Section 4

Vehicle Classification Monitoring

**SECTION 4**

**CONTENTS**

*Section* *Page*

**CHAPTER 1---INTRODUCTION 4-1**

Variability 4-1

Data Collection to Account for Truck Movements 4-2

Integration of Classification Counting Programs 4-3

Required Outputs from the Vehicle Classification Data Collection Program
4-4

Classification Schemes 4-4

The FHWA 13-Category Classification System 4-5

Calibration and Testing of Classifiers 4-6

Alternative Classification Schemes 4-6

**CHAPTER 2---USER NEEDS 4-8**

Uses for Classification Data 4-8

Summary Vehicle Classification Statistics 4-9

Classification Data Needs for Pavement Design 4-12

**CHAPTER 3---VEHICLE CLASSIFICATION DATA COLLECTION DESIGN 4-13**

Short Duration Counts 4-13

Classification Coverage Counts 4-14

Other Special Needs Counts 4-17

Project Counts 4-17

Urban Classification Count Programs 4-18

Integration of the Coverage Count Program with Other Programs 4-18

Duration of Short Counts 4-19

Permanent/Continuous Classifiers 4-20

Introduction to Continuous Classification Counts and Factors 4-21

Vehicle Classes Used for Factoring 4-22

Alternative Factor Procedures 4-22

Roadway-Specific Factors 4-23

The Traditional Factor Approach 4-24

Create Initial Factor Groups 4-25

Determine the Variability of Group Patterns 4-28

Test the Quality of the Selected Groups 4-29

Determine the Precision of Factors 4-31

Refine the Factor Groups 4-31

Determine the Number of Locations to Monitor 4-32

Collect Additional Data and Refine the Established Process 4-32

**CHAPTER 4---COMPUTATION AND REPORTING OF ANNUAL,\
SUMMARY, VEHICLE CLASSIFICATION STATISTICS 4-34**

Computation of AADTT 4-34

Estimating Daily Volumes from Less-than-Daily Counts 4-34

Estimating Annual Average Daily Traffic Volumes from a 24-Hour\
Classification Count 4-36

Estimating Annual Average Daily Traffic Volumes from More\
than 24-Hour Counts 4-36

Computation of Axle Correction Factors 4-36

Factors for Converting Daily Classification Counts to AADTT by Class
4-38

**CHAPTER 5---VEHICLE CLASSIFICATION DATA COLLECTION EQUIPMENT 4-39**

Manual Counts 4-40

Axle Sensor Based Counters 4-41

Vehicle Length Based Counters 4-43

Machine Vision Based Equipment 4-44

Other Technologies 4-45

**REFERENCES 4-46**

**APPENDIX 4-A---FREQUENTLY ASKED QUESTIONS 4-47**

**APPENDIX 4-B---EXAMPLE TRAFFIC FACTOR GROUP COMPUTATIONS\
USING THE \"TRADITIONAL\" METHOD 4-50**

**APPENDIX 4-C---FHWA VEHICLE TYPES 4-63**

**APPENDIX 4-D---RESOURCES ON TRAFFIC DATA COLLECTION 4-66**

**FIGURES**

*Figure* *Page*

4-3-1 Ratio of Average Weekday Traffic per Month to Average Annual
Daily\
Traffic for Combination Trucks at Interstate Sites 4-30

4-B-1 Seasonal Patterns by Vehicle Type on Interstates in Washington
4-51

4-B-2 Ratio of Average Monthly Weekday Traffic to Average Annual Daily\
Traffic for Passenger Vehicles 4-52

4-B-3 Ratio of Average Monthly Weekday Traffic to Average Annual Daily\
Traffic for Single-Unit Trucks 4-52

4-B-4 Ratio of Average Weekday Traffic per Month to Average Annual
Daily\
Traffic for Combination Trucks 4-53

4-B-5 Ratio of Average Monthly Weekday Traffic to Average Annual Daily\
Traffic for Multi-Trailer Vehicles 4-53

**TABLES**

*Table* *Page*

4-2-1 Conversion Chart 4-11

4-3-1 Example Truck Factor Groups 4-27

4-4-1 Calculation of Average Travel by Time of Day for Combination
Trucks\
at an Example Continuous Counter Site 4-35

4-4-2 Number of Axles per Vehicle 4-37

4-A-1 Length Based Classification Boundaries 4-49

4-A-2 Misclassification Errors Caused By Using Only Total Vehicle
Length\
As The Classification Criteria 4-49

4-B-1 Seasonal Adjustment Factors (MAWDTT/AADTT) for Cars 4-54

4-B-2 Seasonal Adjustment Factors (MAWDTT/AADTT) for Single-Unit Trucks
4-55

4-B-3 Seasonal Adjustment Factors (MAWDTT/AADTT) for Combination Trucks
4-56

4-B-4 Seasonal Adjustment Factors (MAWDTT/AADTT) for Multi-Trailer
Trucks 4-57

4-B-5 Comparison of Seasonal Adjustment Factors for Different Interstate
Groups 4-58

Section 4 vehicle classification monitoring\
\
Chapter 1\
Introduction

This section addresses the collection, manipulation, and reporting of
vehicle classification data to meet the needs for vehicle classification
information. It includes examples of how to apply the general data
collection principles discussed in Section 2 to the subject of vehicle
classification counting.

This chapter summarizes the material presented in Section 2, with
specific emphasis on vehicle classification counting. Chapter 2
discusses the basic user requirements that the vehicle classification
program needs to meet. Chapter 3 presents the recommended process for
selecting the size, frequency, and duration of vehicle classification
counts to meet those needs. Chapter 4 discusses the computation of
annual summary information and provides ideas on how to present that
information effectively. Finally, Chapter 5 discusses the data
collection equipment that is currently available and the need for both
acceptance testing and field validation tests after the equipment has
been installed.

Variability

Traffic volumes vary over time on all roads. Traffic volumes also vary
dramatically from one road to another. These variations in traffic
volume are even more apparent when volumes for specific vehicle types
(classification) are analyzed. Consequently, the vehicle classification
data collection program must gather sufficient data on traffic patterns
of important vehicle types to accurately quantify the truck traffic
stream to meet the needs of users.

The same sources of variation that are present in traditional traffic
volumes apply to vehicle classification estimates. These include:

• time of day

• day of week

• time of year

• direction

• geography.

Complicating the monitoring of these traffic patterns is the fact that
not much data has been collected by classification and not much
analytical work performed. Thus, many of these patterns are not well
understood at the State and individual roadway levels. Further
complicating matters is the fact that travel patterns for trucks are
usually quite different than those for cars, and the data collection
plans currently used tend to be structured around understanding the
movements of cars not trucks.

Thus, the structure of the recommended traffic data collection plan has
been expanded to understand and account for the movement of trucks.

Data Collection To Account For Truck Movements

The recommended structure of a good classification counting program
parallels that traditionally followed for volume counting. The
classification counting program should include both extensive,
geographically distributed, short duration counts and a smaller set of
permanent, continuous counters.

A fairly large number of short duration classification counts should be
performed to monitor and capture truck movements taking place on
individual roads. These counts should be collected by equipment capable
of providing hourly volume summaries. They should normally include data
for all lanes and directions for a given location, since truck traffic
varies considerably from lane to lane and often by direction. This data
collection effort yields the basic truck traffic statistics needed on
any given road including the geographic variability of truck movements,
and the time-of-day distribution at a variety of locations.

These data serve as the starting point for other statistics needed
including truck VDT (or VMT), freight flows (tonnage) carried by trucks
on specific roadways and along specific corridors, and traffic load
design statistics (ESAL and axle load distributions).

However, without adjustment, short duration classification counts yield
biased estimates. Thus, as with traditional traffic volume counting,
classification coverage counts must be supplemented by the use of
permanent, continuously operating, vehicle classification counters
(CVC). The permanent counters provide an understanding of how truck
travel varies by day of the week and season of the year.

As with traditional volume counting, the permanent classifiers should be
used to compute adjustment factors that can be applied to short duration
classification count data to convert a daily count into an estimate of
annual average daily volume for that roadway. The difference between the
recommended counting program for vehicle classification information and
the traditional volume counting program is that the factor groups used
for volume counts do not usually create accurate factors for adjusting
vehicle classification data.

Truck volumes patterns are heavily affected by local economic activity.
They are also heavily influenced by the presence or absence of large
through-freight movements. For example, a high percentage of through
trucks on a road tends to result in higher weekend truck traffic and
higher nighttime truck traffic than would otherwise be expected.
Similarly, the lower the volume of through-traffic, the lower the volume
of trucks in the evening, and the more heavily oriented truck travel is
to weekdays. Because typical volume factor groups do not differentiate
among these types of roads, total volume-based adjustment factors do not
accurately account for these factors.

Finally, traditional volume factoring groups are oriented toward
functional classification of roadway, which may or may not correlate
well with truck travel patterns. Consequently, to better estimate the
annual average travel by trucks on the roads, most States will need to
develop a classification factoring process specifically to factor short
duration classification (truck) counts.

Integration of Classification Counting Programs

The vehicle classification counts required should not be considered
separate from the volume counts traditionally performed. Instead, they
should be integrated with the traditional volume counts. Because
classification counts provide both classification and total volume
information, they can replace traditional volume counts reducing
duplication and error. This is true for both short duration counts and
permanent traffic recorders.

Traffic surveillance equipment used as part of advanced traffic
management systems (ATMS) or advanced traveler information systems
(ATIS) can be used to supply both total volume and vehicle
classification information. Intelligent transportation system (ITS)
technology and its resulting data are often present at high profile
locations as part of safety enhancement systems.[^1] These systems can
supply useful, continuous traffic monitoring data, while also
accomplishing their primary ITS safety/operations objective. Other
agencies and even multiple divisions within a State DOT collect
classification information that can be routinely incorporated into the
statewide traffic counting database. Coordinating these traffic
monitoring activities can lead to significant improvements in the amount
of data available to users, while at the same time reducing the cost of
data collection.

Excellent sources of classification data can often be found at locations
associated with freeway operation surveillance systems, long-term
pavement performance monitoring, vehicle weight enforcement, and toll
facility revenue collection points. Finding these sources and developing
the procedures to obtain and make the data available to others, can be
significant tasks. However, the benefits of cooperation in the data
collection process are substantial and long lasting. The benefits from
the effort are well worth the cost.

Required Outputs From the Vehicle Classification Data Collection Program

It is difficult to describe all of the outputs that can result from the
vehicle classification data collection effort. In general, a State DOT
should be able to provide users with an estimate of the amount of truck
traffic by type of truck by road segment. Truck volume and percentage
estimates should be made available for the date when data were collected
and as annual average estimates corrected for seasonal and day-of-week
variation. States should always provide annual average daily truck
volumes by truck type, but other average statistics, such as average
peak hour truck volume, may also be appropriate.

Classification Schemes

Highway agencies use a large number of vehicle classification schemes.
For many analyses, simple vehicle classification schemes (passenger
vehicles, single unit trucks, combination trucks) are more than
sufficient. In other cases, more sophisticated vehicle classification
categories are needed. For example, in the early 1990\'s Canada
investigated the creation of a classification scheme that would have
included the type of hitch used between tractors and trailers. This
would have allowed much more reliable research on the crash history, and
consequently the safety benefits, of alternative hitch types.
Unfortunately, the available data collection technology could not
accurately classify vehicles by hitch type.

As was found in Canada, the classification schemes that can be used are
a function of the data collection equipment available. The three types
of sensors most frequently used for collecting truck volume information
(visual, axle, and presence sensors) each provide a different mechanism
for classifying vehicles. Within each of these three broad categories
are an array of sensors with different capabilities, levels of accuracy,
performance capabilities within different operating environments,[^2]
and output characteristics. Each type of sensor works well under some
conditions and poorly in others.

Further complicating matters is the fact that different manufacturers
attach different types of electronics to those sensors and analyze,
store, and report sensor outputs differently. Some data collection
equipment is capable of maintaining large amounts of data that provide
very descriptive classification information. For example, some systems
store individual vehicle images (either video images or axle weights and
spacing information) and differentiate among a wide variety of vehicle
types. Other, less powerful systems can differentiate only a few general
classes, based largely on measures of overall vehicle length, and store
and report the data only as summary totals for specified time intervals.

The result is an array of options for classifying vehicles, and an even
wider array of ways in which the resulting vehicle counts are stored and
reported from the field. Many States use a variety of equipment for
different conditions and therefore are confronted with the task of
dealing with different vehicle classification schemes at different
points in the network. This is not necessarily a bad situation. **The
key is to understand how the different classification schemes relate to
one another**. That is, if the State normally uses axle classification
schemes (such as the FHWA 13 categories) but relies on inductance loop
classifiers on urban freeways, it must determine the appropriate
length-based classification boundaries that allow accurate comparison
between these two schemes. This can be accomplished by comparing
estimates of overall vehicle length stored on individual vehicle weight
records obtained from a WIM scale that uses inductance loops for
detecting vehicle presence with the FHWA 13-category classification
scheme associated with each vehicle. **States should maintain and be
able to report the classification algorithm used to define each vehicle
category they collect.**

The FHWA 13-Category Classification System

In the 1980's, the FHWA developed the 13-category scheme used for most
federal vehicle classification count reporting (Appendix 4-C). The
scheme was a compromise among several factors: the manual (vision based)
classification schemes used before that time, the need to create a
nationally consistent classification scheme, the automated counters
being developed at that time, and the need to provide basic information
on different truck types as input to a variety of policy issues.

All States currently use this classification scheme or some variation of
it for classifying vehicles, although few use it exclusively. Many
States separate one or more of the FHWA categories into two or more
additional classifications to track vehicles of specific interest to
them. They then aggregate the categories back together when reporting to
the FHWA. This allows each State to meet both its own needs and the
FHWA's needs. In addition, many States use other classification schemes
in places where axle sensors do not work effectively (e.g., congested
urban conditions) or where non-intrusive sensors are needed.

Since the earliest work done by Maine DOT (Wyman, Braley and Stevens
1985) on classification algorithms[^3], it has been apparent that
different States have trucking fleets with slightly different axle
spacing characteristics. Thus, even when States use the same FHWA
classification scheme, the algorithm they use to convert axle-sensor
information into vehicle counts by category differs. In most cases, the
vehicle classification algorithm provided by each manufacturer needs to
be "fine tuned" to accurately convert that State's truck fleet axle
spacing characteristics into an accurate measure of truck volumes for
the FHWA categories.

Fine-tuning the classification algorithm is needed because the visual
basis of the FHWA 13 categories does not translate to an exact set of
axle spacings. For example, classes 2 and 3 (passenger car and other
two-axle, four-tire, single-unit vehicles) are easily identified
visually. However these classes are often inter-mixed by
axle-sensor-based classification counters. This is because larger cars
often have wheelbases equal to or longer than those of small trucks.
Consequently, it is not possible to create an algorithm that uses only
axle spacing information to differentiate between these two classes of
vehicles.

These types of problems exist in a variety of vehicle classes.
Recreational vehicles are particularly hard to classify with axle
sensors. In many cases, States can do little about these problems (such
as the class 2 and 3 problem mentioned above). Difficult choices are
made to fine-tune the classification algorithms to limit the effects
these errors have on the data they collect. This usually means ensuring
that the algorithms correctly classify "important" truck categories and
only have problems classifying types of trucks that are rare and of less
importance to the highway community. Highway agencies must educate their
users on the limitations in the data collected.

Calibration and Testing of Classifiers

**Each State must periodically calibrate, test, and validate the
performance of its classification equipment to ensure that the equipment
is operating as intended.** This includes testing each new shipment of
classifiers received from the manufacturer, and a short field test
whenever a classifier is placed in traffic to ensure that the counter is
working correctly. The quality of the data collected is highly dependent
on the quality of the calibration/testing operation.

Alternative Classification Schemes

When the FHWA 13 categories cannot be used (because the data collection
equipment can not collect them), or the FHWA classes do not meet State
needs, it is recommended that the classes be either a subset of the FHWA
classes or a clean disaggregation of the FHWA classes. That is, the
State should strive to create classes that consist of either several
FHWA classes added together (e.g., a "single-unit truck" category that
consists of FHWA classes 5, 6, and 7), or FHWA categories split into two
or more classes (e.g., dividing FHWA Class 13 into the two classes,
"triple-trailer trucks" and "other seven or more axle multi-trailer
vehicles").

