The sample size of schemes 

34M-36M is over 6,700

Data Collection and Annual Average

Daily Traffic (AADT) Estimation for

Non-Federal Aid System (NFAS) Roads

FHWA Safety Program

https://safety.fhwa.dot.gov/
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

NOTICE

This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

QUALITY ASSURANCE STATEMENT

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. 

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Cover Photo Sources: Shutterstock and TTI Communications.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

i

TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-SA-20-064

2. Government Accession 

No.

3. Recipient's Catalog No.

4. Title and Subtitle

Informational Guide on Data Collection and Annual Average Daily Traffic 

(AADT) Estimation for Non-Federal Aid-System (NFAS) Roads

5. Report Date

March 2020

6. Performing Organization Code

7.Author(s) 

Ioannis Tsapakis, William Holik, Subasish Das, Edgar Kraus, and Paul Anderson

8. Performing Organization Report No.

9. Performing Organization Name and Address 10. Work Unit No. 

Texas A&M Transportation Institute (TTI)

3135 TAMU

College Station, TX 77843

11. Contract or Grant No. 

DTFH61-16-D-00004L 

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety 

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

August 2016 – April 2020

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Marc Starnes. 

16. Abstract

This is an Informational Guide on traffic data collection and estimation of annual average daily traffic (AADT) for nonFederal aid-system (NFAS) roads. NFAS roads refer to rural minor collectors (6R) and both rural and urban local roads (7R 

and 7U). This Informational Guide describes four preparation steps for safety data integration and an eight-step process for 

developing a random stratified sampling scheme and AADT estimates for NFAS roads. The intent of the Informational 

Guide is to assist both experienced traffic monitoring personnel and those who are new to traffic data collection and AADT 

estimation. The Informational Guide includes methods suitable for agencies that do not collect data nor estimate AADT for 

NFAS roads, as well as for those desiring to improve their practices and the accuracy of their AADT estimates. Readers are 

encouraged to take those portions most relevant to their needs to develop a stratification scheme and AADT estimates. 

Agencies do not have to adopt all the activities described in the Informational Guide.

17. Key Words: 

annual average daily traffic, AADT, traffic count, 

random stratified sampling, decision tree, traffic 

data, low-volume roads, non-federal aid-system

roads

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

146

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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TABLE OF CONTENTS

LIST OF TABLES ...................................................................................... VI

LIST OF FIGURES........................................................................................I

ACRONYMS .............................................................................................. III

EXECUTIVE SUMMARY.......................................................................... IV

CHAPTER 1 — INTRODUCTION .......................................................... 1

1.1BACKGROUND.......................................................................................................1

1.2SCOPE.......................................................................................................................2

1.3FRAMEWORK..........................................................................................................3

1.4AUDIENCE...............................................................................................................4

1.5AADT ESTIMATION ..............................................................................................6

1.6REQUIREMENTS.....................................................................................................8

1.7ORGANIZATION..................................................................................................10

CHAPTER 2 — TRAFFIC MONITORING CONCEPTS......................13

2.1INTRODUCTION..................................................................................................13

2.2TRAFFIC COUNTS...............................................................................................13

2.2.1 Continuous ...................................................................................................13

2.2.2 Short-Duration ............................................................................................14

2.3TRAFFIC DATA.....................................................................................................15

2.3.1 Variation.......................................................................................................15

2.3.2 Types.............................................................................................................17

2.4TRAFFIC PARAMETER COMPUTATION.........................................................17

2.4.1 AADT............................................................................................................18

2.4.2 Factors ..........................................................................................................19

CHAPTER 3 — PREPARATION FOR AADT ESTIMATION .............22

3.1INTRODUCTION..................................................................................................22
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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3.1.1 Safety Data Integration Framework .........................................................22

3.2STEP 1: LAY THE FOUNDATION .....................................................................24

3.3STEP 2: CONDUCT GAP ANALYSIS ................................................................27

3.3.1 Traffic Volume Data ...................................................................................28

3.3.2 Network Segmentation ..............................................................................29

3.3.3 Counted and Uncounted Segments ..........................................................30

3.3.4 Other Elements for Review........................................................................30

3.3.5 Practices and Procedures ...........................................................................31

3.4STEP 3: ESTABLISH DATA GOVERNANCE PROCESS.................................34

3.4.1 Leadership....................................................................................................34

3.4.2 Quality ..........................................................................................................34

3.4.3 Prioritization................................................................................................37

3.4.4 Cooperation.................................................................................................37

3.4.5 Flexibility ......................................................................................................39

3.4.6 Utilization.....................................................................................................41

3.5STEP 4: DEVELOP DATA COLLECTION AND INTEGRATION PLAN ......42

CHAPTER 4 — RANDOM STRATIFIED SAMPLING .........................44

4.1INTRODUCTION..................................................................................................44

4.1.1 Basic Definitions and Terms ......................................................................44

4.1.2 Sampling Methods .......................................................................................45

4.2STEP 1 – GATHER AND PROCESS NON-TRAFFIC DATA...........................47

4.3STEP 2 – PERFORM DATA EXPLORATORY AND CORRELATION 

ANALYSIS ...................................................................................................................52

4.4STEP 3 – DETERMINE PERFORMANCE AND SAMPLE SIZE NEEDS OF 

CURRENT SCHEME...................................................................................................57

4.4.1 Accuracy of Default AADT Estimates ......................................................57

4.4.2 Within-Stratum and Scheme Variability..................................................59

4.4.3 Sample Size..................................................................................................61

4.4.4 Precision and Confidence Interval.............................................................65

4.5STEP 4 – DEVELOP NEW STRATIFICATION SCHEME ................................68

4.5.1 Stratification Without Traffic Volume Data............................................68

4.5.2 Stratification With Traffic Volume Data..................................................70

4.6STEP 5 – COMPARE SCHEMES AND SELECT ONE.......................................82

4.7STEP 6 – SELECT SAMPLES................................................................................87
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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4.8STEP 7 – ESTABLISH AND EVALUATE CONTINUOUS COUNT 

PROGRAM...................................................................................................................90

4.9STEP 8 – CONDUCT COUNTS AND DEVELOP DEFAULT AADT 

ESTIMATES.................................................................................................................93

CHAPTER 5 — CONCLUSION.............................................................95

REFERENCES ............................................................................................99

APPENDIX A – IMPACT OF AADT ESTIMATION ERRORS ON 

SAFETY ANALYSIS................................................................................107

SUMMARY.................................................................................................................107

A.1 BACKGROUND.................................................................................................107

A.2 OVERVIEW OF EB METHOD .........................................................................108

A.3 METHODOLOGY..............................................................................................109

A.3.1 Data Preparation and Exploratory Data Analysis.....................................110

A.3.2 SPF Development .........................................................................................111

A.3.3 Sensitivity Analysis .......................................................................................115

A.4 RESULTS.............................................................................................................115

A.5 REFERENCES.....................................................................................................121

APPENDIX B – AUTOMATED ESTIMATION OF TRAFFIC 

VOLUMES USING TRIP GENERATION METHOD ...........................122

SUMMARY.................................................................................................................122

B.1 OVERVIEW OF VDOT’S TRIP GENERATION METHOD...........................122

B.2 SELECTION OF TRAFFIC LINKS ...................................................................123

B.3 TRAFFIC VOLUME ESTIMATION..................................................................124

B.4 COST AND OTHER CONSIDERATIONS .....................................................128

B.5 METHOD AUTOMATION AND VALIDATION ...........................................128

B.6 REFERENCES .....................................................................................................132

ACKNOWLEDGMENTS ........................................................................133
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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List of Tables

Table 1. Total Road Length by Functional Class and Rural/Urban Designation.(4)....2

Table 2. Total Roadway Length (Miles) by Functional Classification, Rural/Urban 

Designation, and Ownership.(4).........................................................................................5

Table 3. Readily Available U.S. Census Bureau Variables. .........................................49

Table 4. Current Scheme and Default AADT Estimates of Agency A......................58

Table 5. Calculation of Median Absolute Percent Error.............................................59

Table 6. Calculation of Coefficient of Variation and WACV......................................61

Table 7. Value of Z for Different Confidence Levels. ..................................................62

Table 8. Required Sample Sizes for Different Confidence-Precision Levels. ...........64

Table 9. Precision of Default AADT Estimates for Different Confidence Levels.....67

Table 10. Old and New Default AADT Estimates of Agency A. ................................71

Table 11. Accuracy Metrics of Updated Default AADT Estimates. ..........................72

Table 12. Characteristics and Performance of New Traditional Stratification 

Scheme. ............................................................................................................................74

Table 13. Characteristics and Performance of County-Based Scheme. ...................75

Table 14. Characteristics and Performance of Non-County-Based Scheme............76

Table 15. Example of a Decision Tree in a Table Format. .........................................79

Table 16. Example of Independent Variable Importance. ..........................................80

Table 17. Examples of Traditional Schemes and Decision Trees. .............................83

Table 18. Crash Counts by Injury Type and Year......................................................111

Table 19. SPF Characteristics. .....................................................................................112

Table 20. Number of Electronic Building Footprints by State.................................130
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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List of Figures

Figure 1. Flowchart. Process to Collect Data and Estimate AADT for NFAS Roads.

............................................................................................................................................. v

Figure 2. Flowchart. Process to Collect Data and Estimate AADT for NFAS Roads.

.............................................................................................................................................4

Figure 3. Chart. Types of AADT Estimation Methods for NFAS Roads. ....................7

Figure 4. Flowchart. Nine-Step Safety Data Integration Framework.(5)...................23

Figure 5. Scanned Document. NYSDOT Traffic Counting MOA.(46).........................26

Figure 6. Flowchart. Basic Steps of Developing and Revising a Random Stratified 

Sampling Scheme. ...........................................................................................................45

Figure 7. Graph. Example of AADT Frequency Histogram, Boxplot, and 

Descriptive Statistics Indicating Existence of Outliers................................................53

Figure 8. Graph. Examples of Correlation Coefficients Calculated for Two 

Agencies............................................................................................................................55

Figure 9. Graph. Number of Samples Required for Different Coefficients of 

Variation, Confidence Levels, and Precision Levels.....................................................63

Figure 10. Flowchart. Example of a Decision Tree Developed in R. .........................78

Figure 11. Graph. Median APE, Required Sample Size, and WACV of Various 

Stratification Schemes. ...................................................................................................84

Figure 12. Flowchart. Establishment and Evaluation of Continuous Count (Traffic 

Volume) Program.(1)........................................................................................................91

Figure 13. Flowchart. Process to Collect Data and Estimate AADT for NFAS 

Roads.................................................................................................................................95

Figure 14. Flowchart. Parametric SPF Development Process.(1).............................110

Figure 15. Scatterplots. Observed vs. Expected Crashes. ........................................112

Figure 16. Graph. Cumulative Residuals vs. Segment Length..................................113

Figure 17. Graph. Cumulative Residuals vs. AADT...................................................114
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 18. Graph. Percentile Rank Change for Different AADT Groups (7R). ......117

Figure 19. Graph. Percentile Rank Change for Different AADT Groups (7U).......118

Figure 20. Graph. Percentile Rank Change for Different AADT Groups (6R). ......119

Figure 21. Screenshot of Map. Examples: (a) Step 1; (b) Step 2. Photo Courtesy 

VDOT Northern Region(48, 49)........................................................................................126

Figure 22. Screenshot. Examples: (a) Fariba Drive; (b) Pine Knot Drive. Photo 

Courtesy VDOT Northern Region.(48,49)......................................................................127

Figure 23. Screenshot. Example of Building Footprints in a Residential Area. ......129

Figure 24. Scatterplot. Traffic Volumes from Trip Generation Method vs. Counts in 

North Carolina...............................................................................................................131

Figure 25. Scatterplot. Traffic Volumes from Trip Generation Method vs. Counts in 

Vermont..........................................................................................................................131
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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ACRONYMS

AADT Annual average daily traffic

AASHTO American Association of State Highway and Transportation Officials

ACS American community survey

ADT Average daily traffic

AEGIST Applications of Enterprise GIS for Transportation

ARNOLD All road network of linear referenced data

CART Classification and regression trees

CCS Continuous count station

CFR Code of Federal Regulations

CMF Crash modification factor

DOT Department of Transportation

DVRPC Delaware Valley Regional Planning Commission

FDE Fundamental data elements

FHWA Federal Highway Administration

GIS Geographic information systems

HPMS Highway Performance Monitoring System

HSIP Highway Safety Improvement Program

IT Information technology

LEHD Longitudinal Employer-Household Dynamics

LRS Linear referencing system

MAP-21 Moving Ahead for Progress in the 21st Century Act 

MIRE Model inventory of roadway elements

MOA Memorandum of agreement

MOU Memorandum of understanding

MPO Metropolitan Planning Organization

NYSDOT New York State DOT 

NFAS Non-Federal aid-system

PTR Portable traffic recorder

QA Quality assurance

QC Quality control

SEMCOG Southeast Michigan Council of Governments 

SPF Safety performance function

TMG Traffic Monitoring Guide

TRCC Traffic Records Coordinating Committee 

VDOT Virginia DOT

WIM Weigh-in-motion
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

iv

EXECUTIVE SUMMARY

The purpose of this Informational Guide is to provide transportation agencies with information 

about collecting data and developing annual average daily traffic (AADT) estimates for nonFederal aid-system (NFAS) roads. NFAS roads refer to roadway functional classifications rural 

minor collectors, and both rural and urban local roads. The expectation is to use the AADT

estimates in data-driven safety analysis, as defined by the Highway Safety Improvement Program 

(HSIP) Final Rule. The Guide presents a framework (Figure 1) that consists of two major parts: 

Preparation and AADT Estimation. 

The first part includes four basic steps that are necessary to prepare for effective data 

collection and sharing of traffic volume data among agencies. These steps are part of a larger 

safety data integration process that FHWA published in 2016. The second part, called AADT 

Estimation, includes eight steps that describe how to improve an existing stratification scheme, 

develop new schemes, collect traffic volume data, and estimate AADT for NFAS roads.

The intent of this framework is to assist both experienced traffic monitoring personnel and 

those who are new to traffic data collection and AADT estimation. The Informational Guide 

includes methods suitable for agencies that a) do not collect data nor estimate AADT for NFAS 

roads, and b) desire to improve their practices and the accuracy of their AADT estimates. For 

this reason, the framework incorporates both traditional (manual) stratification approaches, as 

well as more advanced non-traditional classification methods such as decision trees.

The target agencies of this Informational Guide are Metropolitan Planning Organizations 

(MPOs), Federal, State, Tribal, and local governments that own NFAS roads. The primary 

audience within the target agencies are traffic monitoring officials who typically collect, process, 

analyze, integrate, archive, publish, or report traffic data and AADT estimates. A secondary 

audience is traffic safety managers, analysts, and researchers who use AADT in data-driven 

safety analysis such as performing system-wide network screening analysis, developing crash 

modification factors (CMFs), calibrating and developing safety performance functions (SPFs), and 

evaluating the effectiveness of highway safety improvement projects and countermeasures. 

The Guidebook includes relevant noteworthy practices, lessons learned, and computational 

examples from six pilot studies with various transportation agencies. The two appendices 

describe the impact of AADT estimation errors on data-driven safety analysis and present an 

inexpensive trip generation method that estimates traffic volumes on residential roads. 

Agencies do not have to conduct all the activities described in the Informational Guide. Readers 

are encouraged to adopt those elements most relevant to their needs given their available 

resources. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 1. Flowchart. Process to Collect Data and Estimate AADT for NFAS Roads.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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CHAPTER 1 — INTRODUCTION

1.1 BACKGROUND

Annual average daily traffic (AADT) is one of the most widely 

used data elements in transportation engineering. The FHWA 

Traffic Monitoring Guide (TMG)—the main reference for 

transportation agencies on traffic data collection, processing, 

analysis, and reporting—defines AADT as the “total volume 

of vehicle traffic of a highway or road for a year divided by 

365 days”.(1) Transportation agencies use AADT to meet data 

reporting requirements, better inform decision-making, and 

support various agency functions related to planning, design,

operations, safety, maintenance, environmental analysis, etc. 

State Departments of Transportation (DOTs) are required 

to report AADT every year to the Highway Performance Monitoring System (HPMS) for the 

full extent of mainlines, samples, and ramps on all Federal-aid facilities.

(2)

In addition, the 2016 

Highway Safety Improvement Program (HSIP) Final Rule requires States to have access to 

AADT along with other Model Inventory of Roadway Elements (MIRE) Fundamental Data 

Elements (FDE) for all public paved roads, including non-Federal aid-system (NFAS) roads, by 

year 2026.

(3) According to the HSIP Final Rule: 

The MIRE FDE are beneficial because collecting this roadway and traffic data and 

integrating those data into the safety analysis process would improve an agency's ability 

to locate problem areas and apply appropriate countermeasures, hence improving 

safety.

The NFAS includes roads that are functionally classified as: 

• Minor collectors in rural areas (6R).

• Local roads in rural areas (7R). 

• Local roads in urban areas (7U). 

According to 2016 Federal Highway Administration (FHWA) Highway Statistics, these functional 

classes together account for more than 75 percent of the total roadway mileage in the United 

States.

(4) Table 1 shows the total roadway length by functional classification and rural/urban 

designation.

Chapter 1 at a Glance

1.1 Background

1.2 Scope

1.3 Framework

1.4 Audience

1.5 AADT estimation

1.6 Requirements

1.7 Organization
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Table 1. Total Road Length by Functional Class and Rural/Urban Designation.

(4)

Federal 

Aid

Functional Classification and 

Rural/Urban Code

Road Length 

(Miles) Percent

Yes 1R - Rural Interstates 29,133 0.7%

Yes 2R - Rural Other Freeways and Expressways 6,378 0.2%

Yes 3R - Rural Other Principal Arterials 89,728 2.2%

Yes 4R - Rural Minor Arterials 133,809 3.2%

Yes 5R - Rural Major Collectors 407,650 9.8%

No 6R - Rural Minor Collectors 258,477 6.2%

No 7R - Rural Local Roads 2,002,878 48.4%

— Subtotal 2,928,054 70.7%

Yes 1U - Urban Interstates 19,058 0.5%

Yes 2U - Urban Other Freeways and Expressways 12,255 0.3%

Yes 3U - Urban Other Principal Arterials 66,137 1.6%

Yes 4U - Urban Minor Arterials 112,384 2.7%

Yes 5U - Urban Major Collectors 129,173 3.1%

Yes 6U - Urban Minor Collectors 16,961 0.4%

No 7U - Urban Local Roads 856,085 20.7%

— Subtotal 1,212,054 29.3%

— Total 4,140,108 100.0%

A dash denotes not applicable.

Existing Federal guidance does not fully address (as of the publication of this document) how 

States should collect data and estimate AADT for NFAS roads to meet the new requirements 

of the HSIP Final Rule. Similarly, the 2016 FHWA HPMS Field Manual does not provide 

information on AADT volume group ranges, precision levels, and sampling procedures for 

NFAS roads.

(2) The HSIP Final Rule asks States to collect additional MIRE FDEs as needed to 

support system-wide or site-specific safety analysis.(3)

1.2 SCOPE 

The purpose of this Informational Guide is to provide transportation agencies with information 

about collecting data on NFAS roads and developing AADT estimates for use in data-driven

safety analysis, as defined in the HSIP Final Rule. Considering that many transportation agencies 

perform stratified sampling to develop default AADT estimates for NFAS roads, this 

Informational Guide focuses on the improvement of potentially existing stratification schemes 

and development of new random stratified sampling schemes. The Guide will help readers to:

• Understand basic traffic monitoring concepts.

• Understand the importance and need for producing AADT estimates for NFAS roads.

• Conduct a self-assessment on existing resources and data. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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• Establish data sharing practices with other agencies.

• Validate existing stratification scheme and quantify the accuracy of current AADT 

estimates, if any. 

• Develop new stratification schemes manually as well as by applying the classification 

method decision trees.

• Compare effectiveness and data needs of different stratification schemes.

• Collect traffic volume data and develop new AADT estimates.

The intent of this Informational Guide is to assist both experienced traffic monitoring personnel 

and those who are new to traffic data collection and AADT estimation. The Informational 

Guide includes methods suitable for agencies that do not collect data nor estimate AADT for 

NFAS roads, as well as for those desiring to improve their practices and the accuracy of their 

AADT estimates. 

1.3 FRAMEWORK

This Informational Guide presents a framework (Figure 2) that consists of two major parts. The 

first part, called Preparation, includes four basic steps that are necessary to prepare for effective 

data collection and sharing of traffic volume data among target agencies. These steps are part of 

a larger safety data integration process that FHWA published in 2016 (Informational Guide for 

State, Tribal, and Local Safety Data Integration).

(5) The second part, called AADT Estimation, 

includes eight steps that describe how to improve an existing stratification scheme, develop 

new random stratified sampling schemes, collect data, and estimate AADT for NFAS roads.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 2. Flowchart. Process to Collect Data and Estimate AADT for NFAS Roads.

1.4 AUDIENCE

The target agencies of this Informational Guide are Metropolitan Planning Organizations 

(MPOs), Federal, State, Tribal, and local governments that own NFAS roads. Table 2 shows the 

total length of NFAS roads in the United States by roadway functional classification, rural/urban 

designation, and roadway ownership.(4)
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Table 2. Total Roadway Length (Miles) by Functional Classification, Rural/Urban 

Designation, and Ownership.(4)

Agency - Roadway 

Owner

Rural Minor 

Collectors 

(6R)

Rural 

Local 

(7R)

Urban

Local 

(7U)

Total Percent

County 175,471 1,184,588 181,047 1,541,106 49.4%

Town, Township, 

Municipal1 12,757 532,539 631,302 1,176,598 37.7%

State Highway Agency 60,015 121,994 32,393 214,402 6.9%

Federal Agency27,632 119,767 7,901 135,301 4.3%

Other Jurisdictions32,602 43,990 3,442 50,034 1.6%

Total 258,477 2,002,878 856,085 3,117,441 100.0%

Percent 8.3% 64.2% 27.5% 100.0% NA4

1

Prior to 1999, municipal was included with other jurisdictions. Some States may have incomplete and missing 

ownership data.

2 Roadways in Federal parks, forest, and reservations that are not a part of the State and local highway systems. 

3

Includes State park, State toll, other State agency, other local agency, and roadways not identified by ownership.

4 Not applicable.

Counties own around 50 percent of all NFAS roads in the United States, followed by towns 

that own approximately 38 percent. The remaining 12 percent belongs to Federal, State, and 

other agencies. Rural local roads account for the majority (64 percent) of all NFAS roads, 

followed by urban local roads (28 percent) and rural minor collectors (8 percent).

The primary audience within the target agencies are traffic monitoring officials (e.g., managers, 

planners, data analysts, database administrators, Geographic Information Systems [GIS] analysts, 

information technology [IT] staff, and data technicians) who typically collect, process, analyze, 

integrate, archive, publish, or report traffic data and AADT estimates. A secondary audience is 

traffic safety managers, analysts, and researchers who use AADT in data-driven safety analysis 

such as performing system-wide network screening analysis, developing crash modification 

factors (CMFs), developing and calibrating safety performance functions (SPFs), and evaluating 

the effectiveness of highway safety improvement projects and countermeasures.(6)
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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1.5 AADT ESTIMATION

Many agencies estimate AADT using variations of a traditional method that Drusch first 

introduced in 1966(7)and is recommended by FHWA’s TMG.(1) The traditional approach 

combines traffic data from permanent and portable traffic counting equipment. Permanent 

stations, known as continuous count sites (CCSs), collect traffic data 24 hours a day, seven days 

a week for either all days of the year or extended periods of time.(1) Although CCSs capture 

actual traffic volumes at a specific site, the purchase, installation, operation, and maintenance 

cost of these counters is high. Due to budgetary constraints, agencies tend to install them at 

select locations. For many segments where CCS do not exist, agencies collect short-duration 

(e.g., 24, 48, 72 hours, one week) or seasonal data using portable traffic recorders (PTRs). The 

goal is to accurately adjust and expand the short-duration counts to AADT estimates. The main 

steps of the traditional AADT estimation method are:

• Gather traffic volume data from CCSs and calculate several adjustment factors (e.g., 

monthly, day-of-week, axle correction factors, etc.) for each CCS. Chapter 2 provides 

more information about calculating basic traffic parameters and adjustment factors.

• Establish temporal (monthly or others) pattern groups that are reasonably homogenous. 

Create groups of CCSs or factor groups based on traditional grouping approaches (i.e., 

roadway functional classification and geographical stratification), cluster analysis, volume 

factor groups, by roadway, by area of influence, or a combination of these methods. 

• Compute adjustment factors for each group of CCSs. The group adjustment factors are 

computed from the individual factors of the sites contained in each group. 

• Assign short-duration counts to the previously determined factor groups. 

• Multiply the average daily traffic (ADT) of a short-duration count with the appropriate 

group adjustment factor(s) to generate an AADT estimate for the counted segment.(1)

Unlike higher roadway functional classifications that agencies routinely count for various 

purposes including meeting HPMS requirements, NFAS roads typically have significantly lower 

count coverage. Further, conducting a high number of counts on the extensive NFAS road 

network can be financially difficult. To reduce data collection costs, many agencies combine the 

traditional AADT estimation approach with stratified sampling techniques that include the 

following general steps:

• Stratify NFAS road network to create as homogenous groups of roads (or strata) as 

possible in terms of AADT. The stratification is often based on one or multiple 

attributes (e.g., roadway functional class, rural/urban designation, etc.). 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

7

• Select and count a sample of roads from each group (or stratum). The goal of the 

counted samples is to provide similar traffic volumes that are representative of all the 

roads within each stratum. 

• Calculate a default AADT estimate for each stratum using the counts collected in the 

previous step. The default AADT value of each stratum is assigned to every segment 

(within the stratum) where traffic volume data are not available – these segments are 

widely known as uncounted segments. On the other hand, counted segments are those 

for which traffic volume data exist. 

While most agencies use traditional stratified sampling approaches to develop default AADT 

estimates for NFAS roads, there are other types of AADT estimation methods that can be 

broadly divided into three major groups, as shown in Figure 3.

Figure 3. Chart. Types of AADT Estimation Methods for NFAS Roads.

