   0.91   0.9    0.89   0.89   0.96   1.01   1.06   11     1.01   11.6

  12    7       1.41   1.43   1.27   1.12   0.98   0.87   0.75   0.71   0.84   0.98   1.08   1.16   11     1.05   22.8

  13    8       1.13   1.13   1.03   1.01   0.98   0.94   0.97   0.93   0.97   0.95   1      1.01   12     1      6.44

  14    19      1.12   1.12   1.04   1.01   1.02   0.95   0.97   0.94   0.95   0.92   0.98   1.02   12     1      6.63

  15    16      1.12   1.19   1.08   1.05   1.01   0.95   0.92   0.89   0.9    0.96   0.99   1.03   12     1.01   9.13

  16    1       1.16   1.14   1.06   1.02   0.92   0.92   0.99   0.94   0.95   0.96   1      1      14     1.01   7.86

  17    13      1.14   1.13   1.05   1.01   0.96   0.94   0.96   0.93   0.91   0.93   1.07   1.02   14     1.01   7.83

  18    3       1.14   1.16   1.05   0.99   0.99   0.95   0.99   0.87   0.95   0.98   1      1.01   14     1.01   8.02

  19    4       1.44   1.2    1.17   1.07   0.92   1.05   0.84   0.84   0.85   0.81   0.98   1.17   14     1.03   18.8

  20    12      1.19   1.19   1.03   0.98   0.94   0.88   0.98   0.96   0.91   0.94   1.03   1.06   16     1.01   9.81
  ----- ------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

**Table 2-B-3 Ward's Minimum Variance Cluster Analysis**

**Ward's Minimum Variance Cluster Analysis**

Eigenvalues of the Covariance Matrix

  -------------- ---------------- ---------------- ---------------- ----------------
                 **Eigenvalue**   **Difference**   **Proportion**   **Cumulative**

  1              0.136135         0.072741         0.586741         0.58674

  2              0.063393         0.048811         0.273226         0.85997

  3              0.014582         0.007335         0.062848         0.92281

  4              0.007247         0.003818         0.031234         0.95405

  5              0.003429         0.000509         0.014777         0.96883

  6              0.00292          0.000804         0.012585         0.98141

  7              0.002116         0.001131         0.009119         0.99053

  8              0.000985         0.000503         0.004243         0.99477

  9              0.000481         0.000078         0.002074         0.99685

  10             0.000403         0.000159         0.001736         0.99858

  11             0.000244         0.00016          0.001053         0.99964

  12             0.000084                          0.000364         1
  -------------- ---------------- ---------------- ---------------- ----------------

Root-Mean - Square Total -- Sample Standard Deviation -- 0.13905

Root Mean -- Square Distance Between Observations -- 0.681202

  ----------- ----------- --------- ------------ ------------- ------------
  Number of   Clusters    Joined    Frequency of Semipartial   R-Squared
  Clusters                          New Cluster  R-Squared     Tie

  19          8           19        2            0.000387      0.999613

  18          22          1         2            0.001211      0.998402

  17          CL19        3         3            0.001369      0.997033

  16          CL18        13        3            0.001578      0.995455

  15          60          16        2            0.001741      0.993715

  14          CL16        CL17      6            0.001778      0.991936

  13          CL14        12        7            0.003005      0.988931

  12          18          7         2            0.003399      0.985532

  11          CL13        CL15      9            0.006515      0.979017
  ----------- ----------- --------- ------------ ------------- ------------

**Table 2-B-4: Cluster Analysis Dendogram**

## CLUSTER ANALYSIS

## (Continuous ATR Data)

Ward\'s Minimum Variance Cluster Analysis

STNUM

2 2 1 1 1 6 1 1 2 1 1

6 4 9 5 6 2 1 3 8 9 3 2 0 6 8 7 2 0 5 4

0.45
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0.40 \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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S \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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e \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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m 0.35 \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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i \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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p \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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i 0.25 \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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a \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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l \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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0.20 \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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R \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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\- 0.15 \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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s \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXX .

q \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXX .

u 0.10 \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXX .

a \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXX .

r \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXXX .

e 0.05 \|XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
XXXXXXXXXX XXXX .

d \|XXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXX
. . .

\|XXXXXXXXXXX . XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXX . .
.

\|XXXXX . . .XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXX . . .

0.00 \| . . . .XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXX XXXXXX . . . .

