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ssejp
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functional classification and total volume, can have very high or low truck percentages,

depending on the nature of the traffic.

9.00%

8.00%

7.00% 7 wee
PSHE

6.00%

5.00%

4.00%

3.00%

ay
2.00% al

1.00%

Percnt of Daily Travel

0.00%

1 3 5 7 9 1 13 15 17 #19 21 23
Hour of the Day

Figure 11. Rural Truck Percentages
by Funciionai Ciass and Time-of-Day

vers
—%~VC 8

ja ves
—e-VC 10
VC 6
—sVC7

;-eLVC 5

In summary, as with most other distributions, at any individual site the timing and nature of

directional peaking characteristics can vary substantially from national averages and from those

common patterns given as examples above. These locational differences depend on the level of

recurring congestion, the nature of the traffic using the road, and the location and nature of

commercial development and other traffic generators.



Report No. 2. Govemment Accession No. 3. Recipient's Catalog No.
FHWA-PL-97-025

Title and Subtitle 5, Report Date
Vehicle Volume Distributions by Classifications June 1997

6. Performing Organization Code

7. Author(s) 8. Performing Organization Report
Mark Hallenbeck, Mian Rice, Betty Smith, Cindy Cornell-Martinez, and No,
Joe Wilkinson
9. Performing Organization Name and Address 40. Work Unit No. (TRAIS)
Chaparral Systems Corporation
649 Harkle Road, Suite E 41, Contract or Grant No.
Santa Fe, New Mexico 87505 DTFH61-83-C-00078
12, Sponsoring Agency Name and Address 13. Type of Report and Period
Long-Term Pavement Performance Division Sone ;
Federal Highway Administration echnical Report
way we April 1996 - July 1997
6300 Georgetown Pike
McLean, Virginia 22101-2296 14. Sponsoring Agency Code
HNR 30
15. Supplementary Notes
William Grush, Federal Highway Administration, HPM-30
16. Abstract
This report documents the findings of a study of variability in traffic volumes by vehicle classification. It is
meant to update the report, Highway Performance Monitoring System, Vehicle Classification Case Study,
by Douglas MacTavish and Donald Neuman (1982). This project studied data from a broader
geographic distribution of those sites and inciuded a larger data collection period at each site.
17. Key Words 18. Distribution Statement
Vehicle volume distributions, vehicle No restrictions, This document is available to the public
classification, temporal distribution, factor through Chaparral Systems Corporation, 649 Harkle Road,
groups, functional classification Suite E, Santa Fe, New Mexico 87505.
19. Security Classif (of this report) 20. Security Classif. (of this page) | 21. No. of Pages 22, Price
Unclassified Unclassified 104
Form DOT F 1700.7 (8-72) Reproduction of completed page authorized



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Day of Week
| geCars : —m- Single Unit Trucks i
| —#— Combination Trucks —— Multi-Trailer Trucks

Figure 5. Day-of-Week Patterns at Nebraska Site 3023

As with other day-of-week and time-of-day patterns discussed in this report, local knowledge
(obtained by either understanding the traffic using the site or by collecting enough data on the
traffic patterns to understand their variation) is needed to accurately assign and apply day-of-
week patterns to specific road segments. This requirement causes difficulties with FHWA’s

desire to have a factoring process that can be applied when states do not submit data correctly

adjusted to represent average annual conditions.

14



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= Classes 11-13 are multi-trailer trucks.

it is also possible to include class 5 trucks (2-axle, 6-tired trucks) as part of the passenger
vehicle category, since it can be difficult to separate vehicle class 5 from class 3 when only
axle spacing information is used. (Class 5 has been placed with the single-unit trucks because
the FHWA’s definition of the class indicates that it is primarily intended to contain small urban

delivery trucks, rather than extra large passenger vehicles.)

Although these aggregated classes suffer from the problem of each class containing a variety of
vehicles with different characteristics (gross vehicle weight [GVW], load/axle, types of

movements), the 13 FHWA classes also suffer from these problems, but to a lesser extent.

SEASONAL PATTERNS

General Findings

At the national level, both rural and urban sites exhibit a general pattern of lower volumes in
the winter months and higher volumes in the late spring through early fall. These basic
patterns are true for all four aggregated vehicle categories. However, although the national
average monthly traffic patterns are similar for all four aggregated vehicle classes, the monthly
patterns of the four vehicle classes at individual sites tend to be different. Very few sites have
monthly automobile travel patterns that are similar to the monthly patterns for the truck
classifications. At the national level, however, the averaging necessary to compute mean
statistics smoothes out a great deal of this variation, leaving only the most basic trends visible
{see Figure 13 and Table 7).

The basic trend visible in Figure 13 and Table 7 supports the generally held assumption that
people travel less in the winter and more in the summer. This assumption seems to hold true

for truck travel as well, although the timing of the travel peaks during the middle of the year is

31



Average Day of Month/
Average Annual Daily Traffic

1.250
1.200 |
1.150
1.100
1.050
1.000
0.950
0.900
0.850
0.800
0.750 |
> > § EE FP 2 fF BE &B B FS
s § € 5 $ § 3 3 g 4
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Ss 9 Pa
uw
Month a =z

7 - 1
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|
—*-FC7 —e-FC12 —.-FC14 —FC16 |

Figure 15. Monthly Passenger Vehicle Volume
Patterns by Functional Class

38



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Even relatively small numbers of errors in classifying vehicle categories with low
volumes can have a large impact on the adjustment factors computed for those

classes.

= Attempting to calculate monthly adjustment factors for all 13 FHWA vehicle classes
will cause seasonal adjustment values to become even more variable (unstable) and
inaccurate, primarily because of the mathematical problems caused by low traffic

volumes for many of the categories.

= Extremely low- or high-volume traffic months can occur during any month at a

given site.

These points are discussed in more detail below.

Seasonal Variation by Vehicle Classification

For the sites at which LTPP data are available, automobiles tend to follow fairly traditional
patterns. This “classic” automobile volume pattern shows a fairly continuous rise in traffic
volumes from January to a peak in the summer, followed by a decrease throughout the rest of
the year. However, in most cases, car volumes vary only moderately from month-to-month

from the average annual condition during the year.

For example, almost 60 percent of the months of data examined at all sites had average daily
car volumes within 10 percent of the average annual daily condition (see Table 8) The average
monthly car volumes were more than 30 percent different from the average condition only

6 percent of the time (that is, only 6 percent of the time is the ratio of the monthly average
daily traffic [MADT]/annual average daily traffic [AADT] greater than 1.3 or less than 0.7),

This includes winter months.

34



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SUMMARY

This review of the temporal distribution of car and truck volumes reached the following basic
conclusions. Although the LTPP database is large in terms of the number of sites included in
the analysis, the wide distribution of these sites across the nation, the limited number of sites
for which seasonal truck volume information has been submitted, the relatively large number
of different traffic patterns, and the large number of both geographically distinct regions and
functionally different roads make accurately identifying, codifying, and applying truck volume

trends and patterns difficult at the national level.

Volumes by Day-of-Week
In general, the following observations can be made from the LTPP database:
= Sunday traffic volumes are the lowest of the week for truck classes at most sites,
but average car volumes on Sundays can be fairly high at many rural sites.

«Mondays have slightly lower truck volumes than other weekdays, although this

difference is not significant.
= Tuesday through Thursday volumes are similar for all truck classes.

= Friday is the highest volume day of the week for cars but is similar to other

weekdays for truck travel.

= Saturday volumes tend to be low for trucks but, like Sundays, can be either high or

low for cars, depending on the location.

There are many exceptions to these general findings. The most significant difference is that
many sites in the Great Plains and some Rocky Mountain states have different day-of-week
patterns. These differences are caused by the large through-movements of both cars and trucks

traveling to and from the western states.



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once, although significant findings are mentioned in multiple sections to ensure that a reader

examining only a part of the report sees these findings.

VOLUMES BY DAY-OF-WEEK

The analysis of traffic volumes by day-of-week illustrated both a high degree of consistency in
many travel patterns and a high degree of variation in other travel patterns. At times, the
functional ciasses of a road, the region of the country, and the type of vehicie being examined
had an impact on whether a specific factor was stable or highly variable, but there are
exceptions to all “rules” that are described in this report. In general, the following

observations can be made of all sites:
= Sunday traffic volumes are the lowest of the week for truck classes at most sites,
but average car volumes on Sundays can be fairly high for many rural sites.

= Mondays have slightly lower truck volumes than other weekdays, although this

difference is not significant.
= Tuesday through Thursday volumes are similar for all truck classes.

= Friday is the highest volume day of the week for cars but is similar to other

weekdays for truck travel.

= Saturday volumes tend to be very low for trucks but, like Sundays, can be either

high or low for cars depending on the location.

Table 3 shows the average day-of-week ratio of daily traffic to annual traffic for each of the

four combined vehicle classes.



List of Figures

PYenr

States Represented in the Study (number of sites indicated)...
Functional Classes of Roadways .
13 FHWA Vehicle Classes
Percentage of Sites with Sunday Day-of- Week Factors for Combination
Trucks within a Specified Range...
Day-of-Week Patterns at Nebraska Site 3023
Basic Time-of-Day Patterns.............cee
Business Day Trucking Pattern.......ccecsesseeeeeseeeteeseneenees 19
Directional Time-of-Day Distribution for Multi-Trailer Trucks at Site 5009
Weekday/Weekend Truck Percentages..........-s10+
Difference in Combination Truck Travel by Direction ..
Rural Truck Percentages by Functional Class and Time-of-Day
Weekday versus Weekend Automobile Patterns .
Seasonal Factors by Aggregated Vehicle Class
Example of Single-Unit Truck Seasonality ......
Monthly Passenger Vehicle Volume Patterns by Functional Class..
Monthly Single-Unit Truck Volume Patterns by Functional Class.
Monthly Combination Truck Volume Patterns by Functional Clas:
Monthly Multi-Trailer Truck Volume Patterns by Functional Class..

List of Tables

RYN er

aw

Number of Sites per State for Each Functional Class...
Number of Sites per Region for Each Functional Class
Day-of-Week Patterns for Urban and Rural Roads.