The traffic count-based methods or simply count-based methods require traffic volume data 

that agencies typically obtain from continuous and short-duration traffic counters, as described 

above. The count-based methods mainly include traditional stratified sampling methods that 

most agencies use to develop default AADT estimates;

(8, 9, 10, 11, 12, 13, 14, 15, 16)

and non-traditional 

approaches such as statistical and machine/deep learning methods that directly estimate AADT 

by avoiding the errors inherent in each step of the traditional approach described above.(17, 18, 19, 

20, 21)

Estimating AADT using a combination of traffic, passively collected data (e.g., from cell 

phone and navigation devices), and other types of data (e.g., demographic and socioeconomic 

data) has attracted some attention over the last few years. Ongoing FHWA pooled-fund study 

Exploring Non-Traditional Methods to Obtain Vehicle Volume and Class Data is currently examining 

the accuracy of AADT estimates developed from non-traditional data sources and methods.(22)

AADT Estimation Methods

A) Traffic CountBased

B) Non-Traffic 

Count-Based

Traditional 

Stratified 

Sampling

NonTraditional 

Methods

Disaggregated 

Estimates at 

the Segment 

Level

Aggregated 

Estimates 

(e.g., County 

Level)

C) Travel 

Demand Models
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

8

The non-traffic count-based methods or non-count-based methods allow the direct estimation 

of AADT using alternative types of data such as traffic, roadway, demographic, socioeconomic, 

passively collected (e.g., GPS and location-based services data from mobile devices), land use, 

area of influence (e.g., distance from permanent sites and distance from major roads), temporal 

variables (e.g., hour of day, day of week, weekday, weekend, month, season), and administrative 

data (e.g., county boundaries and city limits). These methods can produce a) disaggregated 

AADT estimates at the roadway segment level,

(23, 24, 25, 26, 27, 28)

and b) AADT estimates 

aggregated by functional class, county, ZIP code, U.S. Census geographical units (e.g., block 

groups and tracts), area, region, and other geographical levels.(29, 30, 31, 32, 33, 34, 35)

Travel Demand Models (TDMs) incorporate mathematical equations to capture travelers’ 

decisions and allocate trips to roads.(28, 36, 37, 38, 39) MPOs and local agencies typically develop and 

use TMDs to model primarily small- to medium-size areas and regions.(40) The traditional travel 

demand modeling process consists of four steps: trip generation, trip distribution, mode choice, 

and trip assignment. The main data inputs in travel demand modeling are AADT, transportation 

network, land use, survey, and socioeconomic data. 

1.6 REQUIREMENTS

The HSIP is legislated under Section 148 of Title 23, United States Code (23 U.S.C. 148) and 

regulated under Part 924 of Title 23, Code of Federal Regulations (23 CFR Part 924).(41, 42) The 

purpose of the HSIP is to achieve a significant reduction in fatalities and serious injuries on all 

public roads. The HSIP requires a data-driven, strategic approach to improving highway safety 

with a focus on performance. The Moving Ahead for Progress in the 21st Century Act (MAP-21) 

and the Fixing America's Surface Transportation Act continue the HSIP as a core Federal-aid 

program. The MAP-21 requires States to collect a subset of MIRE FDE [23 U.S.C. 148(e)(2)(A)]

that are necessary to perform system-wide network screening, locate problem areas, and 

implement appropriate countermeasures to improve safety.

Federal requirements that govern the collection of MIRE FDE, including AADT, are:

• 23 CFR Part 924.11(b) requires States to incorporate specific quantifiable and 

measurable anticipated improvements for the collection of MIRE fundamental data 

elements into their Traffic Records Strategic Plan by July 1, 2017. 

• 23 CFR Part 924.11(b) requires States to have access to a complete collection of MIRE 

FDE on all public roads by September 30, 2026. Public roads refer to any highway, road, 

Among the various AADT estimation methods that exist in the literature, this Informational 

Guide focuses on stratified sampling that many agencies use to estimate AADT for NFAS 

roads.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

9

or street under the jurisdiction of and maintained by a public authority and open to 

public travel, including non-State-owned public roads and roads on tribal land (23 CFR 

Part 924.3). Open to public travel means that the road section is available during extreme 

weather or emergency conditions (except during scheduled periods), passable by fourwheel standard passenger cars, and open to the general public for use without 

restrictive gates, prohibitive signs, or regulation other than restrictions based on size, 

weight, or class of registration. Toll plazas of public toll roads are not considered 

restrictive gates [23 CFR 460.2(c)].

(43)

• 23 CFR Part 924.17 requires States to collect a series of MIRE FDE based on the 

functional classification and surface type of a road, as follows: 37 MIRE FDE on non-local 

paved roads, 9 MIRE FDE on local paved roads, and 5 MIRE FDE on unpaved roads.

AADT is required for non-local paved roads and local paved roads. This Informational 

Guide focuses on data collection and AADT estimation for paved rural minor collectors, 

and both rural and urban local paved roads.

FHWA Guidance on State Safety Data Systems(44)clarifies that transportation agencies can use 

HSIP funds to collect and analyze safety data, including AADT, to identify safety problems and 

countermeasures, prioritize projects, and evaluate the effectiveness of highway safety 

improvement projects and programs [23 U.S.C. 148(a)(4)(B)(xiv)]. The term highway safety 

improvement project is defined in 23 U.S.C. 148(a)(4) as "strategies, activities, and projects on a 

public road that are consistent with a State strategic highway safety plan and...correct or improve a 

hazardous road location or feature...or address a highway safety problem." Based on this definition, 

the collection, analysis and improvement of safety data is an eligible project. The term data 

improvement activities means "a project or activity to further the capacity of a State to make more 

informed and effective safety infrastructure investment decisions." [23 U.S.C. 148(f)(1)(A)]. Safety 

data collection, analysis, and improvement activities are provided throughout section 148. 

Examples of activities eligible for HSIP funding are:

1. Collecting, maintaining, and sharing safety data, including MIRE, on all public roads and 

related systems associated with analytical usage of the data that directly supports HSIP 

implementation efforts [23 U.S.C. 148(f)(1)(B)].

2. Creating, updating or enhancing a highway basemap of all public roads [23 U.S.C. 

148(f)(1)(B)].

Transportation agencies can use HSIP funds to collect and analyze safety data, including 

AADT [23 U.S.C. 148(a)(4)(B)(xiv)].
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

10

3. Improving the State's ability to identify the number of fatalities and serious injuries on all 

public roadways in the State with a breakdown by functional classification and ownership 

in the State [23 U.S.C. 148(c)(2)(D)(v)].

4. Improving data timeliness, accuracy, completeness, uniformity, integration, and 

accessibility [23 U.S.C. 148(c)(2)(A)(i)].

5. Evaluating the effectiveness of safety data system improvement efforts [23 U.S.C. 

148(c)(2)(a)(ii)].

6. Evaluating the effectiveness of highway safety improvement projects [23 U.S.C. 

148(c)(2)(F)(i)].

7. Improving the ability to link State safety data systems with other data systems within the 

State [23 U.S.C. 148(c)(2)(A)(iii)].

8. Improving the compatibility and interoperability of safety data with other State 

transportation-related data systems and the compatibility and interoperability of State 

safety data systems with data systems of other States and national data systems [23 

U.S.C. 148(c)(2)(A)(iv)].

9. Improving the ability to collect data on non-motorized (e.g., pedestrian and bicyclist) 

crashes [23 U.S.C. 148(c)(2)(A)(vi)].

10. Creating, updating, or enhancing a highway basemap of all public roads in a State [23 

U.S.C. 148(f)(1)(B)(i)].

11. Storing and maintaining safety data in an electronic manner [23 U.S.C. 148(f)(1)(B)(iii)].

12. Developing analytical processes for safety data elements [23 U.S.C. 148(f)(1)(B)(iv)].

13. Acquiring and implementing roadway safety analysis tools [23 U.S.C. 148(f)(1)(B)(v)].

1.7 ORGANIZATION

The remaining chapters and appendices in this Informational Guide are:

• Chapter 2 – Traffic Monitoring Concepts. This chapter introduces basic traffic 

monitoring terms and concepts that relate to different types of traffic counts, data, and 

computation of basic traffic parameters.

• Chapter 3 – Preparation for AADT Estimation. This chapter provides 

information on how to prepare and plan for effective collection and sharing of traffic 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

11

volume data among target agencies. The chapter examines the first four steps (Figure 2) 

of FHWA’s safety data integration framework: 1) lay the foundation, 2) conduct gap 

analysis, 3) establish data governance process, and 4) develop data collection and 

integration plan.

• Chapter 4 – Random Stratified Sampling. This chapter describes eight steps of the 

AADT estimation process shown in Figure 2. The steps include: 1) gather and process 

non-traffic data, 2) perform data exploratory and correlation analysis, 3) determine 

performance and sample size needs of current stratification scheme, 4) develop new 

stratification schemes, 5) compare schemes and select one, 6) select samples, 7) 

establish and evaluate continuous count program, and 8) conduct counts and develop 

default AADT estimates. FHWA selected six agencies—Georgia DOT, Minnesota DOT, 

Montana DOT, New Mexico DOT, North Carolina DOT, and Pennsylvania DOT—for 

pilot studies that involved analyzing data and developing an AADT estimation work plan 

for each pilot state. Chapter 4 includes various calculation examples from some of these 

studies.

• Chapter 5 – Conclusion. This chapter describes the most important elements that 

transportation agencies should take into consideration to improve an existing 

stratification scheme, develop a new scheme, collect data, and estimate AADT for NFAS 

roads.

• Appendix A – Impact of AADT Estimation Errors on Safety Analysis. This 

appendix describes a sensitivity analysis that determined the impact of AADT estimation 

errors on the results of data-driven safety analysis.

• Appendix B – Automated Estimation of AADT Using Trip Generation 

Method. This appendix describes an AADT estimation procedure that incorporates a 

trip generation method. The method is suitable for residential roads that have one point 

of entry (i.e., cul-de-sacs). This method has two advantages over many traditional and 

non-traditional AADT estimation methods. First, it eliminates the need to carry out 

traffic counts in the field by yielding time and cost savings. Second, it provides more 

accurate AADT estimates, particularly on urban local roads where the correlations of 

candidate stratification variables with AADT tend to be weak to negligible.

In the first phase of the project, researchers identified noteworthy practices in data collection 

and AADT estimation for NFAS roads. Four agencies—Delaware Valley Regional Planning 

Commission (DVRPC), New York State DOT (NYSDOT), Southeast Michigan Council of 

Governments (SEMCOG), and Virginia DOT (VDOT)—helped develop detailed case studies of 

their practices that are available online at 

https://safety.fhwa.dot.gov/rsdp/safety_casestudies.aspx. The four case studies are:
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

12

• DVRPC: Innovative Traffic Data Sharing Practices. This case study highlights 

how DVRPC promotes traffic data sharing with other agencies and makes traffic and GIS 

data available to the public.(45)

• NYSDOT: Engagement of Local Agencies in Traffic Volume Collection and 

Random Sampling Procedures. This case study highlights two noteworthy practices 

at NYSDOT on traffic data collection agreements with local agencies and random 

sampling procedures for selecting traffic count locations.

(46)

• SEMCOG: Innovative Traffic Data Quality Assurance/Quality Control 

Procedures and Automating AADT Estimation. This case study highlights two 

noteworthy practices at SEMCOG regarding short-duration traffic count validation 

procedures and an automated AADT estimation process.(47)

• VDOT: Innovative Procedures in Traffic Volume Estimation. This case study 

presents how VDOT estimates traffic volumes on secondary local roadways using a trip 

generation method instead of taking short-duration counts in the field.(48)

Examples and summaries of noteworthy practices from these four agencies are provided in 

green rectangles throughout this Informational Guide.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

13

CHAPTER 2 — TRAFFIC MONITORING CONCEPTS

2.1 INTRODUCTION

The purpose of traffic monitoring is to describe the use 

and performance of the roadway system through the 

collection, processing, and analysis of traffic data. This 

chapter introduces basic traffic monitoring terms and 

fundamental concepts that readers need to understand 

before reading the remaining chapters of this Informational 

Guide. This chapter does not provide a comprehensive 

review of traffic monitoring theory but presents the most 

important terms and concepts necessary to compute or 

estimate AADT for NFAS roads. These concepts relate to different types of traffic counts, data, 

and computation of basic traffic parameters. The FHWA TMG provides more information on 

various components of traffic monitoring programs.

(1) Other national references include the 

American Association of State Highway and Transportation Officials (AASHTO) Guidelines for 

Traffic Programs and Volumes I and II of the FHWA Traffic Detector Handbook.

(50, 51, 52)

2.2 TRAFFIC COUNTS

A count refers to the tabulation of the quantity of vehicles at a specific point along a road. This 

point is known as a count station or site. Based on the duration and the traffic equipment used, 

traffic counts are grouped into two primary categories: continuous and short-duration. 

2.2.1 Continuous 

Continuous count stations are permanently-installed traffic counters that automatically collect 

traffic data 24 hours a day and 7 days a week for all days of the year or at least for several 

months or seasons. Ideally, these stations record traffic data every day of the year; however, 

for various reasons (e.g., construction, equipment failure, seasonally closed road, inclement 

weather conditions), gaps in data can occur. Continuous counts refer to volume counts 

covering all lanes derived from CCSs for 24 hours of each day over all days in a year. 

Continuous data programs or permanent count programs are part of a broader travel 

monitoring program and involve maintaining, storing, accessing, and reporting data from CCSs.

AADT is only one of the traffic parameters that can be computed using CCS data.

CCSs employ different sensor technologies, broadly categorized into two groups: intrusive and 

non-intrusive. Intrusive sensors refer to traffic monitoring devices that are installed on top of, 

under, or in the pavement. Examples of intrusive sensors are inductive-loop detectors, piezosensors, strain gauge sensors, fiber optics, magnetometers, and weigh-in-motion (WIM) 

Chapter 2 at a Glance

2.1 Introduction

2.2 Traffic counts

2.3 Traffic data

2.4 Traffic parameter 

computation
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

14

sensors. Non-intrusive sensors are located above or to the side of the roadway. Examples of 

non-intrusive technologies are video image processors, microwave radar sensors, laser radar 

sensors, magnetometer sensors, passive infrared sensors, ultrasonic sensors, and passive 

acoustic sensors.

CCSs are an important traffic data source but have high installation, operating, and maintenance 

costs. Accordingly, agencies install them at a few carefully selected locations, limiting their

spatial coverage on the network. For locations where CCSs do not exist, more affordable 

short-duration counts can be used, as described below.

2.2.2 Short-Duration

Short-duration or short-term count stations collect traffic data for a specified period that is less 

than 365 days per calendar year.

(1) The duration of a count typically ranges between a few hours

to a couple of weeks. Short-duration count stations can collect data by hour of day or shorter 

time intervals such as 15 minutes or by individual vehicle storage. The ADT is one of the main 

outputs that can be computed from short-duration counts. The TMG defines ADT as: 

The total traffic volume during a given time period (in whole days), greater than one 

day and less than one year, divided by the number of days in that time period. ADT is 

also known as raw data and unadjusted or non-factored data. 

One of the goals of taking short-duration counts is to expand and convert their ADT into 

AADT estimates. This AADT estimation process is widely known as factoring and involves 

calculating the ADT of a short-duration count and multiplying it with one or multiple 

adjustment factors (e.g., axle correction factor (if a road tube count), time (hour) of day, day of 

week, month of year (season), and change rate year to year factor). The adjustment factors are 

typically derived from CCS data. Highway agencies use the counts and the AADT estimates to 

supply information for individual projects (e.g., corridor studies, pavement design, maintenance, 

repair, rehabilitation, reconstruction, traffic control studies), develop lane closure policies, 

safety, meet federal reporting requirements, and provide broad knowledge of roadway use.

Short-duration counting programs refer to the management aspects of non-continuous data 

collection activities that are part of an overall travel monitoring program. Short-duration count 

programs are typically divided into coverage count and special needs count subsets. The 

coverage count subset encompasses the roadway system on a periodic basis to meet both 

point-specific and area needs, including HPMS reporting requirements. The special needs subset 

comprises additional counts necessary to meet specific project needs of other users.

(1)

The location and frequency of short-duration counts is a function of each agency’s policies, 

funding levels, geographic areas of responsibility, and needs. The ways in which agencies balance 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

15

the benefits and costs of addressing their objectives against their limited traffic-counting budgets 

have led to different data collection programs nationwide. Some agencies consider a weeklong 

count conducted every seven years with data recorded for every hour of each day to be 

adequate. Others conduct 48-hour counts every three years and record daily counts. The 

distance between short-duration counts along a roadway is also subject to agency discretion

but should follow the HPMS Field Manual. The most common types of short-duration traffic 

counters are PTRs. PTRs are mobile vehicle counters or vehicle classifiers that are temporarily 

installed in or along the infrastructure and can be easily moved from one location to another. 

Common counting equipment used on NFAS roads include low-cost traffic counters connected 

to hollow rubber tubes that are laid perpendicular across the monitored lanes.

(53) When a 

vehicle passes over a hollow rubber tube, the change in air pressure causes an air switch to 

close, which sends an electric signal to the counter. The last decade has seen increased use of 

video recording technologies for traffic volume data collection. Portable video units are secured 

along the roadside for up to several days and then the recorded traffic video is post-processed 

using video image processing software to generate hourly or daily vehicle counts and/or 

classification data.

2.3 TRAFFIC DATA 

2.3.1 Variation

Traffic volume patterns can vary temporally and spatially. For example, volumes often change 

throughout the day. Passenger car and truck traffic typically have different time of day patterns. 

Urban passenger car volumes tend to peak during the morning and evening commutes, while 

rural passenger car volumes tend to increase slowly until the evening when the volumes 

decrease. Truck volume patterns change too depending on several factors such as local 

activities that generate or attract truck traffic.

Day of week patterns exist for all vehicle types.(1)For example, passenger car volumes are 

typically constant on weekdays and then decline on weekends. Certain roads used for 

recreational travel will have constant volumes on weekdays and volumes will increase on 

weekends. Long-haul truck volumes often remain constant seven days a week for all hours of a 

day. Short-haul truck volumes remain constant on weekdays and then fall on weekends more 

than passenger cars. Passenger cars have various uses, whereas short-haul trucks are typically 

business related and are used infrequently on weekends.

Traffic volumes vary depending on the month of the year. There are numerous factors that 

contribute to month of year traffic volume variations depending on local socioeconomic 

activities and land uses. For example, when schools are out of session during summer months 

and winter break, less school related traffic (buses) is generated. Roads near beaches will have 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

16

lower volumes in the winter whereas roads near ski resorts may have higher volumes in the 

winter. Truck traffic experiences monthly variations especially in areas with agriculture or 

distribution hubs.

Traffic volumes can vary from year to year. When new developments are built, a road with 

traditionally low and stable traffic volumes can see rapid volume growth. During economic 

downturns, traffic volumes can decrease from one year to the next. Areas heavily dependent on 

one economic sector can see dramatic changes in traffic volume from year to year depending 

on the growth or decline in the sector. For example, when oil prices are high there may be an 

increase in truck traffic on rural roads with energy development activity until oil prices 

decrease.

Traffic volumes vary spatially at different geographical scales. For example, traffic volumes vary 

by State, in different parts of a State, from one roadway to another, and even at different 

locations along a road. Similar differences occur for motorcycles, cars and trucks. Roads that 

belong to the same functional class and are nearby to each other could have different traffic 

volumes due to geographic, socioeconomic, demographic, land use, and other characteristics.

Geographical classification can be used as a surrogate measure to describe roads with similar 

traffic characteristics, including AADT. Geographic stratification is particularly important when 

different parts of a State experience different travel behavior. For example, roads that 

experience heavy recreational activity tend to have different traffic patterns and AADTs than 

roads that do not carry recreational traffic. Even in urban areas where travel is more stable 

throughout the year, cities with heavy recreational movements have different patterns than 

cities in the same State without recreational travel.

Some roads experience directional variation where traffic may predominately travel in one 

direction in the morning and the opposite direction in the afternoon. For example, this pattern 

can occur in suburban areas where vehicles travel toward the city center in the morning and 

away from the city center in the afternoon. Although it is important to understand that traffic 

can vary by direction, this Informational Guide focuses on data collection and AADT estimation 

methods that capture the total vehicle volume in both directions of travel, not separately in 

each direction. 

This Informational Guide focuses on data collection and AADT estimation methods that 

capture the total vehicle volume in both directions of travel, not separately in each 

direction.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

17

2.3.2 Types

Traffic monitoring devices collect one or more types of motorized and non-motorized traffic 

data. The main types of motorized data include vehicle volumes, vehicle classification, speeds, 

axle spacing, vehicle and axle weight, vehicle length, signature, gap, headway, and lane 

occupancy. Non-motorized data mainly refer to bicycle and pedestrian volumes. 

Most continuous and short-duration traffic counters that agencies employ on NFAS roads 

record total vehicle volumes. Agencies use vehicle classification counters less frequently mainly 

because they tend to be more expensive. Typical situations where vehicle classification data are 

collected include, but are not limited to, when there is a need to characterize traffic, determine 

traffic composition, and measure AADT of a particular vehicle class or group of vehicle classes 

(e.g., trucks).

Existing traffic data collection technologies count vehicles either directly or indirectly. Some 

technologies count each passing vehicle and others count the number of axles or record array 

events. Axle correction factors are required to convert axle counts into vehicle volumes. Axle 

correction factors are computed using vehicle classification data. Section 2.4 of this 

Informational Guide provides formulas for computing axle correction factors along with other 

basic traffic parameters. 

2.4 TRAFFIC PARAMETER COMPUTATION

This section presents formulas for computing basic traffic parameters using data from CCSs and 

short-duration counts.

The ADT captures the total vehicle volume at a particular site within a day. ADT can be 

computed using both CCS and short-duration counts for a given period that is greater or equal 

to one day, but less than one year. The ADT is calculated as follows:

Equation 2.1:

Where:

VOLi = total traffic volume on both directions of travel for the i

th day.

n = number of days in a year for which traffic volume is collected (1≤n<365).

ADT =

1

n

 VOLi

n

i=1
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

18

2.4.1 AADT

The AADT is the total vehicle volume at a site on a typical day of the year. In this Informational 

Guide, the term AADT computation refers to the calculation of AADT using traffic volume 

data collected by CCSs. On the other hand, the term AADT estimation refers to annualizing 

short-duration traffic counts by applying one or more adjustment factors or using nontraditional prediction models (e.g., regression) that may account for various types of traffic and

non-traffic data (e.g., demographic and socioeconomic data).

The TMG provides different methods to calculate AADT. The most direct method is to

calculate the average of all ADT values that are available in a given year. Although this method 

is simple to apply, it introduces biases when data are missing. To avoid potential sources of bias, 

the TMG recommends using the following formulation for computing AADT:(1, 54)

Equation 2.2:

Equation 2.3:

Where:

MADTm= monthly average daily traffic during the mth month of the year.

VOLi,h,j,m = total volume for the i

th occurrence of the hth hour of the day within the jth day of the 

week during the m

th month. 

i = occurrence of a particular hour of day within a particular day of the week in a 

particular month for which traffic volume is available (i = 1, 2, ..., nh,j,m).

h = hour of the day (h = 1, 2, ..., 24).

j = day of the week (j = 1, 2, ..., 7). 

m = month of the year (m = 1, 2, ..., 12). 

MADTm =

 Wj,m 

1

nh,j,m

 VOLi,h,j,m

nh,j,m

i=1

 

24

h=1

7

j=1

 Wj,m

7

j=1

AADT =

 dm

12

m=1 ∗ MADTm

 dm

12

m=1
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

19

nh,j,m = number of times the h

th hour of the day within the jth day of the week during the mth

month has available traffic volume (nh,j,m ranges from 1 to 5 depending on hour of day, day of 

week, and data availability).

Wj,m = the weighting for the number of times the jth day of week occurs during the mth month 

(either 4 of 5). The sum of the weights in the denominator is the number of calendar days in 

the month (i.e., 28, 29, 30, or 31).

dm = the weighting for the number of days (i.e., 28, 29, 30, or 31) for the mth month in a 

particular year.(54)

This method does not require complete data from all 24 hours of a day and is more accurate 

than other AADT calculation formulas, especially in cases where data may be missing during 

some hours or days of a year. 

2.4.2 Factors

Short-duration counts usually require several adjustments to expand a raw count into an AADT 

estimate. Adjustments are necessary to reduce the effects of temporal bias and convert the 

number of axles, if available, to number of vehicles. The specific set of adjustments needed is a 

function of when a count was taken within a year, the duration of the count, and the equipment 

used to collect the count. These adjustments are made by developing and applying appropriate 

factors.

Factors are unitless ratios mainly computed from CCS data. There are several types of factors

such as temporal adjustment factors, axle correction factors, K-factor, D-factor, and peak hour 

factors. This Informational Guide presents temporal adjustment factors and axle correction 

factors that are needed to develop AADT estimates from short-duration counts.

2.4.2.1 Temporal Adjustment Factors

Temporal adjustment factors are used to expand short-duration counts into annual estimates

(i.e., AADT). The goal of using these factors is to correct temporal bias in short-duration 

counts by hour of day, day of week, month of year, season, or year (if count not conducted in 

current year). Adjustment factors can be calculated by dividing the AADT of a CCS by the 

average traffic volume capturing the duration of the desired adjustment factor. For example, a 

monthly adjustment factor is the ratio of AADT to monthly average daily traffic. For more 

information see FHWA’s TMG.(1)

Sometimes, two or more adjustment factors are combined. For example, day of week and 

monthly factors can be combined to create a single factor for each day of week of each month, 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

20

resulting in 84 adjustment factors per year. In this example, the adjustment factor is the ratio of 

AADT to monthly average weekday traffic.

Ideally, a short-duration count collected in a certain year should be adjusted with factors 

developed from continuous count data for that same year. This can create delays in timing of 

developing adjustment factors because annual continuous count data will not be available until 

the end of the year. One solution provided in the TMG is to develop temporary adjustment 

factors by applying last year's factors; computing an average of the three previous year’s factors; 

or computing a 12-month rolling average (for example, the temporary August 2018 factor 

would be computed using data from July 2017 through August 2018).

(1)

2.4.2.2 Axle Correction Factors

Axle correction factors are used to adjust axle counts into vehicle counts. Axle correction 

factors are developed by dividing the total number of vehicles counted by the total number of 

axles on those vehicles. Axle factors must be obtained from per vehicle classification or WIM 

site data. Axle correction factors can vary significantly on different roads. Ideally, the axle 

correction factor applied to an axle count should come from a vehicle classification count taken 

nearby on the same road and during the same period. When these types of axle correction 

factors are not available, the TMG recommends applying a system-wide axle correction factor. 

In general, a short-duration axle count is converted to an AADT estimate with the following 

formula:

Equation 2.4:

Where:

AADTi = estimated annual average daily traffic at location i.

VOLi = average daily axle volume at location i.

Fm = applicable monthly (seasonal) factor.

Fd = applicable day of week factor (if needed).

Fh = applicable hour of day factor (if needed).

Fa,i = applicable axle-correction factor for location i (if needed).

AADTi = VOLi × Fm × Fd × Fh × Fa,i × Fg
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

21

Fg = applicable yearly change rate factor (if needed).