The aggregation of the FHWA 13 classes into three or four classes is
specifically recommended for the seasonal factoring of truck volumes.

Highway agencies are also encouraged to collect and analyze detailed
vehicle characteristic data (i.e., actual axle weight and spacing
information) every few years from several WIM locations to examine
changes in fleet characteristics over time. Examination of detailed axle
spacing information will allow currently emerging vehicle
characteristics such as split tandems and changing axle spacing
relationships to be tracked. Such changes can result in the need to
update vehicle classification algorithms.

The detailed data needed for this type of analysis (number of axles,
axle spacings, total vehicle length, and trailer configuration
information) can be obtained from individual vehicle records collected
by WIM equipment. For some analyses, these data need to be supplemented
by a video record with a visible time recording to allow the video
record to be matched against the WIM vehicle record.

Chapter 2\
User Needs

User needs drive the data collection process. **A key to making the data
collection process valuable to agency's decision makers (a requirement
for adequate funding) is the ability of the traffic monitoring program
to supply users with the data they need.** This can be a difficult
problem because in many cases data users and collectors do not
communicate well with each other.

Data collectors and data users are frustrated by the lack of
communication. Data collectors often know little about how the data will
be used but are asked to provide data and summary statistics. Many of
these statistics require the application of professional judgment, and
that judgment is hard to apply when the impact of different assumptions
on analytical outcomes are not fully known. Data users often receive
data with no explanation. The precision of the estimates is rarely
provided. In many cases users settle for the data or summary information
provided. Generally, better data and information will result if
communication between the data and user groups are improved.

This chapter discusses briefly some of the uses of traffic
classification data. It is intended to start the communication process
by helping data collectors begin to understand how data may be used and,
thus, what summary statistics are needed. Data collection personnel are
encouraged to actively investigate the data needs of their agency and
then work creatively to meet those needs.

Uses For Classification Data

Vehicle classification data are of considerable use to agencies involved
in almost all aspects of transportation planning and engineering. The
need for information on truck volumes and freight movements is growing
with the recognition of the role that freight mobility plays in the
economy, and as highway engineers realize the importance of truck volume
and operating characteristics on the geometric and structural design of
roadways and bridges. Common uses of truck volume information include
the following:

• pavement design

• pavement management

• scheduling the resurfacing, reconditioning, and reconstruction of
highways based on projected remaining pavement life

• prediction and planning for commodity flows and freight movements

• provision of design inputs relative to the current and predicted
capacity of highways

• development of weight enforcement strategies

• vehicle crash record analysis

• environmental impact analysis, including air quality studies

• analysis of alternative highway regulatory and investment policies.

In short, vehicle classification data are extremely important and will
become even more important as transportation agencies and legislatures
grapple with increasingly older, more congested roadways that need long
lasting repair and rehabilitation in order to ensure statewide economic
vitality.

Summary Vehicle Classification Statistics

The many uses for classification data require the production of a wide
variety of summary statistics. For example, a State wishes to develop
the annual average daily traffic by class for each roadway. To comply,
several actions are required which the program must be capable of
producing. A sufficient base of short duration classification counts
must be taken. The short-duration classification counts must be adjusted
to account for changing traffic volumes on different days of the week
and different months of the year. The adjustment process must be
developed and applied, which assumes the availability of continuous
classification counter data. The resulting statistics are then capable
of meeting defined user needs.

The average classification estimates can also be combined with load data
to produce annual average loading conditions useful for monitoring the
growth of trucks over time, for determining the loads being placed on
pavement and bridges, and for tracking freight movements on the road
system.

In addition to annual average conditions, users may want to be able to
describe truck traffic by time of day during the average weekday. These
estimates can be useful for scheduling road closures and in examining
the effects of new development. Weekdays are also a normal design
consideration from a traffic operations perspective, and understanding
the mix of traffic is an important input to the design of an operational
control strategy. Special procedures based on the available data can be
developed to produce the desired information

Many statistics can be extracted from the vehicle classification data
available from a comprehensive program. Several current revenue
distribution formulas require vehicle distance traveled (VDT)
information, and given the sensitivity of pavement design procedures to
truck volume and load, it is quite possible that at some point, truck
VDT could also be used in this fashion. Other common uses of VDT
statistics by vehicle class include:

• air quality emission calculations

• crash statistics by type of vehicle

• general trend monitoring.

VDT by classification is a critical input to vehicle safety studies
(crash rate and exposure calculations), cost responsibility studies, and
vehicle size and weight studies. Many States track the use of specific
types of vehicles. For example, Oregon tracks the use of triple-trailer
combination trucks. Computing statewide or systemwide VDT by
classification allows the State to compare not just total travel trends,
but differences between trends for the subject vehicle class and for
other similar classes. Oregon can not only measure the growth of
triple-trailer truck travel (and changes to the crash exposure of these
vehicles) but can also compare the growth of travel in that category of
trucks to the increased travel of vehicles in other large truck
classifications.

Developing data collection programs and aggregation methodologies that
allow the computation of average facility statistics from average daily
traffic estimates allows users to target these types of analyses to much
lower levels (for example, how much is heavy truck travel growing on
Interstate highways?). These statistics in turn facilitate improved
geometric design work, more accurate safety analyses, and improvements
to other critical engineering tasks.

The user may not always be interested in producing results using all 13
of the FHWA vehicle classes. In many cases three or four simple
categories may suffice to meet user needs. Four traditional categories
often used are:

• passenger vehicles (cars and light pick-ups)

• single unit trucks

• single combination trucks (tractor- trailer)

• multi-trailer trucks

Each of these categories is an aggregation of existing FHWA classes.
Reporting and use of these simpler categories has the advantages of
requiring less work (by the data collectors and the user) and of
providing more statistically reliable truck volume counts for many
analyses. Several of the FHWA categories contain so few vehicles that it
is not possible to count and accurately report them. Using a smaller
number of vehicle classes increases the confidence in the volume
estimates reported.

Where practical, a State highway agency should collect data in the 13
FHWA vehicle classes but perform the majority of its data reporting with
a more aggregated classification system, such as the four categories
described above. This has the advantage of providing most users with as
much information as they can use, while allowing those users who require
more detailed information to obtain it. Such an approach is successful
as long as users understand that more detailed data are available at
some locations but that the quality of the details may be lower.

Axle correction factors are derived from classification data.
Conventional volume counting performed with road tubes or other single
axle sensors provides axle counts that must be converted into vehicle
volume estimates. If all vehicles were passenger cars, dividing the
number of axle hits by two would provide a good estimate of the total
traffic volume. However, the more multi-axle vehicles that are present,
the less accurate an adjustment factor of 2.0 becomes.

Where classification counts exist (particularly those that use the 13
FHWA classes), a much more accurate axle correction factor can be
computed by assigning an average number of axles to each of the 13
classes and then calculating the average number of axles per vehicle for
each vehicle on that road. This number can then be used to factor
conventional axle counts taken on that road near the classification
count. Table 4-2-1 provides a sample conversion chart that can help
compute axle correction factors from classification counts that use the
FHWA 13 vehicle classes.

Table 4-2-1\
Conversion Chart

  ----------------------------------- -----------------------------------
  **Vehicle Class**                   **Average Number of Axles Per
                                      Vehicle**

  1                                   2

  2                                   2

  3                                   2

  4                                   2.2

  5                                   2

  6                                   3

  7                                   4

  8                                   4

  9                                   5

  10                                  6

  11                                  5

  12                                  6

  13                                  7
  ----------------------------------- -----------------------------------

Axle correction factors should be taken from classification counts
performed on the same days of the week as the volume count (or at least
weekday counts need weekday classification counts) because the
difference in vehicle mix from weekday to weekend at many sites can
create significant error.

Axle correction factors from multiple counts within a group of roads can
also be averaged to provide an estimate of an "expected" axle correction
factor for roads within that group. This procedure is useful for
computing axle correction factors for roads on which no recent
classification count has been computed.

States should look to expand on the classification data they provide to
users. Several States publish "truck volume" maps and/or "freight flow"
maps. These maps, analogous to traditional traffic flow maps, show truck
volumes (or truck freight tonnage) in a graphic form by roadway. They
allow visual inspection and comparison of the magnitude of freight
movement carried by alternative routes. The information can be used to
prioritize alternative road improvement projects, provide public
information needed to reach consensus on required transportation
alternatives, and provide a resource for engineers and planners who are
trying to balance funding constraints with the need to support freight
movement.

Lastly, States have traditionally reported a basic "truck percentage"
statistic for most roadways. This statistic can be easily computed and
reported on the basis of the annual average vehicle classification
estimates. Time of day and day of week biases can affect the calculation
of truck percentages and can also affect the use of this statistic. For
example, the truck percentage calculated from a 24-hour classification
count taken on a Wednesday, while being accurate for the period where
the data were collected, probably overstates the true annual truck
percentage. This percentage is also probably incorrect if it is used to
estimate peak period truck traffic. Peak hour and peak period truck
percentage statistics should be produced and reported specifically to
meet those data needs.

Classification Data Needs For Pavement Design

The new pavement design guide currently being developed by the NCHRP[^4]
requires considerably more traffic data than traditional pavement design
procedures. Location-specific truck volume estimates are crucial to the
accurate estimation of pavement loads. The annual average daily truck
traffic statistics discussed above meet the primary need for current
truck volume estimates necessary to compute pavement loadings.

However, the new pavement design guide also requests knowledge about
seasonal variation in truck volumes and time of day distributions in
those volumes. Seasonal variation will be input to the guide as monthly
factors. Seasonality is important to pavement design, because the
structural response of most pavements changes with environmental
conditions. Thus, the timing of loads is important to the accurate
prediction of pavement life.

Time-of-day distributions are used in the draft NCHRP design guide to
predict the day and night temperature differentials on the curling and
warping of PCC pavements.

Chapter 3\
Vehicle Classification Data Collection Design

As with traditional traffic volume counts, a vehicle classification
counting program should consist of both short duration and continuous
counts. The short duration counts provide geographic coverage and the
continuous counts provide the information needed to account for
day-of-week and seasonal variations when the short duration counts are
converted into annual estimates.

Data on volumes by vehicle classification come from a variety of
sources. The majority of the spatial data will come from short duration
counts. Other sources include WIM sites, urban traffic management
centers, toll facilities, and other agencies that collect truck volume
information. Obtaining data from these external sources greatly
increases the data available for agency use, often at a far lower cost
to the highway agency than if it had collected the data directly.

The key to a successful classification data collection program is not
the source of the data, but the ability to routinely obtain it, verify
its validity, summarize it into useable formats, report it in a manner
that is useful to data users, and manage the process efficiently. Major
portions of the management function involve understanding the need for
both short duration and continuous vehicle classification counts and
creating a program that collects the appropriate amount of data within
both categories.

Short Duration Counts

Short duration vehicle classification counts serve as the primary
mechanism for collecting information on truck volumes. They provide the
geographic distribution necessary to meet the general agency needs and
the needs of its customers, as well as the site-specific knowledge
needed for the more detailed technical analyses of users.

Large numbers of transportation analyses are starting to require more
and better truck volume information. Truck volume information has become
particularly important for pavement design, freight mobility, planning,
safety, and project programming decisions.

Earlier versions of the TMG recommended the collection of 300 vehicle
classification counts during a three-year data collection cycle. This
recommendation stemmed from research performed in the early 1980s, when
automated vehicle classifiers were just beginning to be adopted by
highway agencies. However, 100 vehicle classification counts per year is
not adequate to meet the current truck volume data needs of most State
highway agencies, and many currently collect far more classification
data than this.

A more comprehensive approach is needed to provide the classification
data. The recommendation is based on the following objectives:

• increasing the accuracy and availability of truck volume data

• improving the truck volume data for national studies

• improving the truck volume data used for site-specific studies

• decreasing the cost of collecting the truck volume data by making it a
primary focus of the traffic data collection program.

Short duration counts by themselves, however, are only part of the data
collection process. Research has shown that truck volumes vary
dramatically during the day, often differ significantly between weekdays
and weekends, and can change as well from one season to the next season.
If adjustments are not made for day-of-week and seasonal variation, the
result is likely to be erroneous analytical conclusions. For example,
safety research that uses truck crash rates computed only from weekday
counts will significantly under-estimate the truck crash rate for most
locations because unadjusted weekday volumes tend to over-estimate
annual average daily volumes. A base of continuous classification
counters is used to support the temporal factoring process.

Classification Coverage Counts

**The classification coverage count program should be designed to
operate like a traditional volume coverage program to provide a minimum
level of truck traffic data on all system roads.** The basic coverage
program would be supplemented by special counts as needed to meet
site-specific data needs.

To develop a classification coverage program, the highway system should
be divided into vehicle classification (truck) segments akin to what is
currently done for volume and described in Section 3. Vehicle
classification segments should, in theory, carry a homogeneous volume of
trucks, where trucks are defined as the aggregation of FHWA classes 4 to
13. In practice, development of these section definitions is a judgment
call since the definition is usually based on the available
classification data combined with specific knowledge of the system. The
more classification data and the better knowledge of trucks available,
the easier and better the definition will be. The availability of truck
or commercial vehicle flow maps during the road segmentation process is
very useful.

Most vehicle classification segments are expected to span several
traffic volume segments because truck traffic can remain fairly constant
despite changes in total traffic volume (that is, changes in car volumes
do not necessarily result in changes in truck volume). With time, as
more data and information become available, the definition of segments
will improve.

As with traffic volume, the classification segments will change over
time as roadway and traffic characteristics change and as more
classification data helps to better define the segments. Periodic
reassessments will be necessary to maintain the classification segment
inventory current.

Many caveats apply to the development of the classification coverage
count program. Each agency will have to develop a classification
inventory system to cover its roads that meets its needs. In some cases,
the truck traffic may not change over large expanses of road and a small
number of classification segments will cover the road. In the Interstate
system, for example, classification segments may extend over several
interchanges and be very long. The character of the highway and the
traffic it carries will play a major role in the definition of these
segments and in the number of classification counts needed. Roads that
service truck traffic generating activities will necessitate more
classification segments, more classification counts, and more frequent
revision than roads through regions that experience little trucking
activity.

Lower functional systems, where truck traffic may be sporadic, may
require long segments in some areas and shorter segments in others,
particularly, where truck traffic generators are found. Judgment will
play a large role in the roadway segmentation and the classification
count planning in these areas. Additional classification counting may be
needed to better identify where significant changes occur and how these
affect the definition of segments.

The structure of the road system is superimposed by a system of traffic
volume segments that allow the traffic counting program to cover it.
Likewise, the traffic volume segments will be covered with a smaller
subset of vehicle classification segments that allow the establishment
of a vehicle classification program that covers the system and provides
comprehensive truck data.

The vehicle classification segment inventory will allow a determination
of how much classification counting is needed and how many of the volume
counts should be classified. **A general rule of thumb is that 25 to 30
percent of the coverage volume counts should be classified.** This, of
course, depends on the actual volume coverage program in operation, the
character of the road system covered, and many other considerations. The
general rule of thumb applies to the traffic volume program
recommendation using a coverage program over a 6-year cycle.

Common sense and judgment are greatly needed to determine how to
integrate classification and volume counting. Different agencies will
make different decisions depending on many considerations. In some
cases, the availability of low cost classification equipment can almost
justify the conversion of all counting to classification. The gain in
information on trucks combined with the elimination of the error
introduced by axle correction may justify the extra cost. Many of the
newer counters perform classification and many agencies that have
acquired the new equipment classify rather than count.

On the other hand, changes in program direction, the acquisition of
newer equipment, and the implementation of program changes do not occur
overnight. Many organizations depend on available counters, have
long-term data collection contracts, or do not have established
classification programs.

Many lower volume roads do not have the volume of classified vehicles
(trucks) to justify the full conversion of volume counting to
classification. These are the roads where the installation of
classifiers based on road tubes is easier and where equipment
limitations are not a problem. However, once a classified count is
taken, additional repetitive counts may not improve the truck volume
estimates. In these cases, a decision to save a little time, effort, and
funding could be appropriate.