National Average Truck Percentages by Functional Classification
of Roadway
Vehicle Percentage Chart for Each Functional Class .
Number of Sites per LTPP Region for Each Functional Class....
Seasonal Factors for Aggregated Vehicle Classes by Urban/Rural.
Designation for All Roadway Classes Combined

Size of Monthly Tavel Factors by Vehicle Class........ccccssssssssserseseeseenesseeseenseenseneenee



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41



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Center
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VEHICLE VOLUME DISTRIBUTIONS
BY CLASSIFICATION

Prepared by

Mark Hallenbeck, Director
Mian Rice, Research Assistant
Washington State Transportation Center (TRAC)

Betty Smith, Researcher
Cindy Cornell-Martinez, Director of Research
Joe Wilkinson, Chairman of the Board

Chaparral Systems Corporation

July 1997



TABLE OF CONTENTS

INTRODUCTION AND BACKGROUND MATERIAL.
Source of the Study Data ...
Applicability of Findings to Other Sites in the Nation
Vehicle Classification Categories Used.
Report Organization

VOLUMES BY DAY-OF-WEEK
Creation of Factor Groups
Geographic Differences
Impacts of Local Conditions .

VOLUMES BY TIME-OF-DAY
Directional Differences

Weekday/Weekend Differences .....cccssecssssssssssesesessssssssssesasarsassessesesansneeresnsseenseseete 25
URBAN VERSUS RURAL VOLUMES .wesessesssesssressessssssessssseseeenecsesceescsessnseneesneenenseeets 26
VEHICLE PERCENTAGES BY FUNCTIONAL CLASS....secsssscsscnrersseonsennesennecnseceee. 28
SEASONAL PATTERNS

General Findings
Seasonal Variation by Vehicle Classification
Differences in Factors by Functional Classification of Road
Effects of Low Volumes

SUMMARY
Volumes by Day-of-Week
Time-of-Day Patterns.....
Urban versus Rural Volumes...
Vehicle Percentages by Functional Class of Road.
Seasonal (Monthly) Patterns .
Other Findings

APPONGiX occ eesesnsecsssresssessesessssesseesscsnecsessessueenessssesssneessessarsnentarsesesnssesneenennsensersanee AS

iii



Table 5. Vehicle Percentage Chart for Each Functional Class

Functional

Ciass Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7
1 0.2% 63.1% 12.3% 0.3% 2.3% 0.8% 0.1%
2 0.2% 72.4% 16.0% 0.3% 2.5% 0.8% 0.1%
6 0.1% 72.3% 18.1% 0.3% 2.2% 0.7% 0.1%
7 0.0% 74.5% 18.2% 0.4% 3.7% 0.6% 0.0%
1 0.2% 75.7% 15.7% 0.2% 1.6% 0.8% 0.0%
12 0.2% 71.3% 17.4% 0.2% 2.5% 0.6% 0.3%
14 0.1% 74.2% 16.2% 0.4% 2.0% 0.7% 0.1%
16 0.0% 74.5% 19.9% 0.1% 1.9% 0.4% 0.0%

Functional

Class Class 8 Class 9 Class 10 Class 14 Class 12 Class 13 _
4 1.8% 16.8% 0.3% 1.5% 0.2% 0.3%
2 1.5% 5.6% 0.4% 0.2% 0.1% 0.2%
6 1.6% 3.5% 0.1% 0.1% 0.0% 0.0%
Zz 0.9% 1.5% 0.0% 0.0% 0.0% 0.0%
11 1.1% 3.9% 0.2% 0.2% 0.1% 0.6%
12 1.4% 3.92% 0.6% 0.3% 0.4% 0.8%
14 1.1% 4.5% 0.2% 0.3% 0.1% 0.2%
16 1.2% 1.3% 0.3% 0.0% 0.1% 0.2%

Table 6. Number of Sites per LTPP Region for Each Functional Class

REGION | FC1 | FC2 | FC6 | FC7 |FC 114 |FC12 | FC 14 {FC 16 | Total
ATLANTIC 4 9 2 41 2 0 2 ) 20
CENTRAL 9 8 8 2 5 2 3 (e) 37

SOUTH 5 5 7 2 5 9 2 1 27

WEST 5 5 0 oO 1 1 2 1 15
Total 23 27 17 5 13 3 9g 2 99

A second observation about Table 5 is that many of the vehicle types make up a fairly small
percentage of the traffic stream, regardless of the functional classification of roadway. Ona

high-volume interstate, a very low percentage (less than 0.25 percent) can still represent a

29



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Figure 4. Percentage of Sites with Sunday Day-of-Week Factors
for Combination Trucks within a Specified Range

« Automobile and truck day-of-week patterns are essentially unrelated. Knowing the

day-of-week pattern for cars does not improve the ability to determine the day-of-

Consequently, factor groupings that would work for cars are likely to be
inappropriate for trucks (e.g., just because sites A, B, and C should be treated as a
factor group for automobiles does not mean that those same three sites should be

treated as a factor group for trucks).

This research does not conclude that factor groups cannot be constructed. Rather, there is

insufficient data in the LTPP database at this time to support the creation of these groups. In

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%0 0 %0'0 |%0°0 %0°0 %8 € {0 0 %0'O %0'0 %9 0 %0'0 "90 %L'0 %0'0 00:70 puayeem | ZL
%0'0 %0'0 %0'0 %0'0 %8 %0'O %0'O %0'°0 %0'b %0'0 %o0'b Ye %0'0 (00:10 pusyoom |Z
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%0'0 %0'0 %0'0 %0'0 or LO |%*OO ob %0S Waa 0% B'S %E'S 00:22 Kepyaam | 2
%0'0 %0'0 %0'0 %0'0 %O% Koa 00 Yb %0'E vb Le %6'E %ES 00:12 Aepyaam | L |
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%0'O %0 0 %0'0 %0 0 %b9 %8'9 %0'0 62 BL %6 6 %0°8 obd MES 00:S4 Aepyaom |Z

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This means that, in a large percentage of cases, collecting short-duration counts of car volumes
will lead to a reasonably® accurate measure of annual conditions, regardless of when those
counts are taken. In the remaining cases, adjustments are necessary, but the maximum error
caused by monthly variation in car volumes should be around 30 percent at most non-

recreational sites.

Table 8. Size of Monthly Tavel Factors by Vehicle Class

Percent of Percent of Percent of Percent of Percent of Percent of
Months with Months with Months with Months with Months with Months with
Factors Factors Factors Factors Factors Factors
Different from | Different from | Different from | Different from | Different from | Different from
1.0 by Less 1.0 by Less. 41.0 by Less: 1.0 by Less 1.0 by Less 1.0 by Over

than 5% than 10% than 20% than 30% than 40% 40%
Cars. 33 58 82 94 o7 3
Single Unit Trucks 14 33, 60 74 84 16
Combination Trucks 26 49 76 88 96 4
Multi-Tratler Trucks 18 4 57 73 82 18

The data in Table 8 show that combination vehicles are only slightly more variable from
month-to-month than cars. In almost 50 percent of months, average day-of-the-month
combination truck volumes are within 10 percent of annual average daily conditions, whereas
in only 12 percent of the months do average monthly volumes vary more than 30 percent from

the average annual daily condition.

In contrast, the other two aggregated truck classes have much higher levels of month-to-month
variation. Average daily single-unit truck volumes by month fall within 10 percent of average
annual daily conditions in less than 33 percent of the months; for muiti-irailer trucks, this

figure rises to only 34 percent of the months. Perhaps more importantly, for single-unit trucks

in over 16 percent of the months, average day-of-the-month volumes vary more than

6 In this case, “reasonably” is defined based on the pavement life analyses for which the LTPP data was
intended. If load was proportional to car volume (which it is not), the error present by not adjusting for
seasonality would be considered acceptable for many pavement life analyses.

35



Vehicle Percentages by Functional Class of Road

The distribution of vehicles among vehicle classes changes dramatically by geography and, toa
lesser extent, by functional class of roadway. In particular, the presence or lack of multi-
trailer trucks tends to be geographically based. These larger trucks seem to be uncommon in

eastern and southern states and much more common in the western states.

Thus, “national average” vehicle distributions are misleading for sites in most states. The data
available to this study through the LTPP traffic database are insufficient at this time to provide
a list of which states have significant volumes of specific vehicle types and which do not.
These data should be available from the LTPP database after all states have completed
submitting traffic data for their test sites, particularly if those data are then combined with
existing Highway Performance Monitoring System (HPMS) submittals.

Seasonal (Monthly) Patterns

Cars and trucks at most sites follow different seasonal patterns. Applying factors computed for
cars or total volume to trucks volumes or individual truck classification categories will create
bias in the annual truck volume estimates for a site. However, at the national level, the

“average” seasonal trends are reasonably similar for both cars and trucks.

Factors created to seasonally adjust car and truck volumes by vehicle class tend to be unstable
when a given vehicle class has low vehicle volumes. This problem is particularly common of
the multi-trailer truck and motorcycle classes, but it also applies to FHWA class 7 and
occasionally to FHWA class 4 vehicles. Consequently, it is recommended that seasonal
adjustment factors be developed and applied for more aggregated vehicle classes. The project

team recommends the use of the following four categories:

= Cars (including motorcycles).

47



Vehicle Percentage Chart (Aggregation Scheme 1) for Each Functional Class
Functional | Class | Class | Class | Class | Class [Class 6-| Class 8-|Class 11-| Total
Class 1 2 3 4 5 7 10 13
4 0.17%|63.06%] 12.29%] 0.32%] 2.34%) 0.89%} 18.97%] 1.96% 100%
2 0.16%[72.11%/ 15.98%] 0.34%] 2.49%! 0.89%] 7.43% 0.60% 100%
6 0.08%[72.31%|19.08%| 0.29%| 2.17%] 0.80%] 5.15%] 0.12% 100%
7 0.03%|74.53%]18.15%| 0.39%] 3.72%] 0.66%]  2.46%| 0.06% 100%
1 0.21%|75.66%| 15.67%} 0.20%] 1.58%! 0.81%] 5.17%] 0.70% 100%
12 0.24%|71.29%|17.35%| 0.24%] 2.46%! 0.97%! 5.91%] 1.62%] 100%
14 0.12%] 74.17%} 16.17%] 0.36%] 2.02%} 0.80%] 5.79% 0.58% 100%
16 0.00%|74.51%|19.93%| 0.11%] 1.87%| 0.43%] 2.89%] 0.27% 100%
All 0.13%]72.21%|16.83%| 0.28%] 2.33%] 0.78%] 6.72%] 0.73% 100%
Functional
Classes
Functional
80.00% Class
70.00% —~-i
60.00% 2
50.00% ks
40.00% [7
30.00% mie
20.00% ~en12
10.00% ae
——16
0.00%
Class Class Class Class Class Class Class Class
2 3 4 5 67 8-10 14413
52


= Single unit trucks.
= Combination trucks.

