Traffic Volume Collection and Estimation on NonFederal Aid System Roads 

SOUTHEAST MICHIGAN COUNCIL OF GOVERNMENTS

INNOVATIVE TRAFFIC DATA QUALITY 

ASSURANCE/QUALITY CONTROL PROCEDURES AND 

AUTOMATING AADT ESTIMATION 

CASE STUDY

FHWA-SA-17-035

Federal Highway Administration 

Office of Safety 

Roadway Safety Data Program 

http://safety.fhwa.dot.gov/rsdp/

March 21, 2017
SOUTHEAST MICHIGAN COUNCIL OF GOVERNMENTS CASE STUDY 

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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. 

Standards and policies are used to ensure and maximize the quality, objectivity, utility, and 

integrity of its information. FHWA periodically reviews quality issues and adjusts its programs 

and processes to ensure continuous quality improvement. 
SOUTHEAST MICHIGAN COUNCIL OF GOVERNMENTS CASE STUDY 

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TECHNICAL DOCUMENTATION PAGE 

1. Report No.

FHWA-SA-17-035

2. Government Accession 

No.

3. Recipient's Catalog No.

4. Title and Subtitle

Innovative Traffic Data QA/QC Procedures and Automating AADT Estimation

5. Report Date

March 2017

6. Performing Organization Code

7. Author(s) 

William Holik, Ioannis Tsapakis, Anita Vandervalk, Shawn Turner, John 

Habermann

8. Performing Organization Report No.

9. Performing Organization Name and Address

Texas A&M Transportation Institute (TTI)

Texas A&M University System 

3135 TAMU

College Station, TX 77843-3135

10. Work Unit No.

11. Contract or Grant No.

DTFH6116D00004

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

Case Study, August 2016–February 

2018 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Stuart Thompson, Office of Safety. 

16. Abstract

This case study highlights two noteworthy practices at the Southeast Michigan Council of Governments (SEMCOG) 

regarding short-duration traffic count validation procedures and an automated annual average daily traffic (AADT) 

estimation process. SEMCOG maintains a centralized traffic count database and receives traffic counts from the local 

agencies in southeast Michigan. SEMCOG conducts 46 validity checks on all traffic count data to identify invalid data 

available in the database but not adequate for analysis. After implementing the system, SEMCOG reduced labor costs 

associated with converting and entering data and was able to spend more time analyzing data. SEMCOG developed an 

algorithm that works inside its geographic information system (GIS) to improve AADT estimates by searching for 

uncounted segments with nearby counted segments. When a counted segment is identified, the algorithm calculates the 

weighted average of two nearby segments and assigns that AADT to the uncounted segment. This process was automated 

using Python scripts, which results in an increase in the number of AADT estimates without requiring additional field data 

collection. 

17. Key Words: 

short-duration traffic counts, AADT estimation, 

data quality control, data sharing, data viewer

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

 24

22. Price

Form DOT F 1700.7 (8-72) Reproduction of Completed Pages authorized 
SOUTHEAST MICHIGAN COUNCIL OF GOVERNMENTS CASE STUDY 

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ACRONYMS 

Table 1. Acronyms. 

Acronym Description

AADT Annual Average Daily Traffic

AWDT Average Weekday Daily Traffic

DOT Department of Transportation

GIS Geographic Information System

LRS Linear Referencing System

MPO Metropolitan Planning Organization

NFAS Non-Federal Aid System

QA/QC Quality Assurance/Quality Control

SEMCOG Southeast Michigan Council of Governments
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EXECUTIVE SUMMARY 

This case study highlights two noteworthy practices at the Southeast Michigan Council of 

Governments (SEMCOG) regarding short-duration traffic count validation procedures and an 

automated annual average daily traffic (AADT) estimation process. SEMCOG maintains a 

centralized traffic count database and receives traffic counts from local agencies in southeast 

Michigan. SEMCOG conducts 46 validity checks for all traffic count data to ensure quality data 

are used for analysis. Some of these data serve a purpose for a local agency and do not meet 

SEMCOG’s requirements for short-duration traffic count data used for AADT estimation. Thus, 

SEMCOG identifies and excludes these invalid data from AADT analysis, but the data remain in 

the database. After implementing the system, SEMCOG reduced labor costs associated with 

converting and entering data and is able to spend more time analyzing data. SEMCOG 

developed an algorithm that works inside of its geographic information system to improve 

AADT estimates by searching for uncounted segments with nearby counted segments on either 

side. When such a segment is identified, the algorithm calculates the weighted average of the 

two nearby segments and assigns that AADT to the uncounted segment. This process was 

automated using Python scripts, which results in an increase in the number of AADT estimates 

without requiring additional field data collection. 
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TABLE OF CONTENTS 