On higher road systems, repetitive classification may greatly enhance
the understanding of truck volume variability and result in better truck
volume estimates. However, on these roads the collection of
classification data is much more problematic. In the higher volume
systems, portable equipment installation may not be safe or effective
and the installation of more expensive equipment the only solution.

Such constraints may dictate a slower conversion from the current data
collection program to the recommended program that emphasizes
classification counting. Still, all highway agencies need to understand
the use of their roadways by trucks, and thus counting of trucks is an
important task. To help achieve that objective, **another useful rule of
thumb is that a minimum of one vehicle classification count should be
taken on each road each year** to insure a minimum of data available
annually to represent each road. Where practical, these counts should be
taken at existing HPMS volume sample sections to insure the quality of
classification data reported to the HPMS.

Many caveats apply to this rule of thumb as well. For long roads (such
as roads that extend across an entire State), far more than one count
must be taken. For roads that change character (e.g., a route may be
primarily a farm to market road in one place but become a major freight
hauling road in another) several classification counts may be
appropriate.

Roads that experience significant changes in truck traffic due to
changes in industrial activity and/or junctions that lead to truck
generators may need classification counts on either side of the
junctions where truck activity levels change. For minor routes, a single
classification count may be all that is needed. Finally, some agencies
may decide to take additional vehicle classification counts whenever
resources permit simply because truck volume data play a major role in
defining coverage program segments and to insure quality data are
available to meet user needs.

The implementation of a comprehensive classification coverage program
requires direct integration into the standard volume counting program
activities. The manner of scheduling, equipment, staff, and resources
must be adequately considered.

It may not be necessary to perform vehicle classification counts at the
same location every year. Any placement within the defined segment
should provide adequate representation and any additional counts taken
help to verify the annual estimate provided. Likewise, classification
counts need not be taken at the same time each year because the
conversion to annual estimates accounts for the temporal variability. In
fact, counts taken at different times of the year provide independent
estimates that will help to verify and/or improve the segment estimate.
Careful scheduling of the data collection effort may also be necessary
to measure important, seasonal truck movements such as those due to
harvesting or other highly seasonal events.

**The recommended length of monitoring for vehicle classification data
remains 48 hours. The recommended cycle of monitoring for the
classification program is also 6 years.** The schedule of counts should
be developed to insure that coverage of each classification segment
occurs at least once within a 6-year cycle.

Whenever possible, vehicle classification counts should be taken within
the HPMS volume sample sections. This results in direct estimates for
each sample section, thereby allowing the expansion of the truck percent
variables in the HPMS to valid system estimates of truck travel.

Other Special Needs Counts

As with traditional volume counting, the vehicle classification count
program requires special counts in addition to those collected for
coverage to meet needs that the coverage program does not cover.
Traditionally, these counts have been primarily project related.

Project Counts

In many States, the majority of classification counts are project
related. Most commonly these counts are taken to determine the truck
traffic on a road segment that requires a traffic load estimate as an
input for a pavement rehabilitation design. Collection of the data
specifically for the road segment being rehabilitated ensures that the
count data reflect current conditions and that the data used in the
geometric and structural design procedures are accurate enough to ensure
adequate performance of the new pavement over the design life of the
project. Common reasons for project counts include pavement design,
operational design (e.g., signal timing or testing the need for truck
climbing and/or passing lanes), geometric design, and corridor studies.
Each project count can have different requirements for duration, spatial
frequency, and types of summary measures that must be produced.

The establishment of a classification coverage program will allow a more
complete understanding of truck traffic on the highway systems and
hopefully limit the need for additional counting to special cases.

Urban Classification Count Programs

The need for classification data in urban areas is pressing.
Unfortunately, these are some of the most difficult places for current
data collection equipment to operate. Existing counter technologies have
significant difficulty classifying vehicles in conditions where vehicles
do not operate at constant speed, where vehicles follow very closely, or
where stop and go traffic occurs. This is particularly true for
equipment that relies on inductance loops and axle detectors.

However, this does not mean that vehicle classification counts cannot be
taken in urban areas. Agencies must simply take special care in
selecting both the technologies they use and the locations where they
place the equipment to ensure that the data collected are valid.
Research efforts to investigate new technologies should continue.
Several new technologies, particularly video and various laser-based
technologies, can classify accurately in urban conditions when they are
correctly placed and calibrated.

Studies can be undertaken to identify the classification segments where
classification data needs exist. The first step may be identifying
current installations where classification data may already be collected
by ITS installations, State permanent counters, tolls, bridges, traffic
signals, etc. Retrieving that data reduces the need for the use of
portable data collection equipment at many sites. Second, identify the
remaining locations where the portable data collection program can
collect data using current technology. Subtracting these sites from the
set of all needed locations should result in a set of locations where
data cannot be collected using current means. The use of visual counts
is often a last resort in cases where data cannot be collected by other
means. Finally, a determination can be made of the
counting/classification program needed to provide system coverage and
meet special count needs.

Classification data also offers the additional advantage of providing
speed data that are often used in air quality analysis and other urban
studies. Likewise, speed studies provide classification data. Thereby
offering an opportunity for coordination and reduced data collection.

Integration of the Coverage Count Program with Other Programs

At first glance, the coverage program recommended for classification
counts can seem large. It is true that the recommended program is an
expansion over previous recommendations. The expansion is due to the
maturation of vehicle classification technology and an explosion of the
need for truck data. However, many States that already actively collect
substantial amounts of classification data to meet their own data needs
may find that the current recommendations do not significantly increase
the size of the program.

The first level of integration is that classification counts should
replace traditional volume counts on road sections where classification
counts are taken. Thus, for every classification count taken, one less
volume count is needed.[^5] Use of classification counters to provide
total daily volume estimates also has the advantage of providing direct
measurement of daily volume since the need for axle correction is
eliminated.

The coverage count program should also be integrated as much as possible
with the project count program. Existing project counting activities can
eliminate the need for coverage counts. Similarly, existing coverage
counts can often supply project information, if the existing coverage
count meets the informational needs of the project.

Finally, the classification count program should be integrated with
other traffic surveillance systems, particularly those involving
regulation of the trucking industry (such as mainline sorting scale
operations upstream of weight enforcement stations), as well as
surveillance systems installed as part of traffic management, safety,
and traveler information systems.

Duration of Short Counts

**The period of monitoring recommended for vehicle classification counts
is 48 consecutive hours.** Other count durations can produce reasonable
results in some cases, but are not recommended for general use.
Equipment that can collect data in hourly traffic "bins" should be used
for the general program. In urban areas or for special studies, the use
of shorter intervals, such as 15 minutes, may be appropriate. The use of
48-hour periods is recommended because:

• the accuracy of the annual load estimates of 48-hour counts is better
than that of 24-hour counts

• significant improvement in quality control capabilities become
possible with the comparison of one day's hourly traffic counts against
the second day's counts

• axle sensors will normally stay in place for 48 hours if correctly
installed.

Counts for less than 24 hours are not recommended unless they are
intended to provide project specific information (such as turning
movement counts for signal timing plans). This is because truck travel
changes significantly during the day, and some sites can experience
relatively large truck volumes at times when other traffic volumes are
light. Counting throughout the day is important to determine accurate
daily truck volumes, particularly in roads that carry substantial
numbers of trucks.

Counts of less than 24 hours are usually taken as a last resort when
other data collection alternatives are not available. These counts need
to be adjusted to daily totals using a daily adjustment factor to
convert the shorter period to a 24-hour estimate. This adjustment factor
should be obtained from more extensive classification counts on similar
roads because the time-of-day distribution of truck volume is not the
same as that for total volume. The daily volume must also be converted
to an annual estimate by using the appropriate day-of-week and monthly
factors.

Vehicle classification counts of longer than 48 hours are useful,
particularly when those counts extend over the weekend, since they
provide better day-of-week volume information. However, in many
locations it is difficult to keep portable axle sensors in place for
periods that significantly exceed 48 hours. Many highway agencies have
also experienced difficulty in developing cost-effective staff and
equipment utilization plans when using 72-hour or longer count
durations. Whether a highway agency can conduct longer counts is a
function of coverage area size, staff utilization, and other factors.

While a strong case can be made for a number of other count durations,
the benefits of 48-hour counts are supported by recent research
findings. In particular, a study of truck volume variability and the
effect of factoring classification counts showed that an improvement of
between 3 and 5 percent in estimation of annual average volumes could be
achieved by increasing the duration of the classification count from 24
to 48 hours (Hallenbeck and Kim 1993). A study of total traffic volume
counts by Cambridge Systematics found that lower volume roads tend to
have much greater day-to-day volume fluctuations (in percentage terms)
than higher volume roads. These roads showed the greatest improvement
when traffic counts were extended from 24 to 48 hours (Cambridge
Systematics et al 1994).

Permanent/Continuous classifiers

Research has shown that truck travel does not follow the same
time-of-day, day-of-week, and seasonal patterns as total volume
(Hallenbeck and Kim 1993, Weinblatt 1996, Hallenbeck et al 1997).
Analysis of continuously collected data sets also seems to indicate that
truck volumes on many roads (even high volume Interstate) can change
dramatically as a result of changes in the national and local economy.
Continuously operating classification counters are needed to monitor
truck flows so that these patterns can be detected and accounted for in
engineering and planning analyses. **Each State highway agency needs to
operate a set of continuous classification counters to measure truck
travel patterns and provide the factors to convert short classification
counts to annual averages.**

All State highway agencies have been operating permanently installed,
continuous traffic counters (commonly referred to as ATRs) for many
years. It has only been since the mid-1980s that technology allowed the
installation and operation of similar counters to collect continuous
classification data. A significant increase in the number of these
counters has taken place since 1990, as a result of the start of traffic
data collection for the Strategic Highway Research Program's (SHRP) Long
Term Pavement Performance (LTPP) project. Many States have also
converted ATR installations to classification as the old equipment wore
out and was replaced.

Data from these continuous classification devices have shown that truck
volumes have time-of-day, day-of-week, and seasonal variations that are
very different from those of cars. In addition, sources of continuous
classification data may be obtained from installations from regulatory,
safety, and traffic management systems installed to operate and manage
the infrastructure. To obtain these existing data, highway agencies
often must create close working relationships with other public
agencies. The effort may result in considerable improvement to the
available classification data.

Introduction to Continuous Classification Counts and Factors

The objective of seasonal factor procedures is to remove the temporal
bias in current estimates of truck volume. There are four primary
reasons for installing and operating permanent, continuously operating
vehicle classifiers for traffic monitoring purposes. These include the
ability to:

• provide a highly accurate measure of truck volumes at a limited number
of specific sites around the state

• track the changes in those volumes over time with a high degree of
accuracy

• determine the travel patterns of different truck types on different
roadways across the State

• create adjustment factors and factor groups that allow application of
the factors for converting short duration classification counts into
annual average estimates of vehicle volume by vehicle type.

This section discusses ways to establish a continuous vehicle
classification count program, and presents two alternative methods for
the development of factor groups for classification. The continuous
vehicle classification data collection program is related to, but can be
distinct from, the traditional ATR program. In addition, factoring of
vehicle classification counts (i.e., truck volume counts) may be
performed independently from the process used to compute AADT from short
duration volume counts.

There is still a significant lack of data in most States concerning the
travel patterns of trucks. Much work needs to be done to gain the
knowledge needed to refine the vehicle classification factor groups and
factor procedures. The first step has been taken to recommend a process
that will improve the quality of truck volume data and information. To
become effective the process will have to be implemented and given time
to mature and fully develop as knowledge is gained and improvements
made.

Vehicle Classes Used for Factoring

Regardless of the approach taken for the computation and application of
factors, **it is recommended that adjustment factors be computed only
for three or four "generalized" vehicle classes.** The groups
recommended are:

• passenger vehicles (motorcycles, cars, and light trucks)

• single-unit trucks (including buses)

• single-unit combination trucks (tractor-trailers), and

• multi-trailer combination trucks.

In States with few multi-trailer trucks (often the case East of the
Mississippi River), three vehicle classes may be sufficient. In these
cases, single-trailer and multi-trailer combination trucks should be
combined. Highway agencies may adjust these categories to best reflect
their vehicle fleets and travel patterns, as well as the capabilities of
the classification equipment in their programs.

Several reasons support the recommendations. The factoring process does
not work well with very low traffic volumes. With low volumes, even
small changes result in high percentage changes that make the computed
factors highly unstable and unreliable. Even on moderately busy roads,
many of the 13-category vehicle classes will have mathematically
unstable vehicle flows simply because their volumes are low. Aggregating
the vehicle classes provides for more stable and reliable factors.

A second reason is that computing factors for 13 vehicle classes, in
what is clearly recognized as the pioneering development of
classification factoring processes, may introduce too much complexity
and may create a computational and application nightmare. There is no
gain in separately annualizing extremely variable and rare vehicle
classification categories at this relatively primitive stage of the
process.

A third reason is that many issues relating to the quality of the
classification data available from continuous and portable counters
remain unanswered. Adequate editing procedures, resolution of the
assignment of vehicles to classification categories, inability of
equipment to collect a standard set of vehicle classes in all
conditions, and tremendous disparity in equipment already present major
challenges to the factor development and application process.
Unnecessary complications at this stage of development should be
avoided.

Alternative Factor Procedures

Two alternative truck volume factor procedures are presented. Both have
advantages and disadvantages. Both are very complementary and can be
combined as appropriate. Unlike procedures for total traffic volume that
have benefited from many years of trial and error by 50 State highway
agencies, the procedures discussed here are in their infancy.
Recognizing that fact, flexibility is offered to apply these or any
other alternatives that effectively remove temporal bias.

The first procedure involves the use of roadway-specific factors. The
second is an extension of the traditional traffic volume factoring
process involving the creation of groups and the development of average
factors for each of the groups.

Either applying factors to a road or fitting road segments into groups
involves making decisions to resolve difficulties. A factor process may
result in one set of factors for cars, another set of factors for
trucks, and the combination of both to arrive at a total volume. A
factor process may also require more than one set of factors for trucks
when different truck types are factored separately. Some roads could
conceivably fit in one factor group for cars, a second factor group for
single unit trucks, and a third factor group for combination trucks.
Resolutions have to be made by each State between the need for accuracy,
and reductions in unnecessary complexity in the approach to removing
temporal bias. The current state-of-the-art has not progressed to the
point where detailed guidance can be given on the precise number of
factor groups that need to be created or on the resolution of the many
issues that will arise.

There are two basic parts to the factoring process, the computation of
the factors that will be applied to the short counts, and the
development of a process that assigns these factors to specific counts
taken on specific roadways. The roadway-specific and the traditional
procedures approach these two aspects of the factoring process
differently. The result is two very different mechanisms for creating
and applying factors, each with its own strengths and weaknesses.

Roadway-Specific Factors

This process was developed by the Virginia Department of Transportation
(VDOT) in the late 1990\'s. The VDOT operates continuous counters on all
major roads and the counters are used to develop road-specific factors.
A short classification count taken on a specific road is adjusted using
factors taken from the nearest continuous classification counter on that
road. A factor computed for a specific road is not applicable to any
other road.

As a result, a continuous classification counter must be placed on every
road for which an adjustment factor is needed. This requires a large
number of continuous vehicle classification counters and substantial
resources. However, it ensures that a road can be directly identified
with an appropriate factor and provides considerable insight into the
movement of freight and goods within the State. The rule for assigning
factors to short counts is simple and objective.

Identifying a specific road with a specific factor removes a major
source of error in the computation of annual traffic volumes by removing
the "location" error associated with applying an average factor.
Further, it produces factors that are applicable to all trucks using
that road. The fact that different truck classes (single-unit versus
combination trucks) exhibit different travel patterns is irrelevant,
since all patterns are computed for that road.

Having road-specific continuous counters also greatly reduces the number
of short duration counts that are needed, since the continuous counters
provide classification data for road sections near the count locations.
The quality of data from continuous counters is usually superior to that
of short counts.