This formula needs to be modified, as necessary, to account for the traffic count’s specific 

characteristics. For example, if the short-duration count is taken with an inductive loop 

detector instead of a conventional pneumatic axle sensor, the axle correction factor (Fa,i) is 

removed from the formula. Similarly, if the count is taken for seven consecutive days, the day of 

week factor (Fd) can be removed from the equation. Lastly, change rate factors are only needed 

if the count was taken in a year other than the year for which AADT is being estimated.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

22

CHAPTER 3 — PREPARATION FOR AADT ESTIMATION

3.1 INTRODUCTION

This chapter provides information on how to 

prepare and plan for effective collection and 

sharing of traffic volume data among target 

agencies. The chapter recommends following the 

first four steps of a safety data integration 

framework published by FHWA.

(5) The four steps 

include: 1) lay the foundation, 2) conduct gap 

analysis, 3) establish data governance process, and 

4) develop data collection and integration plan.(5)

Within these four steps, this chapter presents 

noteworthy data sharing practices at state and 

local agencies and provides lessons learned from a 2017 peer exchange on safety data 

integration and AADT estimation for NFAS roads.

3.1.1 Safety Data Integration Framework

In 2016, FHWA published an Informational Guide for State, Tribal, and Local Safety Data 

Integration.

(5) The Informational Guide defines data integration as “...the linking of multiple data 

sources to meet user’s needs.”

(5)

Safety data refers to three core data sources: crash, roadway 

inventory, and traffic volume data such as AADT. The goal of the 2016 Informational Guide is 

to help State, Tribal, and local agencies meet federal requirements pertaining to safety data 

integration and use of integrated safety data:(55)

Consistent with the purpose and scope of the HSIP, a State shall have in place a safety 

data system to perform safety problem identification and countermeasure analysis [23 

U.S.C. 148 (c)(2)(A)]. The statute also specifies that a State shall advance the 

capabilities of the State for data collection, analysis, and integration in a manner that 

includes all public roads, including non-State-owned public roads and roads on tribal 

land in the State [23 U.S.C. 148 (c)(2)(D) and (D)(ii)].

The 2016 Informational Guide introduced a safety data integration framework (Figure 4) that 

includes nine steps. The first four steps are part of the Preparation phase of the integration 

process. These preparation steps are also necessary to establish AADT data collection and 

sharing practices. Step 5 involves identifying training needs and connects the preparation steps 

with the implementation Steps 6 through 9.

Chapter 3 at a Glance

3.1 Introduction

3.2 Step 1: Lay the foundation

3.3 Step 2: Conduct gap analysis

3.4 Step 3: Establish data 

governance process

3.5 Step 4: Develop data collection 

and integration plan
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 4. Flowchart. Nine-Step Safety Data Integration Framework.

(5)
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The following sections describe the preparation steps (Steps 1 through 4) of the safety data 

integration framework by focusing on those elements that relate to AADT data collection and 

sharing. However, larger safety data integration efforts need to consider many MIRE-FDEs, not 

just AADT.

3.2 STEP 1: LAY THE FOUNDATION

Laying the foundation for effective cooperation and coordination among target agencies is the 

first preparation step for collecting and sharing AADT data. Considering the extensive and 

diverse use of traffic volumes in several fields, various stakeholders from all target agencies

should participate in this effort:

• Executive managers and leaders.

• Traffic monitoring staff including data managers, data analysts, and data technicians.

• Safety program managers, safety engineers, and crash data analysts.

• GIS managers, enterprise data managers and roadway inventory data stewards.

• Information technology staff.

• Planners and design engineers/managers.

• Maintenance engineers/managers.

• Infrastructure managers.

The leaders of each target agency need to determine the staff that should participate in this 

collaborative effort. Frequently, state agencies take the lead in a safety data integration effort, 

due to the greater spatial extent of their jurisdiction over a road network. However, other 

structures, where local agencies provide leadership, also exist.

(5) This Informational Guide does 

not suggest that a specific agency should take the lead. It is emphasized, nonetheless, that 

collaboration, partnerships, and frequent coordination among target agencies is critical for the 

success of this effort.

A critical activity during the first step of data integration is the development of effective multiagency coordination. For the success of a data integration project, it is important that:

• The executive management is fully invested in the project and project leaders have

gained approval for a strategic data integration plan and secured required resources.(56)

• Project leaders need to be mindful of proprietary interests of stakeholders, longstanding practices for processing, using and accessing the data, and existing independent

systems that various agencies may have developed over time.(56)
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25

• Stakeholders need to develop a sense of ownership of the final product to achieve buyin, trust, and cooperation. This can be achieved by informing and involving all project 

participants and by collecting and addressing each participant’s concerns.(56)

• Participants need to articulate clear goals, establish formal data sharing agreements and 

memoranda of understanding (MOUs), and foster productive communication by 

enforcing rules for respectful dialogue during group meetings.(57)

The FHWA Informational Guide for State, Tribal, and Local Safety Data Integration describes four 

case studies with the Arizona DOT, the Indiana DOT, the Navajo DOT, and the Rhode Island 

DOT. Based on the case studies, the report provides a list of lessons learned about 

partnerships, summarized as follows:

• There might be agencies that are not directly related to transportation issues but might 

add value to a data integration project.

• A data integration project might require more time and resources from participating 

agencies than initially expected.

• A data sharing MOU can be helpful to define the purpose of the project, expectations, 

shared responsibilities, and set a project mission and goals.

New York State Department of Transportation – Partnership Agreements with Local 

Agencies for Data Collection(46)

The New York State Department of Transportation (NYSDOT) estimates AADT annually 

on 115,000 miles of public roads using data from thousands of short-duration counts.(46)

NYSDOT conducts only 2 percent of these counts. Contractors and local agencies conduct 

84 percent and 14 percent of the counts, respectively. NYSDOT executes partnership 

agreements with local agencies to conduct counts. NYSDOT uses a memorandum of 

agreement (MOA), Figure 5, to facilitate these partnerships. 

As part of this agreement, NYSDOT purchases and provides traffic counters and supporting 

equipment, software, and training to local agencies, which in turn have to conduct a 

minimum of two counts per counter per year for five years. Local agencies take the majority 

of counts on NFAS roads that NYSDOT would not have otherwise counted. If a county 

does not fulfill its obligation to the state, it must return the equipment to NYSDOT. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

26

Source: Michael Fay, Director, Highway Data Services Bureau, NYSDOT

Figure 5. Scanned Document. NYSDOT Traffic Counting MOA.(46)

AGREEME T

The following terms and conditions are applicable to the Traffic Counting Partnership between 

County and New York State Department of Transportation. ---------

New York State Department of Transportation agrees to pro,·ide: 

1. cw automatic traffic recorders. 

2. All necessary software, training and support. 

________ County agrees to: 

I. Accept ownership of the automatic traffic recorders after fulfilling the qualified 

count obligation. 

2. Assume responsibility for all repairs to the counters. 

3. Supply all peripheral equipment (road tube, nails, clamps, etc ... ). 

4. Return the automatic traffic recorders, Pocket PC, and software to New York 

State Department of Transportation, if the County decides not to participate in the 

Traffic Counting Partnership. 

NYSDOT Reprcsentati,·e Date County Representative Date 

Im·entory of Counters deli,·ered to ------County 
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In summary, Step 1 Lay the Foundation should incorporate the following activities:

• Secure the commitment of executive management.

• Forge partnerships with data integration stakeholders.

• Establish MOUs and/or data sharing agreements.

• Establish communication processes to inform and involve all stakeholders.

3.3 STEP 2: CONDUCT GAP ANALYSIS

A gap analysis compares the current state to a desired state of a program, determines existing 

assets and program strengths, and identifies needs, potential deficiencies, and necessary

improvements to reach the desired conditions. In the case of AADT, the gap analysis involves 

not only identifying counted and uncounted segments, but also reviewing and determining other 

key elements of a program including, but not limited to, existing resources, costs, equipment,

data, technical capabilities, and business practices and procedures.

The main benefits of conducting a gap analysis include time and cost savings related to data 

collection, management, and accessibility, and the identification of opportunities to improve

data quality and completeness. Target agencies can realize these benefits by maximizing the use 

of existing resources, minimizing unnecessary data collection efforts, eliminating inefficient

processes, and implementing cost-effective practices and procedures to meet their needs and

MIRE FDE requirements.

Lessons learned from NYSDOT’s partnerships with local agencies are:(46)

• A mutually beneficial partnership will reduce costs for the DOT and provide

hardware, software, training, and data analysis to the local agency.

• The minimum number of counts can be set so that the DOT will at least break even

when providing local agencies with traffic counters and related equipment, training,

and support compared to the cost of outsourcing data collection activities.

• Working with the manufacturer of the traffic data recorders is crucial to ensure data

compatibility.

• Adequate training and standards are necessary to ensure valid data collection.

• Developing good working relationships with other agencies and understanding their

needs, priorities, and objectives can improve the probability of success.
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According to the TMG, transportation agencies should conduct a comprehensive strategic 

evaluation of their entire traffic monitoring program at least once every five years.(1) This 

comprehensive evaluation should include all aspects of a program covering all roadway 

functional classes, including NFAS roads. A targeted gap analysis that focuses on NFAS roads 

can be a part of the comprehensive evaluation of an agency’s entire program, but agencies can 

also conduct it independently, depending on their schedule and priorities.

It is important to establish a diverse team to perform the gap analysis. The team should include 

subject matter experts from different functional areas, departments, or sections of an agency. 

The team members should collectively have the experience and knowledge required to identify, 

gather, review, and in some cases analyze various traffic, roadway, financial, and administrative 

elements, as well as relevant business practices and procedures. The following subsections 

describe the most important activities, elements, processes, and procedures that a gap analysis 

should consider.

3.3.1 Traffic Volume Data

Analysts should gather all historical and recent traffic volume data from all partner agencies and 

compile them, if possible, into a single database. For completeness, analysts should include data 

from CCSs, if available on NFAS roads, and mark them accordingly to distinguish them from 

short-duration counts. 

The main data attributes of interest include, but are not limited to:

• Unique identification (ID) number of count or CCS.

• HPMS functional class.

• Traffic volume counting method (e.g., CCS, PTR, statistical method, etc.).

• Count technology (e.g., total volume, vehicle classification).

• Entity that took the count and provided the data (e.g., contractor, in-house staff, partner 

agency).

• Traffic equipment vendor and software version.

• Start and end dates and times of a count.

• Road name.

Gathering as many traffic counts as possible from both internal and external sources can 

reduce the number of counts required for sampling purposes (see section 4.4.3), resulting in 

data collection cost savings.
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• Linear referencing system (LRS) route ID.

• LRS location point.

• Latitude and longitude (or other linear reference point if geographic coordinates are not 

available).

• AADT (or ADT if counts are not factored).

• AADT year.

• Direction and/or lane of travel reported.

• AADT estimation method, if multiple methods are in place (e.g., factoring, trip 

generation method, regression model, neural network). 

• Amount of missing data, if any at all.

• Restrictions such as:

o Construction or other activity affected traffic flow, but not traffic pattern.

o Traffic counting device problem (e.g., malfunction or overflow).

o Weather affected traffic flow, but not traffic pattern.

o Construction or other activity affected traffic flow and traffic pattern.

o Weather affected traffic flow and traffic pattern.

3.3.2 Network Segmentation

Agencies need to review their LRS roadway network, and update it, as needed, by adding 

potentially new and missing NFAS roads. The development of a sampling plan requires a 

complete and well-defined universe of roads that has been properly sectionalized. Ideally, 

agencies should segment the network using existing AADT data. Though this may be possible 

on higher functional classification roads that have high coverage with count data, many states 

have limited traffic volume data on NFAS roads. 

To overcome this challenge, agencies should create intersection-based segments. That is, each 

segment should start and end at consecutive access points such as intersections and ramps, so 

that the AADT does not vary along each segment. The intersection-based segmentation can 

more accurately capture the actual counted and uncounted portions of the network, compared 

to other types of roadway segmentations that agencies may use for inventory and other 

purposes. For example, many agencies create homogenous sections, so that all roadway 

inventory elements (e.g., number of lanes, lane width, shoulder width, etc.) are constant along 

each section, even if AADT does not vary from one section to the next. This may result in 

multiple consecutive segments that can have the same AADT. From an AADT estimation 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

30

perspective, consecutive segments that do not have intermediate access points (i.e., the AADT 

does not vary from one segment to the next) can be merged into a single segment. This will 

result in longer segments that reduce the number of samples (i.e., short-duration counts) 

required to develop default AADT estimates, as explained in Chapter 4.

It is important to document the exact location of each segment to assure that cyclical updates, 

field reviews, traffic counts, etc., are performed on the appropriate roadway sections.(2)

Agencies should follow FHWA’s HPMS Field Manual; All Road Network of Linear Referenced Data 

(ARNOLD) Reference Manual; and Applications of Enterprise GIS for Transportation (AEGIST) 

Guidebook to make necessary modifications to their roadway network.(2, 58, 59)

3.3.3 Counted and Uncounted Segments

One of the main gaps that this analysis aims to identify and quantify is the total roadway mileage 

and the corresponding percent of uncounted segments on NFAS roads. To fill this gap, analysts 

need to geolocate available traffic counts and identify the counted and uncounted segments on 

the network. Each counted segment can contain one or more AADT values that may stem 

from different years, AADT estimation methods, or agencies. If multiple AADT values are 

available for the same location or segment, a process needs to be in place to determine the 

official AADT value for each year. This process needs to incorporate criteria for selecting the 

authoritative count type, AADT estimation method, and data source. For more information see 

FHWA’s All Road Network of Linear Referenced Data (ARNOLD) Reference Manual, and Applications 

of Enterprise GIS for Transportation (AEGIST) Guidebook.

(58, 59)

After assigning AADT values to the network, analysts should summarize at a minimum the 

number, the total roadway mileage, and the corresponding percent of counted segments and 

uncounted segments by functional classification and rural/urban designation (i.e., 6R, 7R, and 

7U), as well as for all NFAS roads together. These aggregate statistics can help analysts better 

understand what percent of the NFAS network does not have AADT data and where agencies 

need to devote future data collection and AADT estimation efforts and resources. Note that 

the number of counted and uncounted segments is used to calculate the number of shortduration counts required for sampling purposes, as described in Chapter 4.

3.3.4 Other Elements for Review

In addition to determining counted and uncounted segments, the gap analysis should consider, 

at a minimum, the following elements:

• Data collection costs: Estimate average costs required to conduct counts in-house 

and costs for outsourcing data collection activities. 
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o In-house: Estimate direct and indirect costs for collecting different types of 

continuous and short-duration traffic data in-house. Examples of direct costs 

include, but are not limited to, the capital, labor, and materials required to 

purchase, install, operate, and maintain traffic equipment, yearly calibration of 

equipment and costs to gather and process data. Materials may include the traffic 

equipment, peripherals (e.g., cables), tools, and hardware. Examples of indirect 

costs include employee benefits, computer operations, administration, vehicle 

depreciation, and others. Target agencies should develop separate cost 

estimates, at a minimum, for each type of traffic equipment (e.g., CCS, portable 

traffic recorder) and produced data type (e.g., total volume, vehicle classification, 

speed, weight). In cases where data collection costs for the same type of 

equipment and output vary geographically, target agencies should consider 

developing separate cost estimates by region. 

o Outsourcing: Estimate average costs required by contractors to conduct counts. 

Similar to the in-house cost estimates, target agencies should develop separate 

cost estimates by data type and region, if needed.

• Budget. Estimate budget, if available, for traffic volume data collection on NFAS roads

for the next data collection cycle or similar periods (e.g., for the next year, three years, 

five years, and 10 years).

• Traffic equipment. Determine number of available CCSs and portable traffic data 

recorders by type of equipment (e.g., total volume, classification, WIM), data output

(e.g., volume, speed, weight, number of axles), and status (e.g., active, inactive, 

abandoned). In the case of CCSs, determine the geographic location (e.g., route ID and 

distance from origin, coordinates, etc.) of each station, along with the name and the 

FHWA functional classification of the road where the station is located.

• Personnel. Determine number of traffic monitoring program staff, review their roles 

and responsibilities, and identify those who are tasked with the collection, processing, 

management, and archiving of short-duration traffic data. Agencies need to develop a 

clear understanding and quantify the manpower and resources required to conduct and 

process a certain number of counts (e.g., 5,000) within a year.

3.3.5 Practices and Procedures

The gap analysis should involve reviewing, and ideally documenting, practices and procedures 

related to AADT estimation. Below is a list of relevant questions that will help agencies not 

only conduct this review, but also communicate and share their practices with their partners. It 

is important to answer these questions before developing a data collection and integration plan:
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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• Roadway stratification

o Have you stratified the NFAS roadway network by creating groups of roads with 

similar AADT and developing default AADT values for each group? 

o If yes, did you stratify the network manually or by applying a statistical method?

o What data attributes did you use to stratify the network and why?

o Have you validated the accuracy of the default AADT estimates or the variability 

of each roadway group?

• Sampling

o Do you apply sampling procedure(s) to select locations or roadway segments of 

future counts?

o If yes, what sampling procedure(s) do you use?

o How do you apply the sampling procedure(s) (e.g., spreadsheet, statistical 

program, scripts, other)?

• Data collection

o Do you conduct short-duration counts at predetermined locations, known as 

historical sites?

o What types and how many counts do you take in-house per year?

o What types and how many counts do you outsource per year?

o What types and how many counts do other agencies take per year?

o What types and how many counts do you obtain from other agencies per year?

• Factoring

o Do you apply temporal adjustment factors to annualize short-duration counts?

o If yes, what types of temporal adjustment factors do you use?

o Do you calculate adjustment factors using data from CCSs located on NFAS 

roads or higher functional classes?

o Do you apply axle correction factors to the counts?

o If yes, how do you calculate the axle correction factors?

• Non-traditional AADT estimation

o Do you estimate AADT using non-traditional methods such as statistical (e.g., 

regression) and machine learning (e.g., neural network) methods?

o If yes, what is the cost of developing the models?
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o What is the cost of applying the models?

• Data management

o In what format(s) do you receive or gather traffic volume data?

o Where do you store the data and who manages the database(s)?

o Have you established data quality assurance (QA) and quality control (QC) 

criteria?

o How do you apply QA/QC procedures (e.g., spreadsheet, software program, 

other)?

o Have you established criteria for deleting, retaining, and archiving data?

o How do you share the data internally and in what format?

o How do you share the data with external entities and in what format?

Lessons learned about gap analysis conducted in previous safety data integration case studies

include:

(5)

• Local agency participation is fundamental to the success of the gap analysis and can be 

encouraged by providing benefits from the partnership. These benefits might include 

better data, more efficient reporting, improved asset management tools, and other 

analytic support.

• The gap analysis is only as good as the feedback and data provided by participants and 

may require an iterative approach to update data sets and refine stakeholder needs over 

time.

• The gap analysis is linked to the data governance step and should be repeated as 

needed. A data governance group may be useful to coordinate activities among partners 

and define data needs and gaps.

• Data gap analyses are essential to every data integration project and should include 

stakeholders from all affected agencies and business units. Small changes to data 

integration plans at the beginning of a project can have huge positive impacts and 

outcomes. Data gap analyses are the foundation for subsequent steps in the data 

integration process.

In summary, Step 2 Conduct Gap Analysis should incorporate the following activities:

• Gather traffic volume data from all partner agencies and compile them, if possible, into a 

single database.
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• Update the LRS roadway network by including all NFAS roads and determine the total 

roadway mileage and the corresponding percent of counted and uncounted segments.

• Review and quantify, if possible, other important elements of traffic monitoring 

programs such as data collection costs, budget, existing permanent and portable traffic 

equipment, personnel, and other available resources.

• Review and document relevant business practices and procedures such as roadway 

stratification, sampling, data collection, factoring, AADT estimation, and data 

management.

3.4 STEP 3: ESTABLISH DATA GOVERNANCE PROCESS

Data governance addresses data systems and management issues by defining data standards, 

data definitions, data authority and ownership, data access rights and usage, and data lifecycles.

Data governance helps agencies assure that their plans to address data gaps will result in 

desired improvements. The FHWA Data Governance Plan defines six strategic data governance 

goals and related objectives: leadership, quality, prioritization, cooperation, flexibility, and 

utilization.(60)

3.4.1 Leadership

Agencies need to identify champions and establish a data governance committee to monitor 

data integration. The committee is responsible for:

• Understanding data needs of all partner agencies.

• Identifying how partner agencies use and manage existing data.

• Developing metadata for existing data sources.

• Establishing data standards, QA/QC requirements, and data workflows.

• Documenting data governance procedures and developing training material. (5)

The data governance committee should consist of managers, data collectors, and users to 

ensure that efficient processes are developed in support of all user needs.

3.4.2 Quality

Agencies need to establish data standards, QA/QC procedures, and measures of data quality to 

ensure data are sufficient for the intended uses. It is important to define how partner agencies 

should collect, process, archive, secure, maintain, and disseminate data internally and externally.

Agencies can quantify data quality in relation to timeliness, accuracy, completeness, uniformity, 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

35

interoperability, and accessibility. These measures allow data users to make decisions about 

how the data might support a specific decision-making process.

In the case of traffic volume data, agencies can adopt TMG’s traffic monitoring formats and 

instructions (TMG, Chapter 7) that States follow to report traffic data to FHWA.(1) TMG 

provides data definitions, naming conventions, data structures, field descriptions, reporting 

requirements, and examples not only for traffic volume data, but also for station, speed, vehicle 

classification, weight, and non-motorized data. The ASCII text format is acceptable for all 

motorized data. 

In addition to data standards, the TMG discusses the importance of implementing automated 

QA/QC procedures for traffic data. It provides a series of QC checks that the Travel 

Monitoring Analysis System and the HPMS submittal software perform on AADT values that 

states submit to FHWA. It also presents six case studies that describe innovative QA/QC 

procedures.(1)

New York State Department of Transportation – Data Requirements for Collecting ShortDuration Counts(46)

NYSDOT developed a guide, titled New York State Traffic Monitoring Standards for Short Count 

Data Collection, to help NYSDOT staff, local agencies, and contractors meet data collection 

requirements.(61) The standards help NYSDOT ensure that the data collected by local agencies 

integrate seamlessly into NYSDOT’s database. The guide includes minimum standards for shortduration counts, safety, traffic count sites, and other data types. NYSDOT requires traffic counts 

to comply with FHWA’s TMG.(1)Participating agencies transfer data to NYSDOT usually weekly 

through email or file transfer protocol following NYSDOT procedures for file naming and data 

transfer. NYSDOT requires specific information to identify count locations and ensure data 

validity. They also conduct quality control checks of all traffic count data using manual 

procedures or automatic checks built into a traffic count software. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

36

Southeast Michigan Council of Governments – Innovative Traffic Data QA/QC 

Procedures(47)

SEMCOG receives every year around 3,000 short-duration counts from local agencies and 

conducts validity checks to ensure quality data are used for analysis. Since SEMCOG does 

not collect traffic data itself, local agencies upload their traffic counts to a central database. 

Some of these data serve local agencies’ specific needs, and do not meet SEMCOG’s 

requirements for AADT estimation. Therefore, SEMCOG identifies and excludes invalid 

data from AADT analysis, but the data remain in the database so that the local agency that 

collected the data can view and access them.

SEMCOG works with a software vendor to ensure that the database can support all traffic 

data collection systems that local agencies use. This eliminates the need to convert data and 

expedites the upload process by allowing local agencies to upload data themselves. When 

local agencies upload data, the software runs 15 validity checks that are built into the 

system. The system flags any errors that appear, and local agencies can remove the flagged 

data or submit all the data. Of the 15 validity checks built into the database system, the 

software provider developed some, and SEMCOG specifically requested others. These 

procedures check for missing count intervals, duplicate counts, tolerance compared to 

previous counts, directional split, and vehicle classification percentages. In addition to the 15 

built-in checks, SEMCOG developed 46 validity checks to clean and filter data downloaded 

from third-party software to SEMCOG’s internal database. These procedures provide an 

additional layer of data cleaning and filtering to complement the data cleaning processes 

embedded in the third-party software database. 

Lessons learned from SEMCOG’s validity checks include the following:

• Vendor selection is critical. With the number of agencies and amount of 

customization required to fully implement the procedures, an off-the-shelf product 

would not have worked. SEMCOG vetted the vendor to ensure it would be able to 

customize its software to meet the specific needs of all agencies.

• Having in-house technical expertise helps ensure that the program accomplishes the 

intended purpose and is sustainable. This requires coordination and buy-in from IT 

personnel. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

37

3.4.3 Prioritization

Agencies need to prioritize efforts to address data gaps and needs. In the case of AADT, the 

gap analysis will likely identify more uncounted segments than what many agencies can 

practically count using their existing budgets and resources. As a result, agencies may select to 

count only a representative sample of segments on their network. Chapter 4 provides more 

information on how to calculate the sample size required within a stratum.

At the beginning of a year, agencies should develop a clear understanding of all the counts that 

need to be conducted. The partner agencies need to work together to decide which data gaps 

are critical in the short-term and which can wait for some time. For example, agencies should

identify and prioritize potential uncounted segments for which traffic data are needed not only 

to develop AADT estimates, but also for other purposes and projects (e.g., special projects, 

safety improvement projects, pavement maintenance projects). This prioritization can minimize 

the need for taking additional or duplicate counts. The data governance committee can help 

agencies communicate and prioritize their gaps and needs. By carefully reviewing traffic count 

segments, location, and data requirements, it is often possible to significantly reduce the total 

number of counts required to meet various needs of several users.

3.4.4 Cooperation

Agencies need to collaborate, establish data sharing practices, and execute MOUs. Partners 

should coordinate to reduce duplication in the number and location of permanent and shortduration counts. A single count can supply information (e.g., vehicle volumes, weight, speed) 

that many agencies can potentially use for different purposes including intersection studies, 

signal warrants, pavement design, turning movements, safety analysis, and environmental 

studies. Communicating, coordinating, and sharing traffic volume data can yield several benefits 

for all stakeholders. Access to additional permanent and short-duration counts will provide data 

• Gathering and using data collected by local agencies can increase the number of 

traffic counts and eliminate duplicate data collection efforts. However, local agencies 

may collect data for purposes other than AADT estimation, thus creating the need 

for validity checks.

• Developing validity checks that exceed the procedures built into third-party software 

is necessary to better customize procedures and handle erroneous data. 

• Creating validity checks that flag and exclude erroneous data can greatly increase the 

accuracy and reliability of data used in different analyses.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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for filling data gaps, quality assurance, saving money, and facilitating data reporting and analysis if 

all data are integrated and stored in a central database.

However, sharing data among agencies may create several challenges and concerns that the 

data governance committee should identify and address by accounting for all partners’ needs 

and developing appropriate implementation strategies. Examples of relevant challenges and 

recommended strategies to address them are:

• Difficulties in integrating traffic volume data that can have different data format, type, 

structure, resolution, duration, and outputs. The data governance committee should 

identify and address these issues by establishing common data standards, data sharing 

practices, and MOUs. The need for formal data sharing agreements is as important as it 

is in laying the foundation (Step 1). Having a scalable enterprise-wide data warehousing 

solution in place can benefit all stakeholders. 