=" Multi-trailer trucks.

Buses may be either treated separateiy (usuaiiy appropriate for urban areas) or as part of the
single-unit truck category (usually appropriate for rural areas), depending on the needs of the

user.

The four categories of vehicles listed above can be approximated fairly easily by simply

aggregating data collected in the 13 FHWA vehicle categories.

Other Findings

Large, site-specific differences were found within all of the data stratifications researched for
this study. Local conditions appear to have an extremely large impact on the basic time-of-day
and day-of-week patterns exhibited by, or the vehicle mix present on, a given roadway,
regardless of roadway type or level of traffic volume on that roadway. It is not possible to
predict these variations from the average condition without more information than is commonly

available in national traffic databases.

48



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seasonal factors within functional classes of roads means that, if FHWA decides to adjust truck
volumes submitted by the states, these factors should be developed by aggregating data for all
classes of roads. This aggregation is necessary because it is not possible at the national level
to obtain the information needed to apply a more definitive value with the required level of
accuracy. If this grouping is too generic, FHWA should consider using three basic factoring
categories—rural interstates, other rural roads, and urban roads—perhaps by broad geographic

region of the country.

Although the available LTPP data do not indicate that this stratification would lead to
Statistically better truck factors, there is a logical basis for assuming that these crude categories
of roads could improve the precision of monthly adjustment factors. The logic behind these

categories is the following.

= Although the existing functional classification system does not account for the
nature of truck traffic using a road, urban traffic patterns can be assumed to be
fundamentally different from rurai patterns, and rural interstates are likely to carry

more long-distance traffic than other rural roads.

= Long-distance travel is likely to be subject to different economic stimuli than local

traffic and, therefore, may be somewhat different than locally generated traffic.

= Regional economic (and weather) differences are likely to lead to differences in
trucking patterns by geographic location. (This is heavily supported by the

significant differences in types of vehicles present in different parts of the country.)

Nevertheless, as noted earlier, the research team was not able to determine statistically
significant differences in vehicle volume patterns that could be applied to short-duration counts

with confidence.

42



DISCLAIMER

The contents of this report reflect the views of the authors, who are r r for th
facts and the accuracy of the data presented herein. The contents do not necessarily
reflect the official views or policies of the Federal Highway Administration. This report

does not constitute a standard, specification, or regulation.



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Vehicle Percentage Chart for Each Functional Class

Functional |Ciass 1| Class 2 | Class 3 | Class 4| Class 5| Class 6| Class 7|Class 8| Class 9 | Class 10 |Class 11|Class 12] Class 13] total
Class

1 017%) 63 06%” 1229%) 032%)" 234%)" 079%) O11%! 165%) 16 60%] 148%] 020%} 028%] _—*100%
2 0.16%] 72.11%] 15.98%] 0.34%] 249%! 0.82%] 0.07%] 1.46%] 5.57% 0.24%] 0.14% 0.22%) 100%
6 0.08%] 72.31%] 19.08%] 029%] 217%] 0.75%! 005% 010%} —0.01%| 001%! 100%|
7 0.03%| 74.53%| 18.15%] 0.39%! 3.72%| 0.63%) 0.03% 0.05%] 0.01%] 0.00%] 100%
4 0.21%) 75.66%] 15.67%] 0.20%| 1.58%! 0.77%! 0.03% 076%) 0.14%) 0.41%! 100%
12 0.24%] 71.29%| 17.35%] 0.24%) 2.46%] 0.64%] 0.33% 142%] 3.93%) 0.28% 0.43%] 0.82%] _100%|
14 0.12%] 74.17%| 16.47%] 0.36%| 2.02% 0.75%| 0.05%] 1.08%! 4.53% 0.35%] 0.05%] 0.18%] 100%)
16 0.00%] 74.51%] 19.93% 0.17%) 1.87%) 0.42%! 0.00%! 4.23%) 1.32% 0.02%! 0.07% 0.18%] 100%
All 0.13%] 72.21%! 16.83%| 0.28%] 2.33%| 0.70%] 0.08%! 1.33%] 5.13% 0.26% 0.33% 0.13%] 0.26% 100%
Functional
Classes
Functional
Class
80.00% et
70.00% 2
60 00% . —k-6
50.00% ~K-7
40 00% et
30.00% ee
20 00% ae

——16
0.00% * seep

Class Class Class Class Class Class Class Clas» Class Class Class Class Class
1 2 3 4 $ 6 7 8 9 10 W 12 13

5l



Weekday/Weekend Differences

The time-of-day distributions for cars change from weekdays to weekends in urban areas. On
weekend days, there is usually no morning peak in the time of day distribution, and the
afternoon peak is often a lower percentage of daily traffic than that of weekdays.
Consequently, vehicles are more evenly distributed during the middle of the day (see Figure

12). Similar patterns occur on all of the larger functional classes of urban roads.

9.00%
8.00%
7.00%
6.00%
5.00% ee
4.00% | —o— Weekday |
3.00% ; —m- Weekend

2.00%
1.00%
0.00%

Percent of Average
Daily Traffic

- 2m © &e O@ = 2 PF DB TF
Se Se re Tr =F NN

Hour of the Day

Figure 12. Weekday versus Weekend Automobile Patterns

Weekday/weekend changes are not as apparent for urban truck travel except for individual
roads that show marked weekday peaking in specific truck classes. Although truck volumes
drop considerably in urban areas on weekends, the time-of-day distribution of trucks tends to
not change appreciably. (Roads with unique trucking patterns experience unique changes

based on the nature of the local truck trip generator.)

Similar changes for both cars and trucks occur on rural roads, except that recreational roads
usually experience significant increases in automobile traffic on weekends (often including
Fridays). The timing of these increases is a function of the nature of the recreational activity

and the distance that road section is from major population centers. That is, rural roads that

25



late at night (see Figure 9). This pattern is true for both urban and rural areas, although the
percentage of trucks is usually higher in rural areas than urban areas. (The available data sets
also contain relatively few small urban roads, where truck percentages are small to begin with

and may decrease to near zero late at night.)

2 60.00%
3
g e 50.00%
& § 40.00%
2
5 > 30.00%
g & 20.00%
as
BF 10.00%
o
2 0.00% >
a - o TM ~~ oa = oO ” nt o Dd iJ
e228 F&F 2S@aark
Hour of the Day 4 Urban Weekday
~—»— Urban Weekend

—=- Rural Weekday
—e—Rural Weekend |

Figure 9. Weekday/Weekend Truck Percentages

What these graphs show is that motorists get the impression that very large truck movements
take place at night. The reality is that, in most cases, these late night truck movements are not
that big; they just seem large because the percentage of trucks on the road is so high. Note
that where long-haul, through-trucks make up the majority of trucks on the road, traffic
volumes may decrease very little, if at all, making the night time truck percentage even higher

and the perception of high truck volumes even stronger.

Directional Differences

It was difficult to determine nationally applicable statistics for time-of-day travel by direction,
since directional characteristics are site-specific. The LTPP database contains numerous sites

for both urban and rural functional classes that exhibit either very directional traffic (that is,

21



Table 7. Seasonal Factors for Aggregated Vehicle Classes by Urban/Rural
Designation for All Roadway Classes Combined

| \ i '

Urban Roads} JAN FEB MAR APR MAY JUN JUL AUG SEP oct NOV DEC

Urban Cars 0.88 0.99 0.93 0.96 4.01 1.05 1.05 1,07 1,03 1.04 0.98 0.98

Urban Single
Unit Trucks | 0.72 | 084 | 09 | 14.02 | 1.06 | 1.13 | 1.09 | 1.06 | 1.145 | 1.17 | 0.96 | 0.96

Urban
Combination
Trucks 0.86 0.93 0.92 0.93 4.07 4.08 0.97 4.13 1.05 4A4 4.01 0.91

Urban Multi-
Trailer Trucks | 0.94 | 1.01 | o99 | 09 | o94 | oo98! o91 | o99 | 1.23 | 1.23 | 099 | 0.93

Rural Roads} JAN | FEB | MAR | APR | MAY | JUN | JUL | AUG | SEP | OCT | NOV | DEC

Rural Cars 0.81 | 087 | 092 | 099 { 1.07 | 4.08 | 1.14 | 4.11 | 1.09 1 0.95 | 09
Rural Single Unit]
Trucks 0.73 | 082 | 087 | 0.95 | 1.01 | 4.17 | 112 | 4.13 | 445 | 743 | 0.99 | 0.87
Rural
Combination
Trucks 0.88 0.93 1.01 1.01 1,07 1.14 4.04 1,04 1.08 1.04 0,92 0.81

If the monthly traffic patterns are examined for individual sites and individual vehicle classes,

the followin

ta
wing ad ni aclu:

Cars and combination trucks have much lower levels of seasonal (monthly) variation
than that of single unit trucks and muiti-trailer trucks. This is true for both month-

to-month variation at a site, and variation from one site to another.

Conseauen te seasona 1 adtustments to short-dur:
= Consequently, seasonal adjustments to short-dur

estimates will be much more precise than those associated with single-unit trucks or

tmulti-trailer vehicles.