Finally, this approach has the advantage of simplifying the calculation
of adjustment factors, the application of those factors, and the
maintenance of the program. For example, there is no need to develop
groups and the application is done one road at a time. Problems with
continuous counters only apply to the affected roads and prioritization
of counter problem correction can be based on road priority.

There are also disadvantages with this approach. The most important is
cost. It is expensive to install, operate, and maintain large numbers of
continuous traffic counters. The larger the system that must be covered
the larger the cost. Even for smaller States, the cost to install a
large counter base may be prohibitive. However, this approach may apply
effectively to the Interstate, where sufficient continuous counters may
be available. It can also be applied to roads where current counters are
installed.

A second disadvantage is that many roads are quite long and the
character of truck traffic over their length can change drastically.
This is why short count coverage programs are needed at all. An
adjustment factor taken on a road segment may not be applicable to
another segment a few miles down the road, particularly if large truck
generation activities take place along that stretch of roadway. Truck
patterns change because of economic activity, traffic generators, or
road junctions. Not only does this further increase the number of
continuous counters required, it also creates difficulty in selecting
between the two permanent counters when a short count falls in between.

The next problem is maintenance. Because of the large number of counters
required, some counters will always be down. The inability to quickly
repair failed continuous counters results in a lack factors for those
roads.

One solution may be to develop and use the "traditional" method
described below as a "back-up" for places where a specific road factor
is not available. That is, specific road factors may be used for the
most important truck roads and the traditional factor groups for routes
without continuous counters. When continuous counters fail, traditional
factoring techniques can then be used to provide adjustment factors on
those roads. This combination of the traditional and "roadway specific"
factors may be an effective compromise between these two techniques

One final problem with the "roadway specific" technique is that there is
no mathematical mechanism that allows computation of the
accuracy/precision of the factors as they are applied to a given roadway
section. When these factors are applied to count locations that are
close to the continuous counter, they can be assumed to be quite
accurate. However, as the distance between the short count and the
permanent counter grows, and particularly as more opportunity exists for
trucking patterns to change, the potential for error in the factor being
applied grows, and at an unknown (but potentially substantial) rate.

The Traditional Factor Approach

The traditional factor process involves categorizing roads that have
similar truck traffic patterns. A sample of data collection locations is
then selected from within each group of roads, and factors are computed
and averaged for each of the data collection sites within a group. A
definition for each group describes characteristics that "explain" the
observed pattern and which is used to allow the objective assignment of
short counts to the groups.

For traffic volume, the traditional "characteristics" for grouping roads
have been the functional class of the road (including urban or rural
designation) and geographic location within the State. These groups are
then supplemented with an occasional "recreational" designation for
roads that are affected by large recreational traffic generators.

This same technique can be applied to truck traffic patterns. However,
the characteristics that need to be accounted for can be very different.
Functional class of roadway has been shown to have a very inconsistent
relationship to truck travel patterns (Hallenbeck et al 1997). Instead,
truck travel patterns appear to be governed by the amount of long
distance "through" truck traffic versus the amount of locally oriented
truck traffic, the existence of large truck traffic generators along a
road, such as agricultural or major industrial activity, and the
presence or absence of large populations that require the delivery of
freight and goods. Understanding how these and other factors affect
truck traffic is the first step toward developing truck volume factors.

Create Initial Factor Groups

States must depend on available classification data and knowledge to
begin the development of basic truck traffic patterns. Truck traffic
patterns are governed by a combination of local freight movements and
through-truck movements. Extensive through-truck movements are likely to
result in higher nighttime truck travel and higher weekend truck travel.
Through-traffic can "flatten" the seasonal fluctuations present on some
roads, while creating seasonal peaks on other roads that have nothing to
do with economic activity associated with the land abutting that roadway
section. Similarly, a road that primarily serves local freight movements
will be highly affected by the timing of those local freight movements.
For example, if the factory located along a given road[^6] does not
operate at night, there will likely be little freight movement on that
road at night.

Functional road classification can be used to a limited extent to help
differentiate between roads with heavy through-traffic and those with
only local traffic. Interstates and principal arterials tend to have
higher through-truck traffic volumes than lower functional classes.
However, there are Interstates and Principal Arterial highways with
little or no through-truck traffic, just as some roads with lower
functional classifications can carry considerable through-truck volumes.
Thus, functional classification of a road by itself has been shown to be
a poor identifier of truck usage patterns. To identify road usage
characteristics, additional information must be obtained from either
truck volume data collection efforts or the knowledge of staff familiar
with the trucking usage of specific roads.

Local truck traffic can be generated by a single facility such as a
factory, or by a wider activity such as agriculture or commercial and
industrial centers. These "point" or "area" truck trip generators create
specific seasonal and day-of-week patterns much like recreational
activity creates specific passenger car patterns. Truck trips produced
by these generators can be highly seasonal (such as from many
agricultural areas) or fairly constant (such as flow patterns produced
by many types of major industrial plants). Where these trips predominate
on a road, that road has truck travel patterns that match the activity
of the geographic point or area that produces those trips. In addition,
note that changes in the output of these facilities can have dramatic
changes in the level of trucking activity. For example, a labor problem
at one West Coast container port may produce dramatic shifts in
container truck traffic to other ports. This results in significant
changes in truck traffic on major routes serving those ports. Expansion
or contraction of factory production at a major automobile plant in the
Midwest can cause similar dramatic changes on roads that serve those
facilities.

Truck trip generators can also affect the types of trucks found on a
road. Specific commodities tend to be carried by specific types of
trucks. However, State-specific truck size and weight laws can mean that
trucks typical in one State may not be common in others. For example,
multi-trailer trucks are common in most western States, while they make
up a much smaller percentage of the trucking fleet in many eastern
States. Understanding the types of trucks used in a State to carry
specific commodities (e.g., coal trucks in Kentucky and Pennsylvania) is
critical to understand the trucking patterns that should be expected on
a road and how those patterns are likely to change.

There are many other patterns that affect truck travel. For example,
construction trucks operate in an area\'s roads until the construction
project is completed and then they move somewhere else. This type of
truck movement is difficult to quantify. Roads near truck travel
generators, such as quarries or trash dumps, carry consistent truck
traffic and the type of truck is well known.

Summarizing the different patterns in a way that allows creation of
accurate factor groups is difficult. Obviously, the more knowledge that
exists about truck traffic on a road, the easier it is to characterize
that roadway.

Geographic stratification and functional classification can be used to
create truck volume factor groups that capture the temporal patterns and
are reasonably easy to apply. An initial set of factor groups might look
something like that shown in Table 4-3-1. Roads might then be moved
between these initial starting groups as needed.

Definitions like those presented above group roads with as homogenous
truck travel patterns as possible, and also provide easy identification
of the groups for application purposes. They present a starting point to
begin the identification process necessary to form adequate groups.

Performing a cluster analysis using truck volumes (as done in Section 3
for total volume) will help to identify the natural patterns of
variation and to place the continuous counters in variation groups. This
will help in identifying which groups may be appropriate and in the
determination of how many groups are needed. One of strengths of the
cluster analysis is that it identifies groups but only by variation. The
weakness is that it does not describe the characteristics of the group
that allow application of the resulting factors to other short counts.

The example definition in Table 4-3-1 does exactly the opposite. It
clearly establishes group characteristics but cannot indicate whether
the temporal variation is worth creating separate groups or not. As is
the case for AADT group procedures, a combination of statistical methods
and knowledge must be used to establish the appropriate groups.

Table 4-3-1\
Example Truck Factor Groups

  ----------------------------------- -----------------------------------
  [Rural]{.underline}                 [Urban]{.underline}

  Interstate and arterial major       Interstate and arterial major truck
  through-truck routes                routes

  Other roads (e.g., regional         Interstate and other freeways
  agricultural roads) with little     serving primarily local truck
  through traffic                     traffic

  Other non-restricted truck routes   Other non-restricted truck routes

  Other rural roads (e.g., mining     Other roads (non-truck routes)
  areas)                              

  Special cases (e.g., recreational,  
  ports)                              
  ----------------------------------- -----------------------------------

Determine the Variability of Group Patterns

All roads within the defined factor groups should have similar truck
volume patterns. To verify that, the continuous counter data available
within the groups must be examined. For each continuous classification
counter in a group, compute the temporal adjustment factors of interest
(day-of-week, month, or combined) for each of the vehicle types desired
and then compute the mean and standard deviation for the group as a
whole. Plots of the volumes and the factors over time can also help to
determine whether the travel patterns at the continuous sites are
reasonably similar.

In most cases only a few roads within each group will have data
(continuous counters) needed to estimate travel patterns. The
assumptions this analysis makes are similar to those made for AADT
factors. The implication is that the continuous counters typify the
existing temporal variation. Then the continuous counter variation
reflects the variation existing at locations where there are no
continuous counters. A combined monthly and weekday factor can be
computed as follows[^7]:

Adjustment Factor ~C,\ June~ = AADTT ~C~ / MAWDTT ~C,\ June~ (4-1)

where Adjustment Factor ~C,\ June~ = a multiplicative factor for a
specific vehicle type used to convert a 24-hour count taken on any
weekday in June to an estimate of annual average daily traffic.

AADTT ~C~ = annual average daily (truck) traffic volume for a specific
vehicle type

MAWDTT ~C,\ June~ = monthly average weekday (truck) traffic volume for
the month of June for a specific vehicle type.

Computing the mean (or average) for the June factor for all sites within
the factor group yields the group factor for application to all short
counts (weekdays in June) taken on road segments within the group. The
standard deviation of the factors within the group describes the
variability of the group factor. The variability can be used to
determine whether a given factor group should be divided into two or
more factor groups, to compute the precision of the group factor, and to
estimate the number of continuous counter locations needed to compute
the group factor within a given level of precision.

The variability of each statistic computed for the factor group will
have a different level of precision. For example, the June factor will
have different precision than the July factor. The precision will also
vary for each of the vehicle types analyzed.

Test the Quality of the Selected Groups

The information on variability must be reviewed to determine whether the
roads grouped together actually have similar truck travel patterns. A
number of methods can be used to determine whether various sites
"belong" together. A statistically rigorous approach to testing the
precision of the selected groups requires the use of fairly complex
statistics, an examination of all the truck classes used, the comparison
of statistical reliability for all the different types of statistics
produced with the reliability users need for those statistics.

This is a complex and difficult analysis. The analysis can be simplified
by concentrating on the most important vehicle classes and statistics
produced. However, even with the simplifications suggested, trade-offs
are necessary. No designed group will be optimal for all purposes or
apply perfectly to all sites.

For example, in one group of roads, the single tractor-trailer volumes
on roads within each group may have similar travel characteristics, but
the single-unit truck volume patterns are very different from each
other. By changing the road groups, it may be possible to classify roads
so that all roads have similar travel patterns for single-unit trucks,
but then the single tractor-trailer patterns become highly variable.

At some point, the analyst will need to determine the proper balance
between the precision of the group factors developed for these two
classes of trucks, or they will have to accept the fact that different
factor groups are needed for different vehicle classes. Then each road
may end up in multiple factor groups depending on what vehicle
classification volume is being factored. Use of multiple groups may
result in a more accurate factor process but will certainly result in a
more complicated and confusing procedure.

The trade-offs between alternative factor groups can only be compared by
understanding the value of the precision of each statistic to the data
user. In most cases this is simply a function of determining the
relative importance of different statistics. For example, if 95 percent
of all trucks are single tractor-trailer trucks, then having road groups
that accurately describe tractor-trailer vehicle patterns is more
important than having road groups that accurately describe single-unit
truck patterns. Similarly, if single-unit trucks carry the predominate
amount of freight (this occurs in mineral extraction areas), then the
emphasis should be on forming road groups that accurately measure
single-unit truck volume patterns.

The quality of a given factor group can be examined in two ways. The
first is to graphically examine the traffic patterns present at each
site in the group. Figure 4-3-1 gives an example of a set of monthly
truck volume patterns for a group of sites in the State of Washington
that could be considered a single factor group. Graphs like these give
an excellent visual description of whether different data collection
sites have similar travel patterns.

The second method is to compute the mean and standard deviation for
various factors that the factor group is designed to provide. If these
factors have small amounts of deviation, the roads can be considered to
have similar characteristics. If the standard deviations are large, the
road groupings may need to be revised.

![](media/image1.wmf){width="5.995833333333334in" height="4.1in"}

Figure 4-3-1: Ratio of Average Weekday Traffic per Month to Average
Annual Daily Traffic for Combination Trucks (FHWA Classes 8 - 10) at
Interstate Sites

Determine the Precision of Factors

An estimate of the precision of the group factor can be derived from the
standard deviation. For example, the precision of the June adjustment
factor computed above can be estimated using the standard deviation of
that estimate. The precision of the group factor can be estimated with
95 percent confidence as approximately[^8] plus or minus 1.96 times the
standard deviation divided by the square root of the number of sites in
the group.

Increasing the number of continuous counter locations within a group
will improve the precision of the group factor. However, increasing the
number of continuous counter locations only marginally improves the
precision of the group factor application at specific roadway sections.
That is, increasing the sample size makes the group factor itself a
better measure of the mean for the group, but the mean value may or may
not be a good estimate of the pattern at any given roadway section
within that group. The standard deviation of the group factor measures
the diversity of the site factors within the group.

There can be cases where the factors will not improve the annual volume
estimates, particularly in high variability situations. An alternative
is to take multiple site-specific classification counts at different
times during the year to directly measure seasonal change. This can be
an effective way to accurately estimate annual truck traffic for high
profile projects that can afford this additional data collection effort.
This alternative can also be used to test the accuracy of the annual
estimates derived from the group factors.

Refine the Factor Groups

If the factor groups selected have reasonably homogenous travel patterns
(i.e., the variability of the factors is low), then the groups can be
used for factor development and application. If the factors for the
group are too variable, then the groups may need to be modified.

These modifications can include the creation of new groups (by removing
the roads represented by some continuous counters from one group and
placing them in a new group), and the realignment of counters within
existing groups (by shifting some counters and the roads they represent
from one existing factor group to another). This process continues until
a judgment is made that the groups are adequate.

Be aware, as noted earlier, that if very precise adjustment factors are
desired, it is possible that the factor process will require different
factor groups for each vehicle class. That is, traffic patterns for
combination trucks may be significantly different (and affected by
different factors) than the traffic patterns found for smaller,
short-haul trucks. These patterns may in turn be sufficiently different
than passenger vehicle patterns that three different factor groupings
may need to be developed. In such a case, passenger car volumes may need
to be adjusted using the state's existing factor process (since total
volume tends to be determined by passenger car volumes in most
locations); while single unit trucks are factored with data obtained
from different groups of counters and combination trucks are factored
with counts obtained from those same counters but aggregated in a
different fashion. Then the three independent volume estimates need to
be added to produce the total AADT estimate.

Determine the Number of Locations to Monitor

Once groups have been established and the variability of the group
factors computed, it is possible to determine the number of count
locations needed to create and apply factors for a given level of
precision. Note that because each statistic computed for a group has a
different level of variability, each statistic computed will have a
different level of precision.

The first step in determining the number of sites needed per group is to
determine which statistics will guide the decision. In general the key
statistics are those that define the objective of the formation of
groups, that is, the correction for temporal bias in truck volumes. The
combined day-of-week and monthly factor, computed for the truck-trailer
combination vehicles during the months when short duration counts are
taken, may well be the most appropriate statistic to guide the group
size, at least for the Interstate/arterial groups. For other groups, the
single-unit truck may be more appropriate.

If counts are routinely taken over a nine-month period, the one month
with the most variable monthly adjustment factor (among those nine
months) should be used to determine the variability of the adjustment
factors and should thus be used to determine the total sample size
desired. In that way, factors computed for any other month have higher
precision.

For most factor groups, at least six continuous counters should be
included within each factor group. This is an initial estimation based
on AADT factor groups. If it is assumed that some counters will fail
each year because of equipment, communications, or other problems, a
margin of safety may be achieved by adding additional counters.