• Data security and safety concerns because of increased exposure of data to new users. 

Further, data owners and custodians may be bound by certain agreements that limit data 

sharing, use, or access to the data. The data governance committee should address 

these issues separately for each data set and agency by establishing formal data 

governance policies.

• Different data management and maintenance practices among agencies raise questions 

about who should update the data, when, and how often. Agencies set up their data 

refresh cycles based on their needs, statutory requirements, and Federal regulations. 

Partners must agree on responsibilities for managing traffic volume data and maintaining 

the basemap or LRS that provide a common foundation for the transportation data that 

partner agencies share. These responsibilities include notifying the data custodians for 

new traffic volumes, network changes, updating the basemap or LRS, and releasing 

updated versions of the basemap or LRS. Maintenance and updates of the LRS and traffic 

volume data should occur at intervals feasible and acceptable to partner agencies. LRS 

updates should define the date of the change to a roadway feature, so that analysts can 

determine when a roadway modification occurred, what the AADT was on the modified 

segment, and take the effect of the change into account. A simple mechanism to engage 

local partners and promote system adoption is to fund the system maintenance using 

state funds. The FHWA Guide for State, Tribal, and Local Safety Data Integration provides 

more information on how to address these issues. (5)

• Query performance issues because of integrating multiple data layers and systems that 

are physically in different locations. Data queries might execute significantly slower if 

data layers and tables reside in distributed systems, compared to a unified physical 

master data system. Although it might be easier to deal with data maintenance and 

custodian issues through distributed systems, it is critical to consider minimum user 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

39

requirements to ensure that analytical tools can use the data effectively. One solution is 

to use web services and web data feeds. (5)

3.4.5 Flexibility

Agencies need to communicate creative and innovative solutions that meet all stakeholders’ 

needs. The data governance committee should help agencies share their experiences and ideas 

with all stakeholders and generate solutions by considering input from all partners.

Southeast Michigan Council of Governments – Traffic Data Sharing Practices(47)

Before 2001, SEMCOG received traffic data from local agencies in over 10 different formats 

and spent too much time converting and uploading the data into their system. To address 

these issues, SEMCOG now maintains a centralized traffic count database that local agencies 

use, so all data are compatible and easily transferable to the system. SEMCOG provides the 

database software and login information to each local agency so users can deploy the 

software locally. SEMCOG works with the software vendor to ensure that the software can 

support all traffic data collection systems used by local agencies. This allows each agency to 

collect traffic data with its own equipment and processes, just as it did before using the 

software. With the common software, each agency downloads data from its counters and 

uploads the data to the centralized database system. The software has built-in validity checks 

that require each agency to certify that the data are valid; however, agencies can bypass 

these checks. This typically only happens when an agency collects data for a specific project, 

such as peak-hour counts or one-directional counts. These data are not appropriate for 

AADT estimation but must be retained in the database for use by the agency that collected 

the data.

Local agencies have their own data collection plans and equipment. SEMCOG and local 

agencies have a user’s group that meets to discuss data collection schedules and locations. 

SEMCOG occasionally requests local agencies count specific locations. They will hire 

consultants to collect data when local agencies are not able to take counts. Most of the 

counts that local agencies collect are short-duration counts, but may also take counts that 

cover shorter periods or fewer lanes or directions to meet their needs. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

40

Delaware Valley Regional Planning Commission – Innovative Traffic Data Sharing 

Tool(45)

The DVRPC has developed an interactive web platform, the Traffic Count Viewer, that 

provides public access to traffic and non-traffic data that DVRPC and other external entities 

collect. DVRPC gathers different types of short-duration counts from various agencies,

cleans and processes the data, and stores them in a regional database. Traffic Count Viewer 

communicates with the database, allowing users to view, filter, and download traffic counts. 

Users can directly connect to DVRPC’s ArcGIS server map services and extract GIS layers, 

metadata, and traffic data in a tabular format. Further, DVRPC provides disaggregated data 

that reside in its database. DVRPC, transportation engineers, planners, developers, market 

analysts, and the general public have realized several benefits from this tool: 

• Increased efficiency of the data collection program by creating and maintaining a 

central data depository. Users do not have to extract and process data from multiple 

remote data sources.

• Elimination of duplicate data collection efforts, resulting in time and cost savings for 

data partners and users.

• Quick and easy access to data regardless of users’ objectives, background, and 

knowledge level in database administration and data management.

• Improved relationships with external entities by offering no-cost access to a 

comprehensive data source.

• Improved data transparency by allowing users to access DVRPC’s ArcGIS server and 

download different types of data.

Lessons learned from DVRPC’s practices include: 

• Consider ways to collect data once with elements that meet the evolving needs of 

different users. 

• Try to gather traffic data from various sources and create a central data repository 

while making sure the data meet the needs of as many users and agency functions as 

possible. 

• Consider using open-source code and data libraries to develop tools that can have 

multiple end-users within and outside the organization. 

• Try to have people with a background in GIS and database administration, which can 

benefit tool development and implementation.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

41

3.4.6 Utilization

Agencies need to improve the access and use of data. The data governance committee should 

help agencies communicate and overcome potential technical barriers and data access 

restrictions that may exist because of sensitive information and federal and state laws. Partner 

agencies need to develop user guidelines on how to access and properly use the data for 

analytical purposes. 

In summary, Step 3 Establish Data Governance Process should incorporate the following activities:

• Identify data governance leadership, establish a data governance committee to monitor 

and guide data integration, and define the responsibilities of the committee. 

• Establish a data quality assurance program including data standards, data accuracy, and 

measures of data quality.

• Establish priorities to address data gaps and needs identified in Step 2.

• Establish policies that set criteria and limits for using, sharing, and accessing each data 

type. 

• Identify opportunities for cross-organizational collaboration, data sharing, and data 

integration, and establish related MOUs.

• Communicate innovative solutions among stakeholders.

• Promote appropriate data usage among stakeholders.

• Inform managers and executives on cost savings and other benefits achieved through 

more-efficient processes. 

• Foster people’s willingness to change existing systems and business practices, learn 

how to use new technologies, and adapt to new, fast-evolving environments. 

• Seek to have the right people in the room, which helps with decision-making. 

• Involve all data users from various business areas and consider the needs and 

preferences of different agency functions. This helps secure broad buy-in and 

support.

• Relate the costs for data collection and management to the hidden cost of not having 

data or a data warehouse. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

42

3.5 STEP 4: DEVELOP DATA COLLECTION AND INTEGRATION PLAN

A data collection and integration plan specifies the actions needed to reach the desired state 

defined in the gap analysis (Step 2). The plan describes the activities and the responsible parties 

in each of the previous steps. A coordinating body of stakeholders should participate in the plan 

development. The Traffic Records Coordinating Committee (TRCC) of each state or the data 

governance group could take a leadership role in the plan development. The joint FHWA and 

NHTSA report State Traffic Records Coordinating Committee Noteworthy Practices describes 

common practices of effective TRCC groups.(57)

In the case of AADT, the plan should describe at a minimum the AADT estimation methods

that partner agencies will use along with relevant data collection needs and requirements for 

each method. Chapter 4 provides information that can help agencies choose appropriate 

stratification methods and determine data and other relevant requirements. For example, if 

partner agencies choose to apply traditional traffic-count based methods, their plan should 

describe how they will conduct sampling, who will take counts in the field, where, when, how 

often, and for how long. 

General lessons learned from previous pilot studies that developed data collection and 

integration plans are:

• Starting the process to develop the plan can be difficult. A kickoff meeting with project 

stakeholders has been a useful strategy. Project stakeholders for the data integration 

plan could be the same or similar to the data governance committee or the state’s 

TRCC. It is important that participating agency representatives are prepared to discuss 

objectives, work plans, and their role in the data integration project at the kickoff 

meeting. MOUs might help formalize the group and formally task it to deliver a plan that 

partner agencies can endorse and adopt.

• Establishing data integration project roles and commitments is critical. Unless 

cooperative relationships already exist, each participating agency should commit to a 

level of data sharing appropriate for the agency’s available data and capabilities in terms 

of technology and staffing. Agencies should identify whether they plan to participate as 

data integrators, data providers, or data maintainers. Data version control is an 

important element that agencies should also address. Having a data version control 

process or system in place will yield benefits for several internal and external data users 

in the short-term, but particularly in the long run. 

• Document how the resulting data resources meet the needs identified in the gap 

analysis. This can be achieved by documenting the requirements of business and analysis 

applications for various stakeholders, and then defining how the resulting data resources 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

43

meet these requirements. If the data integration plan requires the development of a new 

database, the database developer should ensure that applications in use by stakeholders 

will be able to use the data in the format produced by the new database, or that 

procedures are in place to convert the data into a usable format.

• The data collection and integration plan should include a description of use cases for the 

integrated data to ensure that all stakeholder needs are met.

• It might be advantageous to structure the plan in a way that allows for quick successes, 

prioritizing activities that are comparatively easy to implement.

• Highlight dependencies and necessary preceding activities so that it becomes evident 

how the plan depends on activities within and outside the scope of the data integration 

project. This will make it easier to understand necessary schedule changes if related 

activities are not proceeding as planned.

In summary, Step 4 Develop Data Collection and Integration Plan should incorporate the following 

activities:

• Start data integration plan activities in conjunction with gap analysis and data governance 

efforts.

• Identify data integrator, provider, and maintainer roles for the data integration project.

• Identify a list of necessary tasks to complete the data integration project, including a list 

of actions within each task and a definition of how tasks depend on each other.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

44

CHAPTER 4 — RANDOM STRATIFIED SAMPLING

4.1 INTRODUCTION

This chapter describes an eight-step process that 

agencies can use to develop AADT estimates for 

NFAS roads. The process aims to improve 

potentially existing stratification schemes and 

develop new random stratified sampling schemes 

using readily available data such as U.S. Census 

Bureau data. The eight steps are shown in Figure 6. 

Before describing these steps, it is important to 

review relevant statistical terms and concepts that 

are used in this chapter.

4.1.1 Basic Definitions and Terms

Sampling refers to the process through which a 

sample of objects is extracted from the entire 

population of objects. In this Informational Guide, 

each NFAS road segment or section is an individual 

object, and the population refers to all NFAS road segments of a state transportation network 

(Section 3.3.2 Network Segmentation describes how agencies should partition the LRS network). 

The term sample refers to a subset of segments that have been extracted from a population and 

need to be counted. The selected segments are often called sample segments or simply samples.

The purpose of sampling is to develop as accurate AADT estimates as possible, without 

counting every segment on the extensive NFAS road network. There are two broad categories 

of sampling: random and non-random sampling. In random or probability sampling every roadway 

segment of the network has a known positive probability of being included in the sample. 

Conversely, in non-random or non-probability sampling the analyst selects the sample based on 

subjective criteria. The main advantage of random sampling is that the selected sample tends to 

be more representative of the entire population. Step 6 (section 4.7) provides more 

information about selecting samples, including research findings according to which random 

sampling results in more accurate AADT estimates than non-random sampling. (10, 13, 62)

Chapter 4 at a Glance

4.1 Introduction

4.2 Step 1: Gather non-traffic data

4.3 Step 2: Conduct statistical 

analysis

4.4 Step 3: Determine 

performance of current scheme

4.5 Step 4: Develop new 

scheme(s)

4.6 Step 5: Compare schemes

4.7 Step 6: Select samples

4.8 Step 7: Establish and evaluate 

continuous count program 

4.9 Step 8: Conduct counts 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

45

Figure 6. Flowchart. Basic Steps of Developing and Revising a Random Stratified 

Sampling Scheme.

4.1.2 Sampling Methods

There are various methods to design a random sampling scheme. The methods mainly differ in 

whether and how the population is divided into smaller groups, and the rules and procedures 

by which samples are selected. Some of the most common and well-documented random 

sampling methods are:

• Simple sampling – It involves selecting a sample of objects from the entire population

without partitioning the population into strata. It is the simplest form of sampling and

frequently used as a baseline to determine potential gains obtained (in the accuracy of a

target estimate) when more advanced sampling methods are used.

• Stratified sampling – It involves dividing the population into groups, called strata, and

then drawing a sample from each stratum. Due to limited availability of AADT data on

NFAS roads, the stratification of the network is typically based on one or multiple

characteristics such as functional classification, rural/urban designation, population

density, etc. If the AADT variability is lower within individual strata than across strata,

then stratification will permit an appreciable reduction in the total number of the

samples required to develop default AADT estimates for a given confidence and

precision level.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

46

• Systematic sampling – It involves creating an ordered list of all objects in the population, 

then randomly selecting an object from the first k objects, and thereafter, selecting 

every kth object on the list.

• Cluster sampling – It involves dividing the population into naturally formed groups, 

called clusters, and then drawing a sample of clusters from the population. Note the 

difference between stratified sampling and cluster sampling. In stratified sampling, the 

sample includes objects from each stratum, whereas, in cluster sampling the sample 

includes objects only from the chosen clusters.

• Multistage sampling – It involves selecting a sample by using a combination of different 

sampling methods.

Each method has specific objectives, uses, strengths, and weaknesses. For more information on 

these methods see Cochran (1977), Lehtonen and Phakinen (1996), and Thompson (2012).(63, 64, 

65) Among the various types of sampling methods that exist in the literature, this Informational 

Guide recommends using random stratified sampling to develop default AADT estimates for 

NFAS roads. The purpose of stratifying the NFAS road network is to create strata (i.e., groups 

of roads) that are internally homogenous in terms of AADT. The main challenge is that many 

states have limited traffic volume data on NFAS roads and therefore, cannot use AADT as a 

stratification variable. To overcome this challenge, agencies should stratify the network using 

roadway, administrative, and other non-traffic variables such as demographic and

socioeconomic characteristics that are readily available and may be good surrogates for AADT. 

Note that stratified sampling can provide gains over other sampling techniques when the 

stratification variables are correlated with AADT and hence can explain some of the AADT 

variability.

(8, 9, 63, 64, 65) Therefore, before developing a stratification scheme, it is important to 

determine which non-traffic variables may be good candidates for stratification purposes.

It is important to highlight that for safety analysis applications the goal is to develop default 

AADT estimates that are as close (in magnitude) as possible to actual traffic volumes at 

individual sites. In other applications (not examined in this Informational Guide) where stratified 

sampling is employed, the intent is to predict a precise mean value for a group of objects as a

whole. This distinction is important because, as explained in the TMG (Chapter 3), the key to 

meeting the first purpose is to have more groups and very homogenous groups of roads, 

Among the various types of sampling methods that exist in the literature, this Informational 

Guide recommends using random stratified sampling to estimate AADT for NFAS roads. 

The purpose of stratifying the NFAS road network is to create strata that are internally 

homogenous, to the extent possible, in terms of AADT.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

47

whereas in the second purpose the key is to have fewer groups but more samples within each 

group.(1)

The stratified sampling methodology described herein accounts for the following:

• Resource constraints within target agencies.

• Limited availability of permanent and short-duration count data on NFAS roads.

• Potential existence of a stratification scheme and default AADT estimates.

• Statistical needs of target agencies.

• Variability of AADT data on NFAS roads.

Before developing a random stratified sampling scheme, agencies should follow the four 

preparation steps described in Chapter 3. Analysts should place emphasis on Step 2 Gap 

Analysis and ensure that they conduct, at a minimum, the following activities:

• Gather traffic volume data from various internal and external sources, as described in 

section 3.3.1.

• Update and segment the NFAS road network, as described in section 3.3.2.

• Geolocate traffic counts and determine counted and uncounted segments, as described 

in section 3.3.3. 

These activities are important because they can potentially reduce the number of sample counts 

and yield cost savings (for more information read section 4.4). The remaining eight sections in 

this chapter describe the eight steps of the AADT estimation process illustrated in Figure 6.

4.2 STEP 1 – GATHER AND PROCESS NON-TRAFFIC DATA

The first step of the process is to gather and process various external non-traffic variables, 

some of which may be good surrogates for AADT. A frequently asked question is: “Which 

external variables should agencies gather and use to stratify their NFAS road network?” Based on 

previous findings(26, 28, 30, 31)and research conducted during the development of this Guide, Table 

3 provides a short list of readily available U.S. Census Bureau variables that, in general, tend to 

have higher correlations with AADT compared to other Census variables. To facilitate the data 

extraction process, Table 3 provides for each Census variable its data source, table name, field 

name, and web link(s) to download the data. 

This Informational Guide recommends calculating the geographical density of each variable

listed in Table 3. This calculation involves dividing a variable, measured within a given 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

48

geographical unit (e.g., total population within a Census block group), by the land area (in 

square miles) of that unit. In general, as shown in section 4.3, the density of a Census variable

(e.g., population density within a Census block group) typically has a higher correlation with 

AADT than the raw unadjusted Census variable (e.g., total population within a Census block 

group) that does not account for the size of the geographical unit of interest (e.g., area of 

Census block group). Agencies can also develop composite variables by combining two or more 

variables into a single variable. A typical example of a composite variable is population and 

employment density [Pop_Empl_Den=(Pop+Empl)/ALAND], which captures the number of 

residents and employees per square mile of land.

(11) Another example is the density of jobs in 

residence and workplace areas [RAC_WAC_Den=(RAC+WAC)/ALAND].
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

49

Table 3. Readily Available U.S. Census Bureau Variables.

Variable Description (Abbreviationa) Data Source(s) Table Name Field Name

Total population (Pop) ACSb X01_Age_and_Sex B01001e1

Total housing units (HU) ACS X25_Housing_Characteristics B25001e1

Occupied housing units (OHU) ACS X25_Housing_Characteristics B25002e2

Aggregate number of rooms (Agg_Rooms) ACS X25_Housing_Characteristics B25019e1

Aggregate number of vehicles (Agg_Veh) ACS X25_Housing_Characteristics B25046e1

Workers (>16 years) who did not work at home (Workers) ACS X08_Commuting B08011e1

Aggregate income in the past 12 months (Agg_Inc) ACS X19_Income B19313e1

Aggregate earnings in the past 12 months (Agg_Earn) ACS X19_Income B19061e1

Employment (labor force 16 years and over) (Empl) ACS X23_Employment_Status B23025e2

Area of land (in square meters) (Area) ACS/TIGERcIt varies by shapefile ALAND

Total number of jobs by residence area (RAC) Census LEHDd RAC_S000_JT00 C000

Total number of jobs by workplace area (WAC) Census LEHD WAC_S000_JT00 C000

Total population (C_Pop) Decennial CensuseSummary_File_01 Pop10

Total housing units (C_HU) Decennial Census Summary_File_01 Housing10
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

50

a The abbreviations were developed by the authors (not by the source agencies) and used for simplicity 

throughout this Informational Guide.

b American Community Survey (ACS) data estimates are provided by the U.S. Census Bureau at 

different geographical levels and formats at:

• https://www2.census.gov/geo/tiger/TIGER_DP/

• https://data.census.gov/

• https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-data.html

• https://www2.census.gov/programs-surveys/acs/summary_file/

• https://factfinder.census.gov/faces/nav/jsf/pages/download_center.xhtml

c The area of the land (ALAND field) is one of several data attributes included in various geodatabases. 

Each geodatabase contains five-year ACS estimates developed for a specific year, state, and geographical 

unit. For example, the geodatabase “ACS_2017_5YR_BG_13.gdb.zip” contains 2017 ACS estimates at 

the Census block group level for Georgia (13). The geodatabases are available at:

• https://www2.census.gov/geo/tiger/TIGER_DP/

d Longitudinal Employer-Household Dynamics (LEHD) data are provided by the U.S. Census Bureau at 

the block level at: https://lehd.ces.census.gov/data/

e Decennial census data are provided by the U.S. Census Bureau at different geographical levels and 

formats at:

• https://data.census.gov/

• https://www2.census.gov/

• https://www.census.gov/cgi-bin/geo/shapefiles/index.php

• ftp://ftp2.census.gov/

• ftp://ftp2.census.gov/geo/tiger/

• https://factfinder.census.gov/faces/nav/jsf/pages/download_center.xhtml

The data sources included in Table 3 are briefly described below:

• ACS: The American Community Survey collects data continuously and develops 

demographic, housing, socioeconomic, employment and other types of annual and fiveyear estimates. One-year estimates are available for selected geographic areas with at 

least 65,000 people. One-year estimates are disaggregated at the Census tract level. On 

the other hand, ACS developed five-year estimates at the block group and tract levels

using a much larger sample size extracted from more geographical areas.(67)

• Census LEHD: The U.S. Census LEHD Origin-Destination Employment Statistics 

(LODES) datasets include employment data at the Census block level. In particular, they 

release two datasets, called residence area characteristics (RAC) and workplace area 

characteristics (WAC). RAC contains the number of jobs/workers by home Census 

block, whereas WAC includes the number of jobs/workers by work Census block.

(68)

Analysts should aggregate both variables at the Census block group level or tract level 

to match the geographical resolution of the ACS estimates.

• Decennial Census: Summary File 1 (SF1) contains data gathered during the population 

census that is conducted every ten years. The data are available at the block level and 

include housing, demographic, social, and economic characteristics.(69) Analysts should 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

51

aggregate the data at the Census block group or tract level to match the geographical 

resolution of the ACS and LEHD estimates, if used in the analysis.

This Informational Guide recommends downloading U.S. Census data at the block group level 

or tract level.1 The reason being is that Census variables disaggregated at the block level or 

aggregated at large geographical units (e.g., by county) tend to have lower correlations with 

AADT resulting in less homogenous strata. 

Note that the list of variables in Table 3 is not meant to be exhaustive. Agencies can use it as a 

starting point and potentially expand it by gathering additional variables from other data sources 

that may be unique to each state or region. For example, in the absence of a national land use 

database, analysts should search if land use data exist for their area of jurisdiction and analyze 

them in Step 2 along with other external variables. In general, land use data (e.g., residential, 

industrial, agricultural, commercial, educational land use, etc.) can partially capture some of the 

AADT variability and may be potentially useful for roadway stratification purposes.(21, 26, 28, 29, 34)

After downloading and processing external datasets, analysts should ideally integrate the various 

non-traffic variables into the LRS network following FHWA’s All Road Network of Linear 

Referenced Data (ARNOLD) Reference Manual, and Applications of Enterprise GIS for Transportation 

(AEGIST) Guidebook.

(58, 59) Agencies should use the updated network to perform the exploratory

analysis described in Step 2, as long as traffic volume data exist for NFAS roads. If no volume 

data are available, agencies can skip Steps 2 and 3 and proceed directly to Step 4. In this case, 

after agencies collect traffic volume data on NFAS roads, they should revise the original 

stratification scheme by revisiting all eight steps of the entire process.

 

1 Many Census variables are available at different geographical levels of aggregation such as Census block, block 

group, tract, zip code, county subdivision, county, etc. For example, the Census block is the smallest geographical 

unit. Multiple Census blocks form a block group, and multiple block groups form a Census tract.

This Informational Guide recommends calculating the geographical density of specific Census 

variables aggregated at the Census block group or tract level.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

52

In summary, Step 1 Gather and Process Non-Traffic Data should incorporate the following 

activities:

• Download Census data.

• Calculate geographical density of Census variables at the block group or tract level.

• Gather state-specific land use data, if available.

• Integrate data into the LRS network.

4.3 STEP 2 – PERFORM DATA EXPLORATORY AND CORRELATION 

ANALYSIS

The purpose of this step is to conduct a data exploratory and correlation analysis to better 

understand the data, identify potential outliers, explore the relationships among variables, and 

ultimately determine potentially good surrogates for AADT for use in Step 4. The analysis 

should involve calculating basic descriptive statistics and developing frequency histograms, 

boxplots, scatterplots, and correlation matrices. It is reasonable to perform the analysis for 

distinct roadway groups defined by one or more variables such as roadway functional 

classification and rural/urban designation. 

Some general trends that are frequently observed within the three roadway functional 

classifications of interest (i.e., 6R, 7R, and 7U) include the following:

• Suspiciously high AADT values may be present in the data suggesting further 

investigation. Analysts need to identify and remove potential outliers. Possible causes of 

outliers include, but are not limited to, incorrect (functional) classification of roadway 

segments, unusual traffic volumes due to special events, human errors, equipment 

malfunction, etc. Figure 7 shows an example of an AADT frequency histogram, boxplot, 

and descriptive statistics. The figure shows several high AADT values that may be 

potential outliers. As a general rule of thumb, agencies should review counts that are 

above the following traffic volume values:

o 6R: >5,500 vehicles per day (vpd)

o 7R: >3,500 vpd

o 7U: >12,000 vpd

Note that these are general criteria for reviewing suspiciously high traffic volumes. 

These threshold values may vary from one state to another. For example, the median 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

53

AADT within 6R in Minnesota is 303 vpd, whereas that in North Carolina is more than 

triple (1,100 vpd). Therefore, the threshold values will be higher in the case of North 

Carolina. Likewise, other descriptive statistics may vary by state. These differences show 

how much traffic can vary geographically, highlighting the need to perform this data 

exploratory and correlation analysis separately for each state. 

• The standard deviation is often higher than the mean AADT. In other words, the

coefficient of variation (=standard deviation/mean) is typically greater than one (>1.0).

• Among the three functional classifications, urban local roads (7R) tend to have higher

traffic volumes, followed by rural minor collectors (6R), and rural local roads (7R).

Figure 7. Graph. Example of AADT Frequency Histogram, Boxplot, and 

Descriptive Statistics Indicating Existence of Outliers. 

An important activity in Step 2 is to calculate the degree of correlation between AADT and 

each of the non-traffic variables prepared in Step 1. The Pearson product-moment correlation

coefficient, r, is a measure of the relationship between two continuous variables. It describes 

the extent to which two sets of data are related. Most spreadsheets and statistical software can

Sample size 3250

Mean 469.84

Median 302.50

Mode 4 0

Std. Deviation 537.65

Coeff. of Var. 1.14

Variance 289065.67

Skewness 3.65

Minimum 5

Maximum 7800

Percentiles 2 5 155.00

5 0 302.50

7 5 560.00

Sample siz

e

3906

Mean 233.73

Median 115.00

Mode 4 0

Std. Deviation 359.34 Coeff. of Var. 1.54

Variance 129125.63

Skewness 5.45

Minimum 5

Maximum 6500

Percentiles 2 5 50.00

5 0 115.00

7 5 265.00

Sample size 4339

6R

7R

7U
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

54

compute the Pearson r.

2

Figure 8 shows examples of correlation coefficients between AADT 

and various Census variables for two states. The black bold numbers (e.g., 0.37) range between 

0.04 and 0.57 and indicate statistically significant correlations at the 0.01 level (two-tailed). The 

black (not bold) numbers (e.g., 0.04) range from 0.03 to 0.05 and suggest statistically significant 

correlations at the 0.05 level (two-tailed). Values less than or equal to 0.02 are not statistically 

significant. The non-significant correlations are shown in dark red and parentheses (e.g., (0.01)). 