= Much of the high variability (instability) in single-unit and multi-trailer truck
volumes can be traced to the fairly low daily volumes at many sites of these

vehicles,

= It is suspected (although no proof is available within the LTPP database) that some

ey 4
i used

33



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Truck percentages on significant rural roads are commonly higher than those on comparable
urban roads (for example, compare the interstate classes of 1 and 11, or the principal arterial
classes 2 and 14 in Table 4.) Within the LTPP database, over 40 percent of the rural interstate
sites have truck percentages of over 20, whereas no urban site in the LTPP database has a
truck percentage that reaches 20 percent. On the other hand, rural interstates can also have
very low truck percentages. One quarter of the rural interstate sites have truck percentages
below 10 percent, with the low falling under 2 percent. There are also several urban freeways

base tha ve very low trick nercentas:

te the T TDP datahac,
in the Lirr database if

Table 4. National Average Truck Percentages

ctional Clacsification of Roadway

by Functional Classification of Roadway

Functional Cars Single-Unit Combination Multi-Trailer
Class of Trucks® Trucks Trucks

Roadway*
1 75.5% 3.5% 19.0% 2.0%
2 88.3% 3.7% 74% 0.6%
6 91.5% 3.3% 5.2% 0.1%
7 92.7% 4.8% 2.5% 0.1%
11 91.5% 2.6% 5.2% 0.7%
12 88.9% 3.7% 5.9% 1.5%
14 90.5% 3.2% 5.8% 0.6%
16 94.4% 2.4% 2.9% 0.3%

Similarly, the larger roads tend to have higher truck percentages than the smaller roads
typically found within functional classes 6, 7, 8, and 16. However, just as a number of
freeways and expressways have relatively small truck percentages, some rural minor arterials
in the LTPP database have truck percentages of just under 10, which is higher than 20 percent

of the rural and urban interstate sites.

4 Note that functional class 12 is not present in this table because of the very small number of sites present in
the test data set that fell within this classification of road.

27



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Table 3. Day-of-Week Patterns for Urban and Rural Roads

Urban Roads Sunday Monday | Tuesday |Wednesday| Thursday Friday Saturday
Cars 0.87% | 0.98% | 0.98% 1% 1.03% 1.15% | 0.99%
Single Unit Trucks 0.42% 1.11% 1.19% 1.20% 1.21% 1.24% 0.63%
Combination Trucks 0.42% 1.13% 1.23% 1.24% 1.23% 1.19% 0.55%
Multi-Trucks Trucks 0.46% 1% 1.21% 1.23% 1.25% 1.23% 0.61%

Rural Roads Sunday Monday Tuesday |Wednesday| Thursday Friday Saturday
Cars 1.01% 0.95% 0.91% 0.93% 0.98% 1.16% 1.05%
Single Unit Trucks 0.54% 1.07% 1.15% 4.15% 1.18% 1.21% 0.70%
‘Combination Trucks 0.50% 1.10% 1.20% 1.23% 1.21% 1.15% 0.60%
Multi-Trailer Trucks 0.46% 1.04% 1.19% 1.23% 1.25% 1.17% 0.66%

There are many exceptions to these general findings. The most significant exception is that
many sites in the Great Plains (and some Rocky Mountain) states have very different day-of-
week patterns (see Geographic Differences below). These differences are caused by large
“through”-movements of cars and trucks traveling between the larger midwestern cities and the

western states,

Through-traffic also appeared to play a major role in much of the variation seen in the LTPP
data. For example, both urban and rural truck travel tends to decrease significantly on the
weekends. However, many of the sites whose location indicated the potential for large
through-truck movements had more modest declines in weekend truck travel than sites that
appear to carry much lower through-travel volumes. This could easily be explained by the fact
that through-trucks are much more likely to travel over the weekend to arrive at their

destination during the weekday than trucks with a local itinerary.

A second common difference appeared to be caused by the effect of recreational travel on
automobile volumes. All urban sites in the LTPP dataset have lower Sunday volumes than
other days of the week. Some rural sites have this same pattern. However, many rural sites

have very high Sunday volumes, which can be divided into two distinct patterns:



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The data used in this study had all passed through the LTPP quality assurance! process and
were further screened as part of this analysis. In addition to removing invalid data, the
screening found some questionabie traffic volumes for individual days and sites. These data
errors were minor in relation to the overall analyses, and tests on the effects of these data
showed that their impacts were marginal. However, the presence of these minor perturbations
in the data, despite the extensive LTPP quality control and quality assurance tests, showed the
need for individual states to adopt and extend these kinds of tests. Data presented in the
appendices of this report are based on the “cleanest” possible data within the LTPP dataset.

Applicability of Findings to Other Sites in the Nation

The majority of results presented in this report are for the “average” conditions found in the
data. Considerable variation was found in the data reviewed for individual locations.
Therefore, readers using these results should be aware that individual sites may vary
considerably from these patterns. This is particularly true of the percentage of vehicles by
vehicle category, since some vehicle categories are not commonly found in some parts of the
country.

Where possible, this report includes measures of data variability to describe the range of values
that can be reasonabiy expected at any given site. However, it is important to realize that tocal
conditions significantly affect the traffic patterns found on any roadway and, therefore, that

patterns at any given location can differ greatly from those illustrated in this report.

Vehicle Classification Categories Used

FHWA requests that vehicle classification information be submitted in 13 vehicle categories.

These vehicle categories are shown in Figure 3. In some of the tables presented in this report,

1 A brief description of the LTPP quality assurance process can be found in “WIM Data Quality Assurance” in
Volume II of the National Traffic Data Acquisition Conference, 1994, pages 251-266.

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gs Day Trucks
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z | Trucks

i
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Hour of Day

Figure 6. Basic Time-of-Day Patterns

The presence of any one of these patterns is determined by geographic location and roadway
function, which are not necessarily equivalent to roadway functional classification. The
specific height of the curves for percentage of traffic by time-of-day and the exact location of
the peak travel periods vary somewhat from location to location, depending on the volume of
traffic on a road (particularly in relation to roadway capacity) and the proximity and nature of
traffic generators. (As with all generalities, there were significant exceptions to these patterns.
For example, I-15 between Los Angeles and Las Vegas experiences a large night-time travel
movement, particularly on Friday nights, because of the vehicles going from Los Angeles to
Las Vegas for the weekend. These types of site-specific peaks can be expected near major,

unique traffic generators.)

The rural automobile pattern consists of a single peaked distribution, with the peak normally
occurring in the afternoon. It is commonly found on rural roads that are not affected by

nearby urban areas, and it is also found on weekends in both urban and rural areas. Traffic



relation to the nearby activity centers. Similarly, individual traffic generators (e.g., gravel
pits) may create site-specific time-of-day patterns that differ dramatically from these generic

patterns.

Time-of-day analyses can be (but are not always) affected by directional distributions, e.g.,
time-of-day traffic patterns at many sites are different in one direction than in the other
direction. The size of the directional differential varies from site to site and depends
significantly on local conditions rather than on functional class or other routinely stored
parameters. Thus, the time-of-day tables presented along with this report, which are not

directional, can be used for both directions of traffic unless local knowledge suggests the

ion of direction-speci!

Urban versus Rural Volumes

Many roads exhibit traffic patterns that are not characteristic of their urban/rural functional
classification coding (a large number of “rural” roads act like urban roads, and vice versa).
Roads in areas designated as “rural” but located near urban areas often exhibit traffic patterns
with urban characteristics (e.g., highly directional, peak period automobile volumes).

have yet to
be significantly developed, and where externally generated traffic is a predominant portion of
the total traffic volume. These roads tend to exhibit traffic patterns more commonly expected

in rural areas (lack of commuter peaks, high weekend volumes).

Basic trucking patterns tend to be reasonably similar in both urban and rural areas. The
proportion of through-traffic to local traffic, rather the urban or rural designation of a site,
appears to be the major factor in determining truck patterns. However, the percentage of
through-truck traffic is more often (but by no means always) higher in rural areas than in urban

areas.

46



Appendix Table of Contents

Vehicle Percentage Chart for Each Functional Class..............cccecccsesceceeseeeeereetesenenee
Vehicle Percentage Chart (Aggregation Scheme 1) for Each Functional Class. .
Vehicle Percentage Chart (Aggregation Scheme 2) for Each Functional Class... te
Weekday/Weekend Ratios for Each Functional Class (Revised) ............ccssccecseeeseeeveeee
Weekday/Weekend Ratios (Aggregation Scheme 1) for Each Vehcile Class ...............66

Weekday/Weekend Ratios (Aggregation Scheme 2) for Each Functional
Class (Revised) ..........

Monthly Ratios for Each Functional Class (Revised) .
Monthly Ratios (Aggregation Scheme 1) for Each Functional Class (Revised) ..
Monthly Ratios (Aggregation Scheme 2) for Each Functional Class (Revised) ..
% Hourly Weekday/Weekend Traffic for Each Vehicle Class and

Functional Class .........0.ccccccececesevececcusaeececeseessecesecesuerereseseeeeeneeseeseaameeeneees 69
% Weekeday/Weekend Traffic per Hour Across Vehicle Class and
Functional Class...........ccscseeceeseeeesceceseceeeeeeeeconscccessccaeeeeteencccesestenstsaeesteeees 83
50



6L

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classification scheme that specifically accounts for truck usage. (Note that studies performed
in both Washington? and Virginia? have shown that it is possible to create seasonal factor
groups for adjusting short-duration truck counts to more accurately estimate annual truck
volumes.) It is particularly important to have a surrogate value that indicates whether a

specific road carries large volumes of long-distance, through-truck travel.

Similar problems arose with automobile factors and total volume factors. Many states have
more than one rural factor group (for adjusting total volume counts) to account for regional
differences, and many also maintain specific recreational travel patterns to account for the
traffic conditions near large recreational areas. The LTPP database does not contain variables
that describe the local geographic information needed to account for these variations in

automobile and total traffic volume.

Although the LTPP database is large in terms of the number of sites included in the analysis,
the wide distribution of these sites across the nation, the limited number of sites for which
seasonal truck volume information has been submitted, the relatively large number of different
traffic patterns, and the large number of both geographically distinct regions and functionally
different roads make accurately identifying, codifying, and applying truck volume trends and

patterns difficult at the national level.

Geographic Differences

An excellent example of the effects of specific local conditions can be found in the data for the
Great Plains states. Data in the LTPP database (particularly from Nebraska and Wyoming)

illustrate the effects of large through-travel movements on day-of-week travel. (This

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

3 Factoring of Short-Duration Classification Counts, presented at The National Traffic Data Acquisition
Conference, by Herb Weinblatt, Cambridge Systematics, May 5-9, 1996.