Collect Additional Data and Refine the Established Process

Much needs to be learned about vehicle classification. **States are
encouraged to convert as many of their ATR continuous counters to
classification as possible and to analyze the available data to better
understand truck travel patterns and variation.**

A substantial continuous vehicle classification program allows States to
refine the classification count factoring process as needed. The
addition of new continuous count locations allows the comparison of
newly measured truck travel patterns with previously known patterns.
This is true even for the road-specific factoring procedure, since
traffic patterns along a road can change dramatically from one section
to another. One way of adding new count locations is to move counter
locations when equipment or sensors fail and need replacement at an
existing continuous site.

If a new data collection site fits well within the expected group
pattern, that site can be incorporated into the factor group. However,
if a new site shows a truck travel pattern that does not fit within the
expected group pattern, a reassessment of the truck volume factoring
procedures may be appropriate. Modifications include moving specific
roads or road sections from one factor group to another, creating new
factor groups, and even revising the entire classification factoring
process.

The factoring process should be reviewed periodically to ensure that it
is performing as intended. For the first few years after initial
development or until the process has matured, these evaluations should
be conducted every year. After that, the classification process should
be reviewed periodically every 3 years or the same review cycle used for
the AADT group factor process.

Chapter 4\
Computation and Reporting of Annual, Summary, Vehicle Classification
Statistics

This chapter presents basic procedures for computing statistics or
estimates derived from the vehicle classification program. Statistics
discussed include:

-   AADTT (annual average daily truck traffic)

-   axle correction factors

-   factors for converting daily truck traffic counts into estimates of
    AADTT (by class)

-   factors that allow conversion of AADTT estimates (by class) into
    average day of week estimates for use in the draft NCHRP 1-37A
    Pavement Design Guide.

Computation of AADTT

Computation of AADTT (by vehicle class) from a short duration count
requires the application of one or more factors that account for
differences in time-of-day, day-of-week, and seasonal truck traffic
patterns. These adjustments are the same as those applied to traditional
volume counts, except that they must be applied by individual vehicle
classification when working with classification count data.

Estimating Daily Volumes from Less-than-Daily Counts

**Classification counts should be taken for 48 consecutive hours. W**hen
it is not possible to collect at least 24 hours of data, time-of-day
adjustments are needed to expand the short counts to daily estimates.
Most classification counts are taken in hourly increments. When these
hourly volumes add up to less than 24 hours (usually with visual
counts), it is necessary to expand them to 24-hour estimates.

This should be accomplished using adjustments from data collected by
permanent vehicle classification counters. Adjustment tables should be
created for specific types of roadways (using the factor groups
discussed in the previous chapter of this section if a better system is
not available) and specific hours of the day. In this manner, the factor
applied to adjust a very short count to an estimate of daily traffic
volume (by class) will depend not just on how many hours were counted
but on which hours were counted, as well as on which class of vehicles
is being adjusted. For example, the adjustment for a 6-hour count taken
from 8 AM to 2 PM may be very different than the adjustment that should
be applied to a 6-hour count taken from 2 PM to 8 PM.

These adjustment tables can be created by simply computing the
percentage of daily traffic that occurs during any one hour of the day
for each vehicle class for each type of day of the week. These
percentages can then be added together as needed to create an adjustment
percentage for any series of hours of data collection.

Table 4-4-1\
Calculation of Average Travel by Time of Day for Combination Trucks at
an Example Continuous Counter Site

  ----------------------- ------------------------ -----------------------
  Hour                    Average Weekday Volumes  Percentage of Traffic
                          By Hour                  

  Midnight - 1 AM         20                       1.9%

  1 AM - 2 AM             30                       2.8%

  2 AM - 3 AM             10                       0.9%

  3 AM - 4 AM             10                       0.9%

  4 AM - 5 AM             20                       1.9%

  5 AM - 6 AM             40                       3.7%

  6 AM - 7 AM             80                       7.4%

  7 AM - 8 AM             100                      9.3%

  8 AM - 9 AM             60                       5.6%

  9 AM - 10 AM            80                       7.4%

  10 AM - 11 AM           70                       6.5%

  11 AM - Noon            80                       7.4%

  Noon - 1 PM             50                       4.6%

  1 PM - 2 PM             60                       5.6%

  2 PM - 3 PM             90                       8.3%

  3 PM - 4 PM             80                       7.4%

  4 PM - 5 PM             50                       4.6%

  5 PM - 6 PM             40                       3.7%

  6 PM - 7 PM             30                       2.8%

  7 PM - 8 PM             20                       1.9%

  8 PM - 9 PM             10                       0.9%

  9 PM - 10 PM            20                       1.9%

  10 PM - 11 PM           10                       0.9%

  11 PM - Midnight        20                       1.9%

                          1080                     100%
  ----------------------- ------------------------ -----------------------

To compute the daily total traffic volume estimated by the short count,
the simple formula below is used:

Daily Traffic Volume = ![](media/image2.wmf) (4-2)

Thus, if a 6-hour count was taken from 6 AM to noon on a weekday, and
260 combination trucks were counted, then using Table 4-4-1, the total
daily combination truck volume would be estimated as 600 trucks (260 \*
100 / 43.6 = 596 ≈ 600).

Estimating Annual Average Daily Traffic Volume from a 24-Hour
Classification Count

This calculation is equivalent to converting ADT to AADT. It requires
the application of two adjustments, a day-of-week adjustment and a
seasonal adjustment. These two factors can be applied as one combined
factor (usually a ratio of AADT / MAWDT, or annual average daily traffic
to average weekday traffic for a given month), or as two separate
factors, a seasonal adjustment (usually AADT to monthly average) and a
day-of-week adjustment.

Both of these techniques work with roughly the same accuracy, if the
factor groups that are used to compute and apply those factors are
correctly formed.

Estimating Annual Average Daily Traffic Volumes from More Than 24-Hour
Counts

If the data collected cover more than 24 hours, the data should be
summarized to represent a single daily count. This can be done in two
ways, depending on how the factoring process is performed.

If individual day-of-week factors are used (e.g., a different factor for
Tuesdays than for Wednesdays), then each 24-hour count can be converted
into an estimate of annual average daily traffic, and the different
daily values averaged into a single estimate of AADTT.

If a general day-of-week adjustment (e.g., a single weekday to average
day-of-week adjustment), the individual hourly volumes[^9] can be
averaged. These averages are then totaled to produce a single daily
volume, which can then be adjusted for seasonality and day of week.

Computation of Axle Correction Factors

Emphasis on the collection of classification data should minimize the
need for axle correction. Whenever possible, axle correction factors
needed to convert axle counts to vehicles should be developed from
vehicle classification counts taken on the specific road. In addition,
the classification count should be taken from the same general vicinity
and on the same day of week (a weekday classification count is usually
sufficient for a weekday volume count) as the axle count it will be used
to adjust. Where a classification count has not been taken on the road
in question, an "average" axle correction factors can be estimated from
the WIM and continuous classification sites.

The computation is the same whether the data come from a single short
duration count or from a continuous WIM scale. Table 4-4-2 illustrates
the process.

In the table, vehicle volume is computed by dividing the total number of
axles counted by the average number of axles per vehicle. Thus, an axle
count of 4,520 axles would be equal to a vehicle volume of 1,949 (4,520
/ 2.32 = 1,949).

Multiplicative axle correction factors can be derived as the inverse of
the average number of axles per vehicle. In the above example, the
factor would be 0.43 (the inverse of 2.32. The number of vehicles
(1,949) would then be estimated by multiplying the number of axles
(4520) times the factor (.43).

Table 4-4-2[^10]\
Number of Axles per Vehicle

  -------------- ------------------ ------------------ ------------------
  **FHWA Vehicle **Daily Vehicle    **Average Number   **Total Number of
  Class**        Volume**           of Axles Per       Axles**
                                    Vehicle**          

  1              100                2                  200

  2              1,400              2                  2,800

  3              45                 2                  90

  4              15                 2                  30

  5              20                 2                  40

  6              40                 3                  120

  7              5                  4                  20

  8              15                 4                  60

  9              120                5                  600

  10             5                  6                  30

  11             15                 5                  75

  12             5                  6                  30

  13             10                 7                  70

  Total Volume   1,795              Total Number of    4,165
                                    Axles              

                                    Average Number of  2.32
                                    Axles Per Vehicle  
  -------------- ------------------ ------------------ ------------------

Factors for Converting Daily Classification Counts To AADTT By Class

The calculation of factors for converting average daily traffic (by
class) to annual average conditions begins by computing average
day-of-week, average-day-of-month, and annual average daily traffic
statistics at each continuous count location. The ratios from each
continuous count location are then averaged within the factor groups to
produce the average factor for the group.

The first step in computing day-of-week adjustment factors is to compute
an average day of week for each month. For example, the average Monday
is computed by adding the Monday traffic volumes in the month, and then
dividing by the number of Mondays in the month.

An average-day-of-month can be computed by simply averaging the seven
daily values within each month. This is preferable to calculating a
simple average for all days of the month, because then average monthly
statistics can be compared from one year to the next without worry that
in one year there were more weekend days than in another year.

Annual average daily traffic for each day of the week for each vehicle
class can then be computed as the average of the 12 months. The best
computational procedure is recommended in the AASHTO Guidelines for
Traffic Data Programs and can be shown mathematically as follows:

![](media/image3.wmf) (4-3)

where: ADTT~c~ = daily truck traffic for class c, day k, of day-of-week
i, and month j

i = day of the week

j = month of the year

k = 1 when the day is the first occurrence of that day of the week in a
month, 4 when it is the fourth day of the week

n = the number of days of that day of the week during that month
(usually between 1 and 5, depending on the calendar and the number of
missing days).

Chapter 5\
Vehicle Classification Data Collection Equipment

A variety of equipment can be used to classify the traffic stream.
Available technology allows use of axle, vehicle length, and machine
vision classifiers. New technologies are rapidly evolving. As a last
resort, human observation is used.

For each of the technology solutions (axle, length, or vision) there are
generally a number of different sensor technologies. Each sensor has its
own advantages and disadvantages regarding cost, reliability, accuracy,
life span, ease of set up, and type of information provided.

Each of the basic classification and sensing technologies has strengths
and weaknesses that allow some classification techniques to work better
than others under specific environmental and traffic conditions. No
technology has proven to be the best under all conditions.

The different sensor technologies also require a variety of different
vehicle classification schemes because the vehicle characteristic
information provided by each sensor differs. The ideal vehicle
classifier would be able to measure a wide variety of vehicle
characteristics to differentiate trucks on the basis of several
different factors and to meet the needs of different users.
Unfortunately, such a sensor does not currently exist at an affordable
price. Consequently, agencies must select the technologies that provide
the data they most need to provide the classification information they
require, at the locations where those data are needed, at prices they
can afford.

For most engineering tasks the primary issue is separating "heavy"
vehicles from "light" vehicles, because heavy vehicles cause more
pavement damage and tend to have poorer acceleration and braking
characteristics. However, weight is not the only issue, since total
vehicle size (length, width, height) has a major impact on the geometric
design needed for safe roadway operation. Other desired vehicle
classification attributes include the type of connection used on
multi-unit vehicles (the connection has major safety implications) and
the type of engine that provides the power (since the type of engine
affects the amount and type of pollutants emitted). Unfortunately, these
last two vehicle characteristics are extremely difficult to obtain from
conventional classification equipment, and as a result, these vehicle
characteristics are normally collected as part of special studies, not
as part of the traffic monitoring effort.

The FHWA 13-category classification system is a direct result of the
compromises forced on highway agencies by the limitations in affordable
vehicle sensors. The FHWA 13-category classification scheme is a
compromise between a classification scheme based on standard axle
sensing technology, and a classification scheme based on observation of
the traffic stream by human observers. Like all compromises, the FHWA
13-category scheme is not perfect. However, its strengths and weaknesses
are viewed differently by different groups, each of which needs a
different type of data to perform a particular important analysis. **The
FHWA 13-category scheme does provide an excellent mechanism for
classifying vehicles, given available technology. Its use is recommended
as the basic classification scheme for highway agencies.** However,
agencies may choose to expand on the FHWA scheme to meet their own
needs, and they may use other classification systems in locations where
non-axle based vehicle sensors are in operation.

The remainder of this chapter introduces the available vehicle sensor
technology. However, because this field is changing rapidly, the reader
is encouraged to access current research results when exploring vehicle
sensors. Good starting points for further research in this field are the
Vehicle Detector Clearinghouse operated by the New Mexico State
University and the North American Travel Monitoring Exhibition and
Conference, held every two years.

The basic strengths and weaknesses of the commonly available
technologies are presented below.

Manual Counts

Historically, truck counts (classification counts) could only be done by
visually counting the traffic stream. Visual counts are traditionally
called manual counts. Manual classification of the traffic stream has
several advantages. Visual identification can classify trucks on the
basis of a vehicle's body style (tank trucks versus dump trucks versus
flat bed trucks versus delivery trucks). Looking at trucks can also
increase the accuracy with which an individual truck is classified into
being either "potentially heavy" or "not likely to be heavy". That is, a
human observer can easily determine the difference between a car pulling
a light trailer and a tractor pulling a semi-trailer, when these two
vehicles have the same number of axles and possibly even similar axle
spacing characteristics. Thus, "classification errors" from human
observers are usually small when the data collector is highly motivated.
Visual short counts can potentially be taken in all conditions.

Unfortunately, manual classification counts are expensive and prone to
error. It is very difficult for a person to count accurately for more
than about three consecutive hours. After three hours, the concentration
of most observers tends to wander, causing the number of errors to
increase. Counting traffic can be a very boring job for a person.
Substantial supervision is needed to ensure the quality of the data
reported. In addition, most human observers cannot count accurately
under high volume, multi-lane conditions (additional observers are
needed, further increasing the cost of data collection).

Axle Sensor Based Counters

Automated classification was developed to help resolve the limitations
of manual counting. Automated classifiers became common in the 1980\'s
with the advent of microchip equipment that relied on two carefully
spaced axle sensors (usually road tubes). These counters measure the
number of axles associated with each passing vehicle and the spacing
between axles. The axle spacing is computed from the speed of the
vehicle and the time between axle pulses on each sensor. Vehicle speed
is commonly computed by measuring the time it takes for the front axle
to travel from the first axle sensor to the second (a known distance).
The number and spacing of axles is then fed into an algorithm that
associates a given number and spacing of axles with a particular class
of vehicles.

The accuracy of axle sensor based counters is a function of several
factors, including (but not limited to) the following:

• the accuracy of the distance measurement between the two axle sensors

• the need for constant vehicle speed over the two sensors (changing
vehicle speeds cause errors in the axle spacing computation)

• the need for a vehicle to stay in a single lane until it has passed
completely over both sensors

• the speed with which the axle sensor can respond to axles crossing the
sensor,

• the accuracy of the axle sensors themselves (that is how often they
either report non-existent axles (ghost axles) and/or miss axles that
pass over them)

• the presence of different types of vehicles with similar axle spacing

• the care with which the classification algorithm was developed that
converts the number and spacing of axles into vehicle.

Some of these factors are a function of the type of axle sensor used.
Others are a function of the roadway geometry at the site where the
sensors are placed. Others are a function of the quality of the
equipment installation and/or the pavement on which the sensors are
placed. Others are simply a function of the types of vehicles that
actually operate on the road site.

Most classifiers report not only the number of vehicles in each class
but also the number of vehicles that crossed the sensors but could not
be classified. These "unclassified" vehicles normally fall into two
categories, "errors" and "unclassified." Errors are normally vehicle
measurements in which the two axle sensors reported different numbers of
axles (usually because the vehicle changed lanes as it crossed the
sensors); significant changes in vehicle speed occurred over the sensor,
making it impossible to accurately measure axle spacing; or extraneous
noise in the sensors made the system unable to determine the type of
vehicle passing. Unclassified vehicles are normally vehicles for which
the system measurements are complete but that do not fit within any of
the proscribed vehicle categories.

Each agency should carefully examine the types of vehicles that are not
being successfully classified so that it can both improve its
classification algorithm over time and allocate the reported
"unclassified" vehicles to the appropriate vehicle classes. This is
necessary, or the reported volumes by class will underestimate the true
number of vehicles. **Agencies should identify and document the
classification scheme being used to collect classification data and the
procedures used to assign "unclassified" vehicles to the standard 13
categories.**

In general, axle sensors either are designed for portable operation
(they are taped or nailed to the pavement on a temporary basis) or are
permanently imbedded in the pavement by cutting a slot in the roadbed
and using an adhesive to fix the sensor in that slot.