2 The Microsoft Office Excel functions =correl() and = pearson() return correlation coefficients between two 

datasets. If the Pearson r is applied to variables that are nonlinearly related, it will underestimate the relationship 

between the variables; in these cases, other correlation coefficients may be more appropriate.(71)
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

55

Figure 8. Graph. Examples of Correlation Coefficients Calculated for Two 

Agencies.

General observations and findings from Figure 8 and other similar correlation analyses 

conducted in the pilot studies of this project are summarized below:

• Overall, the density variables (see abbreviations in Table 3) that often exhibit the highest

correlations with AADT are:

o Employment density (= Empl / ALAND)

6R 7R 7U 6R 7R 7U

Agg_Earn 0.21 0.10 0.10 0.28 0.11 0.10

Agg_Inc 0.22 0.11 0.09 0.27 0.11

Agg_Rooms 0.17 0.10 0.10 0.20 0.09 (0.02)

Workers 0.24 0.11 0.11 0.23 0.11

0.08

0.08

Agg_Veh 0.27 0.17 0.10 0.19 0.06 (0.00)

Empl 0.25 0.12 0.11 0.23 0.11 0.08

HU 0.16 0.11 0.08 0.17 0.07 0.04

OHU 0.19 0.12 0.09 0.22 0.09 0.05

Pop 0.20 0.10 0.11 0.22 0.09 0.04

C_Pop 0.19 0.09 0.09 0.19 0.08 (0.02)

C_HU 0.13 0.10 0.07 0.14 0.06 0.03

WAC 0.33 0.28 0.20 0.21 0.18 0.18

RAC 0.20 0.09 0.10 0.25 0.09 0.06

WAC_RAC 0.35 0.26 0.21 0.29 0.19 0.18

Pop_Empl 0.22 0.11 0.11 0.23 0.10 0.06

Agg_Earn_Den 0.39 0.21 0.05 0.38 0.27 0.22

Agg_Inc_Den 0.43 0.23 0.04 0.40 0.26 0.22

Agg_Room_Den 0.39 0.20 0.05 0.38 0.29 0.27

Worker_Den 0.54 0.31 0.06 0.41 0.27 0.28

Agg_Veh_Den 0.37 0.20 0.06 0.41 0.29 0.29

Empl_Den 0.57 0.32 0.06 0.40 0.27 0.28

HU_Den 0.39 0.22 0.04 0.36 0.26 0.26

OHU_Den 0.40 0.22 0.04 0.38 0.26 0.27

Pop_Den 0.44 0.23 0.06 0.39 0.27 0.27

C_Pop_Den 0.38 0.20 0.05 0.38 0.26 0.27

C_HU_Den 0.34 0.20 (0.02) 0.35 0.26 0.26

WAC_Den 0.53 0.45 0.11 0.18 0.21 0.15

RAC_Den 0.41 0.22 0.05 0.40 0.26 0.27

WAC_RAC_Den 0.53 0.42 0.11 0.29 0.25 0.18

Pop_Empl_Den 0.48 0.26 0.06 0.40 0.27 0.28

Functional Class & Rural/Urban Code Variable 

Abbreviation

State A State B

Functional Class & Rural/Urban Code

level (two-tailed). The black (not bold) numbers (e.g., 0.04) suggest statistically significant 

correlations at the 0.05 level (two-tailed). The red numbers (e.g., 0.01) indicate that correlations 

are not statistically significant.

Density (den) of U.S Census Bureau Variables

U.S. Census Bureau Variables
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS 

56

o Worker density (= Workers / ALAND)

o Population density (= Pop / ALAND)

o Occupied housing unit density (= OHU / ALAND)

o Density of jobs by workplace area (= WAC_S000_JT00_C000 / ALAND)

o Density of jobs by residence area (= RAC_S000_JT00_C000 / ALAND) 

However, the correlations may vary from one state to another. A variable that may be 

strongly correlated with AADT within a specific roadway functional classification of a 

given state may have a weaker relationship with AADT in another state. Therefore, the 

recommendation in this Informational Guide is to separately perform a correlation 

analysis for each state if count data are available on NFAS roads. If count data are not 

available, agencies should initially stratify their network (in Step 4) using one or more of 

the variables listed above in combination with other readily available data such as 

roadway functional classification and rural/urban designation. After collecting traffic 

volume data in subsequent years, agencies should revise the initial stratification scheme.

• The geographical density of Census variables tends to have higher correlations with 

AADT than the original Census variables that users can download online.

• Higher correlations are expected for densities calculated at the Census block group or 

tract levels, as opposed to smaller or larger geographical units.

• The correlations tend to decrease in the following order: rural minor collectors (6R), 

rural local roads (7R), and urban local roads (7U). 

• Correlations calculated by functional classification and rural/urban code (6R, 7R, and 7U) 

are typically higher compared to those calculated for all NFAS roads together at the 

state level.

The variables Total Population (field B01001e1) and Total Housing Units (field B25001e1) that are 

estimated every year by ACS tend to have higher correlations with AADT than the 

corresponding decennial census variables (Total Population and Total Housing Units from the 2010 

census) when the analysis is performed for the nine-year period (e.g., 2001-2019) following the 

last decennial census. Therefore, agencies should consider using the decennial census variables 

Total Population and Total Housing Units if they are planning to conduct a correlation analysis for 

a particular census year (i.e., 2000, 2010, 2020, 2030, etc.). For all other years, agencies should 

use the corresponding ACS estimates.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

57

In summary, Step 2 Perform Data Exploratory and Correlation Analysis should incorporate the 

following activities:

• Calculate basic descriptive statistics.

• Develop frequency histograms and boxplots.

• Perform correlation analysis by roadway functional classification and rural/urban 

designation.

4.4 STEP 3 – DETERMINE PERFORMANCE AND SAMPLE SIZE NEEDS OF

CURRENT SCHEME

The purpose of this step is to validate the performance and determine the sample size needs of 

an existing stratification scheme, if any. Agencies that have not developed a stratification 

scheme and default AADT estimates cannot perform the calculations in Step 3; however, these 

calculations are necessary for each new scheme developed in Step 4. In Step 3, agencies should 

determine the following:

a) The accuracy of default AADT estimates.

b) The AADT variability within each stratum, as well as the overall variability of a scheme. 

c) The number of samples needed within a stratum, as well as for the whole scheme. 

d) The precision and confidence intervals of default AADT estimates.

Among these performance metrics, the accuracy of the default AADT estimates is of the 

utmost importance for safety analysis. The remaining sections describe how to calculate these 

metrics, whereas Step 5 explains how to use them to compare current and new schemes, and 

ultimately select one.

4.4.1 Accuracy of Default AADT Estimates

Agencies should determine the accuracy of potentially existing default AADT estimates, if any. 

The recommendation is to first calculate the absolute percent error (APE) for each counted 

segment as follows:
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

58

Equation 4.1:

Where:

APEk = absolute percent error at counted segment k.

AADTDefault, i = default AADT of stratum i, which contains counted segment k.

AADTk = AADT calculated from a CCS or other traffic monitoring device or derived 

from a short-duration count at segment k.

After calculating APEs, analysts should determine the median APE for each stratum, as well as 

for the entire scheme using simple spreadsheets. Agencies should use the same equations to 

determine the accuracy of new AADT estimates to be developed in Step 4.

Example 1: Calculation of median APE of existing stratification scheme.

Agency A stratified their NFAS roads by functional classification and rural/urban designation 

(6R, 7R, 7U) several years ago. The scheme includes three default AADT estimates – one 

estimate per stratum as shown in Table 4. 

Table 4. Current Scheme and Default AADT Estimates of Agency A.

Stratum Code Default AADT

6R 124 

7R 58 

7U 736 

To calculate these estimates, agency A used permanent and short-duration count data that 

were available when they developed the scheme. Over the last few years, agency A has 

conducted 1,024 counts on NFAS roads: 146 counts within 6R, 439 counts within 7R, and 439 

counts within 7U. Table 5 shows some of the counted segments within each stratum and the 

corresponding APEs. The columns are: 

• Column A = number of counted segment (k = 1, 2, ..., 1,024)

APEk =

 AADTDefault , i − AADTk

 

AADTk

× 100

Among several performance metrics, the AADT accuracy, calculated as the median APE 

within each stratum, is the most important for safety analysis applications.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

59

• Column B = AADTk

• Column C = code of stratum which contains counted segment k

• Column D = AADTDefault, i

• Column E = APEk, e.g., E2 = (ABS(D2-B2)/B2)*100 = 83.8%

• Column F = Median APE by stratum, e.g., for 6R: F2 = median(E2:E147) = 80.5%

• Column G = Median APE of scheme, e.g., G2 = median(E2:E1025) = 83.5%

Table 5. Calculation of Median Absolute Percent Error.

# A B C D E F G

1

Count 

Number (k)

AADTk

Stratum 

Code AADTDefault,i APEk

Median 

APE per 

Stratum

Median 

APE of 

Scheme

2 1 765 6R 124 83.8% 80.5% 83.5%

3 2 450 6R 124 72.4% 80.5% 83.5%

... ... ... ... ... ... 80.5% 83.5%

147 146 950 6R 124 86.9% 80.5% 83.5%

148 147 1050 7R 58 94.5% 93.8% 83.5%

149 148 340 7R 58 82.9% 93.8% 83.5%

... ... ... ... ... ... 93.8% 83.5%

586 585 320 7R 58 81.9% 93.8% 83.5%

587 586 1860 7U 736 60.4% 74.2% 83.5%

588 587 2120 7U 736 65.3% 74.2% 83.5%

... ... ... ... ... ... 74.2% 83.5%

1025 1,024 2,470 7U 736 70.2% 74.2% 83.5%

The goal of agency A is to improve the accuracy of these estimates (83.5%) either by improving 

the current scheme or developing a new one. Step 4 provides information on how to address 

this goal.

4.4.2 Within-Stratum and Scheme Variability

As previously explained, the purpose of stratifying the network is to develop as homogenous 

groups of roads as possible with respect to AADT. To determine the within-stratum 

homogeneity, agencies should calculate the coefficient of variation (C) for each stratum. The 

coefficient of variation is the ratio of the standard deviation of AADT within a stratum to the 

average AADT of the stratum. To calculate C agencies should use AADT values calculated from 

CCSs (if available), other traffic monitoring devices, or short-duration counts within each 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

60

stratum. Analysts can use simple spreadsheets and statistical programs to perform these 

calculations. After calculating the coefficient of variation of each stratum, the next step is to 

calculate a weighted average coefficient of variation (WACV) for the entire scheme as follows:

Equation 4.2

Where: 

WACV = weighted average coefficient of variation of a stratification scheme.

s = total number of strata within the scheme.

ni = number of counted segments within stratum i.

i = 1, 2, ..., s.

Ci = coefficient of variation within stratum i.

Example 2: Calculating WACV of existing stratification scheme.

Using data from the previous example, Table 6 contains the following columns: 

• Column A = stratum code

• Column B = default AADT estimate developed several years ago using historical counts

• Column C = number of counts conducted over the last few years within each stratum

• Column D = mean AADT calculated using recent count data

• Column E = median AADT calculated using recent count data

• Column F = standard deviation of AADT calculated using recent count data

• Column G = coefficient of variation per stratum, e.g., for stratum 6R: G2 = F2/D2

• Column H = WACV of entire scheme

WACV =

 (ni × Ci)

s

i=1

 (ni)

s

i=1
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

61

Table 6. Calculation of Coefficient of Variation and WACV.

# A B C D E F G H

1

Stratum 

Code

Default 

AADT

Number 

of Counts 

(n)

Mean 

AADT

Median 

AADT

Standard 

Deviation

Coeff. of 

Variation 

(C)

WACV

2 6R 124 146 692 590 781.5 1.13 1.23

3 7R 58 439 646 530 847.0 1.31 1.23

4 7U 736 439 1,688 1,320 2013.3 1.19 1.23

The WACV is calculated as follows:

The coefficient of variation of a stratum affects the required number of samples, as shown in 

the next section. 

4.4.3 Sample Size

The HPMS Field Manual (Chapter 6) provides the following sample size estimation formula:(2) 

Equation 4.3:

Where:

ni = required sample size within stratum i

Zi = value of the standard normal statistic (Table 7) for a specific confidence level (two-sided)

Ci = coefficient of variation (described in the previous section) within stratum i

di = desired precision level (or allowable error) as a proportion of the AADT 

Ni = total number of all counted and uncounted segments within stratum i

WACV =

(n1C1 + n2C2 + n3C3)

(n1 + n2 + n3)

=

(146 × 1.13 + 439 × 1.31 + 439 × 1.19)

(146 + 439 + 439)

= 1.23 ni =

Zi

2Ci2

di

2

1 + 

1

Ni

 [ 

Zi

2Ci2

di

2

 − 1]
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

62

Table 7. Value of Z for Different Confidence Levels.

Confidence Level Value of Z Z Squared

90 percent 1.645 2.706

80 percent 1.282 1.644

70 percent 1.040 1.082

FHWA guidelines Travel Estimation Procedures for the Local Functional System(9)and research 

studies that focused on low functional classification roads(8, 11, 12)simplified equation 4.3 by 

retaining only its numerator:

Equation 4.4:

The rationale behind this simplification is that when N is large, the denominator of equation 4.3 

approaches 1.0 and becomes insignificant.(8, 9, 11, 12) As N increases, the required sample size 

reaches a maximum value, which is equal to the one obtained from equation 4.4. In general, 

equation 4.4 results in larger sample sizes than equation 4.3, but when N is large, the two 

equations produce the same results, and therefore equation 4.4 is easier to apply.

Figure 9 shows how the number of samples varies with respect to the three parameters (C, Z, 

and d) included in equation 4.4. The required sample size increases for higher confidence levels 

and tighter precision levels. Considering that analysts select a confidence and precision level 

based on their needs, the required number of samples within a stratum entirely depends on the 

coefficient of variation of the stratum. The more homogenous a stratum, the lower the withinstratum variability (i.e., lower coefficient of variation), and therefore, the lower the number of 

required samples, yielding cost savings. This highlights the importance of stratifying the network 

with the aim to reduce the within-stratum variability rather than to exercise administrative 

convenience (e.g., to easily manage future counts) which is likely to yield internally

heterogeneous strata that will require more counts.

ni =

Zi

2Ci2

di

2
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

63

Figure 9. Graph. Number of Samples Required for Different Coefficients of 

Variation, Confidence Levels, and Precision Levels.

As described at the beginning of this chapter, the accuracy of default AADT estimates is 

important for safety analysis applications. The key to improving the AADT accuracy is to have 

more strata and very homogenous strata, rather than fewer strata and more samples within 

each stratum.

(1) Conducting more counts within a stratum will improve the precision of the 

AADT estimate, but will likely not improve its accuracy (median APE). Note that in this 

Informational Guide, the precision refers to the maximum expected difference between the 

true mean AADT of the entire population within a stratum and an AADT estimate developed 

from a sample extracted from that stratum. Therefore, by increasing the number of samples, 

the default AADT estimate becomes closer to the true AADT of the population of a stratum, 

90-5 90-10 80-10 70-15

2.0 4,330 1,082 657 192

1.9 3,908 977 593 174

1.8 3,507 877 533 156

1.7 3,128 782 475 139

1.6 2,771 693 421 123

1.5 2,435 609 370 108

1.4 2,122 530 322 9 4

1.3 1,829 457 278 8 1

1.2 1,559 390 237 6 9

1.1 1,310 327 199 5 8

1.0 1,082 271 164 4 8

0.9 877 219 133 3 9

0.8 693 173 105 3 1

0.7 530 133 8 1 2 4

0.6 390 9 7 5 9 1 7

0.5 271 6 8 4 1 1 2

0.4 173 4 3 2 6 8

0.3 9 7 2 4 1 5 4

0.2 4 3 1 1 7 2

0.1 1 1 3 2 0

Coefficient of 

Variation (C) 

Confidence Level - Precision Level

Conducting more counts within a stratum will improve the precision of the AADT estimate, 

but will likely not improve its accuracy (median APE).
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

64

but not necessarily closer to the true AADT of each individual segment included in the stratum. 

Steps 4 and 5 provide more information on this topic. 

As a starting point, agencies should use lower confidence and precision levels (e.g., 70-15) to 

calculate the required sample size within each stratum. After taking more counts, analysts 

should calculate a new coefficient of variation for each stratum and reapply equations 4.3 or 4.4 

to determine the need for collecting more counts within each stratum. 

Example 3: Calculation of sample size of existing stratification scheme.

Using the three coefficients of variation calculated in the previous example, one can calculate 

the sample size required for each stratum for different confidence and precision levels. In this 

example, both equations 4.3 and 4.4 were used to illustrate their differences. Table 8 includes 

the following columns:

• Column A = stratum code

• Column B = total number of counted and uncounted segments within a stratum

• Column C = number of counts conducted in recent years within each stratum

• Column D = coefficient of variation per stratum

• Columns E-J = sample sizes calculated using equations 4.3 and 4.4 (shown in 

parentheses) for three pairs of confidence and precision: 90-5, 80-10, and 70-15.

Table 8. Required Sample Sizes for Different Confidence-Precision Levels.

# A B C D E F G H I J

1

Stratum 

Code

Total 

Number 

of 

Segments 

(N)

Number 

of 

Counts 

(n)

Coeff. 

of 

Var. 

(C)

90-5 

(4.3)

90-5 

(4.4)

80-10 

(4.3)

80-10 

(4.4)

70-15 

(4.3)

70-15 

(4.4)

2 6R 2,054 146 1.13 826 1,382 190 210 60 61

3 7R 37,648 439 1.31 1,770 1,858 280 282 82 82

4 7U 13,035 439 1.19 1,372 1,533 229 233 68 68

For example, the sample size calculation for 6R at 70-15 confidence-precision level using 

equation 4.3 is:

The key to improving the AADT accuracy is to have more strata and very homogenous 

strata, rather than fewer strata and more samples within each stratum.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

65

Agency A meets this sample size requirement because it has already conducted 146 counts 

(>60) within 6R. However, if agency A would like to develop a default AADT estimate at 80-10 

confidence-precision level, it would have to count at least 44 (=190-146) new segments. This 

shows the importance of gathering as much traffic volume data as possible from internal and 

external entities during the preparation steps of the process (see Chapter 3).

4.4.4 Precision and Confidence Interval

For a given n, the precision (expressed as a proportion or percentage) of a default AADT 

estimate for stratum i is calculated as follows:(1)

Equation 4.5:

Where: 

di = precision interval as a proportion or percentage of the default AADT of stratum i

= value of student’s t distribution that corresponds to (1-α/2) confidence level and n1 degrees of freedom

ni = number of counts conducted within stratum i

Ci = coefficient of variation within stratum i

Note that a t-score is appropriate when the sample size is small (typically less than 30), whereas 

a z-score when the sample size is large (at least 30). The absolute precision interval is calculated 

by substituting the coefficient of variation with the standard deviation of AADT:

n6R =

Z6R

2 C6R2

d6R

2

1 + 

1

N6R

 [ 

Z6R

2 C6R2

d6R

2

 − 1]

=

1.04021.132

0.152

1 + 

1

2,054 [ 

1.0402 1.132

0.152 − 1]

= 60 samples di = t

1−

α

2

,n−1

Ci

 ni

t

1−

α

2

,n−1
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

66

Equation 4.6:

Where: 

di = absolute precision interval of the default AADT of stratum i

Si = standard deviation of AADT within stratum i

The standard equation for estimating the confidence intervals for a simple random sample is:

Equation 4.7:

Where: 

Bi = upper and lower boundaries of the confidence interval of a default AADT

estimate for stratum i

= average AADT of stratum i

Example 4: Calculation of precision and confidence interval of default AADT 

estimates.

Using data from the previous example and equations 4-5 and 4-6, one can calculate the 

precision of the current default AADT estimates. Table 9 includes the following columns:

• Column A = stratum code

• Column B = number of counts conducted in recent years within each stratum 

• Column C = average AADT within each stratum 

• Column D = standard deviation of AADT within each stratum (see Table 6)

• Column E = coefficient of variation of AADT per stratum (see Table 6)

di = t

1−

α

2

,n−1

Si

 ni

Bi = AADTi

 ± t

1−

α

2

,n−1

Si

 ni

AADTi

 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

67

• Columns F-H = precision interval (proportion) calculated using equations 4.5 (shown in 

parentheses) for confidence level 90 (Column F), 80 (Column G), and 70 (Column H)

• Columns I-K = absolute precision interval calculated using equations 4.6 (shown in 

parentheses) for confidence level 90 (Column I), 80 (Column J), and 70 (Column K)

Table 9. Precision of Default AADT Estimates for Different Confidence Levels.

# A B C D E F G H I J K

1

Stratum 

Code

Number 

of 

Counts 

(n)

Mean 

AADT

St. 

Dev. 

(S)

Coeff. 

Of 

Var. 

(C)

90 

(4.5)

80 

(4.5)

70 

(4.5)

90 

(4.6)

80 

(4.6)

70 

(4.6)

2 6R 146 692 781.5 1.13 0.15 0.12 0.10 106 83 67

3 7R 439 646 847.0 1.31 0.10 0.08 0.07 66 52 42

4 7U 439 1,688 2013.3 1.19 0.09 0.07 0.06 158 123 100

For example, in the case of 6R, the precision of the default AADT is estimated with 70% 

confidence from equation 4-6 as follows:

Based on equation 4-7, the confidence interval of the default AADT estimate for 6R is:

This means that State A can be 70 percent confident that the true AADT of all counted and 

uncounted segments within 6R is between 625 vpd (=692-67) and 759 vpd (=692+67).

In summary, Step 3 Determine Performance and Sample Size Needs of Current Scheme should 

incorporate the following activities:

• Calculate accuracy of default AADT estimates, if any.

• Determine AADT variability within each stratum, as well as the overall variability of a 

scheme. 

d6R = t

1−

α

2

,n−1

S6R

 n6R

= t0.15,145

S6R

 n6R

= 1.04

781.5

 146

= 67 B6R = AADT6R ± t

1−

α

2

,n−1

S6R

 n6R

= 692 ± 67
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

68

• Compute the sample size required within each stratum, as well as for the whole 

scheme. 

• Calculate precision and confidence intervals of default AADT estimates.

4.5 STEP 4 – DEVELOP NEW STRATIFICATION SCHEME

The purpose of Step 4 is to improve potentially existing schemes or develop new schemes that 

produce as accurate AADT estimates as possible by considering budgetary constraints. The 

selection of a scheme will be done in Step 5. Before improving or developing schemes, readers 

need to be aware of the following important considerations:

• Number of strata and sample size: In general, the accuracy of default AADT estimates 

tends to improve (under certain conditions as described in Step 5) as the number of 

strata within a scheme increases. However, by increasing the number of strata, the total 

number of samples required within a scheme increases as well. This makes the selection 

of a scheme and the number of strata within a scheme more challenging. Step 5 provides 

more information on how to compare schemes and select one.

• Homogeneous strata: As explained in Step 3, the number of samples required within a 

stratum (to develop a default AADT estimate at a given confidence and precision level) 

decreases as the homogeneity of the stratum increases. In other words, a stratum 

whose roadway segments have similar AADT will require fewer samples than another 

stratum that has the same number of segments but higher AADT variability (see section 

4.4.3). 

The next section describes how to stratify the network in the absence of traffic volume data on 

NFAS roads, while section 4.5.2 describes how to develop or improve a stratification scheme 

when traffic volume data already exist.

4.5.1 Stratification Without Traffic Volume Data

If traffic volume data are not available for NFAS roads, agencies cannot perform the data 

exploratory and correlation analysis described in Step 2. In this case, the recommendation is to 

initially develop a basic stratification scheme using variables that are likely to capture some of 

the AADT variability. The suggested variables include:

• Roadway functional classification and rural/urban designation (i.e., 6R, 7R, 7U). This can 

be a logical starting point to divide the entire heterogeneous population into three more 

homogenous groups, which should be further subdivided using some of the 

variables/methods listed below.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

69

• Geographic and land use-based stratification. Based on general knowledge of the 

network, and land use data, if available, users should divide the entire population or the 

three functional classifications geographically. The stratification can be based on known 

industrial, agricultural, commercial, residential, recreational, or other geographic and 

traffic characteristics of the network.

• U.S. Census Bureau data. Analysts should divide the network or the groups created

from the aforementioned methods using one or more of the following composite 

variables (see field names in Table 3) that, generally speaking, tend to have stronger

relationship with AADT compared to raw Census variables that are available online:

o Employment density (= Empl / ALAND)

o Worker density (= Workers / ALAND)

o Population density (= Pop / ALAND)

o Occupied housing unit density (= OHU / ALAND)

o Density of jobs by workplace area (= WAC_S000_JT00_C000 / ALAND)

o Density of jobs by residence area (= RAC_S000_JT00_C000 / ALAND) 

The simplest way to divide (or discretize) each of these continuous variables into bins is 

to calculate and use percentile values3. For example, one can use the 33rd and 66th

percentiles to divide a variable into three bins: low, medium, and high. Creating four bins 

would require using the 25th, 50th, and 75th quartile values as bin thresholds. A more 

effective way to discretize continuous variables is to use decision trees (see section 

4.5.2.3); however, decision trees require the use of statistical software.

After developing an initial stratification scheme, agencies should randomly select samples as 

described in Step 6, establish and evaluate a continuous count program (Step 7), and start 

taking counts (Step 8). Then, they need to revise the original scheme by following all eight steps 

of the process. 

 

3 The Microsoft Office Excel functions =percentile() and =quartile() return percentile and quartile values of a 

continuous variable, respectively.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

70

4.5.2 Stratification With Traffic Volume Data

During the preparation phase (Chapter 3), agencies should make efforts to gather potentially 

existing traffic volume data from internal and external sources, so that they can enhance the 

stratification process and reduce data collection costs. Because many agencies have counted a 

small portion of their NFAS road network, this section provides information on how to 

develop or improve a stratification scheme using limited or larger amounts of traffic volume 

data. Assuming that the quality of the data is acceptable, the higher the portion of counted 

segments on the network, the higher the anticipated efficiency of a scheme.

The remaining sections describe how to:

• Improve an existing scheme by updating its default AADT estimates. 

• Develop a new scheme by applying the traditional (manual) stratification.

• Develop a new scheme by applying decision trees that are predictive modeling methods 

used in statistics and machine learning.

Among these three options, the decision trees usually provide the most homogeneous strata 

because they create groups by minimizing the within-stratum variability. However, they require 

the use of statistical programs. 

4.5.2.1 Update Current Scheme

A simple way to improve an existing scheme without modifying its strata, is to update its default 

AADT estimates. Agencies should first calculate the median AADT within each stratum using 

recent count data. The median AADT values should be used as the new default AADT 

estimates of the scheme. Then, analysts should determine the accuracy of the updated default 

AADT estimates as described in section 4.4.1 and illustrated in the example below. 