12



Weekday/Weekend Ratios for Each Functional Class (Revised)
Functional] Week [Class 1[Class 2] Class 3] Class 4 Class 5| Class 6 | Class 7 | Class 8] Class 9| Class 10| Class 11 | Class 12] Class 13|

Class Group
1 Weekday | 94.6%] 94.0%] 98.6%] 101.5%} 106.2%] 112.9%] 117.4%] 106.1%] 1103%] 1186%] 110.0%] 112.0%] 115.0%
1 Weekend | 113.6%] 114.9%] 103.4%| 96.2%] 84.6%| 67.7%| 56.4%| 84.8%] 74.2%] 53.6%] 74.9%] 69.9%] 62.5%
2 Weekday 93.1%} 98.3%} 103.8%] 114.6%] 117.3%] 123.9%] 128.9%] 113.9%] 125.1%| 127.8% 120.9%] 118.7% 125.1%
2 Weekend | 117.2%! 104.2%} 90.4%! 63.6%! 56.8%! 40.2%! 27.9%! 65.2%! 37.3%! 30.5% 47 8% 53.1%! 37.3%!
6 Weekday | 89.0%] 99.3%] 104.6%] 121.8%] 114.9%] 125.6%| 125.8%] 109.7%] 125.0%] 126.4%] 120.1%] 125.2%| 112.7%
6 Weekend | 127.4%] 101.7%] 88.6%] 45.6%} 62.7%] 36.0%) 35.4%] 75.8%) 37.4% 34.0% 49.7% 37.1% 68.2%
7 Weekday 95.8%} 102.1%] 108.1%] 121.0%} 113.2%] 128.3%) 114.0%} 111.3%] 128.8% 129.7% 123.7% 107.2%} 111.7%
7 Weekend | 110.5%] 946%! 79.7%) 47.6%] 67.0%/ 29.3%! 65.0%} 71.7%] 28.0%! 25.8% 40.8% 82.1% 70.7%
4 Weekday | 103.1%] 103.0%] 107.8%] 114.8%] 117.1%) 122.2%} 112.0%] 115.4%] 120.7%) 123.6%! 119.0%) 118.4%! 118.3%
1 Weekend 92.3%} 92.4%) 80.5%) 63.0%) 57.2%] 44.4%] 70.1%] 61.6%) 48.4% 41.0% 52.5% 54.0%! 54.4%
12 Weekday 103.6%] 101.9%} 108.5%] 120.6%} 116.5%] 128.9%) 123.4%] 117.6%] 122.7% 120.5% 122.7% 120.4%| 116.7%
12 Weekend 90.9%) 95.2%! 78.6%] 48.5%! 588%! 27.7%! 41.6%) 56.1%} 43.3% 48.7% 43.2% 49.0% 58.2%
14 Weekday | 99.8%] 103.6%} 109.5%] 119.8%) 120.3%) 125.4%] 124.6%} 119.3%] 124.0%] 126.1%} 119.0%)! 111.7%]  122.2%|
14 Weekend | 100.6%! 91.0%] 76.2%] 505%] 493%} 365%] 38.4%] 51.7%] 39.9% 34.6%) 52.5% 70.7% 44.6%
16 Weekday | 113.1%] 96.7%] 101.9%] 123.1%] 113.0%] 123.6%] 136.8%] 102.8%] 123.3%] 128.4%| 130.6%]  109.2%| 99.4%
16 Weekend | 67.4%] 108.2%] 95.3%] 42.2%] 67.5%] 41.0%] 8.0%] 93.0%] 41.8%) 29.1%] 23.6%] 77.1%] 101.4%
1 '
34



VEHICLE VOLUME DISTRIBUTIONS
BY CLASSIFICATION

INTRODUCTION AND BACKGROUND MATERIAL

This report documents the findings of a study of variability in traffic volumes by vehicle
classification. It is meant to update the report, Highway Performance Monitoring System,
Vehicle Classification Case Study, by Douglas MacTavish and Donald Neuman (1982). This
project studied data from a broader geographic distribution of those sites and included a larger

data collection period at each site.

Source of the Study Data

Data for the study came from the Central Traffic Data Base of the Long-Term Pavement
Performance (LTPP) project. The dataset contained 99 sites from 19 states. Table 1 shows
the distribution of sites by state and functional classification. Figure 1 shows their geographic
distribution. Table 2 shows how these sites are distributed among LTPP regions. These tables
reveal that relatively few sites were included in the two smallest functional classifications of
roads, class 7 and class 16. (See Figure 2 for a definition of the Federal Highway
Administration [FHWA] functional classifications of roads.) These are under-represented
because these types of roads are relatively scarce within the LTPP experiment, and because
states participating in the LTPP experiment have tended to collect data for larger volume,

higher functional classification roads.

To be
To be

than 9 months of the year. For the larger functional classes of roadway (classes 1, 2, and 11),
only sites that contained data for all lanes and both directions of traffic were selected. For the
other roadways, sites that had data for only one direction of traffic were also accepted,
although some analyses looked only at the subset of sites with data for both directions of
traffic.



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exhibit urban travel characteristics tend to lose the morning peak in the time-of-day
distribution, whereas rural roads that do not experience significant commuter traffic tend to

have relatively similar weekday and weekend travel patterns.

URBAN VERSUS RURAL VOLUMES

One of the major findings from the LTPP data is that many roads exhibit total volume traffic
patterns that are not characteristic of their functional classification coding, that is, a large
number of “rural” roads act like urban roads, and vice versa. As noted in the time-of-day
section above, many “rural” roads are located close enough to urban areas to exhibit urban
time-of-day travel characteristics. Similarly, some “urban” roads are located in areas with
relatively little urban development and appear to have characteristics that are more rural, since
the volume of through-traffic is much larger than the locally generated “urban” traffic. In
most cases, automobile volumes cause a “rural” road to exhibit “urban” traffic patterns, since

tural and urban truck patterns are similar.

The primary differences between urban and rural travel patterns involve the patterns of car
usage and the through-truck traffic. In urban areas car travel fits the standard weekday
commute pattern (with the commute period peak in either one or two directions), with lower
weekend volumes. Rural roads generally fit into one of two patterns, one oriented toward
recreational travel and one without significant recreational attributes. Both of these patterns
tend to have a single time-of-day peak in the afternoon. In the rural recreation car pattern,
weekend traffic volumes are higher than the average day, whereas in the non-recreational

pattern, weekend automobile volumes decrease like those normally found on urban roads.

For trucks, the classic “business day” time-of-day pattern occurs on both urban and rural
roads. However, on some rural roads, the percentage of long-haul, through-trucks is high
enough that the time of day pattern for total trucks is quite flat. On most urban roads and on
many rural roads, though, truck traffic drops substantially late at night.

26



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Hour of the Day

Figure 7. Business Day Trucking Pattern

Note that, although the “classic” business trucking pattern has only one “hump”, the actual
pattern on a given roadway may be directionally oriented, with trucking movements in one
direction on a road during some parts of the day and in the opposite direction during other
parts of the day. This type of pattern is typical for distribution services, where specific styles
of trucks leave a warehouse in the morning and return in the late afternoon. Figure 8
illustrates how, at one individual site, the very different directional patterns combine to equal

the “typical” business day pattern.

> 020 —eDirection1 — |
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Hour of the Day

Figure 8. Directional Time-of-Day Distribution
for Multi-Trailer Trucks at Site 5009

19



40+ percent from the annual daily average. For multi-trailer trucks, this condition is true for
18 percent of the months. These compare to less than 4 percent of the months for both cars
and combination trucks. (Note that these estimates exclude data from sites that counted fewer
than 10 trucks per day within a given vehicle class, since monthly factors computed for these

sites tended to be unstable.)

This variation in truck volumes is not a simple matter of low-volume winter months and high-
volume summer months. For the single-unit and multi-trailer truck categories, volumes often
change dramatically from month to month. Although the basic pattern of low winter and high
summer volumes holds true at most sites, this pattern is often not a steady increase and
decrease over time but a highly variable “jagged line”, with volumes increasing or decreasing
radically from one month to the next. Figure 14 shows some examples of single-unit truck

volume patterns found in the LTPP data.

It is unclear if this variation is caused by real changes in the truck volumes within these classes
(often the truck volumes within even the combined vehicle classes are low enough so that
modest increases in truck volumes will cause significant changes in the monthly factor) or by
the limitations of the data collection equipment. For example, if there is a road with 10,000
vehicles per day and 60 percent of those vehicles are class 2 (6,000 vehicles) and 2 percent are
class 6 (200 vehicles), a 1 percent misclassification rate of class 2 vehicles (assuming they are
misclassified as class 6, because of either lane changes over the sensor or ghost axles from the
sensor) results in an additional 60 vehicles (a 30 percent increase) in the single-unit truck
category. This same type of misclassification error (usually caused by misclassifying closely
following vehicles as a multi-trailer truck) may also be responsible for much of the variability
in the multi-trailer truck class, particularly in those states where these trucks are rarely found.
Another common error that affects the number of trucks counted on a road is the inability to
determine the difference between longer 2-axle, 4-tired pick-up trucks (class 3) and short wheel

base 2-axle, 6-tire delivery trucks (class 5).

36



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time-of-day traffic patterns that are substantially different in one direction than they are in the

other) or strongly bi-directional. It is necessary to have local knowledge of a site to be able to

For urban areas, many of the suburb-to-suburb movements that are growing most quickly are
multi-directional. Morning commuter traffic moves from City A to City B, and from City B to
City A. This creates urban peak movements (a.m. and p.m. peaks in the time-of-day
distribution) in both directions on major roads linking these cities. At the same time, many
urban roads still demonstrate traditional traffic patterns, with a large inbound peak in the

morning and a corresponding outbound peak in the evening.

The available data do not show the flattened peak periods associated with heavy congestion
during the peak hours. This is not surprising because the LTPP tests sites (from which the

data y where pavement test: auld by
data avement test: id Bi

fairly small disruptions to traffic. Consequently, heavily congested urban roads are not

strongly represented in the LTPP database.

Analysis of the LTPP database showed that trucks also have directional splits in urban areas.
Directional truck travel appears to be similar to that of cars (i.e., trucks travel in the same peak
directions), but these directional differences are often not as large for trucks as they are for
cars. However, the LTPP data do show that directional peak-period differences vary
considerably by vehicle type, and it is likely that local conditions will make the travel patterns

of many urban roadways unique.

vements between cars and trucks in urban

areas is that, although through-trucks are able to avoid peak traffic periods by stopping outside
of the urban area, local truck traffic must operate during the business day to pick up and
deliver goods. This places local trucks in some portion of the peak period. In addition, the
delivery and pick-up of most goods take place at commercial activity centers, the same

locations that generate a large percentage of commute trip automobile traffic.