Portable sensors have the advantage of being usable at many locations.
However, they are usually difficult to place on lanes that are not next
to the shoulder of a road, thus making it difficult to use these
classifiers on multi-lane, undivided arterials. It is also easy to make
a mistake when placing portable sensors so that the distance between the
sensors is incorrectly reported and/or is not consistent from the right
hand edge of the lane to the left. Finally, portable sensors can come
loose during data collection yielding invalid results after the sensor
pulls loose but before the sensor has become completely detached from
the roadway.

Permanent sensors are often used for both long-term data collection
sessions and for collecting data on multi-lane highways, where portable
axle-sensors cannot be placed. The primary drawbacks to permanent
sensors are:

• higher cost to acquire than portable sensors

• more expensive to install

• require lane closure for installation

• can only be used in one location.

In general, axle based classifiers work very well on smaller (two-lane)
rural roads and divided four-lane rural roads where congestion is not a
problem. This type of counter has difficulty counting accurately on
roads where traffic speeds are highly variable. This includes roads that
are frequently congested and roads where vehicles are constantly
accelerating or decelerating, such as on urban arterials. They have
difficulty differentiating between closely spaced vehicles (i.e.,
tailgating cars). Two closely spaced cars are often reported as a single
four-axle combination truck. Lastly, unless these devices use axle
sensors that actually detect axle weight, they are unable to reliably
differentiate between cars pulling trailers and multi-axle trucks,
because of similar axle configurations.

Vehicle Length Based Counters

One of the earliest alternatives to axle sensor based counters was the
"dual loop" classifier. Inductance loops were selected because they
allowed for reliable, long lasting installation of the vehicle detector.
Thus, many of the earliest permanent, continuously operating vehicle
classifiers were dual loop systems.

This style of counter uses two inductance loops to estimate the total
length of vehicles crossing the loops. Vehicle length is computed by
dividing the total time a vehicle is over the loop by the speed of that
vehicle. Vehicle speed is determined by the difference in time taken for
the vehicle to be detected by the first loop and the second loop. This
simple equation is then calibrated to account for each loop's
sensitivity and the fact that the "zone of detection" is not a "point"
on the roadway, so that a vehicle is "detected" for slightly longer than
it takes to pass over the loop. The length of this "detection field" is
a function of a number of factors related to loop sensitivity and
vehicle characteristics.

Dual loop sensors generally classify vehicles into fewer, more general,
categories than the FHWA 13 vehicle classes. This is for several
reasons, including the following:

• Most of the length classifiers are not accurate enough to measure
small differences in vehicle length. Thus, broad vehicle length
categories are used to reduce the amount of misclassification. (That is,
by using only four categories, there are only three boundaries where a
one-foot error in measurement will cause a vehicle to be misclassified.
This leads to more accurate classification.)

• Most length classifiers can not differentiate between a single long
vehicle unit and two smaller units hitched together because the length
between hitched units is too small to detect.

• Length alone is a poor variable for differentiating among vehicle
classes. For example, five-axle, tractor, semi-trailer trucks come in a
variety of lengths. Yet these trucks are commonly classified together.

Still, four classes are sufficient for many analytical purposes. When
this is combined with the inability of axle sensors in many conditions,
the low cost of the basic loop inductance sensor and the general
reliability of these systems; it is easy to see why basic length
classifiers remain popular.

However, length classifiers have many of the same operational problems
that axle classifiers have. That is, on roadways where vehicle speeds
are not constant over the detectors because of congestion,
signalization, street parking, or operational conditions (e.g.,
driveways), the computation of vehicle lengths is not accurate. This
makes the classification inaccurate and results in significant data
collection errors. Dual loops also have difficulty differentiating among
closely spaced cars (tailgaters) and tend to report two closely spaced
cars as one mid-sized truck. Thus, these detectors tend to work most
accurately in locations where free-flow traffic is assured.

Machine Vision Based Equipment

Machine vision systems, most of which are based on video image
processing, were developed in response to the desire by many
transportation agencies to use a vehicle detector that did not have to
be placed on or in the roadway. Camera systems allow the detector to be
placed above or beside the roadway, in a location that is more
accessible to maintenance crews. This provides a significant advantage
for locations where access to the roadway is extremely limited and
expensive, such as high volume urban freeways.

Machine vision systems are a much newer technology than axle sensor and
traditional dual loop counters. As a result, the classification systems
used by vision systems are still being refined. The early vision systems
mimicked dual loop counters. They create "virtual loops" from the camera
images being collected, and then compute vehicle speed and length from
those loops.

These systems have performance characteristics that are similar to
traditional dual loop counters. They are subject to the same limitations
in terms of vehicle speed measurement and problems in differentiating
between closely following vehicles. Image sensing systems are also
subject to inaccuracy caused by occlusion (the blocking of the line of
site by a second vehicle).

The primary advantage these systems provide is that they do not require
installation of sensors in the roadway, thus eliminating one major cause
of equipment failure (freeze/thaw damage to loop wires) and making
sensor maintenance easier and less disruptive. In some cases, cameras
are also able to transmit traditional video images to system operators,
allowing for dual use of the data collection equipment.

Considerable research is being done in the area of image processing. New
approaches to image processing (e.g., edge detection algorithms) are
being developed to improve on the performance of the existing image
processing algorithms. Systems currently on the market still tend to
classify vehicles on the basis of their overall size and are thus likely
to use classification schemes similar to those supplied by current loop
based systems.

Information on image-processing technology for traffic data collection
is available from the New Mexico Vehicle Detector Clearinghouse and from
equipment manufacturers. Research efforts are underway in several
States.

Other Technologies

Development with new technologies is moving very quickly. Research and
development of new sensors using infrared, microwave, and radar
technologies is in progress. Several traffic monitoring systems using
these technologies are on the market and are capable of providing
vehicle volumes by at least length classification. Highway agencies are
encouraged to investigate these devices to determine where and when
these new technologies can provide more cost-effective solutions to the
accurate collection of classification data.

References

Cambridge Systematics, et al, "Use of Data from Continuous Monitoring
Sites, Volume I: Recommendations," FHWA, August 1994.

Hallenbeck, Mark, and Kim, Soon-Gwam, "Final Technical Report for Task
A: Truck Loads and Flows," FHWA, WSDOT Report WA-RD 320.3, November
1993.

Hallenbeck, Mark, Rice, Mian, Smith, Betty, Cornell-Martinze, Cindy, and
Wilkinson, Joe, "Vehicle Volume Distributions By Classification,"
FHWA-PL-97-025, June 1997.

Weinblatt, Herb, "Factoring of Short-Duration Classification Counts,"
presented at the National Traffic Data Acquisition Conference, May 5-9,
1996.

Wyman, John, Braley, Gary, and Stevens, Robert, "Field Evaluation of
FHWA Vehicle Classification Categories," for Maine Department of
Transportation, Final Report for contract DTFH-71-80-54-ME-03 for U.S.
Department of Transportation, FHWA, Office of Planning, January 1985.

Appendix 4-A\
Frequently Asked Questions

How many vehicle classifications should I collect data for?

There is no simple answer. In most cases, when using portable vehicle
classification equipment, the 13 FHWA vehicle classifications have
become the standard. However, it is certainly appropriate to further
sub-divide these classifications to provide data on specific vehicles of
interest. For example, Oregon DOT collects data on the use of triple
trailer vehicles. This classification is a sub-set of class 13
(Multi-trailer vehicles). Thus for their own purposes, these vehicles
are a specific class of trucks. When Oregon reports data to the FHWA,
the "triple trailer" class vehicles are simply combined with the other
multi-trailer vehicles measured, and the total is reported as the volume
in FHWA Class 13. Permanent, axle classifiers and WIM scales should also
collect the FHWA 13 vehicle classes.

On many roadways, it is not possible to place axle sensors so that they
accurately collect the 13 FHWA vehicle categories. However, it is
possible to use two inductance loops or magnetic units to differentiate
vehicles by total length. Four vehicle classes are recommended when
collecting data in this fashion. These classes should reflect cars (and
pick-up trucks), single-unit trucks, single-trailer combination trucks,
and multi-trailer trucks. In some states, the multi-trailer truck
category may be unnecessary, and these trucks can be incorporated into
the "combination" category because there are few multi-trailer trucks.
As the truck fleets and truck size and weight laws change, States not
collecting data on these vehicles may have to revise their data
collection process to collect the data. While use of the simplified
vehicle classes does not meet the desired level of reporting for many
purposes, collecting data in the simplified categories is far better
than collecting no vehicle classification data at all, and it allows
monitoring the presence of trucks in urban traffic.

How many permanent, continuously operating vehicle classifiers should my
State install and maintain?

A reasonable answer to this question cannot be given without first
understanding how the State proposes to factor short duration vehicle
classification counts. If a traditional factoring approach is selected
(i.e., something similar to the ATR program operated by almost all
states), then as many continuous classifiers as ATRs should be operated.
If the State chooses a classification count factoring approach that
measures and applies road-specific factors, the number of counters
required will increase significantly.

How many portable classification counts should my State undertake?

They are many factors to consider in the answer. As a rule of thumb, 25
to 30 percent of volume counts should be classified. In general, each
State should undertake a vehicle classification count at least once
every counting cycle that can be applied to each road segment under its
control. This does not mean that each road segment should be counted.
Instead, "super segments" consisting of combined roadway volume segments
should be counted. Each "super segment" should be "relatively"
homogeneous for truck traffic along its length. Each "super segment"
should be counted at the same interval used by the State for collecting
volume counts. Annual truck travel estimates can be derived from the
counts. The annualized truck percentages can then be converted to
estimates of truck travel for the entire "super segment".

How can my State collect classification data in urban areas?

Traditional vehicle classifiers have difficulties operating accurately
in urban areas because vehicle acceleration/deceleration makes speed and
length calculations inaccurate, and because closely following vehicles
result in misclassification of cars as trucks. On freeways, careful
placement and calibration of either video or loop based classification
equipment can produce accurate truck volume counts. To date, no
inexpensive classifier is available that works accurately under
stop-and-go arterial conditions.

For higher systems, permanent classifiers using loops or video may be
the only alternative. On lower systems, there are locations where axle
or magnetic (length) portable classifiers will work. In many cases,
visual counts may be the last resort.

How do I define a "vehicle classification road segment?"

In simple terms, a traffic road segment is a section of roadway that has
similar (or homogeneous) volume or classification characteristics. The
difficulty comes from the fact that a "homogeneous segment" for traffic
volume may not be a homogeneous segment for other purposes such as
classification or pavement design purposes. For example, the road may
change from asphalt concrete to Portland cement concrete even though the
volumes being carried on that road do not change appreciably. When
developing a count program for vehicle classification, it may be
necessary to create classification roadway segments where truck volumes
do not change significantly. A single classification count taken within
a properly defined "super segment" provides the classification data for
all segments within that super segment. The use of these "super
segments" reduces the number of physical classification counts needed to
provide adequate roadway coverage for truck volume information.

What vehicle lengths should I use for vehicle classification?

An analysis of available data examined this issue. It was determined
that no single set of vehicle lengths worked "best" for all States, as
truck characteristics vary from State to State. The vehicle length
classification scheme that worked "the best" on combined data from all
States is shown in the tables on the next page.

These criteria did an acceptable, but by no means perfect, job of
classifying vehicles into the four general categories. Considerable
"error" was found in how well the length bins (and the corresponding
classification results) performed when estimating aggregations of the
FHWA 13-category classification scheme. A classifier can accurately
measure vehicle length (for example as 34 feet for a given small
tractor-trailer combination), place that count in the correct length bin
(in this example, the bin from 13 to 35 feet), but "incorrectly"
classify that vehicle (in this case calling a small combination
truck-trailer a single unit).

**Table 4-A-1**

**Length Based Classification Boundaries**

+-----------------------+----------------------+----------------------+
| **Primary Description | **Lower Length       | **Upper Length       |
| of Vehicles Included  | Bound**              | Bound**              |
| in the Class**        |                      |                      |
|                       | **\>**               | **\< or =**          |
+-----------------------+----------------------+----------------------+
| Passenger vehicles    | 0 m (0 ft)           | 3.96 m (13 ft)       |
| (PV)                  |                      |                      |
+-----------------------+----------------------+----------------------+
| Single unit trucks    | 3.96 m (13 ft)       | 10.67 m (35 ft)      |
| (SU)                  |                      |                      |
+-----------------------+----------------------+----------------------+
| Combination trucks    | 10.67 m (35 ft)      | 18.59 m (61 ft)      |
| (CU)                  |                      |                      |
+-----------------------+----------------------+----------------------+
| Multi-trailer trucks  | 18.59 m (61 ft)      | 36.58 m (120 ft)     |
| (MU)                  |                      |                      |
+-----------------------+----------------------+----------------------+

Table 4-A-2 shows the errors associated with using vehicle lengths to
estimate the four vehicle categories (cars, single unit trucks,
combination trucks, multi-trailer trucks) shown in Table 4-A-1 when
using the vehicle length boundaries shown in that table.

**Table 4-A-2**

**Misclassification Errors Caused By Using Only Total Vehicle Length As
The Classification Criteria**

  ---------------- ----------- --------- ----------- ---------- ----------
  Classification                                                
  Based on Total                                                
  Vehicle Length                                                

  Classification               PV        SU          CU         MU

  Based on         SU          17.7%     81.9%       0.4%       0%

  Configuration    CU          0%        1.8%        84.2%      14.0%
  and                                                           

  Number of Axles  MU          0%        0.1%        20.8%      79.1%
  ---------------- ----------- --------- ----------- ---------- ----------

Many States will be able to improve on these results by fine-tuning the
length spacing boundaries to account for the characteristics of their
trucking fleets. However, no amount of fine-tuning will lead to a
perfect length classification scheme (where perfection is defined as the
ability to use overall vehicle length to classify vehicles based on the
number of units they include or the number of axles they use). This is
because total vehicle length is not a consistent indicator of vehicle
class as defined by these attributes. Consequently, highway agencies
should be aware of the size and type of misclassification error that
exists, and set their length boundaries to minimize error.

Appendix 4-B\
Example Traffic Factor Group Computations\
Using the "Traditional" Method

This appendix shows an example of how to use the traditional method of
factor group development to create truck factor groups. It uses data
taken from 11 sites on four Interstate highways in Washington State. The
locations include facilities east/west, north/south, and on both the
eastern and western sides of the Cascade mountain range. The initial
assumption going into the factor group creation process was that the
Interstate highways fit a single factor group. However, the economic
development patterns on the eastern side of the Cascades are very
different from those on the western side. In addition, there is a
significant possibility that east/west Interstates will have different
truck travel patterns than north/south Interstates, since the
commodities moving east/west tend to be very different than those moving
north/south. These different "professional judgment" assumptions will be
tested as part of the factor group development process.

In the example, factors are computed for four vehicle types: passenger
vehicles (primarily cars), single-unit trucks (including most of the
recreational vehicle population), combination trucks, and multi-trailer
trucks. The factor group computations are based on the conversion of
monthly average weekday traffic (MAWDT) to annual average daily traffic
(AADT) by vehicle classification. This conversion accounts for both
day-of-week and seasonal (monthly) variation in a single factor. It
converts any count value (by class) taken from a weekday count into an
estimate of AADT (by class). Combining both weekday/weekend and monthly
effects into a single factor does add to the variability of the seasonal
adjustment factors being computed, but it eliminates the need to compute
separate day-of-week and monthly factors, and thus removes the need to
create separate factoring procedures for the two factors.

Figure 4-B-1 shows the means of the four monthly patterns computed for
eleven sites. As expected, the four different vehicle classes have
different seasonal patterns. Single-unit trucks have the greatest change
in volume over the course of the year. The other three vehicle classes
have more consistent volume patterns. Much of the seasonal variation in
the single unit truck classification is caused by the presence of
recreational vehicles (RVs) and the fact that RVs are both numerous and
highly seasonal.