Cluster analysis is used to determine the natural groupings in the data,
in this case reflective of the seasonality from month to month. These
groupings are based on the variation in the data. The differences
between groups and stations within groups can be very large or hardly
detectable depending on the natural variability existing in the
stations. The cluster program computes the differences in groups or
group membership using fixed mathematical algorithms without seeking an
explanation. The process is completely driven by the variability in the
data without other considerations. The cluster program will always
create groups and assign all the stations to groups without recognizing
the size of the differences between and within clusters. Stations will
be assigned to a group because the program must make an assignment.

The results of the cluster analysis are not the ultimate groups or group
assignment. Modifications are to be expected. Statistical programs are
tools used by a trained analyst to understand the variation of data. The
development of the final factor groups must account for variability but
must also include characteristics that define the groups and allow the
assignment of short counts to the groups. Caution and judgment are
necessary to interpret the results of the cluster analysis.

The basic intent of the cluster analysis is to identify variation
patterns to give the analyst the knowledge and insight to develop
grouping criteria to expand short counts to AADT. Since the cluster
analysis program groups only on variation, it provides no definable
characteristic or criteria upon which to form groups. The establishment
of the factor groups requires knowledge of the variation, the
determination of relevant criteria (functional class, geography,
topography, degree of urbanization, etc.), and the use of analytical
judgment to make the necessary trade-offs.

Table 2-B-5 presents the four cluster breaks as extracted from Table
2-B-4. Examining the location of the stations and groups on a map is
very helpful in identifying or distinguishing the characteristics of the
patterns. In this example, the cluster program has identified the
patterns and singled out the extreme variation stations, but no criteria
for assignment of short counts to the groups has been defined. This is
where the descriptive analysis and the use of functional class,
geography, or topography are needed to provide adequate criteria for
group formation.

**Table 2-B-5: Cluster Analysis Example**

  ----------------------- ----------------------- -----------------------
  Cluster 1               ATR Number              Functional Class

                          6, 9                    1

                          5                       6

                          4                       14

                                                  

  Cluster 2               ATR Number              Functional Class

                          22, 26, 60              11

                          8, 16, 19               12

                          1, 3, 13                14

                          12                      16

                                                  

  Cluster 3               ATR Number              Functional Class

                          18                      1

                          2                       6

                          7                       11

                                                  

  Cluster 4               ATR Number              Functional Class

                          15, 20                  2

                                                  

  Cluster 5               ATR Number              Functional Class

                          14                      7
  ----------------------- ----------------------- -----------------------

References

Cambridge Systematics, "Traffic Monitoring Systems Development Study"
(multiple volumes), Virginia Department of Transportation, 1994 and
1995.

Cambridge Systematics and Science Application International Corporation,
"Use of Data from Continuous Monitoring Sites, Volume II:
Documentation," FHWA, August 1994.

Cohen, Harry and Margiotta, Richard, "Statistical Analysis of Continuous
Traffic Counter data", Task 1, Final Report, September 1992.

FHWA, "Highway Performance Monitoring Systems---Field Manual for the
Continuing Analytical and Statistical Data Base," USDOT, January 2000..

Ferlis, R.A. et al, Guide to Urban Traffic Counting, FHWA, February
1980.

Hallenbeck, Mark, and O'Brien, Amy, "Truck Flows and Loads for Pavement
Management" FHWA, January 1994.

Hallenbeck, Rice, Cornell-Martinez, and Smith, "Vehicle Volume
Distributions by Classification," FHWA-PL-97-025, June 1997.

Joint Task Force on Traffic Monitoring Standards, AASHTO Guidelines for
Traffic Data Programs, ISBN 1-56051-054-4, 1992.

SAS Institute, Inc., \"SAS/STAT User\'s Guide\", Release 6.03 Edition,
Cary, North, Carolina, 1988

SAS Institute Inc., \"SAS Language Guide for Personal Computers\",
Release 6.03 Edition, Cary, North Carolina, 1988.

Weinblatt, Herb, "Using Seasonal and Day-of-Week Factoring to Improve
Estimates of Truck VMT," Transportation Research Board, August 1995.

Wright, T., Hu, P., Young, J., and Lu, A, "Variability in Traffic
Monitoring Data, Final summary report," Oak Ridge National Laboratory
for the US Department of Energy, August 1997.