68

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Effects of Low Volumes

The primary problem with low volumes within individual vehicle classes is that small changes
in volume within a vehicle class lead to large changes in percentage of volume. For example,
if a vehicle class contains an average of only 10 vehicles per day (many sites in the east contain
far fewer multi-trailer trucks than this), the addition of two vehicles per day represents a 20

percent increase in volume within that category.

If car volumes are reasonably high at a site (10,000 vehicles per day if FHWA vehicle classes
2 and 3 are added together), it is easy for a few cars traveling close together to be misidentified
by most classification equipment as a multi-trailer truck. Two of these errors in a day would
be inconsequential to the car volume estimate (and well within the acceptable tolerance of the
classification equipment). However, these same errors would have a significant impact on

traffic volumes predicted within the multi-trailer truck category.

In addition to the magnified effects of equipment error, small changes in the local economy can
produce small changes in (true) vehicle volumes within these classes. For example, the start or
end of a construction project can dramatically change the number of dump trucks (which are
commonly found in FHWA vehicle classes 6, 7, and 13, depending on the part of the country)
using a road. Although these changes do in fact occur, these patterns do not represent all
roads of that type in that geographic area since the dump truck traffic may be confined to a
small area and since seasonal adjustment factors that account for these changes are not
applicable to many other sites. Similarly, seasonal adjustment factors computed from this site

are not even applicable the following year at that same site.

Therefore, it is recommended that low-volume vehicle classes be avoided in computing
adjustment factors. This is most easily done by aggregating FHWA’s existing vehicle

classification scheme, as was done for many of the analyses in this report.

43



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%OO |%OO |%0O [%OO [%EL |%9'O |%0O |%~O [%OZ |%l'O [%m AL |%1Bs [%10 [ODPL | PuaHaam
%O'O |%0O [%0O |%OO [EL |%IO |%0'O .|%7O |[%OZ [%lO [%ILE |%0BZ [%l'0 [OO:EL | puexeom
Yeb'O |%00 |%OO [%bO [MEL |%9O [%0O [%7O |%0Z [%LO [%S Lb |%GLL [%lO {00-71 | puaxsan
Yb |%0O |%0O |%lO |%bL |%9O |%0'O |%bPO {%l'Z [%lO |%O'BL |%OLL [%lL'O OO:LL | Pusyaan
%0O |%00 |%0O |%tO |%FL |%9O |%00 |%hO |%EZ |%lO [%H'BL |%EOL |%l-0 00:01 | puayaan
%0'O |%OO |%0O |%ZO [RSL [%l0 |%OO |%GO |%HC |%ZO |%! 6) |%B HL [%ZO {00:60 | PueXaaM
HOO |%OO |%OO {%ZO [WES |%LO |%OO |%GO [%97% |%Z0 |%ZE [MEL |%7O [00:80 | PUEXaaM
00 |%OO |%0O |%0O |%OE [%Ot 1%00 |%Z0 [WOE |%EO |%rOZ|%IIZ |%0'0 [00:20 | puayaen
OO |%OO |%OO |%OO |%GE |%OL |%OO |%SO |WOE [%GO |%ELZ|%EOL |%0'0 [00:90 | PusXaen
%0'O |%0O |%0O |%00 [MPS |%FL |%OO |%GO |%GE |%0'0 |%2'0Z |%929 |%0'0 [00:50 | Puaxaen\
%O'O |%OO |%0'O |%0O 1%78 [%FtE [MNO |%NON [MEE |%OO |%L Gl {%7ZL9 |%00 [OOO | PUdXIaM
OO |%OO |%0O |%OO [%SS |%Lt [WOO (WOO {WHE |%0'O |%6'9t |%G69 |%0'0 [00:0 | PUaXaaM
%O'O |%OO |%OO [%00 |[%99 |%EL [%OO [%00 [%97% |%0O |%B'Sl |%LEL |%0'O [00:20 | PuEeX¥sen
OO |%0O |%0O0 [%00 [%rS [%6O0 [%O0 |%00 [%2~% [WOO |%~S! |%6'SL |%00 [00:10 | puexaeM
%O'O —|%OO |%0O |%OO |%BE f%bt |%00 |%SO [%cz |%OO |%HSI {%6'94 |%00 [00:00 | puexXeam
%Z'O |%ZO J%bhO |%hO [%SL |%ZL |%OO |%¥O |%O? [%ZO [WSL |%BLL |[%ZO jOO-ee | Aepyaem
MLO |%bO |%EO |%EO [WED |%OL |%OO |%EO |%O0Z |%LO [%SSL |%BEL |%tO [O0:~2 | Aepyaem
MLO |%LO |%ZO |%7O |%GS |%6O [%lO |%EO (WO [M70 |%BSl [%b'bL [%10 lOOLe | AePYeM
tO |%lO |%ZO |%EO |%zG |%60 |%tO |%EO |%~Z |%ZO |%I'9I |WBEL [%tO [O00Z | Aepxaem
%ZO tO |%LO |%ZO |%Sv |%VO |%lO |%rO |%hZ |%zZO [%TLL |%EEL |%10 loot | AePHoeM
MLO |%lO |%lLO [%7O |%GE |%BO |%tO [MPO |%HT [%ZO [%FAL |%I PL |%10 [00:81 | Aepyaanr
%b'O |%OO |%LO {%Z0 |%OE |%BO |%lO |%GO |%PZ [%ZO [%LLL |%GbL |%LO jO0:LL | AePrsem
LO |%OO |%LO |%ZO j%CE |%GO |%lO |%L0 JMC |%ZO |%GBl |%L ZL |%eO |00:91 | Aepyaann
LO |%OO |%LO [VEO |%BE |%Ot |%LO |%GO [%ZE [WE'D [%eEL |%L0L |%Z0 [00:51 __| Aepysonn
%ZO {WOO |%LO |%EO [MAb [%ZL [%TO [MLL [MVE [%EO [%161 |%O69 |%-O0 [OO-rL | Aepyeam
ELOA|ZEOA] LE OA] OL OA] GOA | BOA | LDA | 9OA | SOA | POA] EDA] Z9A] LOA | AMOH | dnoig STONNA
(penunuos) SSEID EUOIIUNY Pue Ssejd B}DI4YSA SSOI9Y INOH sod Del pusyoopyAepyoom %

Niatatatatoatalatatantajatatajatatatatatatatatatatayajajpalar



fairly large volume of vehicles, but on a moderate volume rural road (e.g., 10,000 Average

Daily Traffic [ADT]), 0.25 percent would equal only 20 vehicles per day in the traffic stream.

When volumes within a class drop to levels this low, it becomes difficult to accurately compute
adjustment factors to account for time-of-day, day-of-week, or month-of-year variation, since
the factors themselves become unstable with small absolute changes in traffic volumes. This

problem caused considerable difficulty in the computation of national averages.

The research team recommends, therefore, that factors be developed and statistics be reported
for a much smaller number of vehicle classes than the 13 classes FHWA currently uses. Our

recommendation is that most factors be produced for:

= Passenger cars and light trucks (plus motorcycles where collected).
= Single-unit trucks.
= Combination trucks.

= Multi-trailer vehicles.

In urban areas where buses are an important part of the fleet mix, buses can be treated as a
fifth vehicle classification. In rural areas, where monitoring bus travel is less important, buses
can either be counted separately, or they can be treated as part of the single-unit truck

classification.

This classification scheme is a simple aggregation of the 13 existing FHWA classes:

= Classes 1-3 are passenger vehicles.
= Classes 4-7 are single-unit trucks and buses.

= Classes 8-10 are combination trucks.

30



vehicle classes for various aggregations (i.e., mean day-of-week or monthly factors

by functional class of roadway or region of the country).

In many cases, the traffic patterns for many vehicle classes were very similar, and

aggregating the FHWA classes allowed a simplified explanation of the results.

Modern automated vehicle classification equipment often has difficulty
differentiating among specific vehicle types, and the data were more accurately
treated at a more aggregated level. (For example, most vehicle classifiers have
problems reliably differentiating between some class 2, class 3, and class 5
vehicles, since all of these vehicle categories contain two axles and the wheel bases
of some types of vehicles (particularly pickup trucks) fall near the axle spacing

borders of these three categories.

Report Organization

This report is separated into the following sections:

Volumes by day of week.

Volumes by time of day.

Urban versus rural volumes.

Vehicle percentages by functional classification of road.
Seasonal patterns.

A summary of key findings.

Each of these sections is subdivided into additional topics, many of which could have been

discussed in two or more sections of the report (for example, seasonal patterns by functional

classification of road). To reduce the size of this report, individual topics are only covered

ho



A second common difference appears to be caused by recreational travel. All urban sites in the
LTPP dataset have lower Sunday automobile volumes than other days of the week. Some rural

sites have this same pattern. However, many rural sites have very high Sunday volumes.

The research team concluded that the LTPP database contains insufficient data at this time to
support the creation of factor groups that can adequately differentiate between sites with and
without high levels of through-traffic and/or recreational traffic. Local knowledge would need
to be added to the factor group creation and application process for these groups to be useful.
This same type of site identifier would need to be added to other roadway databases to allow
these factors to be applied to individual roadway sections. Currently available variables

(functional class, state, volume) are not capable of identifying these patterns.

Time-of-Day Patterns

There are four basic time-of-day patterns:

= Rural automobile—a single-mode distribution pattern, commonly found in rural
areas that are not affected by urban commute traffic and also found on weekends in

urban areas.

= Urban automobile—a bimodal distribution that reflects morning and evening

commuter traffic.

* Through-trucks—a flat time-of-day distribution that reflects long-haul truck traffic

in areas outside of the cargo’s origin or destination.

= Business day trucking—a single-mode distribution that reflects the fact that most

truck traffic starts and ends during the normal extended business day.

A large degree of variation is present within each of these patterns. For example, the peak

period in any of these distributions is affected by the relative location of a road’s section in

45



data are presented for all 13 vehicle categories; however, many of the report’s tables and
figures contain an aggregated vehicle classification scheme. This classification scheme uses
four broad categories of vehicles:

= Passenger cars,

= Single-unit trucks.