Example 5: Updating and determining accuracy of default AADT estimates.

As explained in Example 1, agency A stratified their NFAS roads by functional classification and 

rural/urban designation (6R, 7R, 7U), and developed three default AADT estimates several 

Decision trees usually create the most homogenous strata but require the use of statistical 

programs.

A simple way to improve an existing scheme without modifying the stratification criteria, is 

to update the default AADT estimates using recent counts.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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years ago. Table 10 shows the original default AADT values estimated when the scheme was 

developed, the number of recent counts, and the median AADT of recent counts.

Table 10. Old and New Default AADT Estimates of Agency A.

Stratum Code Original 

Default AADT

Number of 

Counts (n)

Median AADT (Updated 

Default AADT)

6R 124 146 590 

7R 58 439 530 

7U 736 439 1,320 

In Example 1, the APEs and the median APE of the scheme were calculated by considering the 

original default AADT values as the estimated AADT. In this example, the median AADT values 

are used as the default AADT estimates. Table 11 shows some of the counted segments within 

each stratum and the corresponding errors. The columns are: 

• Column A = number of counted segment (k = 1, 2, ..., 1,024)

• Column B = AADT derived from count k

• Column C = code of stratum which contains counted segment k

• Column D = median AADT of each stratum (see Table 10)

• Column E = APEk, e.g., E2 = (ABS(D2-B2)/B2)*100 = 22.9%

• Column F = Median APE by stratum, e.g., for 6R: F2 = median(E2:E147) = 66.2%

• Column G = Median APE of scheme, e.g., G2 = median(E2:E1025) = 68.3%
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Table 11. Accuracy Metrics of Updated Default AADT Estimates.

# A B C D E F G

1

Count 

Number (k)

AADTk

Stratum 

Code AADTDefault,i APEk

Median APE

per Stratum

Median APE 

of Scheme

2 1 765 6R 590 22.9% 66.2% 68.3%

3 2 450 6R 590 31.1% 66.2% 68.3%

... ... ... ... ... ... 66.2% 68.3%

147 146 950 6R 590 37.9% 66.2% 68.3%

148 147 1050 7R 530 49.5% 70.5% 68.3%

149 148 340 7R 530 55.9% 70.5% 68.3%

... ... ... ... ... ... 70.5% 68.3%

586 585 320 7R 530 65.6% 70.5% 68.3%

587 586 1860 7U 1,320 29.0% 66.1% 68.3%

588 587 2120 7U 1,320 37.7% 66.1% 68.3%

... ... ... ... ... ... 66.1% 68.3%

1025 1,024 2,470 7U 1,320 46.6% 66.1% 68.3%

By updating the default AADT estimates of the current scheme using recent counts, the median 

APE decreased from 83.5% to 68.3%. Note that the three strata (6R, 7R, 7U) of the original 

scheme did not change (i.e., the same strata were used in Examples 1 and 5). Therefore, the 

within-stratum variability, the overall scheme variability, and the required samples will be the 

same as those calculated in Step 3 (Examples 2, 3, and 4).

Updating the default AADT estimates does not require a lot of time and resources and may 

significantly improve the AADT accuracy, particularly if a scheme was developed or last updated 

a long time ago. The median APE of the updated scheme should be the baseline for comparing 

an existing scheme against new schemes. The next logical questions are “Can one further improve 

the accuracy of updated AADT estimates? If yes, how?” The next sections address these questions.

4.5.2.2 Traditional Stratification

The traditional approach involves manually dividing the network into groups of roads using one 

or more variables. The key to developing an efficient scheme is to use stratification variables

that are good surrogates for AADT. As described in section 4.5.1, a potential starting point is 

to perform the first level of stratification based on one or a combination of the following

characteristics:

• Functional classification and rural/urban designation. The pilot studies in this project 

revealed that stratifying the entire population of NFAS roads by 6R, 7R, and 7U can 

capture some of the AADT variability and also provide for administrative convenience

and simplicity for implementation. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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• Geographic and land use-based stratification. Specific areas within a state often carry 

similar traffic volumes. General knowledge of the network and potentially land use data, 

if available, should be used to determine whether the traffic volumes in these areas are 

indeed homogeneous and similar to those initially expected. Industrial, agricultural, 

commercial, residential, recreational, or other geographic characteristics of the network 

may reduce the AADT variability within the entire population or the three functional 

classifications. 

• U.S. Census Bureau Data. Analysts should divide the entire population or the previously 

defined groups using one or more Census variables that (based on the results from Step 

2) have the strongest relationship with AADT. Generally speaking, the higher the 

correlation between AADT and a non-traffic variable, the higher the likelihood that the

variable can better explain the AADT variability and result in more homogenous strata. 

A simple way to split a continuous variable into bins is to use the 25th, 50th, and 75th 

quartiles or other percentile values (e.g., 33rd and 66th percentiles) as break-points. A 

more effective way to construct more homogenous bins within a variable and overall a 

more efficient scheme is to use decision trees (see section 4.5.2.3).

Example 6: Developing a new traditional stratification scheme for agency A.

Agency A wanted to improve the accuracy of the updated scheme presented in the previous 

example. The state divided each of the functional classifications 6R and 7R into three strata: 

low, medium, and high density of housing units (Table 12). Likewise, functional classification 7U 

was divided into three strata: low, medium, and high employment density. The Census variables 

were split based on the 33rd and 66th percentile values of each variable. These variables had 

higher correlations with AADT within these functional classifications compared to other

Census variables. Table 12 includes the following columns:

• Column A = number of stratum

• Column B = functional classification and rural/urban designation

• Column C = housing unit density bin (i.e., low, medium, high)

• Column D = employment density bin (i.e., low, medium, high)

• Column E = number of recent counts within each stratum

The key to developing an effective stratified scheme is to use good surrogates for AADT. 

The higher the correlations between AADT and the stratification variables, the higher the 

likelihood that the produced strata will be internally homogeneous.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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• Column F = median AADT, used as the default AADT of each stratum

• Column G = coefficient of variation of AADT per stratum (see calculation in section 

4.4.2)

• Column H = median APE per stratum (see calculation in section 4.4.1) 

• Column I = required sample size (equation 4.4) for 70-15 confidence and precision 

level 

Table 12. Characteristics and Performance of New Traditional Stratification 

Scheme.

# A B C D E F G H I

1 No.

F. Class. 

Rural/

Urban

Code

Housing 

Unit 

Density

Empl. 

Density

Number 

of 

Counts

Median 

AADT

Coeff. 

of 

Var.

Median 

APE

Sample 

Size

2 1 6R Low -* 76 234 1.16 56.9% 65

3 2 6R Medium - 53 637 0.94 59.4% 42

4 3 6R High - 17 1,485 0.55 31.7% 15

5 4 7R Low - 173 172 1.32 65.2% 84

6 5 7R Medium - 139 409 1.31 64.0% 82

7 6 7R High - 120 600 0.97 70.1% 45

8 7 7U - Low 230 943 1.18 61.1% 67

9 8 7U - Medium 52 1,101 1.15 68.7% 64

10 9 7U - High 157 1,237 0.99 68.2% 47

11 - - - - - - 1.14 63.8% 511

* The dashes mean no data (empty cells).

Compared to the previous scheme shown in Example 5, the new scheme above produced more 

accurate AADT estimates (the median APE improved to 63.8%) and more homogeneous strata 

(the WACV decreased to 1.14). However, because the number of strata increased from three to 

nine, the total number of samples required for 70-15 confidence-precision level increased from 

211 (see Example 3) to 511. Decision trees can further improve the accuracy of AADT 

estimates and the within-stratum variability (see section 4.5.2.3).

Note that traditional schemes that rely entirely on administrative variables such as counties

have practical advantages over other traditional (non-administrative) schemes that may require 

more data, time, and effort to develop. The main advantages are:

• Administrative variables are readily available within all agencies, so there is no need to 

gather and process Census and other types of non-traffic data.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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• It is easy to stratify the network (e.g., by county) and implement administrative strata.

• The strata are clearly defined geographically simplifying the administration of future 

counts.

The main disadvantage of administrative schemes is that they often contain highly 

heterogeneous strata that require a significant number of sample counts, making the schemes

inefficient and expensive. Consider that county boundaries were originally developed for 

administrative purposes – not to capture AADT variability. A county may contain a high 

number of different types of roads that may have significantly different traffic volumes. The 

following example compares a county-based scheme against a non-county-based scheme that 

was developed using three stratification variables.

Example 7: Comparison of county-based and non-county-based schemes.

Agency X developed two simple stratification schemes. Each scheme is based on a single 

variable. The first scheme (Table 13) is a county-based scheme that includes 87 strata – one

stratum for each county. The second scheme (Table 14) includes only four strata that 

correspond to low, low-medium, medium-high, and high density of jobs in residence and 

workplace areas (RAC_WAC_Den). Step 1 (section 4.2) explains how to calculate this variable

using Census LEHD data. The four bins were constructed based on the 25th, 50th, and 75th 

percentile values of the variable.

Table 13. Characteristics and Performance of County-Based Scheme.

# A B C D E F

1

County 

No.

Number of 

Counts

Median 

AADT

Coeff. of 

Var.

Median 

APE

Sample 

Size

2 1 168 655 1.44 72.4% 101

3 2 160 355 1.16 75.2% 66

... ... ... ... ... ... ...

87 86 103 275 1.10 66.7% 59

88 87 64 218 0.91 62.4% 41

89 -* - - 1.18 63.8% 6,783

* Dashes mean no data (empty cells).

Traditional stratification schemes developed manually using administrative variables such as 

counties often result in a high number of heterogeneous strata that require significantly 

more samples than other schemes developed using better surrogates.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Table 14. Characteristics and Performance of Non-County-Based Scheme.

# A B C D E F G

1 No. WAC_RAC_Den Number 

of Counts

Median 

AADT

Coeff. of 

Var.

Median 

APE

Sample 

Size

2 1 Low 2,863 95 1.16 63.5% 66

3 2 Low-Medium 2,881 220 1.08 58.5% 57

4 3 Medium-High 2,878 630 1.19 60.6% 69

5 4 High 2,873 1,450 1.06 59.7% 55

6 -* - - - 1.12 60.6% 247

* Dashes mean no data (empty cells).

The last line in both tables (bold numbers) shows the WACV (Column E), the median APE

(Column F), and the total required sample size at 70-15 confidence-precision level (Column G) 

of each scheme. A quick comparison between the two schemes reveals the following:

• Though the second scheme includes only four strata, the overall variability of the 

scheme (WACV=1.12) is lower than that of the county-based scheme (WACV=1.18). 

This shows that counties are highly heterogeneous in terms of AADT. It also suggests 

that disaggregating the four strata into more groups using good surrogates for AADT 

will likely further decrease the WACV.

• Though the second scheme includes only four default AADT estimates (Column D), one 

for each stratum, the median APE (60.6%) of the scheme is lower than that (63.8%) of 

the first scheme. Disaggregating the four strata into more groups using good surrogates 

for AADT will likely further improve the AADT accuracy.

• The number of samples (Column G) required for the first scheme (247) is substantially

lower than that (6,783) required for the county-based scheme.

In short, the non-county-based scheme provides more accurate AADT estimates and can yield 

significant cost savings compared to the county-based scheme. These benefits outweigh the 

extra time spent to develop the second scheme, which can be further improved by 

disaggregating the four strata into more (homogeneous) groups using other variables that may 

be good surrogates for AADT.

4.5.2.3 Decision Trees

Decision trees belong to the larger umbrella of classification and regression trees (CART) that 

Breiman et al. first introduced in 1984.

(72) The goal of decision trees is to predict the value of a 

target (dependent) variable (e.g., AADT) based on several input (independent) variables (e.g., 

Census variables, functional classification, rural/urban designation, counties, etc.). Classification 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

77

trees are appropriate when the target variable is nominal or discrete, whereas in regression (or 

decision) trees the target variable is continuous such as AADT. Among several purposes, users 

can develop decision trees to:

• Create homogeneous strata with respect to AADT and calculate default AADT 

estimates – one estimate for each stratum.

• Discretize continuous variables.

• Identify most influential variables.

Note that there are other more advanced machine learning methods. A typical example is 

random forests that are simply a collection of multiple decision trees that have been generated 

using random subsets of data.

(73) These methods are more complex and many consider them as 

‘black boxes’ that cannot be easily interpreted. Because of implementation and practical 

considerations associated with these methods, this Informational Guide focuses on decision 

trees that are easier to understand, present, develop, and implement and can provide significant 

gains over the traditional stratification approach.

Figure 10 illustrates an example of a simple decision tree that includes two variables: FC_RU 

(functional classification combined with rural/urban designation), and HU_Den (housing unit 

density). A decision tree is a top-to-bottom (or left-to-right) flow chart that includes nodes 

interconnected with branches. The topmost node in a tree is the root node. Each of the 

remaining nodes that are split into two nodes is an internal node. The nodes that are not split 

are the terminal nodes or the final strata of the decision tree. For example, the decision tree 

shown in Figure 10 includes seven nodes created at two levels of growth: the root node (Node 

1 is at Level 0), two internal nodes (Nodes 2 and 3 are at Level 1), and four strata (Nodes 4 

through 7 are at Level 2). Annotations are shown in red italicized letters. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 10. Flowchart. Example of a Decision Tree Developed in R.

To develop a tree, users need to select a target variable (AADT) and one or more independent 

variables. Decision trees are constructed and numbered from top-down, starting with the root 

node. The CART-development algorithm determines the value (or classes) of the independent 

variable that results in the largest possible reduction in heterogeneity of the target variable. 

After splitting the root node into two child nodes, the algorithm continues to split each new 

node until a predetermined stopping criterion is met. The tree predicts a target value for each 

final node. In other words, it estimates a default AADT value for each stratum. 

Though many splitting rules can be established, most of them are based on the notion of 

‘impurity’, which is a measure of the degree of heterogeneity within a node. When all cases 

within a node have the same value for the dependent variable, the node is considered to be 

homogenous or ‘pure’ and requires no further splitting. A commonly used impurity measure for 

continuous variables is the least-squared deviation, which is computed as the within-node 

variance. 

To interpret a tree, one needs to know the following basic principles:

• The root node and each internal node has a single rule, which can be one of the 

following:

o A categorical independent variable can have one or more class labels. For 

example, in Figure 10 the rule of Node 1 is FC_RU = 6R or 7R.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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o A continuous independent variable can be less, equal, or greater than a specific 

value. For example, in Figure 10 the rule of Node 2 is HU_Den < 46.

• The left branch of a node always means that the rule is met (i.e., the answer is ‘Yes’), 

whereas the right branch means that the rule is not met (i.e., the answer is ‘No’). 

With these principles in mind, one can determine the stratification criteria of each terminal 

node. For example, consider the tree shown in Figure 10. To determine the stratification 

criteria of Stratum 6, the user has to start from Node 1 and follow the path that leads to 

Stratum 6:

• Follow the right branch of Node 1, which means that the first rule is not met. In other 

words, the first stratification criterion for Stratum 6 is FC_RU=7U.

• Follow the left branch of Node 3, which means that the rule of Node 3 is met. In other 

words, the second stratification criterion for Stratum 6 is HU_Den<425.

All 1,494 counted segments within Node 6 meet these two criteria. Table 15 shows the 

characteristics of the decision tree in a table format. Based on the stratification criteria of the 

terminal nodes (Column B in Table 15), analysts can assign a default AADT (Column E) to each 

uncounted segment on the network. For example, assume that an uncounted segment has the 

following attributes: FC_RU=7R and HU_Den=100. According to the tree depicted in Figure 

10, the rule at Node 1 is met, and therefore the user must follow the left branch. Then, the 

rule at Node 2 is not met, therefore, the right branch leads to Stratum 5 (AADT=840 vpd).

Table 15. Example of a Decision Tree in a Table Format.

A B C D E F

Node Stratification Criteria or Rules Parent 

Node

Sample Size

(%)

Mean 

AADT

Improv

ement

1 -* - 11,495 (100%) 918 548,452

2 FC_RU=(6R or 7R) 1 7,156 (62%) 341 27,424

3 FC_RU=7U 1 4,339 (38%) 1,869 85,604

4 FC_RU=(6R or 7R) and HU_Den<46 2 6,079 (53%) 253 -

5 FC_RU=(6R or 7R) and HU_Den≥46 2 1,077 (9%) 840 -

6 FC_RU=7U and HU_Den<425 3 1,494 (13%) 1,212 -

7 FC_RU=7U and HU_Den≥425 3 2,845 (25%) 2,214 -

* Dashes mean no data (empty cells).

Most statistical programs produce decision trees in a diagram and table formats. Some of them 

provide the improvement (Column F) in an impurity measure when a node is split, and the 

overall importance of each independent variable (Table 16). The importance of a variable 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

80

accounts for all the (impurity measure) improvements achieved at the nodes that were split 

based on that independent variable.

Table 16. Example of Independent Variable Importance.

Independent Variable Importance Normalized Importance

HU_Den 657,398 100%

FC_RU 548,452 83%

Decision trees allow users to control several parameters such as:(73, 74)

• Maximum tree depth – It controls the maximum number of levels of growth beneath 

the root node. By increasing the maximum depth, the tree tends to produce more 

strata. Ideally, analysts should produce trees for different depths (e.g., depths three to 

seven) and then compare the trees and select a scheme as described in Step 5.

• Minimum number of objects to split a node – It controls the minimum numbers of 

objects required for splitting a parent node into two child nodes. Increasing the 

minimum value tends to produce trees with fewer nodes. 

• Minimum number of objects within a child node – It controls the minimum numbers of 

objects required within a child node. Increasing the minimum value tends to produce 

trees with fewer nodes. 

• Minimum change in improvement – It is the minimum decrease in the impurity measure 

to split a node. If the minimum value is not reached, the node will not be split. 

Increasing the minimum value tends to produce trees with fewer nodes and vice versa. 

• Maximum risk difference to prune a tree – It controls whether and how a tree is pruned 

to avoid overfitting. After a tree is constructed, users can select to prune it to the 

smallest subtree that has an acceptable risk value. Increasing the maximum risk 

difference tends to produce simpler trees and vice versa.

• Maximum number of sample folds used in cross-validation – It controls the number of 

subsets used for cross-validation purposes. If the user selects to perform crossvalidation, the dataset is divided into a predetermined number of sample folds and 

multiple trees are constructed. The first tree is trained using all objects except those in 

the first sample fold. The second tree is generated based on all objects except those in 

the second sample fold, and so on. Each tree is validated using the sample fold that was 

excluded when the tree was created. A misclassification risk is estimated for every 

validated tree. The end product is one final tree, in which the final risk estimate is the 

average of all risks from all validated trees.
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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The main advantages of decision trees over the traditional stratification approach and other 

methods are:(74)

• They generally produce more homogeneous strata compared to the traditional 

stratification approach, and therefore tend to require fewer samples yielding cost 

savings. In the traditional approach, analysts typically select and use a small number of 

independent variables to develop a scheme, and also manually discretize each variable. In 

contrast, decision trees can automatically scan a much larger number of variables and 

determine the optimal threshold within each variable that provides the best separation 

of a dataset.

• They are intuitive and easy to explain to technical teams and stakeholders. 

• They do not require normalization and scaling of data.

• They are not significantly impacted by outliers and missing values, compared to other 

statistical methods. 

• They are as easy to implement as traditional stratification schemes because both 

methods produce a set of stratification criteria and a default AADT estimate for each 

stratum. In other words, there is no need to implement complex equations or ‘black 

boxes’ that some machine learning methods produce.

• They determine the most influential variables and quantify their importance.

• They can use the same independent variables more than once in different parts of the 

tree. This capability can uncover complex interdependencies among variables.

• They are nonparametric and therefore, do not rely on data having a particular type of 

distribution.

The main advantages of decision trees over the traditional stratification approach are: 

• They generally produce more homogeneous strata and therefore tend to require 

fewer samples yielding cost savings. 

• They are intuitive and easy to explain to technical teams and stakeholders. 

• They do not require normalization and scaling of data. 

• They are not significantly impacted by outliers and missing values, compared to other 

statistical methods. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

82

• They allow users to relax stopping rules, overgrow a decision tree, and then prune it

back to decrease its size. This approach minimizes the probability that important 

structure in the data set will be overlooked by stopping too soon.

• They can incorporate various validation methods, including cross-validation, to assess 

the goodness of fit of a tree more accurately.

The main disadvantages of decision trees are:

• They require statistical programs to develop. 

• They often require more time to train a model than other statistical and machine 

learning methods.

• They can produce different trees by changing some of the parameters listed above, even 

if the same (dependent and independent) variables are used.

• They can be difficult to present to stakeholders if they are large and contain many 

nodes.

In summary, Step 4 Develop New Stratification Scheme should incorporate some of the following 

activities:

• Update default AADT estimates of potentially existing scheme. 

• Develop one or multiple schemes by applying the traditional stratification approach.

• Develop one or multiple decision trees using statistical programs.

4.6 STEP 5 – COMPARE SCHEMES AND SELECT ONE

The purpose of this step is to compare the schemes improved and developed in the previous 

step and select one. To conduct this comparison, agencies should determine at a minimum the 

number of strata, the median APE (section 4.4.1), and the required sample size (section 4.4.3)

of each scheme. The WACV (section 4.4.2) may also help analysts to better understand the 

homogeneity of the schemes. Plotting these metrics will allow analysts to visually examine and 

compare the performance and sample size requirements of the schemes. 

For example, a pilot study conducted in this FHWA project with agency X compared 36

stratification schemes that include the agency’s current scheme, an updated version of the 

current scheme, 28 new manual (M) schemes (i.e., traditional schemes), and six decision trees

(DT). Table 17 shows the schemes sorted in an ascending order by the number of strata per
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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scheme. State X developed their current scheme (35M) several years ago based on three 

variables: city population, counties, and route system, which captures roadway ownership.

Scheme 36M involved updating the current scheme by developing a new set of default AADT 

estimates using recent count data.

Table 17. Examples of Traditional Schemes and Decision Trees.

No. Variablesa(Number of Stratab) No. Variables (Number of Strata)

1McFC_RU (3) 19M FC_RU, WAC (12)

2M Pop (4) 20M FC_RU, Pop_Den (12)

3M HU (4) 21M FC_RU, HU_Den (12)

4M Empl (4) 22M FC_RU, Empl_Den (12)

5M RAC (4) 23M FC_RU, RAC_Den (12)

6M WAC (4) 24M FC_RU, WAC_Den (12)

7M Pop_Den (4) 25M FC_RU, WAC_RAC_Den (12)

8M HU_Den (4) 26DT All – Stopped at Level 4 (16)

9M Empl_Den (4) 27M FC_RU, Pop_Den, Empl_Den (27)

10M RAC_Den (4) 28M FC_RU, Pop_Den, RAC_Den (27)

11M WAC_Den (4) 29M FC_RU, Pop_Den, HU_Den (29)

12M RAC_WAC_Den (4) 30DT All – Stopped at Level 5 (29)

13DTd All – Stopped at Level 2 (4) 31M FC_RU, Pop_Den, WAC_Den (34)

14DT All – Stopped at Level 3 (8) 32DT All – Stopped at Level 6 (48)

15M FC_RU, Pop (12) 33DT All – Stopped at Level 7 (69)

16M FC_RU, HU (12) 34M County (87)

17M FC_RU, Empl (12) 35M Current Scheme (94)

18M FC_RU, RAC (12) 36M Updated Scheme (94)

a The abbreviations of the variables are provided in Step 1.

b Total number of strata within each scheme.

c M = Manual traditional scheme.

d DT = Decision tree. The decision trees are highlighted in bold letters. All independent variables were used as 

inputs to train each decision tree.

Figure 11 displays the metrics of these schemes. The horizontal axis shows the number of 

strata within each scheme. The schemes are shown above each chart, and like Table 17, they 

are sorted by the number of their strata to facilitate the comparison. The six decision trees are 

highlighted in bold and their corresponding data points are enlarged to easily distinguish them 

from those of the manual schemes. The three vertical axes from left to right show the median 

APE, the sample size required at 70-15 confidence-precision level, and the WACV of each 

scheme, respectively. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 11. Graph. Median APE, Required Sample Size, and WACV of Various Stratification Schemes.

The sample size of schemes 

34M-36M is over 6,700

The sample size 

of the current 

scheme (35M) is 

6,958
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Among all 36 schemes, decision tree 26DT (highlighted in an oval) has the advantage of 

producing the fourth most accurate set of default AADT estimates by requiring fewer counts 

compared to other schemes that result in similar levels of AADT accuracy. It also contains 

fewer strata than the three best performing schemes (30DT, 32DT, and 33DT), and therefore, 

it is easier to implement. By comparison, decision tree 30DT improves the accuracy of the

estimates by approximately five percent but requires 87 percent more counts than scheme 

26DT. It also includes 13 more strata that may require additional time from an implementation 

perspective. The second most accurate scheme is 32DT, which compared to scheme 26DT, 

improves the AADT accuracy by seven percent, but requires a much larger sample size (158 

percent increase) and contains 32 more strata. Compared to 32DT, the most accurate scheme 

(33DT) does not provide any improvement in AADT accuracy, yet it requires 33 percent more 

counts. This shows that further dividing the 48 strata of scheme 32DT is meaningless, because 

the available surrogates cannot capture additional AADT variability within smaller bins. 

One of the main findings from this stratification analysis is that scheme 32DT improves the

AADT accuracy of the current scheme (35M) by 30 percent, but most importantly it 

significantly reduces the required number of samples (by a factor of 0.23) from 6,958 to 1,574. 

That is more accurate AADT estimates and substantial cost savings. Further, traditional countybased schemes (34M-36M) tend to produce many internally heterogeneous strata that require a 

large number of samples. Stratifying the network by functional classification and rural/urban 

designation (model 1M) is more effective than using strata developed from single raw 

unadjusted Census variable (2M-6M). Likewise, density variables (7M-12M) are better 

surrogates than raw Census variables (2M-6M).

Overall, agencies should select one of the best performing schemes in terms of AADT accuracy 

(i.e., median APE) by considering available budgets. If, for example, agency X can afford to 

conduct the number of counts required by the second best performing scheme, 32DT, then, 

this scheme should be preferred over scheme 26DT. Agencies should also report and 

communicate the AADT estimation accuracy of the final scheme selected, so that internal and 

external end users are aware of the anticipated levels of error.

In general, the accuracy of default AADT estimates is a function of several interrelated factors, 

of which the most important are: 

• Number of strata.

• Degree of correlation between stratification variable(s) and AADT.

• Stratification method.