Multi-trailer trucks have the "flattest" seasonal pattern. These trucks
travel more consistently throughout the year. Still, these vehicles
exhibit a seasonal volume peak in late summer and early fall, and a
reduction in these volumes during the primary winter months (December
through February). Because so much of the truck travel takes place
during the weekdays, the vast majority (9 of 12) of the monthly
adjustments for multi-trailer trucks are greater than 1.0. This means
that a weekday classification count taken on any "normal" weekday from
March through November is likely to over-estimate annual average
multi-trailer truck volumes for the year, unless those weekday counts
are factored appropriately as shown in Figure 4-B-1.

![](media/image4.wmf){width="5.85in" height="4.000694444444444in"}

Figure 4-B-1: Seasonal Patterns by Vehicle Type on Interstates in
Washington

The seasonal factors in Figure 4-B-1 are the mean values for the 11
locations. If all the Interstates in the State of Washington are treated
as a single factor group, the mean value for each month is the factor
that would be used to adjust any short count taken on the Interstate
system. Thus, the question becomes, are these mean factors "good
enough"? This can be answered by looking at the seasonal patterns for
the individual sites and at the variation inherent in the computed
monthly factors.

Figures 4-B-2 through 4-B-5 illustrate the variation in these seasonal
patterns from site to site. Tables 4-B-1 through 4-B-4 show the
individual seasonal adjustment factors used in these computations. These
tables also show the mean and standard deviation for each adjustment
factor for all sites combined. Not surprisingly, the count locations
exhibit a wide range of traffic patterns, and the amount of variation
present is dependent on the class of vehicles.

In general, there is a reasonably large amount of variation within these
sites. Within any given month, the range between the highest and lowest
of the individual adjustment factors is between 0.2 and 0.6 (or 20 to 60
percent of AADT). Another measure that describes the variability of
these monthly factors is the standard deviation of the mean factor. This
factor is the best of the available statistical measures for examining
the effectiveness of the factoring process.

![](media/image5.wmf){width="5.470138888888889in"
height="3.5347222222222223in"}Figure 4-B-2: Ratio of Average Monthly
Weekday Traffic to Average Annual Daily Traffic for Passenger Vehicles

Figure 4-B-3: Ratio of Average Monthly Weekday Traffic to Average Annual
Daily Traffic for Single-Unit Trucks

![](media/image7.wmf){width="5.840277777777778in"
height="3.6555555555555554in"}

![](media/image8.wmf){width="5.679861111111111in"
height="3.504861111111111in"}Figure 4-B-4: Ratio of Average Weekday
Traffic per Month to Average Annual Daily Traffic for Combination Trucks

Figure 4-B-5: Ratio of Average Monthly Weekday Traffic to Average Annual
Daily Traffic for Multi-Trailer Trucks

Table 4-B-1\
Seasonal Adjustment Factors (MAWDTT/AADTT) for Cars

  -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------- -------
  Site     Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct    Nov    Dec    Route   Rural /
                                                                                                       Urban

  1        0.79   0.87   0.92   1.02   1      1.05   1.17   1.24   1.09   0.96   0.94   0.94   5       R

  45       0.77   0.87   0.93   0.97   0.99   1.07   1.2    1.26   1.11   0.93   0.94   0.96   5       R

  82       0.87   1      0.96   1      0.99   1.04   1.08   1.11   1.03   1.01   0.95   0.96   5       U

  809      0.91   0.93   0.95   0.96   1      1.05   1.07   1.12   1.06   0.99   0.97   1      5       U

  86       0.67   0.71   0.96   1      1.05   1.2    1.29   1.32   1.12   0.99   0.94   0.75   82      R

  6        0.41   0.49   0.59   0.68   0.75   0.84   1.03   1.05   0.87   0.64   0.79   0.64   90      R

  14       0.53   0.71   0.85   0.95   1.09   1.2    1.39   1.46   1.19   0.9    0.88   0.85   90      R

  826      0.78   0.87   0.93   0.94   0.98   1.09   1.19   1.27   1.09   1.03   0.88   0.94   90      R

  36       0.84   0.94   0.71   1.03   1.05   1.1    1.1    1.14   1.07   1.04   0.98   0.98   90      U

  825      0.87   0.92   0.96   0.96   1      1.07   1.09   1.14   1.05   1.03   0.94   0.98   90      U

  824      0.9    0.95   0.98   1      0.99   1.04   1.02   1.09   1.04   1.03   0.98   0.98   405     U

                                                                                                       

  Mean for 0.76   0.84   0.89   0.96   0.99   1.07   1.15   1.20   1.07   0.96   0.93   0.91           
  all                                                                                                  
  Sites                                                                                                

  Stand.   0.16   0.15   0.12   0.10   0.09   0.10   0.11   0.12   0.08   0.12   0.06   0.11           
  Dev. for                                                                                             
  all                                                                                                  
  sites                                                                                                
  -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------- -------

Table 4-B-2\
Seasonal Adjustment Factors (MAWDTT/AADTT) for Single-Unit Trucks

  -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------- -------
  Site     Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct    Nov    Dec    Route   Rural /
                                                                                                       Urban

  1        0.64   0.74   0.85   0.98   1.07   1.15   1.4    1.52   1.3    0.92   0.77   0.67   5       R

  45       0.64   0.72   0.87   0.96   1.05   1.17   1.42   1.52   1.39   0.87   0.74   0.65   5       R

  82       0.71   0.88   0.88   0.93   1.05   1.16   1.32   1.37   1.21   1      0.79   0.72   5       U

  809      0.78   0.77   0.87   0.92   1.06   1.21   1.27   1.35   1.14   0.98   0.85   0.79   5       U

  86       0.46   0.56   0.81   1      1.02   1.24   1.41   1.4    1.29   1.73   0.69   0.42   82      R

  6        0.4    0.47   0.51   0.69   0.92   1.06   1.38   1.34   1.25   0.82   0.61   0.47   90      R

  14       0.39   0.51   0.69   0.96   1.14   1.5    1.78   1.68   1.37   0.85   0.66   0.49   90      R

  826      0.54   0.62   0.76   0.9    1.02   1.32   1.61   1.7    1.31   0.97   0.71   0.56   90      R

  36       0.69   0.8    0.74   1.04   1.1    1.22   1.36   1.32   1.13   1.06   0.83   0.72   90      U

  825      0.56   0.64   0.71   0.74   1.28   1.33   1.53   1.64   1.38   0.94   0.69   0.57   90      U

  824      0.71   0.77   0.86   0.95   1.03   1.16   1.33   1.46   1.22   1.01   0.79   0.7    405     U

                                                                                                       

  Mean for 0.59   0.68   0.78   0.92   1.07   1.23   1.44   1.48   1.27   1.01   0.74   0.61           
  all                                                                                                  
  Sites                                                                                                

  Stand.   0.13   0.13   0.11   0.11   0.09   0.12   0.15   0.14   0.09   0.25   0.07   0.12           
  Dev. for                                                                                             
  all                                                                                                  
  sites                                                                                                
  -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------- -------

Table 4-B-3\
Seasonal Adjustment Factors (MAWDTT/AADTT) for Combination Trucks

  -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------- -------
  Site     Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct    Nov    Dec    Route   Rural /
                                                                                                       Urban

  1        0.79   0.86   0.93   0.99   1.04   1.14   1.22   1.23   1.16   0.98   0.88   0.78   5       R

  45       0.86   0.91   0.97   0.99   1.01   1.1    1.12   1.14   1.08   1.03   0.95   0.84   5       R

  82       0.82   1.03   0.92   0.96   0.99   1.09   1.12   1.18   1.14   1.05   0.88   0.82   5       U

  809      0.91   0.93   0.96   0.95   1.02   1.11   1.09   1.11   1.05   1.02   0.95   0.92   5       U

  86       0.81   0.85   1.09   1.12   1.05   1.06   1.11   1.16   1.18   1.15   0.83   0.6    82      R

  6        0.94   1.07   1.08   1.14   1.25   1.25   1.26   1.35   1.43   1.32   1.09   0.86   90      R

  14       0.79   0.86   0.98   1.08   1.03   1.11   1.16   1.18   1.15   0.98   0.9    0.78   90      R

  826      0.96   0.81   0.82   0.89   0.98   1.06   1.11   1.19   1.04   1.24   1.04   0.89   90      R

  36       0.73   0.8    0.73   1.05   1.07   1.23   1.27   1.32   1.18   1.04   0.85   0.74   90      U

  825      0.93   0.97   1.02   1      0.97   1.02   1.05   1.09   1.06   1.14   0.93   0.82   90      U

  824      0.76   0.8    0.89   0.95   0.99   1.11   1.18   1.26   1.27   1.15   0.88   0.77   405     U

                                                                                                       

  Mean for 0.85   0.90   0.94   1.01   1.04   1.12   1.15   1.20   1.16   1.10   0.93   0.80           
  all                                                                                                  
  Sites                                                                                                

  Stand.   0.08   0.09   0.11   0.08   0.08   0.07   0.07   0.08   0.11   0.11   0.08   0.09           
  Dev. for                                                                                             
  all                                                                                                  
  sites                                                                                                
  -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------- -------

Table 4-B-4\
Seasonal Adjustment Factors (MAWDTT/AADTT) for Multi-Trailer Trucks

  -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------- -------
  Site     Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct    Nov    Dec    Route   Rural /
                                                                                                       Urban

  1        0.86   0.92   0.96   0.98   0.92   1.01   1.01   1.13   1.11   1.1    1.05   0.96   5       R

  45       0.93   0.96   0.99   0.99   0.99   1.07   1.03   1.07   1.03   1      1      0.94   5       R

  82       0.87   1.07   0.89   0.94   0.93   1      1.01   1.12   1.18   1.13   0.97   0.9    5       U

  809      0.97   0.96   1.03   1.05   0.97   1.07   1      1.03   1      1.01   0.98   0.94   5       U

  86       0.84   0.92   1.1    1.1    0.97   0.94   0.94   1.03   1.02   1.11   1.09   0.94   82      R

  6        0.96   1.14   1.17   1.18   1.18   1.27   1.24   1.47   1.53   1.39   1.22   0.99   90      R

  14       0.85   0.92   0.99   1.06   1      1.06   1      1.06   1.08   1.06   1.03   0.9    90      R

  826      0.59   1      1.07   1.09   1.11   1.12   1.07   1.25   1.3    0.9    0.82   0.69   90      R

  36       0.82   0.93   0.8    1.08   1.02   1.04   1.02   1.11   1.11   1.11   1.05   0.94   90      U

  825      0.67   0.7    0.76   0.73   1.18   1.29   1.23   1.39   1.49   0.99   0.86   0.74   90      U

  824      0.73   0.75   0.81   0.89   0.97   1.08   1.04   1.2    1.38   1.24   1.03   0.88   405     U

                                                                                                       

  Mean for 0.83   0.93   0.96   1.01   1.02   1.09   1.05   1.17   1.20   1.09   1.01   0.89           
  all                                                                                                  
  Sites                                                                                                

  Stand.   0.12   0.12   0.13   0.12   0.09   0.11   0.10   0.15   0.19   0.13   0.11   0.09           
  Dev. for                                                                                             
  all                                                                                                  
  sites                                                                                                
  -------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------- -------

Table 4-B-5\
Comparison of Seasonal Adjustment Factors for Different Interstate
Groups

<table style="width:100%;">
<colgroup>
<col style="width: 18%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
<col style="width: 6%" />
</colgroup>
<tbody>
<tr class="odd">
<td>Site</td>
<td>Jan</td>
<td>Feb</td>
<td>Mar</td>
<td>Apr</td>
<td>May</td>
<td>Jun</td>
<td>Jul</td>
<td>Aug</td>
<td>Sep</td>
<td>Oct</td>
<td>Nov</td>
<td>Dec</td>
</tr>
<tr class="even">
<td><h1 id="factors-for-cars"><strong>Factors for
Cars</strong></h1></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Std. Dev. for All Interstate Roads</td>
<td>0.16</td>
<td>0.15</td>
<td>0.12</td>
<td>0.10</td>
<td>0.09</td>
<td>0.10</td>
<td>0.11</td>
<td>0.12</td>
<td>0.08</td>
<td>0.12</td>
<td>0.06</td>
<td>0.11</td>
</tr>
<tr class="even">
<td>Std. Dev. for Eastern Interstate Roads</td>
<td>0.19</td>
<td>0.18</td>
<td>0.16</td>
<td>0.16</td>
<td>0.16</td>
<td>0.17</td>
<td>0.17</td>
<td>0.18</td>
<td>0.14</td>
<td>0.18</td>
<td>0.08</td>
<td>0.14</td>
</tr>
<tr class="odd">
<td>Std. Dev. for Western Interstate Roads</td>
<td>0.06</td>
<td>0.05</td>
<td>0.02</td>
<td>0.03</td>
<td>0.01</td>
<td>0.02</td>
<td>0.07</td>
<td>0.08</td>
<td>0.03</td>
<td>0.04</td>
<td>0.03</td>
<td>0.02</td>
</tr>
<tr class="even">
<td>Std. Dev. for Urban Interstate Roads</td>
<td>0.03</td>
<td>0.03</td>
<td>0.11</td>
<td>0.03</td>
<td>0.03</td>
<td>0.03</td>
<td>0.03</td>
<td>0.02</td>
<td>0.02</td>
<td>0.02</td>
<td>0.02</td>
<td>0.01</td>
</tr>
<tr class="odd">
<td>St. Dev. for Rural East/West Interstate Roads</td>
<td>0.19</td>
<td>0.19</td>
<td>0.18</td>
<td>0.15</td>
<td>0.17</td>
<td>0.18</td>
<td>0.18</td>
<td>0.21</td>
<td>0.16</td>
<td>0.20</td>
<td>0.05</td>
<td>0.15</td>
</tr>
<tr class="even">
<td>Std. Dev. for Rural North/South Interstate Roads</td>
<td>0.06</td>
<td>0.09</td>
<td>0.02</td>
<td>0.03</td>
<td>0.03</td>
<td>0.08</td>
<td>0.06</td>
<td>0.04</td>
<td>0.02</td>
<td>0.03</td>
<td>0.00</td>
<td>0.12</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td><strong>Factors for Single Unit Trucks</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>Std. Dev. for All Interstate Roads</td>
<td>0.13</td>
<td>0.13</td>
<td>0.11</td>
<td>0.11</td>
<td>0.09</td>
<td>0.12</td>
<td>0.15</td>
<td>0.14</td>
<td>0.09</td>
<td>0.25</td>
<td>0.07</td>
<td>0.12</td>
</tr>
<tr class="even">
<td>Std. Dev. for Eastern Interstate Roads</td>
<td>0.14</td>
<td>0.15</td>
<td>0.13</td>
<td>0.16</td>
<td>0.10</td>
<td>0.18</td>
<td>0.20</td>
<td>0.17</td>
<td>0.10</td>
<td>0.42</td>
<td>0.09</td>
<td>0.13</td>
</tr>
<tr class="odd">
<td>Std. Dev. for Western Interstate Roads</td>
<td>0.09</td>
<td>0.09</td>
<td>0.07</td>
<td>0.08</td>
<td>0.09</td>
<td>0.08</td>
<td>0.12</td>
<td>0.13</td>
<td>0.09</td>
<td>0.05</td>
<td>0.05</td>
<td>0.08</td>
</tr>
<tr class="even">
<td>Std. Dev. for Urban Interstate Roads</td>
<td>0.08</td>
<td>0.09</td>
<td>0.08</td>
<td>0.11</td>
<td>0.10</td>
<td>0.07</td>
<td>0.10</td>
<td>0.13</td>
<td>0.10</td>
<td>0.04</td>
<td>0.06</td>
<td>0.08</td>
</tr>
<tr class="odd">
<td>St. Dev. for Rural East/West Interstate Roads</td>
<td>0.08</td>
<td>0.08</td>
<td>0.13</td>
<td>0.14</td>
<td>0.11</td>
<td>0.22</td>
<td>0.20</td>
<td>0.20</td>
<td>0.06</td>
<td>0.08</td>
<td>0.05</td>
<td>0.05</td>
</tr>
<tr class="even">
<td>Std. Dev. for Rural North/South Interstate Roads</td>
<td>0.10</td>
<td>0.10</td>
<td>0.03</td>
<td>0.02</td>
<td>0.03</td>
<td>0.05</td>
<td>0.01</td>
<td>0.07</td>
<td>0.06</td>
<td>0.48</td>
<td>0.04</td>
<td>0.14</td>
</tr>
</tbody>
</table>