= Combination trucks.

= Multi-trailer trucks.

Class 1 = Motorcycles

Class 2 = Passenger cars

Class 3 = Other 2-axle, 4-tire single-unit vehicles
Class 4 = Buses

Class 5 = 2-axle, 6-tire single-unit trucks
Class 6 = 3-axle, 6-tire single-unit trucks
Class 7 = 4+ axle single-unit trucks

Class 8 = 4 or less axle combination trucks
Class 9 = 5-axle combination trucks

Class 10 = 6+ axle combination trucks.
Class 11 = 5-axle multi-trailer trucks

Class 12 = 6-axle multi-trailer trucks

Class 13 = 7+ axle multi-trailer trucks

Figure 3. 13 FHWA Vehicle Classes

These higher aggregations are used for several reasons.

= At many sites, the daily volumes within some vehicle categories are so smail that
the factors (or ratios) produced are not stable. These unstable values caused an

unrealistically high degree of variability in the mean factors computed for those



Average Day of Month/
Average Annual Daily Traffic

2
&
a
c=)

0.750

0.650

9.550
e = C4 > @ > @ 5 s oa -
gs 853222 2 2
2 23 $3 3 > £ €
s 3 <2 8 8 3
3 2 4
Month

—*FC1 mH FC2 —#-FC6 FCT
—*-FC 11 —@-FC 12 —+- FC 14 —— FC 16

Figure 16. Monthly Single-Unit Truck Volume
Patterns by Functional Class

39



volumes generally begin increasing between 5:00 and 6:00 a.m. and continue increasing until
sometime in the afternoon. The exact time of the afternoon peak is determined by site-specific

conditions.

The urban travel pattern consists of the classic two peaks associated with commuter trips. This
pattern is present on most urban roads during weekdays, on many roads with rural functional
classes where commuters live in rural areas on the outskirts of urban areas, and on some rural
roads that are affected by intercity traffic generated by nearby urban areas. (For example, I-5
midway between Seattle, Washington, and Portland, Oregon, exhibits an urban travel pattern
even though it lies 90 miles from either city because the two cities generate a considerable
amount of business day travel between them.) This double peak travel pattern is often
directional. That is, the morning peak occurs only in one direction, and the afternoon peak
occurs in the opposite direction. The peaks are still obvious when volumes for both directions
are combined, but the height of the curve (when expressed as a percentage of daily traffic) is

usually lower when expressed as a fraction of bi-directional volume.

The third pattern is the business trucking pattern. The “business trucking pattern” fits most
truck classifications (when aggregated nationally, only truck classes 11 and 12 did not exhibit
this pattern; see Figure 7). The vast majority of trucking movements in the nation start and
end during the normal, extended business day (that is, between 6:00 a.m. and 6:00 p.m.).
Consequently, most truck traffic occurs during these times. The exact timing of the business
day travel varies from site to site, as well as by vehicle class and direction within each site.
The truck travel pattern differs from the rural automobile pattern in two major respects. The
truck pattern usually peaks earlier in the day (although both patterns show significant increases
in traffic volume by 6:00 a.m.), and the truck pattern usually drops earlier in the day than the

tural automobile pattern.

18



phenomenon also appears to occur in some Rocky Mountain states, but the LTPP data set has
relatively few Rocky Mountain sites, making it hard to determine the true geographic extent of
this pattern.) On many (but by no means all) roads in these states, through-traffic
predominates over local traffic. Much of this traffic is generated by or destined for locations
more than one day’s drive away. Thus, goods that leave their origin on a weekday (e.g.,
Friday) during business hours pass over these roads during non-standard business hours on
other days (e.g., Saturday). Similarly, because only minor amounts of freight leave from or
arrive at these origins or destinations on Saturdays and Sundays, the traffic lull that occurs on

those days reaches or precedes the Great Plains states one or more days later or sooner.

The result is that the low volume days for combination and multi-trailer trucks for a number of
Nebraska roads in the LTPP database are Monday and Tuesday, rather than Saturday and
Sunday (see Figure 5). (Wednesday is also a lower volume day for multi-trailer trucks, but not
for combination trucks.) For many vehicle types, the same 5-day/2-day traffic pattern found in
most other sites also exists at these locations. The difference is that the lower volume days are
not the weekends. Note also that not all truck classes at these sites follow this higher weekend
pattern, nor do they fall neatly into the 5-day/2-day pattern. At many of these higher volume
weekend sites, weekend travel by smaller trucks (e.g., single-unit trucks) is still lower than the
weekly average. (These trucks are less likely to be used in long haul, inter-urban freight

movements and are therefore less likely to be part of the “through”-truck movement.)

This higher volume weekend truck travel pattern is also found in some states outside the Great
Plains, although they are infrequent outside of the Great Plains and Rocky Mountains. The
pattern is apparent on roads that carry higher percentages of through-truck traffic and that are
located a day or two from the sites that generate the traffic. It is also important to note that
this through-travel pattern is normally directional. That is, the lower volume days in the

westbound direction may be different from the lower volume days in the eastbound direction.

13



Table 1. Number of Sites per State for Each Functional Class

STATE [FO4 | FC2 | FC6 | FC7 FC11 FC12,FC14 | FC 16 | Total
Alabama 3 3 4 0) 9) 0) 01 of 7
Arkansas 1 () 0 Cy 0, 0; 0 ot
California i 3 O ) 0, 9, cH 0, 0 3
Florida { of 2 oO 0, 0, Oj fe) 2
Indiana | 3| 4; 4) 1! 0. 0. 1, oO} 18
liowa ee a
Kansas i i) 0, 0; 0 0, 0; i 4
Kentucky I ty) 1 oy _ 0 0| aH Ci) 2
Massachusetts 1 1 ODF 0| 0 oO 6
|Minnesota ) Oo Oo 2 62 4, 0. a 4
Mississippi | 1 9| 0| 0; 1 — o, 9 2
Missouri | 0 0 3! 1 T 0 ) Ca)
{ -- ee ee 4
Nebraska 8 oo 60 2) 9, 4 Oy 1
New York | i) 4 0 0 oO 1 0 2
Pennsylvania | 4 0) ol o, 0} ty) 0 2
[Texas | 0 oc 6 2 4: 0 2) 15
Vermont r Oo 3 o of 0 (a A)
Virginia i 2 3 2 1 2 0 1 oO; 4
Washington, 2. 6 o $F tT 2 “aL 2
i f | |
Total 23] a7 17 5) 13) 3] 9 2, __99

Mississippi

Figure 1. States Represented in the Study (number of sites indicated)

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For rural areas, peak-period directional differences are also apparent at many sites. Some
tural sites do exhibit typical “urban” directional travel, even within some of the heavier truck
classes (for example, Figure 8 is a rural site), but other sites with great differences in peak
period directional travel show a different pattern (see Figure 10). In this second pattern (which
is more common in the LTPP database), travel in one direction appears to be offset by
approximately 1 hour from travel in the other direction. This produces a substantial difference
in the percentage of traffic that occurs during the a.m. or p.m. peak periods (when those
periods are defined as a given time period, such as 6:00 to 9:00 a.m.), but it does not represent

an extreme difference in travel patterns.

0.10

0.08
0.06
0.04

[—#— Direction 4
i = Direction 2 |

0.02

Hour of the Day

0.00 Tt _—— a tame
4 3 5 7 9 14 13 15 17 #19 21 23
Fraction of Daily Traffic

Figure 10. Difference in Combination Truck Travel by Direction

If these same data are used to express truck percentages as a function of the time-of-day, the
result is a graphic such as that shown in Figure 11. Figure 11 illustrates that, although truck
volumes decrease during the late night hours, the truck percentage rises significantly because
car volumes tend to drop even more drastically. Truck percentages are generally higher in
rural areas than in urban areas, and larger roads (functional classes 1 and 2 in rural areas, 11
and 12 in urban areas) are more likely to have high truck percentages than smaller functional
road classes; however, the correlation between truck percentage and functional classification of

road is very poor. Truck percentages are highly route specific. Individual roads, regardless of

23



= At one set of sites, the highest automobile volumes are on Friday and Sunday, with

a small drop in volume (but still greater than average) on Saturday.

= At the other set of sites, the highest automobile volumes are on Saturday, with

slightly lower volumes on Friday and Sunday.

One possible explanation for the first of these patterns is that it represents sites with
recreational roads that attract weekend traffic, with most travelers arriving early and leaving
late in the weekend. (Perhaps the count location was near the location of recreational activity
that normally takes the entire weekend.) The other pattern appears to be more closely related
to the travel pattern expected of a day trip, with more activity on Saturday (when more

individuals engage in recreational activity), and slightly lower activity levels on Friday (when

within) recreational destinations and that it measures internal traffic within the destination.
Attempting to assign one or another of these patterns to a specific site would be impossible

without some site-specific information.

Recreational movements affect only cars; these movements do not appear to have an impact on
any truck travel patterns. With the exception of the Great Plains states (and some Rocky

Mountain states), few locations have high truck volumes on Sundays. Instead, Sundays have

various sites). Thus, to accur:

by far the lowest truck volumes

annual average conditions, a state must adjust short duration, weekday, classification counts to
account for lower weekend traffic volumes. If an adjustment is not made, classification counts
taken only on weekdays will tend to significantly over-estimate annual truck volumes. One
way to do this would be to create adjustment factors that convert weekday counts to average

annual estimates.



Consequently, although it is easy to conclude that freeways tend to have larger truck

percentages, local conditions can make these “national” conclusions incorrect. Local

Table 5 provides information on the average fraction of vehicles by FHWA classification on
each functional class of roadway. These numbers were the basis for the summary discussion
of vehicle percentages for rural and urban roads in the previous section. Before relying
extensively on these numbers, an analyst should be aware that the lower functional classes
inciude relativeiy few data collection sites (see Tabie 6); this makes the statistical ceriainiy
fairly small that these values represent the “true” mean values for these roads in the United
States. This statistical uncertainty is further increased by the fact that truck characteristics
vary among regions of the country and, in many cases, from state to state because of the
variations in state truck regulations. Therefore, the states from which these data were taken do

not equally represent the regions of the country for all functional classifications of roadway.