Generally speaking, as the number of strata increases, the accuracy tends to improve, but the 

number of required samples increases. After a certain point, the accuracy improves at a much 
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lower rate, but the required sample size continues to increase at a high rate (e.g., from 30DT 

to 32DT, and from 32DT to 33DT). The main reason is that even the best surrogates typically 

have moderate correlations (e.g., 0.3-0.6 as shown in Figure 8) with AADT. That is the largest 

percent of the AADT variability that these variables can explain is often captured during the 

early stages of the stratification process, when the entire population is divided into the first few 

strata. Beyond a certain ‘critical’ point, further subdividing existing strata does not significantly 

improve the within-stratum variability. This happens because the surrogates cannot effectively 

capture how AADT varies within smaller volume ranges. The magnitude of this ‘critical’ point 

depends on the degree of correlation between the stratification variables and AADT. In a 

hypothetical scenario where the correlations between AADT and the stratification variables are 

very strong (e.g., 0.8-0.9), the critical point will shift to the right. This means that more strata 

can be created by reducing the within-stratum variability without experiencing a significant 

increase in the required sample size.

Further, some stratification methods are more effective and efficient than others. As described 

in the previous step, decision trees and other machine learning methods such as random forests 

have several advantages over the traditional stratification approach. The main advantage is that 

they tend to produce more homogeneous strata, and therefore, more accurate AADT 

estimates more efficiently. The improvement in homogeneity becomes more evident when 

there is a large number of inputs that the decision tree algorithm can analyze exhaustively, 

repeatedly, and efficiently in every step of the tree growth process. Computationally, this would 

be a difficult and time-consuming task to perform using spreadsheets. Training a decision tree 

using several independent variables increases the likelihood of capturing some of the AADT 

variability within smaller strata, where a single variable may not effectively explain. The main 

disadvantage of spending more time to gather and process data for more variables is 

outweighed by the expected increase in within-stratum homogeneity and AADT accuracy. 

In summary, Step 5 Compare Schemes and Select One should incorporate some of the following 

activities:

• Determine the number of strata, the median APE, and the required sample size of each 

scheme developed in Step 4.

• Develop plots of these metrics and compare the performance and sample size 

requirements of the schemes.

Beyond a certain point, further dividing the strata of a scheme results in a marginal 

improvement in AADT accuracy (median APE), but a significant increase in the required 

sample size. 
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• Determine critical point beyond which the AADT accuracy improves marginally, but the 

required sample size increases substantially.

4.7 STEP 6 – SELECT SAMPLES

The purpose of this step is to select sample locations. In line with FHWA guidelines, the 

recommendation is to randomly select new samples, as opposed to repeatedly count the same 

predetermined ‘historical’ sites that agencies often select based on subjective criteria.

(1, 2, 9) The 

main advantages of randomly selecting and counting different segments in each cycle, compared 

to repeatedly counting the same ‘historical’ locations are:

• Higher accuracy of AADT estimates. Several studies have found that randomly selected 

counts tend to result in more accurate AADT estimates.(8, 13, 62) For example, Frawley 

(2007) compared count volumes from historical stations in Texas against count volumes 

from randomly selected sites.(13) The study found that the median volumes from 

randomly selected sites were significantly lower than those of historical counts in the 

same areas. These volumes were lower because typically the historical sites are located 

on local streets at or near intersections with collectors or arterial streets, where 

volumes tend to be higher. Historical count sites are not usually found deep in 

neighborhoods, away from major streets. The randomly selected sites are distributed 

evenly throughout the network and therefore, provide a better representation of NFAS 

roads.

• More effective stratification and higher precision of AADT estimates. Counting new 

segments in each cycle practically increases the percent of the counted portion of the 

NFAS network and the sample size of the stratification analysis (i.e., the dependent 

variable contains more records). This, in turn, improves the stratification process, the 

reliability of the results, and the precision of the estimates, as shown in section 4.4.4.

Precision and sample size are inversely proportional to the second power. This means 

that halving precision requires four times the sample size.(8)In contrast, counting the 

same historical sites in each cycle does not increase the sample size of the analysis and 

therefore, the aforementioned benefits cannot be realized.

• Ability to validate the accuracy of the AADT estimates from previous years. Counting 

new segments allows analysts to validate the accuracy of potentially existing AADT 

estimates.

The recommendation in this Informational Guide is to randomly select samples, as opposed 

to count the same historical sites in each data collection cycle.
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The main drawback of randomly selecting samples is that it increases the administrative effort. 

More specifically, it involves developing and repeating a random sample selection procedure, 

developing annual counting schedules, identifying sites, and updating relevant systems and 

databases with count and location information. Although the human aspects of traffic counting 

may be complicated by this approach, the equipment aspects do not present much of a problem 

due to low traffic volumes on most NFAS roads.(8)

After computing the number of samples needed in each stratum (see section 4.4.3), analysts can 

randomly select samples using a random number generator available in simple calculation 

spreadsheets. This approach assigns an equal probability of selection to each segment within a 

stratum. First, analysts need to create a list of uncounted segments within a stratum and then 

generate a random number4for each segment. After sorting the segments in an ascending or 

descending order by these random numbers, analysts can select a certain number of samples 

required for each stratum. The selected segments now become the stratum sample. This

random sampling process continues until each stratum has the required number of samples.

Any random sample selected in the described manner is representative. However, the 

possibility of selecting a bad sample does exist. To ensure good representation, the segments in 

the sample should be evenly distributed geographically. To assess the level of representation, it 

may be necessary to plot the selected samples on a map and visually examine if they are evenly 

distributed geographically. If some samples are clustered together rather than geographically 

distributed, it may be necessary to reselect some of the segments to ensure a more dispersed 

sample.(8)

 

4 The Microsoft Office Excel functions “=rand()” and “=randbetween()” generate random numbers.
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After randomly selecting samples, agencies should determine specific projects (e.g., special 

projects, safety improvement projects, pavement maintenance projects, research projects, etc.) 

that will require data in the next year or two. This involves coordinating with other internal and 

external entities that need the traffic volume data or plan to collect data themselves. This 

coordination should occur on a continuous basis to ensure counts for special projects are 

collected when needed. Agencies should determine how counts can be combined to make the 

best use of available resources, and then develop a schedule to use the available data collection 

crews and equipment efficiently. The intent of these activities is to reduce count duplication and 

increase the efficiency of the data collection staff. The TMG provides more information on this 

topic.(1)

Sampling is not needed if an agency has already counted their NFAS segments. In this case, the 

agency can continue to collect traffic volume data on six to ten-year cycles depending on their

needs and available resources. The TMG provides more information on how to structure a 

short-duration count data collection program.(1)For example, one simple approach is to 

randomly separate all roadway segments into groups and count one of these groups each year. 

However, this approach does not always make the most efficient use of data collection staff and 

New York State Department of Transportation – Random Sampling Data Collection 

Procedure(46)

NYSDOT conducted a one-time project to collect additional NFAS traffic data. Over the 

last few years, NYSDOT conducted about 8,000 short-duration counts under a special 

project to collect a random sampling of local roads to support AADT data collection. 

NYSDOT supplemented its existing count data with a target of counting at least 10 percent 

of the mileage in each of the 1,500 municipalities in the state to ensure wide distribution of 

data. NYSDOT used its existing local highway inventory to select count locations. All 

locations were collectively sampled, including mainlines, alleys, cul-de-sacs, and dead ends.

To randomly select samples for counting, NYSDOT extracted NFAS road segments from its 

statewide inventory of public roads. The total mileage of NFAS road segments in each 

municipality was compared to the mileage of segments with traffic counts. A random 

number was generated for each segment in municipalities with counts on less than 10 

percent of the total mileage. NYSDOT sorted each municipality individually by the random 

number and selected segments to be counted until reaching 10 percent of the mileage. 

About 8,000 segments were selected for counts, in addition to the already counted 

segments, to reach the 10 percent coverage of NFAS roads. Contractors then conducted 

short-duration traffic counts and provided the data to NYSDOT. 
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equipment. Grouping counts geographically leads to more efficient collection of data, but 

results in the need to account for geographic bias when computing AADT and growth rates.(1)

This Informational Guide recommends a minimum count duration of 48 hours for NFAS roads.

Research findings have shown that longer duration counts produce modest, but statistically 

significant improvements in count accuracy.(75, 76)For example, Krile et. al. (2015) found that 

AADT estimates derived from 48-hour counts, compared to 24-hour counts, result in 

approximately a five percent increase in the probability that the estimates are within +/- 10 

percent of the actual AADT.(75)

In summary, Step 6 Select Samples should incorporate some of the following activities:

• Select samples randomly using random procedures.

• Review selected samples on a map and make adjustments, if needed.

• Determine special and other projects that require traffic volume data.

• Coordinate with partner agencies to avoid duplication of counts.

4.8 STEP 7 – ESTABLISH AND EVALUATE CONTINUOUS COUNT

PROGRAM

The purpose of this step is to establish and evaluate a continuous count program, in accordance 

with guidelines provided in FHWA’s TMG.(1) The primary (but not the only) objective of a 

program is to develop temporal adjustment factors to expand short-duration counts to AADT. 

This objective is the basis for establishing the number and location of CCSs. Each agency should 

determine a balance point between having more CCSs (increasing the accuracy and reliability of 

analyses that depend on CCS data) and reducing the expenditures required to operate and 

maintain the CCSs. 

Chapter 3 of the TMG describes a seven-step process (Figure 12) that agencies should follow 

to 1) develop a new continuous traffic volume program, 2) check to ensure compatibility with 

TMG guidance, and 3) evaluate a program. 

This Informational Guide recommends a minimum count duration of 48 hours for NFAS 

roads.

This Informational Guide recommends establishing and evaluating a continuous count 

program in accordance with guidelines provided in FHWA’s TMG.
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Figure 12. Flowchart. Establishment and Evaluation of Continuous Count (Traffic 

Volume) Program.(1)
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The seven steps of this process are summarized below:

• Review existing continuous count program. This step involves defining, analyzing, and 

documenting the current program; determining hourly, daily, and monthly traffic 

patterns; understanding how data are adjusted; determining quality control procedures 

and rules, and checking whether they are performed as intended; and creating summary 

statistics from the raw CCS data. These activities are also included in the preparation 

steps described in Chapter 3 of this Informational Guide.

• Develop inventory of continuous count locations and equipment. This step involves 

performing an inventory of existing and planned CCSs, as well as other data collection 

devices that can supply continuous volume data; and determining how the continuous 

count data are currently being used, who the customers are, and which data products 

are being produced.

• Determine traffic patterns to be monitored. This step involves reviewing variations in 

time patterns; computing monthly factors at each site; reviewing how short-duration 

counts are assigned to factor groups; determining hour of day adjustment factors; and 

reviewing how volumes are distributed by day of week.

• Establish monthly pattern groups. This step involves creating monthly pattern groups 

using one or more of the following methods: a) the traditional approach is based on 

general knowledge of the system combined with visual interpretation of monthly 

patterns; b) cluster analysis is a statistical classification method that allows for 

independent determination of similarity between groups and identification of travel 

patterns that may not be intuitively obvious to the analyst; and c) volume factor groups.

• Determine appropriate number of CCSs. This step involves determining the total 

number of locations needed in each factor group to achieve the desired precision level 

for the composite group factors. The recommended reliability levels for NFAS roads are 

10 to 15 percent precision with 95 percent confidence for each individual group, 

excluding recreational groups where no precision requirement is specified. When these 

reliability levels are applied, the number of CCSs needed is usually five to eight per 

factor group, although cases exist where more locations are needed. The actual number 

of locations needed is a function of the variability of traffic patterns within that group 

and the precision desired; therefore, the required sample size may change from group 

to group.
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• Select count locations. This step involves determining which CCSs should be included in 

each factor group to meet the required levels of factor reliability. If there is a shortage 

of current CCSs within a group, the agency should select new locations to place 

continuous counters within this group. If a surplus of CCSs within a group exists, then 

redundant locations are candidates for discontinuation unless needed for other 

purposes.

• Compute temporal factors. This step involves calculating temporal adjustment factors 

that agencies should use to annualize short-duration counts.

Chapter 3 of the TMG provides more information about each step of the process.

(1)

In summary, Step 7 Establish and Evaluate Continuous Count Program should incorporate the 

following:

• Review existing continuous count program. 

• Develop inventory of continuous count locations and equipment. 

• Determine traffic patterns to be monitored. 

• Establish monthly pattern groups. 

• Determine appropriate number of CCSs. 

• Select count locations. 

• Compute temporal factors. 

4.9 STEP 8 – CONDUCT COUNTS AND DEVELOP DEFAULT AADT 

ESTIMATES

The purpose of this step is to collect traffic volume data and develop AADT estimates – one 

estimate for each stratum. This Informational Guide recommends using, if possible, traffic 

equipment that counts vehicles instead of axles. Though this recommendation restricts the use 

of older, less capable equipment and potentially raises the level of complexity, it eliminates the 

need to collect classification data and compute axle correction factors, and most importantly 

avoids additional sources of bias introduced by applying axle correction factors. Magnetic 

detectors placed in the center of a lane, loop detectors, and other types of non-intrusive 

equipment described in the TMG are appropriate for counting vehicles. 
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After taking short-duration counts, agencies should calculate the ADT of each count and 

expand it into an AADT estimate by multiplying the ADT by appropriate temporal adjustment 

factors, as described in the TMG.

(1)

If analysts stratify the network manually, they should simply 

calculate the median5 AADT within each stratum and use it (the median AADT) as the default 

AADT estimate of the stratum. Note that analysts should use the median, not the mean6.

Frawley (2007) describes that medians and means are equal in the case of symmetrical (i.e., 

normal) distributions. However, the mean can be largely affected by potential outliers that 

frequently exist in traffic volume data of NFAS roads.(13)

As agencies collect traffic volume data, the initial strata may change. For the first few years after 

initial development of a scheme, the process should be revisited ideally every year or every two 

years. After that, the stratification process should be revised every three years, if possible.

Change rate factors are necessary to adjust counts taken in previous years. Due to limited CCS 

data on NFAS roads, yearly change rate factors can be estimated using Census population data; 

however, research is needed to examine more advanced yearly change rate estimation methods 

for NFAS roads.

In summary, Step 8 Conduct Counts and Develop Default AADT Estimates should incorporate some 

of the following activities:

• Collect traffic volume data using vehicle counting equipment, if possible.

• Calculate ADT for each count.

• Expand the ADT of each count by multiplying it with appropriate temporal adjustment 

factors to develop an AADT estimate.

• Determine median AADT for each stratum.

• Assign median AADT to each uncounted segment within a stratum.

 

5 The Microsoft Office Excel function “=median()” returns the median of an array of values.

6 The Microsoft Office Excel function “=average()” returns the mean of an array of values.

This Informational Guide recommends using traffic equipment that counts vehicles instead of 

axles.

This Informational Guide recommends calculating the median AADT (as opposed to the 

mean AADT) within each stratum.
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CHAPTER 5 — CONCLUSION

This Informational Guide presents information on how to collect data and estimate AADT for 

NFAS roads. The expectation is to use the AADT estimates in data-driven safety analysis, as 

defined by the HSIP Final Rule. The Guide presents a framework (Figure 13) that consists of 

two major parts: Preparation and AADT Estimation.

Figure 13. Flowchart. Process to Collect Data and Estimate AADT for NFAS 

Roads.
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The first part includes four basic steps that are necessary to prepare for effective data 

collection and sharing of traffic volume data among agencies. These steps are part of a larger 

safety data integration process that FHWA published in 2016. The second part, called AADT 

Estimation, includes eight steps that describe how to improve an existing stratification scheme, 

develop new schemes, collect traffic volume data, and estimate AADT for NFAS roads.

The intent of this framework is to assist both experienced traffic monitoring staff and those

who are new to traffic data collection and AADT estimation. The framework includes methods 

suitable for agencies that a) do not collect data nor estimate AADT for NFAS roads, and b) 

desire to improve their practices and the accuracy of their AADT estimates. For this reason, 

the framework incorporates both traditional stratification approaches, as well as non-traditional 

classification methods such as decision trees. 

Some of the key elements in developing a stratification scheme and default AADT estimates for 

NFAS roads are:

• Among the various types of sampling methods that exist in the literature, this 

Informational Guide recommends using random stratified sampling.

• The purpose of stratifying the NFAS road network is to create strata that are internally 

homogenous and externally heterogeneous, to the extent possible, in terms of AADT.

• The key to developing an effective stratified scheme is to use good surrogates for 

AADT. The higher the correlations between AADT and the stratification variables, the 

higher the likelihood that the produced strata will be internally homogeneous.

• Geographical densities of specific Census variables aggregated at the Census block group 

or tract level tend to be more effective in developing a stratification scheme than raw 

unadjusted Census variables that are available online.

• Among several performance metrics, the AADT accuracy, calculated as the median APE 

within each stratum, is the most important metric for safety analysis applications.

• The key to improving the AADT accuracy is to have more strata and very homogenous 

strata, rather than fewer strata and more samples within each stratum.

• A simple way to improve an existing scheme without modifying the stratification criteria, 

is to update the default AADT estimates using recent counts.

• Traditional stratification schemes developed manually using administrative variables such 

as counties often result in a high number of heterogeneous strata that require 

significantly more samples than other schemes developed using better surrogates.
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• Decision trees have several advantages over traditional (manual) stratification 

approaches. For example, they generally produce more homogeneous strata and 

therefore tend to require fewer samples yielding cost savings; they are intuitive and easy 

to explain to technical teams and stakeholders; they do not require normalization and 

scaling of data; and they are not significantly impacted by outliers and missing values, 

compared to other statistical methods.

• The main disadvantages of decision tress are: they require the use of statistical 

programs; they often require more time to train a model than other statistical and 

machine learning methods; they can produce different trees by changing parameter

values, even if the same dependent and independent variables are used; and they can be 

difficult to present to stakeholders if they are large and contain many nodes.

• Beyond a certain point, further dividing the strata of a scheme results in a marginal 

improvement in AADT accuracy (median APE), but a significant increase in the required 

sample size. 

• Conducting more counts within a stratum will improve the precision of the AADT 

estimate but will likely not improve its accuracy (median APE).

• Randomly selecting samples results in more accurate AADT estimates, as opposed to 

counting the same historical sites in each data collection cycle.

• This Informational Guide recommends the following:

o A minimum count duration of 48 hours for NFAS roads.

o Establishing and evaluating a continuous count program in accordance with 

guidelines provided in FHWA’s TMG.

o Using traffic equipment that counts vehicles instead of axles.

o Calculating the median AADT (as opposed to the mean AADT) within each 

stratum.

Transportation agencies can use HSIP funds to collect, estimate, maintain, and share safety data, 

including AADT, on all public paved roads and related systems associated with analytical usage 

of data that directly supports HSIP implementation efforts. AADT is one of several MIRE-FDE

that agencies must integrate to reduce crashes, fatalities, and serious injuries. By making better 

data available to decision makers, agencies will make more efficient spending decisions which in 

turn results in more lives saved per dollar spent on safety. This view of safety decision-making 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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extends to efforts in several functions within an agency, not just those specifically aimed at 

improving safety.
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APPENDIX A – IMPACT OF AADT ESTIMATION ERRORS ON 

SAFETY ANALYSIS

SUMMARY

This Appendix presents a sensitivity analysis(1)that aimed to determine the impact of traffic 

volumes on the results of data-driven safety analysis. In this analysis the research team used 

2013-2017 traffic count, roadway network, and crash data from North Carolina. The study 

developed six SPFs for the lower functional classifications (6R, 7R, and 7U) and then applied the 

empirical Bayes (EB) method that is recommended in AASHTO’s Highway Safety Manual

(HSM).

(2) The sensitivity analysis included six steps: 1) applying the EB method, 2) determining 

the rank of each segment based on the results obtained from Step 1, 3) determining the 

percentile of the rank of each segment, 4) increasing the AADT of each segment by 10, 50, 100, 

250 and 500 percent by keeping the rest of the segments and variables unchanged, 5) repeating 

Steps 1-4 separately for each segment and AADT percent increase, and 6) calculating the 

percentile rank change of each segment by comparing the initial rank of each segment (no 

AADT change) against the rank obtained when AADT was increased by a certain percent. In 

line with previous research findings(3), the results show that AADT estimation errors do not 

significantly affect the expected crash frequencies derived from the EB method and the 

associated percentile rank changes. The AADT affects the number of crashes predicted by SPFs. 

However, the EB method accounts for both predicted number of crashes and historical crashes, 

but the latter have a higher weight in the calculation of expected number of crashes. 

A.1 BACKGROUND

States must develop an HSIP that “emphasizes a data-driven, strategic approach to improving 

highway safety on all public roads that focuses on performance”.(4) Due to the emphasis on datadriven strategies, many studies have focused on understanding the relationship between crashes 

and external variables. The data-driven methods included in the first edition of the HSM are 

widely used to predict crashes on a specific roadway facility.(2)Part C of this manual provides a 

list of predictive models that can be used to estimate crash counts on a roadway using 

alternative variables such as segment length, traffic volume, and geometric characteristics. These 

models, widely known as SPFs, predict crash frequencies with the aim to prioritize candidate 

safety improvement projects and different design alternatives. The HSM provides a series of 

SPFs for three major facility types: (1) rural two-lane two-way roadways; (2) rural multilane 

highway; and (3) urban and suburban highways. For each location type, these SPFs can be used 

to estimate the number of crashes during a given period under certain base conditions. The 

general recommendation is to calibrate existing SPFs or develop new SPFs using local data, and 

then apply advanced predictive methods such as the empirical Bayes (EB) method that account 

for both observed and predicted number of crashes. 
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A.2 OVERVIEW OF EB METHOD

The EB method calculates the expected number of crashes for a defined period before and 

after a safety treatment was implemented at a particular site. The EB method can handle two 

major data issues: sparse datasets and regression to the mean (RTM) bias. RTM bias occurs 

when a site experiences an abnormally high or low number of crashes in one year, followed by 

a return to a more typical crash frequency the following year. Because crashes do not occur in 

a systematic way, it is unlikely that short analysis periods (e.g., one year) can fully capture the 

true frequency of crashes, resulting in crash prediction errors. To overcome RTM bias, the EB 

method uses both the observed number of crashes at a particular site and the predicted 

number of crashes at similar sites. 

An SPF is an equation that predicts the average number of crashes per year at a specific site as 

a function of exposure (i.e., AADT and segment length) and, in some cases, roadway or 

intersection characteristics. SPFs can be developed for specific facility and crash types using 

local data, or by calibrating existing SPFs (e.g., HSM SPFs). The predicted number of crashes (N) 

at a particular site can be estimated by multiplying three components: base SPF (CPredicted), 

CMFs, and a calibration factor, as shown in the following equation.

Equation A.1:

For example, exposure is represented by the segment length and AADT associated with the 

roadway segment as shown by the following baseline SPF:

Equation A.2:

Where:

CPredicted = predicted crash frequency under base conditions

β0, β1 = parameter coefficients

L = segment length

CMFs account for deviations from base conditions in relation to roadway and geometric 

characteristics, and traffic control devices. In some cases, (e.g., crash data variation between 

different jurisdictions or different time periods within the same jurisdiction), applying a 

N = CPredicted × C × CMF CPredicted = exp⁡[β0 + β1 × ln L) + β2 × ln AADT)]
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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calibration factor may be a more efficient approach than developing a new SPF that requires 

more time and data. Calibration factors can be estimated by: 

Equation A.3:

Where:

NObs,.i = observed annual average crash frequency

NPre,i = predicted annual average crash frequency

n = sample size, equal to the number of sites in the calibration process

The EB method is based on a weighted average principle. It has been widely used in many safety 

studies and is recommended by the HSM. It uses a weight factor, w to combine observed 

(CObserved) and predicted crash frequencies (CPredicted) to estimate the expected crash frequency,

CExpected:

Equation A.4:

Where:

CExpected = expected crash frequency

CPredicted = predicted crash frequency obtained from SPF

CObserved = observed crash frequency

w = weight factor that depends on the over-dispersion parameter (k) of the SPF

A.3 METHODOLOGY

Hauer (2015) states: “a parametric SPF is a mathematical function of traits (variables) and 

parameters. The activity of fitting a parametric SPF to data alternates between choosing the variables 

from which the model equation is to be made, determining the form of the function (i.e., how the 

variables and parameters should combine into an equation), estimating the value of the parameters, 

and examining the goodness of the fit.”

(5) The schematic of this concept is presented in Figure 14.

C =

 Nobs ,i

n

i=1

 Npre ,i

n

i=1

CExpected = w × CPredicted + (1 − w) × CObserved
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Figure 14. Flowchart. Parametric SPF Development Process.(1)

The SPF development for NFAS roads in North Carolina included the following steps:

• Step 1: Data preparation. Acquired crash, roadway inventory, and traffic volume data, 

and assigned crashes to the corresponding segments of the roadway network.

• Step 2: Exploratory data analysis. Compiled data for candidate independent variables, 

performed correlation analysis to identify the best predictors, and conducted 

exploratory data analysis to identify patterns and clusters. 

• Step 3: SPF development. Developed negative binomial models for the desired roadway 

networks separately for all five injury types [fatal injury (K); incapacitating injury (A); 

non-incapacitating injury (B); possible injury (C); and no injury or property damage only 

(O)], as well as for KABC crashes only. The researchers validated models using residual 

plots. If the residual plots were not in the range of the best fit model, decision trees or 

other data partitioning algorithms were developed to identify potential clusters inside 

the data. Instead of using the whole data, researchers developed SPFs for the clustered 

data, cross-examined the residuals, and repeated the previous steps until a good-fit 

model was developed.

A.3.1 Data Preparation and Exploratory Data Analysis

To better understand the relationship between various roadway characteristics and safety 

performance on NFAS roads in North Carolina, it was first necessary to assemble a 

comprehensive database of traffic crash, roadway inventory, and traffic volume data for the 
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study network. The data were obtained from different sources for the five-year period 2013 

through 2017. The precision of SPFs largely depends on the quality of the data. SPF 

development requires a comprehensive crash database that contains various information and 

data such as route id, route name, milepost, control section, geographic coordinates, collision 

type, and severity type among others. 

Table 18 shows annual crash frequencies on NFAS roads in North Carolina. The data show that 

444,863 crashes happened during the study period (30 percent of these crashes involve some 

type of injury). From 2013 to 2017, NFAS roads experienced a 12 percent increase in crashes. 

The crash database does not differentiate between segment and intersection-related crashes, 

though past studies have showed the significance of making this distinction.(6) This study 

considered all crashes without separating segment and intersection-related crashes. The SPFs 

developed in this study can be improved by excluding intersection-related crashes if more data 

become available (e.g., distance of crash location from closest intersection). 

Table 18. Crash Counts by Injury Type and Year.