[^1]: Note that through-truck traffic is still normally generated during
    "normal business hours." Thus, through-traffic generated from any
    one geographic location has the same "5-day on, 2-day off" pattern
    seen in the "local truck" pattern. Where a road carries
    through-truck traffic from a single dominant area, the two-day lag
    in truck volumes is often apparent. However, the lag appears at some
    other time in the week.

[^2]: Among the more common vehicle classifier configurations are two
    inductance loops in series (providing measures of vehicle length),
    two axle sensors in series (providing axle count and spacing
    information), two axle sensors with an inductance loop (providing an
    improved measure of axle counts and spacing), and video image
    processing.

[^3]: That is, accurately predicts static axle weights.

[^4]: This same basic framework can also be used by local jurisdictions,
    although the funding sources and primary program requirements are
    likely to be different at the local level.

[^5]: Where "short duration counts" are those generally taken with
    portable equipment.

[^6]: This can actually be a problem in that collection of count data
    requires time (to schedule the data collection staff efficiently,
    collect and analyze the data, and report them). The lead time
    required to supply the data often exceeds the availability of the
    project funding. This lack of funding must be resolved within the
    State highway agency to achieve any potential efficiency from
    coordination.

[^7]: Data from ATRs with extremely low volumes may be left out of the
    factor grouping and computation analysis, since very low volumes
    tend to produce unstable factors.

[^8]: The word "set" is used because factor groups are applied for all
    factors, not just for a single factor. Thus, for monthly factors,
    the minimum distance is computed for all 12 monthly factors
    combined, not for a single month's factor.

[^9]: Data from ATRs with extremely low volumes may be left out of this
    analysis, since very low volumes tend to produce unstable factors.

[^10]: This discussion assumes that each count being factored represents
    a daily vehicle volume, and therefore, that axle correction and time
    of day corrections are not needed. These adjustments must be applied
    if the initial "raw count" data contain less than 24 hours of data
    and/or are simple axle counts.

[^11]: This discussion concerns primarily the computation of monthly
    factors. It assumes that the computational task starts with daily
    traffic volumes from ATRs. The same basic procedures can be used to
    compute monthly factors by vehicle, as well as weekly factors. The
    factors computed can be for a given day of the week, or for all
    weekdays combined.

[^12]: This assumes that the factor being computed is a multiplicative
    factor computed as the ratio of AADT to MADT. If the state uses the
    inverse of this, then simply change the term "denominator" to
    "numerator" in the discussion.

[^13]: For example, if only two Saturdays and two Sundays are present
    for June, but there are three days of data for all five weekdays, in
    the "simple average" technique, the weekdays would be
    over-represented in the "average June day" computation. In the
    AASHTO procedure, the first computation of the seven average days of
    the week allows the two Saturdays to be used to estimate the
    "average June Saturday" while three Mondays are used to compute the
    "average June Monday." When these seven values are then averaged to
    compute the "average June day" the proper balance between weekdays
    and weekend days can be maintained.

[^14]: This assumes the factor being computed is equal to AADT / some
    value (for example, monthly average daily traffic).

[^15]: Weight data collected at an enforcement scale are often biased in
    comparison to "normal" truck weights, in that the data are likely to
    contain fewer overloaded trucks than normally exist. This is because
    drivers who know that their trucks are overloaded will avoid the
    enforcement site when it is open. However, the basic time-of-day,
    day-of-week, and seasonal volume patterns are likely to be
    representative of the patterns experienced by other similar roads,
    and the "biased" load data may provide accurate measures of the
    loads that specific road is experiencing.

[^16]: The phrase "data mine" (common in computer science and electrical
    engineering circles) can be used to describe the resulting ITS
    database systems. The concept is to store the ITS data in a way that
    the data itself can be viewed as "unrefined ore." Processing of that
    "ore" can result in a variety of useful products, and different
    groups may process that "ore" very differently. Of course, an
    advantage of a data mine versus a conventional mine is that the data
    are not used up when the ore is processed and can in fact be used
    for multiple and different purposes. As a result, in many cases, the
    "database" application that stores the information generated by the
    ITS sensors is less important than the facts that the data are
    placed in an accessible location and that software is provided to
    users that allows them to access the data.

[^17]: Of course, if disaggregated data are kept, users can perform
    research on improving the aggregation process using different
    methods for accounting for missing and invalid data. Conversely, if
    only aggregated data are kept, it is not possible to go back and
    look at how the aggregated data values were developed.)