Be aware, for example, that specific vehicle types, such as class 12 and class 13 vehicles, are
relatively common on many roads in the western United States but are fairly uncommon
throughout most of the eastern United States. Other vehicle types, such as class 7, are
uncommon in most states but can represent a large proportion of vehicle loads on some roads
in some states (for exampie, heavy dump trucks or large Kentucky coai trucks are often 4-axie

single-unit trucks).

5 Includes buses and FHWA class 5 (2-axle, 6-tire) trucks.

28



06

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HOO |%0O |%00 {%OO |%00 |%O0 |%00 |%00 |%00 |%00 |%ZBt |mB LE |%OO O0:%2 | PUaBEM 8
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%O'O |%0O0 ([%00 |%00 |%00 [%00 |%00 |%00 [%00 |%00 [%l're|%BSL |%0'0 00:51 | puexaaM 8
%0'O |%00 j%00 |%00 |%00 [%00 |%00 [%00 |%00 |%00 [*r¥1Z|%9BL |%00 [O01 | puaxaem 8
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%00  |%00 |%0O |%00 |%00 [%00 {%00 [%00 |%00 [%00 |%2SZ|%E PL |%00 [00:60 | puayaen 8
%0'O  |%0O J%00 J%00 [%00 |%00 j'H00 J%00 |%00 J%00 |%00E|%00L }%00 |00:80 | Puareenr 8
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Creation of Factor Groups

One goal of this research was to see if factors could be developed to adjust raw data so that it
better represented average annual conditions. Part of this effort was an attempt to define

specific “factor groups” that could be associated with commonly available site information,

in which that site was loca

such as the functional esion i
such t +3 region in which that site was tocate:

g
&
@
:
&

total volume of the road.

It was not possible to create precise day-of-week factor groups from the data availabie in the
LTPP database. (“Factors” used in this analysis were computed as the ratio of average day-of-
week volume by class divided by average annual volume by class. Thus a “Sunday Truck
Factor” of 0.70 would mean that this site experiences only 70 percent of average daily truck
traffic on Sundays.) The variation in the day-of-week factors at the available sites was too
great to create consistent factor groups, and the cause of that variation could not be extracted
from the available independent variables (functional class, state ID, etc.) However, the

following observations can be made about the data.

= Few discontinuities in the day-of-week patterns were found at different sites. Day-
of-week patterns at different sites followed a smooth continuum from sites with very
low weekend voiumes to those with fairly flat day-of-week patterns. This

continuum is illustrated in Figure 4.

other than the frequent presence of high weekend truck travel in the Great Plains

states and some Rocky Mountain states.

10



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Impacts of Local Conditions

Local conditions can easily affect the day-of-week pattern of specific vehicle classes. An
important part of these conditions is recreational travel by cars, but freight movements (either
local or through-movements) can also create unusual day-of-week conditions. Local conditions
(such as harvest movements in farm areas) can affect entire regions or individual roads (for
example, freight movements caused by factory production schedules). In many respects, large
through-movements of long-haul freight can be considered “local conditions”, even though the

cause for those traffic patterns is located far away from the road section in question.

The effect that local conditions (including through-truck movements) have on day-of-week
vehicle patterns is a function of total volume and the relative influence of other traffic patterns.
Much of the variation in the percentage of truck travel that occurs on weekends at individual
sites may be caused by through-traffic at these sites. On roads that appear to have a lot of
long-haul, through-truck traffic, weekend truck volumes tend to be high. On roads whose
percentages of long-haul, through-traffic appear to be moderate in comparison to local traffic,
weekend truck travel percentages tend to be moderate. On roads with little long-haul, through-

truck traffic, weekend truck travel volumes tend to be low.

It was not possible to correlate these different truck travel patterns with different functional
classes of roads or with particular states. As noted above, the Great Plains states consistently
demonstrated higher weekend truck volumes, but these higher weekend volumes were apparent

in other states as well.
From a functional classification perspective, two broad conclusions were reached:

= Urban roads are less likely to have higher weekend truck volume percentages than

rural roads.

15



The final pattern is the through-truck pattern. This pattern reflects the fact that long distance
trucks are not constrained to the normal business day. Many travel at night to avoid traffic
congestion. In addition, because many long distance drivers are paid by the mile rather than
by the hour, there is incentive to drive whenever possible. Consequently, the percentage of
daily travel at night by these vehicles is much higher than for other vehicle classes. This
pattern has a fairly flat volume pattern with individual site characteristics controlling whether

volumes increase or decrease slightly at night.

The smailer truck classifications (classes 5 through 8) usually follow the business day trucking
pattern. Classes 11 and 12 often follow the through-truck pattern. The remaining truck
classes (9, 10, and 13) switch from one pattern to the other, depending on the nature of truck
traffic on each road. class 9 trucks, in particular, are commonly used for both long distance
and short haul trucking duties in the United States. Consequently, at some sites, class 9
vehicles fall into the business day pattern, at other sites they follow the through-truck pattern,
and at still other sites, they create a combination pattern. The effects of being used in these
multiple roles is apparent in Figure 7, where the class 9 truck pattern has higher late night

travel percentages than the other trucking classes shown.

Buses (FHWA vehicle class 4) follow one of the four patterns mentioned above. Generally,
within urban areas, transit buses appear to predominate within this class. The data show that
these vehicles fall within the general automobile patterns (i.¢., the double-peak urban pattern
on weekdays and the single-peak rural pattern on weekends). In rural areas, intercity buses
predominate (often supplemented by misclassified large recreational vehicles) and tend to

follow the rural automobile pattern.

For all four basic time-of-day volume patterns and all vehicle classes, vehicle volumes
decrease at night (although, in many cases, reductions in through-truck volumes may be very
small). However, car volumes decrease more late at night than truck volumes, thus truck

percentages (computed from truck volumes divided by total traffic volume) tend to increase

20



Appendix

49



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somewhat different for the four vehicle categories. In particular, several of the urban truck
patterns peak in early fall. Because cars make up a large percentage of the total traffic volume

for most roads (and most sites with available data), the monthly pattern for total volume tends

to be similar to that for cars.

—e— Urban Cars

1.25
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—a— Urban Combination Trucks —— Urban Multi-Trailer Trucks

—— Rural Cars —e— Rural Single Unit Trucks

—+— Rural Combination Trucks

——Rural Multi-Trailer Trucks

Figure 13. Seasonal Factors by Aggregated Vehicle Class

32



= Lower functional class roads (classes 6, 7, 8, 14, and 16) are less likely to have
higher weekend truck percentages than higher functional class roads (classes 1, 2,
11, and 12).
However, within each functional classification of road, there was a large degree of variation.
For example, Virginia site 1023, a rural interstate, carries 77 percent of average daily
combination truck traffic on Sundays, whereas Virginia site 1464, also a rural interstate,
carries only 38 percent of average daily combination truck traffic on Sundays. These
differences can be explained by understanding the trucking patterns on these two roads. Site
1023 is on I-95, and carries large volumes of through-truck traffic. Site 1464 is on I-64, near

Norfolk, and its truck traffic predomi

ately carries freight bound to or from that city.

variables (such as functional class) that could be used to accurately predict when these

variations in truck volume patterns would occur.

VOLUMES BY TIME-OF-DAY

As illustrated in Figure 6, the analysis revealed four basic time-of-day patterns. These patterns

are as follows:

= Rural automobile.
= Urban automobile (two humps).

= Business day trucking.

16



09

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Table 2. Number of Sites per Region for Each Functional Class

REGION | FC1 | FC2 | FC6 , FG? | FC11 | FC12 | FC14 | FC16 | Total
ATLANTIC. 4 a 2 4 2 a 2 0 20
CENTRAL 9 8 8 2 5, 2 3 0} 37
SOUTH S| | 7 _2t- 5 (eee 4| 27]
WEST | 5) 5 0 to} 1 1 2 4,15)
Total [ 23) 27 17 5] 73! aL 2|___99

Ciass i = Rurai interstate

Class 2 = Rural Principal Arterial
Class 6 = Rural Minor Arterial
Class 7 = Rural Major Collector
Class 8 = Rural Minor Collector

Class 11 = Urban Interstate

Class 12 = Urban Other Freeways and Expressways
Class 14 = Urban Principal Arterial

Class 16 = Urban Minor Arterial

Class 17 = Urban Collector

Figure 2. Functional Classes of Roadways

The data supplied by the states were coiiected by permanently instaiied vehicie ciassification
and weigh-in-motion (WIM) equipment placed for the LTPP research tests. Because these sites
were not selected to provide information on a specific set of traffic movements (e.g. ,
recreational movements important to a state), they should provide a reasonably unbiased
sample of traffic conditions within the U.S. However, the traffic patterns found in the U.S.

is study was relatively small in comparison to the

range of possible traffic patterns, particularly those found on the lower urban and rural
functional road classes. Thus, some care must be taken when using these study results for

general application.



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2.05
1.95
1.85
4.75
1.65
1.55
1.45

4.25
4.18
1,05
0.35,
0.85
0.75
0.65
0.55
0.45
0.35

Ratio of Average Day-of-Month/Average Annual
Daily Volume by Class

1.35 |

c 2 Bs > € B > 2 3 2 BB
oj oO 3 ¢ & 5 3 3 3 8 {<] ao
Months

| —e—Mass 1003 ~¢—WA 1002 ae Vir 1417

1

| —x— Vir 1419 —e—Ver 1004

Figure 14. Example of Single-Unit Truck Seasonality

Differences in Factors by Functional Classification of Road

Monthly traffic volume patterns were also examined by functional class of roadway (see

Figures 15, 16, 17, and 18). In general, these plots illustrate three basic conclusions:

* The smaller the number of sites in a sample, the more likely that extreme monthly

variation will be observed (functional classes 7, 12, and 16).

37



Average Day of Month/
Average Annual Daily Traffi

1.600

1.400

1.200

0.600

0.400

> 2 § £€ eF © = 8 EB FE R B
5 § 2 3
PPPe PPP Tea?
Ss 8 ea © 2 8

Month = ® z= 0

| —oFC1 —m-FC2 te FCS —<-FC7

[mere —e-FC 12 —+-FC 14 —ros|

Figure 17. Monthly Combination Truck Volume
Patterns by Functional Class

40