Year K A B C O Unknown Total

2013 483 706 6,716 16,685 56,577 2,446 83,613

2014 468 719 6,840 16,998 56,410 2,492 83,927

2015 506 796 7,040 18,955 60,282 2,783 90,362

2016 519 1,009 7,216 19,138 62,585 2,792 93,259

2017 496 1,515 7,545 17,494 63,722 2,930 93,702

Grand 

Total 2,472 4,745 35,357 89,270 299,576 13,443 444,863

For each functional classification (6R, 7R, 7U), two SPFs were developed: one for total crashes 

(KABCO) and another one for KABC crashes. The original dataset contained several candidate 

predictors. Shoulder type, shoulder width, median type, and median width were considered for 

the analysis. After performing a correlation analysis, only two variables were found to be the 

best predictors: segment length (miles) and AADT (vpd). 

A.3.2 SPF Development

SPFs were developed using AADT data from permanent sites and short-term counts. The 

model structure, the overdispersion parameter, and the loglikelihood of each SPF are listed in

Table 19. 
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Table 19. SPF Characteristics.

Func. 

Class.

Crash 

Severity Safety Performance Function

Overdispersion

Parameter

Loglikelihood

7R KABCO N7R,Tot = 2.479 * Length0.962 * AADT0.035 0.353 20090.20

7R KABC N7R,KABC = 0.632 * Length1.005 * AADT0.034 0.348 -5624.30

7U KABCO N7U,Tot = 1.691 * Length0.842 * AADT0.071 0.952 -808.10

7U KABC N7U,KABC = 0.488 * Length1.062 * AADT0.071 0.772 -1,286.60

6R KABCO N6R,Tot = 2.432 * Length0.988 * AADT0.090 0.406 10,188.20

6R KABC N6R,KABC = 0.641 * Length1.023 * AADT0.084 0.326 -826.70

Regression models examine the average effects of the associated variables and ignore subgroup 

effects in the model development. As a result, models are often geared towards the population 

mean, without considering site-specific patterns. This study developed decision tree algorithms 

to determine the clustering effect. The results support the model development for each 

functional classification separately. Figure 15 illustrates the observed versus expected KABCO 

crashes for the three functional classifications. 

Figure 15. Scatterplots. Observed vs. Expected Crashes.

The model fitting is judged by its residuals, which are the differences between the number of 

recorded crashes and the predicted crash counts. A model is thought to fit well if the residuals 

are close to zero. The Cumulative Residual (CURE) plot is a good visualization to show how 

well or poorly an SPF can predict crashes for various values of an independent variable, which is 

plotted on the x-axis of the plot. A horizontal stretch of the CURE plot infers to a region of the 

variable where the estimates are unbiased. On the contrary, in regions where the CURE plot 

consistently drifts up or down the estimates are biased. The CURE plot for an unbiased SPF is 
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needed to be in the boundary of two standard deviations. The CURE plots (KABCO crashes) 

for both segment length and AADT are shown in Figure 16 and Figure 17.

KABCO Crashes - 7R

KABCO Crashes - 7U

KABCO Crashes - 6R

Figure 16. Graph. Cumulative Residuals vs. Segment Length. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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KABCO Crashes - 7R

 

KABCO Crashes - 7U

KABCO Crashes - 6R

 

Figure 17. Graph. Cumulative Residuals vs. AADT. 
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By sorting the data by each variable (AADT, segment length, or any other predictor variable of 

interest), CURE plots can be created to assess the precision of the functional form of these 

variables in the model. The CURE plots developed for 6R, 7R, and 7U are mostly within the 

boundary of two standard deviations. 

A.3.3 Sensitivity Analysis

The goal of the sensitivity analysis was to determine the impact of AADT estimation errors on 

the expected crash frequencies and hence, the final ranking of the study sites. The sensitivity 

analysis was separately performed for each functional classification and crash severity group 

(KABCO and KABC). The analysis involved the following steps:

• Step 1: Apply the EB method.

• Step 2: Determine the rank of each segment based on the results obtained from Step 1. 

• Step 3: Determine the percentile of the rank of each segment.

• Step 4: Increase the AADT of each segment by 10, 50, 100, 250 and 500 percent by 

keeping the rest of segments and variables unchanged.

• Step 5: Repeat Steps 1-4 separately for each segment and AADT percent increase. The 

end result is repeating Steps 1-4 185,930 times = [(3,110 segments in 6R) + (12,386 

segments in 7R) + (3,097 segments in 7U)] x (5 AADT percent increases) x (2 crash 

severity groups).

• Step 6: Calculate the percentile rank change of each segment by comparing the initial 

rank of each segment (no AADT change) against the rank obtained when AADT was 

increased by a certain percent.

A.4 RESULTS

Figure 18 through Figure 20 show the distribution of the percentile rank changes for different 

AADT groups and functional classes in a box-violin plot format. The plots show that higher 

percent increases in AADT are associated with higher percentile rank changes, as expected. 

This trend is visible for all functional classifications. However, even the highest percent (500 

percent) increase in AADT does not have a significant effect on the expected crash frequencies 

and therefore, the final segment ranking. In particular, the highest percentile rank changes are 

on average less than 10 percent in the case of 6R and 7U, and less than 5 percent in the case of 

7R. Overall, the plots show that the average percentile rank changes are higher for 6R and 7U 

compared to those of 7R. Because the traffic volume of most rural local roads is lower than 

2,000 vpd, the box-violin plots for higher volumes (greater than 2,000 vpd) are skewed toward 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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lower percentile rank changes. This trend can be partially attributed to the fact that the AADT 

coefficients (0.035 and 0.034) of the two SPFs developed for 6U (for KABCO and KABC 

crashes respectively) are significantly smaller than those of the other two functional 

classifications. Another potential reason that relates to the small AADT coefficients is that the 

sample size of 7R (12,386) is significantly larger than those of 6R (3,110) and 7U (3,097). 

Further, the CURE plots of 7R show that the residuals for the AADT range 0-10,000 vpd are 

not close to zero, indicating that the SPFs may need to be improved. One possible 

improvement is to divide 7R into subgroups based on geography or roadway/geometric 

characteristics and develop separate SPFs for each subgroup. Other potential improvements 

may include changing the form of the SPFs, adding more variables and interaction terms, and 

changing the objective functions that are used to estimate the parameter estimates. 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 18. Graph. Percentile Rank Change for Different AADT Groups (7R).
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 19. Graph. Percentile Rank Change for Different AADT Groups (7U).
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

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Figure 20. Graph. Percentile Rank Change for Different AADT Groups (6R).

In general, the results show that AADT estimation errors do not significantly affect the 

expected crash frequencies and the associated percentile rank changes. This is consistent with 

previous findings reported by Staats.(3)Staats performed a similar sensitivity analysis to evaluate 

how AADT estimation errors can impact the number of predicted crashes that are used in 

safety analysis (EB method that uses SPFs). The sensitivity analysis included the following steps:

a) Identified all intersections in Kentucky using GIS tools.

b) Classified intersections based on their characteristics (rural or urban roads, number of 

approaches, unsignalized or signalized, and number of lanes in each direction). The 

analysis considered intersections where State roads intersect local roads.
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c) Estimated predicted average crash frequency using SPFs from the HSM for different 

types of intersections such as three-leg and four-leg stop-sign controlled intersections. 

The AADT of the major road of an intersection was obtained from Kentucky’s Highway 

Information System, while the AADT of the local intersecting road was estimated from a 

model.

d) Estimated the expected number of crashes based on the following Empirical Bayes (EB) 

formula:

Expected Crashes in X years = Overdispersion parameter∗N∗CMF∗X + (1−

Overdispersion parameter)∗Previous crashes

Where: 

Overdispersion parameter = obtained from the HSM and calibrated for each SPF

N = the number of crashes predicted by the SPF

CMF = crash modification factor (from HSM or CMF Clearinghouse)

X = the number of years

Previous crashes = the number of crashes at the intersection in the past X years

e) Adjusted AADTs using the following equation:

Adjusted AADT = Estimated AADT/(1 + Percent Error)

Where:

Estimated AADT = AADT generated by a model

Percent Error = maximum percent error (797 percent), minimum percent error (-94 

percent), average positive error (134 percent), or average negative error (-38 percent).

f) Revised SPF values using the adjusted AADTs. The EB method incorporated the updated 

SPF values and used crash data over a 10-year period assuming a CMF of 0.15. Staats 

estimated that the average crash cost was $54,051.

g) Estimated maximum countermeasure construction cost for each prediction error and 

intersection, assuming that the benefit-to-cost ratio was equal to five. 

h) Calculated percent errors for maximum countermeasure costs between the original 

AADT, the estimated AADT, and the adjusted AADTs. This range of errors captured 
DATA COLLECTION AND AADT ESTIMATION FOR NFAS ROADS

121

the variation in the number of predicted crashes due to different AADT estimation 

errors.

Staats found that the average AADT estimation errors ranged between 134 percent 

(overestimated AADTs) to 38 percent (underestimated AADTs). After accounting for the two 

errors and adjusting the AADT values, the author found that the derived crash prediction 

errors were much smaller: 28 percent for overestimated AADTs and 22 percent for 

underestimated AADTs. The maximum AADT estimation error was 797 percent and the 

lowest was 94 percent. The derived crash prediction errors were 54 percent and 253 percent 

respectively. In general, the study showed that the AADT errors had a limited impact on the 

final crash predictions. This happened because the local road AADT only influences the number 

of crashes predicted by SPFs. However, intersection crash predictions must take into account 

both SPFs and historical crash rates and the latter have a higher weight in the calculation of the 

expected number of crashes.(3)

A.5 REFERENCES

1. Das, S., I. Tsapakis, and S. Datta. Safety Performance Functions of Low-Volume 

Roadways. Transportation Research Record: Journal of the Transportation Research Board, 

No. 2672, Transportation Research Board of the National Academies, Washington, 

D.C., March 2019.

2. AASHTO. Highway Safety Manual, 1st Edition. Washington D.C., 2010.

3. Staats, W.N. Estimation of Annual Average Daily Traffic on Local Roads in Kentucky. Master 

Thesis, Civil Engineering, University of Kentucky, 2016.

4. U.S.C. 148. U.S. Code, Title 23, Chapter 1, Section 148, Highway Safety Improvement 

Program.

5. Hauer, E. The Art of Regression Modeling in Road Safety. Springer, 2015.

6. Avelar, R., K. Dixon, and P. Escobar. Evaluation of Signalized-Intersection Crash 

Screening Methods Based on Distance from Intersection. Transportation Research Record: 

Journal of Transportation Research Board, Vol. 2514, 2015, pp. 177-186.
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APPENDIX B – AUTOMATED ESTIMATION OF TRAFFIC 

VOLUMES USING TRIP GENERATION METHOD

SUMMARY

This appendix presents a traffic volume estimation procedure that incorporates a trip 

generation method suitable for residential roads with one point of entry (i.e., cul-de-sacs).(1)

This method has two advantages over many traditional and non-traditional methods. First, it 

eliminates the need to carry out traffic counts in the field by yielding time and cost savings. 

Second, it provides significantly more accurate AADT estimates, particularly on urban local 

roads where the correlations of Census variables with AADT tends to be weak to negligible.

The Virginia (VDOT) first developed this method to manually estimate traffic volumes on 

secondary local roadways instead of taking short-duration counts in the field.(1) According to

VDOT’s approach, region and district staff review candidate segments using aerial photos, 

evaluate roadway eligibility criteria, count the number of houses along residential roads, and use 

a trip generation equation to estimate traffic volumes. 

To reduce the time and effort required to manually perform these steps, the research team 

developed a GIS database that contains electronic footprints of over 100 million buildings in the 

U.S. Then, researchers used GIS scripts and models to apply trip generation concepts and 

automate, to some degree, the estimation of traffic volumes. The research team validated the 

method using permanent and short-duration count data in North Carolina and Vermont, and 

found that the median APEs are 18.0 percent and 20.0 percent, respectively. These errors are 

significantly lower than those obtained from many traditional and non-traditional AADT 

estimation methods.

B.1 OVERVIEW OF VDOT’S TRIP GENERATION METHOD

VDOT estimates traffic volumes on secondary local roadways using a trip generation method 

instead of taking short-duration traffic counts. The use of trip generation estimates(2) was an 

outcome of a review process that aimed to reduce data collection costs and achieve manpower 

savings by establishing a local secondary count program. As part of this program, VDOT 

provided guidelines(3, 4)to its staff on how to identify eligible traffic links and generate traffic 

volume estimates using the trip-generation method. For example, roadways that are subject to 

use of trip generation techniques for traffic volume estimates are those that have one point of 

entry (e.g., cul-de-sacs), are residential in nature, and have a length of 0.5 mile or less. 

According to VDOT’s secondary program, region and district staff performs several actions 

that mainly involve:

• Reviewing candidate segments using aerial photos.
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• Determining potential development and roadway connectivity.

• Evaluating whether specific criteria are met.

• Documenting the trip generation estimate process used.

• Developing and submitting the estimates to the Central Office–Traffic Engineering 

Division. 

B.2 SELECTION OF TRAFFIC LINKS

VDOT developed criteria and guidance to evaluate sites for the trip generation method.(3, 4)

District staff reviews each candidate site using recent aerial images to determine development 

and roadway connectivity. If VDOT staff can make a determination using aerial photos, VDOT 

recommends using the trip generation method only for traffic links that meet the following 

criteria: 

• Land use is limited to single-family detached housing (ITE Trip Generation Code 210).(2)

For other land uses, it may not be possible to determine the number of dwelling units 

(or other independent variables) from an aerial photo. In the case of higher-density 

residential, this limitation also eliminates the need to judge which trip generation code 

to use, which may also be difficult to determine from an aerial photo. The majority of 

cul-de-sacs in the Northern Region consist of single-family homes.

• No alternative means of motor vehicle access is available, even by private streets or 

driveways.

• Unusual land uses are nearby that might cause traffic volumes to differ significantly from 

the trip generation rates. For example, a Metrorail station may induce more bicycle and 

pedestrian trips and fewer vehicle trips, or it may induce more vehicular trips if it 

attracts on-street parking.

• Occasionally, a private street diverges from a VDOT-maintained cul-de-sac. 

Consideration of these cul-de-sacs for the trip generation method is dependent on 

meeting all the criteria above, even though the private streets may lead to additional 

development, as long as the combined length of the public and private streets are less 

than the 0.5-mile threshold.

District staff retains the flexibility to conduct an actual traffic count on any link that exhibits 

unusual traffic conditions that do not meet the criteria defined above.
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B.3 TRAFFIC VOLUME ESTIMATION

According to VDOT, a simple method of computing traffic volumes for cul-de-sacs is to 

multiply the total number of households on the cul-de-sac by the trip generation rate. 

However, this would approximate the volume at the point where the cul-de-sac intersects the 

connecting roadway. The volume on a cul-de-sac is highest at this point since all trips from the 

cul-de-sac must pass it. The volume becomes lower toward the bulb because households along 

the stem typically travel away from the bulb. To capture this trend, the trip generation method 

estimates the average volume per weekday along the cul-de-sac as follows:

Equation B.1:

Where:

ν = number of trips per weekday using a cul-de-sac, averaged according to the length of 

 the cul-de-sac.

Η = households with driveways on the cul-de-sac. 

gn = number of trips per weekday generated by the nth household.

dn = portion of the length of each trip to and from the nth household that uses the cul-de-

 sac, taken as the distance along the cul-de-sac between the nth household’s driveway 

 and the end of the stem.

L = length of the cul-de-sac.

Because VDOT uses this process for only one land use type, it considers the same g value for 

every household. VDOT extracts this value from the Trip Generation Manual.

(2)

For single-family 

detached housing (Code 210), g = 9.57 trips per weekday per dwelling unit, according to the 8th

edition of the manual. VDOT rounds this rate to 10 trips per weekday for computational ease. 

VDOT makes a further distinction between “bulb households,” with driveways in the bulb of 

the cul-de-sac, and “stem households,” with driveways in the stem. Trips to and from bulb 

households typically use the cul-de-sac for approximately its entire length, particularly when 

considering additional maneuvering in the bulb. Stem household trips typically use the cul-de-sac 

only from their driveways to the connecting road. Equation B.1 simplified as follows:

ν =

 (gn × dn)

H

n=1

L
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Equation B.2:

Where:

g = 10 trips per weekday per household.

Hb = number of bulb households.

Hs = number of stem households.

The ratio d/L is a dimensionless constant since both d and L are expressed in units of length. It 

represents the average fraction of the cul-de-sac used by trips to and from stem households. 

Stem household driveways are assumed to be distributed uniformly along the length of the culde-sac, so d/L = 0.5. VDOT further simplifies Equation B.2 as follows:

Equation B.3:

Although it is possible to compute a traffic volume estimate without differentiating between 

bulb and stem households, VDOT does not do so because it wants to produce estimates that 

are more accurate. Most cul-de-sacs do not have driveways distributed uniformly along their 

length. Site plans often distribute driveways more densely in the bulb of the cul-de-sac than 

along the stem. Likewise, connecting roadways often serve households at the corner of a culde-sac resulting in fewer driveways. 

VDOT established the following five steps for estimating traffic volumes for eligible traffic links.

Step 1: Draw an imaginary line at the entrance of the cul-de-sac, where it joins the connecting 

road, and count the number of houses whose primary driveway can only be reached by 

traveling across the line. For example, on the traffic link shown in Figure 21a, there are nine 

houses.

Step 2: Draw another imaginary line between the bulb and the stem of the cul-de-sac and 

count the number of houses whose primary driveway can only be reached by traveling across 

this line. For example, on the cul-de-sac shown in Figure 21b, there are three houses.

ν = g × Hb + Hs ×

d

L

 

ν = 10 × Hb + 0.5Hs

)
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Step 3: Add the results of Steps 1 and 2, and multiply the sum by 5. The calculation in this step 

is based on Equation (3). For example, from the first two figures, the result is (9 + 3) x 5 = 60.

Step 4: Report the result of Step 3 as the number of trips per day on a spreadsheet provided 

by the Central Office. Use the letter “M” to indicate that the value resulted from a “manual” 

estimate rather than a physical count. Also, report the date the estimate was prepared. 

Step 5: Periodically send the spreadsheet back to CO-TED to import it into the traffic 

monitoring system. This occurs at least once per year. The results are reported in annual 

publications.

Figure 21. Screenshot of Map. Examples: (a) Step 1; (b) Step 2. Photo Courtesy 

VDOT Northern Region(48, 49)

1

2

3

4

5

6

7

8

9

1

2

3
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VDOT provides its staff with additional aerial photos (Figure 22) and estimation examples to 

highlight differences between links that have different roadway and geometric characteristics. 

Figure 22. Screenshot. Examples: (a) Fariba Drive; (b) Pine Knot Drive. Photo 

Courtesy VDOT Northern Region.(48,49)

The first three steps of the traffic volume estimation process result in the following:

Figure 22a: 

Step 1: 17 houses.

Step 2: 4 houses.

Step 3: (17 + 4) x 5 = 105 trips per day.

Figure 22b:

Step 1: 38 houses.

(a) (b)
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Step 2: 17 houses.

Step 3: (38 + 17) x 5 = 275 trips per day.

Figure 22a shows a simple case of a traffic link that consists of a bulb and a stem. In Figure 22b, 

the cul-de-sac has two private-street connections through the bulb, taken into consideration in 

Step 2, and two connections along the stem. Distribution of the connections along the stem is 

relatively uniform along the cul-de-sac’s length, so the trip generation computation is valid.

B.4 COST AND OTHER CONSIDERATIONS

In general, the cost to generate traffic volume estimates is significantly lower than collecting 

field data. Some Districts apply the trip generation method in-house, while others hire a 

temporary service contractor. As of February 2016, a typical hourly rate of these contractors 

ranged between $20 and $30 per hour. Depending on the complexity and the number of the 

houses along a roadway link, VDOT staff or its contractors spend between 15-30 minutes to 

generate a traffic volume estimate for one link.

Most contractors are responsible for both conducting counts in the field using portable traffic 

equipment and generating traffic volume estimates using the trip generation method. Some 

Districts ask their contractor(s) not to rely heavily on aerial photos but visit the roadway links 

of interest and collect necessary information and data in the field. To save time and reduce 

travel costs, contractors try to do both (i.e., conduct short-duration counts and visit eligible 

links that are close to the counts) in one trip. Other Districts allow their contractor(s) to apply 

the trip generation method at the office and ask them to visit the field in case the aerial photos 

are old, trees cover driveways and houses, and there is significant construction activity in the 

examined area.

B.5 METHOD AUTOMATION AND VALIDATION

To avoid some of the manual steps described above, particularly those that involve reviewing 

and extracting data from aerial images, the research team used artificial intelligence methods to 

develop a geodatabase that contains footprints of over 100 million buildings in the entire 

country. Figure 23 shows an example of building footprints in a residential area in North 

Carolina. Table 20 shows the approximate number of buildings by state.
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Figure 23. Screenshot. Example of Building Footprints in a Residential Area.

Then, researchers developed GIS models and scripts to automatically: 

• Determine dangle nodes (i.e., cul-de-sacs) on the transportation network.

• Extract location information (e.g., address) for each building.

• Map every building to the network based on the building address.

• Apply trip generation rules and roadway link eligibility criteria.

• Estimate traffic volumes for eligible links.

The research team used the GIS tools to apply the trip generation method in North Carolina 

and Vermont. Researchers compared the traffic volumes estimates generated by the trip 

generation method against permanent and short-duration counts. Figure 24 and Figure 25

show scatterplots between trip generation volume estimates and counts from North Carolina 

and Vermont, respectively. The validation datasets included 123 counts in North Carolina and 

34 counts in Vermont.
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Table 20. Number of Electronic Building Footprints by State.

State Number of 

Buildings (Millions) State

Number of 

Buildings (Millions)

Alabama 2.5 Montana 0.8

Alaska 0.1 Nebraska 1.2

Arizona 2.5 Nevada 0.7

Arkansas 1.5 New Hampshire 0.5

California 10.0 New Mexico 1.0

Colorado 2.1 New York 4.5

Connecticut 1.2 New Jersey 2.5

Delaware 0.3 North Carolina 4.5

District of Columbia 0.1 North Dakota 0.6

Florida 6.5 Ohio 5.0

Georgia 3.5 Oklahoma 2.1

Hawaii 0.3 Oregon 1.8

Idaho 0.5 Pennsylvania 4.5

Illinois 4.5 Rhode Island 0.4

Indiana 3.3 South Carolina 2.2

Iowa 2.0 South Dakota 0.6

Kansas 1.6 Tennessee 3.0

Kentucky 2.4 Texas 9.5

Louisiana 2.1 Utah 1.0

Maine 0.8 Vermont 0.3

Maryland 1.6 Virginia 3.1

Massachusetts 2.0 Washington 3.0

Michigan 4.6 West Virginia 1.0

Minnesota 2.4 Wisconsin 3.1

Mississippi 1.5 Wyoming 0.4

Missouri 3.1 -* -

* Dashes mean no data (empty cells).
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Figure 24. Scatterplot. Traffic Volumes from Trip Generation Method vs. Counts in 

North Carolina.

Figure 25. Scatterplot. Traffic Volumes from Trip Generation Method vs. Counts in 

Vermont.

y = 0.8894x + 12.826

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In the case of North Carolina, the median APE and mean APE were 18.0 percent and 25.3 

percent, respectively. In Vermont, the median APE and mean APE were 20.0 percent and 27.0 

percent, respectively. These errors are generally lower than those obtained from other 

traditional and non-traditional methods.

The case study with VDOT and the automation of VDOT’s approach by the research team 

revealed that the use of trip generation estimates minimizes the need to carry out traffic counts 

on qualified links by yielding time and cost savings. Even if agencies apply the trip generation 

method manually, it may be cheaper to use state resources and labor than to hire contractors 

in many cases. Keys to successful application of the trip generation method include a clear 

implementation plan and specific assignment of roles and responsibilities to different parties 

within the agency. Developing requirements and guidelines can be a time-consuming process, 

but the benefits realized outweigh the development effort. Agencies should estimate the cost 

for generating traffic volume estimates manually or in an automatic fashion, compare it to the 

cost for conducting and processing short-duration counts, and relate it to the hidden cost of 

not having data on secondary local roads. Agencies should inform managers and executives on 

potential cost savings and other benefits achieved through adopting more-efficient procedures

such as the one described above.

(1)

B.6 REFERENCES

1. Tsapakis, I., Holik, W., A. Vandervalk, S. Turner, and J. Habermann. Virginia Department 

of Transportation - Innovative Procedures in Traffic Volume Estimation. Final Repot

FHWA-SA-17-036, Federal Highway Administration, Office of Safety, Washington D.C., 

2017. Available at: https://safety.fhwa.dot.gov/rsdp/downloads/fhwasa17036.pdf.

Accessed December 31, 2019.

2. Institute of Transportation Engineers. (2008). Trip Generation Manual, 8th Edition, User’s 

Guide, ITE, Washington, D.C.

3. Virginia Department of Transportation Northern Region. (2009). Process for 

Implementing the Trip Generation Method as an Alternative to Secondary Local Counts, Rev. 

12/2009, VDOT, Richmond, VA.

4. Virginia Department of Transportation (2015). Use of Trip Generation for Traffic Volume 

Estimation, VDOT, Richmond, VA.
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ACKNOWLEDGMENTS

FHWA: Marc Starnes (General Task Manager), Stuart Thompson (Former General Task 

Manager), Steven Jessberger (Panel Member), and Joseph Hausman (Panel Member).

Case study representatives

DVRPC: Scott Brady and Michael Ruane.

NYSDOT: Michael Fay, Robert Limoges, and Regina Doyle.

SEMCOG: Chade Saghir, Delores Muller, and Brian Mohr.

VDOT: John Bechtold, William Dunnavant, Ali Farhangi, Ralph Jones, Donald Logan, and Brett 

Randolph.

Pilot study representatives

Georgia DOT: Shronica Holland, Robert Binns, Sean Diehl, and LaKenya Rapley.

Montana DT: Becky Duke, Peder Jerstad, Marie Stump, and Don Wark.

Minnesota DOT: Christine Prentice, Darin Mertig, Ian Vaagenes, and Gene Hicks.

North Carolina DOT: Kent Taylor.

New Mexico DOT: Yolanda Duran, Sean Noonen, and John Baker.

Pennsylvania DOT: Gregory Dunmire and Andrea Bahoric.

Other DOT representatives

Vermont DOT: Maureen Carr and Joshua Schultz.
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Texas A&M Transportation Institute researchers: Eun Sug Park, Bobie Guo, Kartikeya 

Jha, Jose Rivera Montes De Oca, Pete Koeneman, Shawn Turner, Raul Avelar Moran, Sruthi 

Ashraf, and Steven Venglar.

Cambridge Systematics Inc. researchers: Richard Margiotta, Annita Vandervalk, and Herb 

Weinblatt.
For More Information:

Visit safety.fhwa.dot.gov/rsdp

FHWA Office of Safety

Marc Starnes

Marc.Starnes@dot.gov

202-366-2186

FHWA-SA-20-064

y = 0.8894x + 12.826

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