Table 4-B-5 (continued)

  ------------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------
  Factors for   Jan    Feb    Mar    Apr    May    Jun    Jul    Aug    Sep    Oct    Nov    Dec
  Combination                                                                                
  Trucks                                                                                     

  Std. Dev. for 0.08   0.09   0.11   0.08   0.08   0.07   0.07   0.08   0.11   0.11   0.08   0.09
  All                                                                                        
  Interstate                                                                                 
  Roads                                                                                      

  Std. Dev. for 0.09   0.12   0.17   0.04   0.10   0.09   0.08   0.10   0.13   0.15   0.12   0.11
  Eastern                                                                                    
  Interstate                                                                                 
  Roads                                                                                      

  Std. Dev. for 0.08   0.08   0.06   0.04   0.02   0.04   0.06   0.06   0.08   0.09   0.06   0.05
  Western                                                                                    
  Interstate                                                                                 
  Roads                                                                                      

  Std. Dev. for 0.09   0.10   0.11   0.04   0.04   0.08   0.09   0.10   0.09   0.06   0.04   0.07
  Urban                                                                                      
  Interstate                                                                                 
  Roads                                                                                      

  St. Dev. for  0.09   0.14   0.13   0.13   0.14   0.10   0.08   0.10   0.20   0.18   0.10   0.06
  Rural                                                                                      
  East/West                                                                                  
  Interstate                                                                                 
  Roads                                                                                      

  Std. Dev. for 0.04   0.03   0.08   0.08   0.02   0.04   0.06   0.05   0.05   0.09   0.06   0.12
  Rural                                                                                      
  North/South                                                                                
  Interstate                                                                                 
  Roads                                                                                      

                                                                                             

  **Factors for                                                                              
  Multi Trailer                                                                              
  Trucks**                                                                                   

  Std. Dev. for 0.12   0.12   0.13   0.12   0.09   0.11   0.10   0.15   0.19   0.13   0.11   0.09
  All                                                                                        
  Interstate                                                                                 
  Roads                                                                                      

  Std. Dev. for 0.06   0.11   0.16   0.05   0.09   0.14   0.13   0.20   0.23   0.15   0.09   0.04
  Eastern                                                                                    
  Interstate                                                                                 
  Roads                                                                                      

  Std. Dev. for 0.14   0.13   0.11   0.12   0.10   0.10   0.08   0.12   0.18   0.11   0.09   0.11
  Western                                                                                    
  Interstate                                                                                 
  Roads                                                                                      

  Std. Dev. for 0.14   0.15   0.13   0.14   0.10   0.10   0.09   0.13   0.18   0.12   0.09   0.11
  Urban                                                                                      
  Interstate                                                                                 
  Roads                                                                                      

  St. Dev. for  0.08   0.16   0.13   0.08   0.13   0.15   0.17   0.29   0.32   0.23   0.13   0.06
  Rural                                                                                      
  East/West                                                                                  
  Interstate                                                                                 
  Roads                                                                                      

  Std. Dev. for 0.05   0.02   0.07   0.07   0.04   0.07   0.05   0.05   0.05   0.06   0.05   0.01
  Rural                                                                                      
  North/South                                                                                
  Interstate                                                                                 
  Roads                                                                                      
  ------------- ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------ ------

It can be seen that for car volumes, two sites on I-90 (sites 6 and 14)
have travel patterns that are rather distinct from the remaining nine
sites. Both of these sites show a higher degree of seasonality than is
present in the other sites. Both have much lower winter volumes than the
remaining sites, and both exhibit a much greater increase in traffic
during the summer months. It turns out that both of these sites are in
eastern Washington. In addition, a third eastern site has a similarly
high summer/low winter travel pattern (site 86) on I-82. These passenger
car patterns suggest that keeping eastern Washington sites separate from
western Washington sites might create better factor groups.

However, looking at the other vehicle classes does not lead to the same
clear-cut decision. For both combination trucks and multi-trailer
trucks, Site 14 has truck volume patterns that fall directly in the
middle of the pattern exhibited by the various sites. Site 6, on the
other hand, does show a traffic pattern for trucks that is slightly more
extreme than for the other sites. Site 6 shows a pattern in which the
weekday adjustment factor is almost always greater than 1, indicating
very low weekend traffic in comparison to weekday traffic, whereas the
other sites have winter volumes that fall below the average daily
volumes.

This mixed result is common when one is trying to apply a single factor
grouping process to multiple types of vehicles. Different classes of
vehicles are affected by different land-use and travel patterns, and as
a result factor groups will predict travel patterns more reliably for
some vehicle classes than for others.

To determine the effectiveness of splitting these roads into two
different factor groups, it is necessary to look at the statistics that
would be generated by splitting these routes. Three different "simple"
splits are possible, given the assumptions presented earlier. The three
alternative groupings to be tested are given below. Each could be
considered "intuitive" to an analyst familiar with the State. The
testing process will show whether the "intuitive" process provides
better analytical results. Each new group does allow easy assignment of
short duration counts collected at specific road locations to an
available factor group.

• All Interstates on the eastern side of the Cascades can be placed in
one group, and all Interstates on the western side of the Cascades can
be placed in the other.

• All east/west Interstates can be placed in one group, and all
north/south Interstates in the other.

• All east/west rural Interstates can be placed in one group, all
north/south rural Interstates can be placed in another, and all urban
Interstates can be placed in a third.

Table 4-B-5 shows the effects of forming these groups and compares them
with the variability found if all Interstates are treated as a single
factor group. Not surprisingly, each of these three changes to the
factor groups results in improvements in some factors (that is, the
factors show a decrease in variability) and increased variation of other
factors. For example, splitting the "All Interstates" factor group into
eastern and western factor groups results in significant improvement (a
decrease in the standard deviation for almost all monthly factors) in
the factors for the car, single-unit, and combination unit factors for
the western Interstate factor groups. However, the multi-trailer truck
factor group for western Interstates shows a minor increase in
variability for all but the summer months. Conversely, the eastern
factor group generally shows an increase in variability for all factor
groups, except for multi-trailer trucks during the winter months. Thus,
separating eastern and western roads only appears to improve the
accuracy of annual traffic estimates west of the Cascade Mountains while
reducing the accuracy of counts in the eastern portion of the state.

The second effort to improve factors is to split roads traveling
primarily east/west from those traveling north/south. Table 4-B-5 shows
that this has the effect of making the factors for north/south roads
universally more accurate than factors for all Interstates combined.
However, factors for the east/west Interstates are almost universally
worse than the factors for all Interstates combined. This sets up the
decision of choosing between a factoring process that more accurately
adjusts short counts on some roads at the expense of poorly adjusting
others. Before making this tough decision, it is important to look at
the last idea for improving these basic factor groups.

The final effort is to remove the urban locations from the east/west and
north/south factor groups. The intent behind this effort is again to
separate roads assumed to have different seasonal and day-of-week
patterns (i.e., urban versus rural). If these patterns are different,
the factors for both east/west and north/south factor groups should have
reduced seasonal variability. Given the results above, the primary
improvement desired is in the area of east/west Interstates, since those
roads have the highest variability between the two groups just tested.

Unfortunately, as shown in Table 4-B-5, removing the urban sites from
the east/west factor group does not significantly improve the
variability of the factor being produced. As with the original east/west
factor group, the rural east/west factor group has monthly factors that
are almost universally more variable than the original monthly factors.
Thus, the original decision point remains, is it better to maintain the
one single factor group that does all Interstate reasonably well, or
split the Interstates into two groups, with one group having more
reliable factors than the other?

It is very difficult to answer this question without including far more
information than can be presented in this report. Issues that would be
important in making this decision include the following:

• How many other factor groups are already used in the State?

• Are any of the roads of higher priority than the others?

• Would it be possible to use road specific factors to deal more
effectively with the higher variation on the east/west roads?

• Are there specific reasons why the east/west roads are more variable
for the year(s) for which data are being examined? (For example, could
an unusual series of weather events have caused more variability than is
normal for those roads?)

• Are either of the factoring groups easier to implement at either the
user level or the database management level? (That is, can the average
user understand which factor to apply for both factor groups? Will the
existing traffic database management system support both types of factor
groups?)

Without the answers to these questions, the decision can only be made on
the basis of statistical reliability. Assuming that the short count
program can be manipulated, it would therefore be in the interest of the
State to select one of the more disaggregated factor groups. The last of
these groups works slightly better than the first two. So it is
selected. However, to allow the best possible computation of annual
traffic estimates, it is recommended that counts in the eastern portion
of the State be collected during the early winter if weather permits or
early spring if reliable winter data collection is not possible, since
these are the times of the year when the adjustment factors for most of
the vehicle classes are most stable.

Appendix 4-C\
FHWA Vehicle Types

The classification scheme is separated into categories depending on
whether the vehicle carries passengers or commodities. Non-passenger
vehicles are further subdivided by number of axles and number of units,
including both power and trailer units. Note that the addition of a
light trailer to a vehicle does not change the classification of the
vehicle.

Automatic vehicle classifiers need an algorithm to interpret axle
spacing information to correctly classify vehicles into these
categories. The algorithm most commonly used is based on the "Scheme F"
developed by Maine DOT in the mid-1980s. **The FHWA does not endorse
"Scheme F" or any other classification algorithm.** Axle spacing
characteristics for specific vehicle types are known to change from
State to State. As a result, no single algorithm is best for all cases.
It is up to each agency to develop, test, and refine an algorithm that
meets its own needs.

FHWA Vehicle Classes with Definitions

1\. ***Motorcycles*** (Optional) \-- All two or three-wheeled motorized
vehicles. Typical vehicles in this category have saddle type seats and
are steered by handlebars rather than steering wheels. This category
includes motorcycles, motor scooters, mopeds, motor-powered bicycles,
and three-wheel motorcycles. This vehicle type may be reported at the
option of the State.

2\. ***Passenger Cars*** \-- All sedans, coupes, and station wagons
manufactured primarily for the purpose of carrying passengers and
including those passenger cars pulling recreational or other light
trailers.

3\. ***Other Two-Axle, Four-Tire Single Unit Vehicles*** \-- All
two-axle, four-tire, vehicles, other than passenger cars. Included in
this classification are pickups, panels, vans, and other vehicles such
as campers, motor homes, ambulances, hearses, carryalls, and minibuses.
Other two-axle, four-tire single-unit vehicles pulling recreational or
other light trailers are included in this classification. *Because
automatic vehicle classifiers have difficulty distinguishing class 3
from class 2, these two classes may be combined into class 2.*

4\. ***Buses*** \-- All vehicles manufactured as traditional
passenger-carrying buses with two axles and six tires or three or more
axles. This category includes only traditional buses (including school
buses) functioning as passenger-carrying vehicles. Modified buses should
be considered to be a truck and should be appropriately classified.

**NOTE:** In reporting information on trucks the following criteria
should be used:

a\. Truck tractor units traveling without a trailer will be considered
single-unit trucks.

b\. A truck tractor unit pulling other such units in a \"saddle mount\"
configuration will be considered one single-unit truck and will be
defined only by the axles on the pulling unit.

c\. Vehicles are defined by the number of axles in contact with the
road. Therefore, \"floating\" axles are counted only when in the down
position.

d\. The term \"trailer\" includes both semi- and full trailers.

5\. ***Two-Axle, Six-Tire, Single-Unit Trucks*** \-- All vehicles on a
single frame including trucks, camping and recreational vehicles, motor
homes, etc., with two axles and dual rear wheels.

6\. ***Three-Axle Single-Unit Trucks*** \-- All vehicles on a single
frame including trucks, camping and recreational vehicles, motor homes,
etc., with three axles.

7\. ***Four or More Axle Single-Unit Trucks*** \-- All trucks on a
single frame with four or more axles.

8\. ***Four or Fewer Axle Single-Trailer Trucks*** \-- All vehicles with
four or fewer axles consisting of two units, one of which is a tractor
or straight truck power unit.

9\. ***Five-Axle Single-Trailer Trucks*** \-- All five-axle vehicles
consisting of two units, one of which is a tractor or straight truck
power unit.

10\. ***Six or More Axle Single-Trailer Trucks*** \-- All vehicles with
six or more axles consisting of two units, one of which is a tractor or
straight truck power unit.

11\. ***Five or fewer Axle Multi-Trailer Trucks*** \-- All vehicles with
five or fewer axles consisting of three or more units, one of which is a
tractor or straight truck power unit.

12\. ***Six-Axle Multi-Trailer Trucks*** \-- All six-axle vehicles
consisting of three or more units, one of which is a tractor or straight
truck power unit.

13\. ***Seven or More Axle Multi-Trailer Trucks*** \-- All vehicles with
seven or more axles consisting of three or more units, one of which is a
tractor or straight truck power unit.

Appendix 4-d\
Resources On Traffic Data Collection

New Mexico Vehicle Detector Clearinghouse,
http://www.nmsu.edu/\~traffic/

"Traffic Detection Technologies for a Modern Transportation
Infrastructure," by Klein, Kelley, and Mills, presented at SPIE
Conference 2592, Collision Avoidance and Automated Traffic Management
Sensors, October 25-26, 1995, Philadelphia.

"Detection Technology for IVHS -- Task L: Final Report," FHWA Contract
DTFH61-91-C-00076, U.S. Department of Transportation, Washington, D.C.,
1996.

"Accuracy of Automatic Vehicle Classifiers," Kansas Department of
Transportation, July 1989.

"Field Evaluation of FHWA Vehicle Classification Categories," by Wyman,
Braley, and Stevens, Maine Department of Transportation, Final Report
for Contract #DTFH-71-80-54-ME-03 for USDOT, 1985.

"Autoscope Evaluation at Trunk Highway 65 and 53^rd^ Avenue North,"
Minnesota Department of Transportation, January 1996.

"Evaluation of Econolite Products Inc., Machine Vision Vehicle Detection
System," Indiana Department of Transportation, 1996.

"Infrared Sensors for Counting, Classifying, and Weighing Vehicles," by
Garner, Lee, and Huang, University of Texas, Report #FHWA/TX 91+1162-1F,
December 1990.

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

[^1]: For example, Colorado DOT operates truck rollover and truck
    speed/braking warning systems that can provide truck use data.

[^2]: That is, some work well at high vehicle speeds but not under
    congested traffic conditions. Some work well in all weather
    conditions, but only when vehicles remain in their lanes, whereas
    others are not affected by poor lane discipline but can be affected
    by weather conditions such as snow or fog.

[^3]: The Maine DOT work, led by John Wyman, created the algorithm
    commonly referred to as "Scheme F," which serves as the basis for
    most current axle-sensor based classification counter algorithms.

[^4]: The new pavement design guide, currently in draft form, will be
    reviewed by AASHTO.

[^5]: In most cases, this still requires an increase in data collection
    resources because it takes more staff time as well as more physical
    data collection equipment to set classification counters than it
    does to set traditional volume counters for the same number of lanes
    of data collection.

[^6]: Not subject to significant amounts of through traffic

[^7]: This formulation assumes a multiplicative application, that is,
    AADTT is equal to the average 24-hour count times the adjustment
    factor. Many states use the inverse of formula 4-1 and apply the
    resulting factor by dividing the average 24-hour volume obtained
    from their short count by the adjustment factor.

[^8]: This is a relatively crude approximation because it assumes that
    the standard deviation calculated from the seven sample sites is
    equal to the actual standard deviation of the population of the
    group of roads. The value 1.96 should be used only for sample sizes
    of 30 sites or more. A more statistically correct estimate would use
    the Student's *t* distribution, which for six degrees of freedom
    (seven classification sites) is 2.45. The calculation also assumes
    that the factors are normally distributed, and that sites are
    randomly selected.

[^9]: Only data for complete hours should be used. Partial hours should
    be discarded.

[^10]: This table provides a conservative estimate of the number of
    axles per vehicle for the 13 FHWA vehicle classes. Appropriate
    numbers must be computed at each site.