INTRODUCTION ....................................................................................... 8

BACKGROUND .......................................................................................... 9

QA/QC PROCEDURES .............................................................................10

DATA COLLECTION ...............................................................................................10

DATA PROCESSING ................................................................................................11

AADT ESTIMATION ................................................................................15

OBJECTIVE ................................................................................................................15

DATA ANALYSIS .....................................................................................................15

APPLICABILITY TO OTHER STATES ..................................................20

QA/QC PROCEDURES ............................................................................................20

AADT ESTIMATION ................................................................................................21

REFERENCES ............................................................................................22

ACKNOWLEDGEMENTS ........................................................................23
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LIST OF TABLES 

Table 1. Acronyms. ............................................................................................................ 4

LIST OF FIGURES 

Figure 1. Sample AADT Interpolation. .........................................................................16

Figure 2. SEMCOG Traffic Volume Data Viewer. .......................................................18

Figure 3. Traffic Volume Information for Individual Link. ..........................................19
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INTRODUCTION 

The purpose of this case study is to highlight two noteworthy practices at the Southeast 

Michigan Council of Governments (SEMCOG) and demonstrate how they can be applied to 

non-Federal Aid System (NFAS) roads: 

 Use of quality assurance and quality control (QA/QC) procedures to clean and filter 

data collected by local agencies. 

 Use of automated annual average daily traffic (AADT) estimation procedures in a 

geographic information system (GIS) environment to reduce data collection 

requirements. 

SEMCOG has a unique relationship with the public agencies in its region in that the local 

agencies and the Michigan Department of Transportation (DOT) collect all traffic data and 

SEMCOG stores, validates, and analyzes the data. Local agencies conduct two types of shortduration counts: those used to develop AADT values for road segments, and those used for 

special projects that may include a shorter period or only a single lane or direction of travel. 

Local agencies send both types of counts to SEMCOG, which then checks and validates the 

counts. To accomplish this task, SEMCOG developed 46 validity checks in a Microsoft Office 

Access database. The main benefit of these procedures is that other agencies can easily apply 

them to identify invalid or questionable data and increase the accuracy and reliability of traffic 

estimates. After implementing the system, SEMCOG reduced labor costs associated with 

converting and entering data and is able to spend more time analyzing data. 

To improve AADT estimates on the 40,355 road segments in the SEMCOG area, SEMCOG 

developed a process to estimate AADT on segments meeting certain criteria instead of 

conducting short-duration counts on each segment. SEMCOG developed an algorithm that 

works inside of its GIS to search for uncounted segments with nearby counted segments on 

either side. When the algorithm identifies an uncounted segment, it calculates the weighted 

average of the two nearby counted segments and assigns that AADT to the uncounted 

segment. SEMCOG reports these values as interpolated segments in its traffic data viewer. This 

process would be time consuming to do manually, so SEMCOG automated the process using 

Python scripts. The benefit of the AADT estimation scripts are an increase in the number of 

AADT estimates that do not require additional data collection and an automated procedure to 

quickly estimate the AADT values. 
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BACKGROUND 

SEMCOG is the metropolitan planning organization (MPO) for seven counties in southeast 

Michigan: Livingston, Macomb, Monroe, Oakland, Saint Clair, Washtenaw, and Wayne Counties. 

SEMCOG does not collect traffic data itself. Instead, it receives around 3,000 short-duration 

counts conducted at various locations every year by local agencies. The traffic data collectors 

apply axle correction factors to the short-duration counts. Additionally, SEMCOG develops and 

applies adjustment factors for day of week and month of year based on permanent stations 

grouped by county and functional class. Michigan DOT maintains the permanent count stations, 

and SEMCOG develops its own adjustment factors. SEMCOG has been archiving traffic data for 

about 15 years. During this period, data collectors have taken roughly 163,500 traffic counts on 

40,355 individual links—or road segments delineated by functional class, county, and 

intersecting roads. 

In the past, SEMCOG spent much time and effort converting data received from each agency 

into a commonly used format compatible with its database. This left little time to analyze the 

data. Now, SEMCOG hosts a common traffic count database system and provides software and 

support to all local agencies in the area to use the system. The system has some QA/QC 

procedures that data go through when uploaded, but SEMCOG validates all data using its own 

46 validity checks when it downloads the data. This ensures all data used for analysis are valid. 

SEMCOG has noteworthy practices for validating data received from other agencies that other 

states can use. Also, SEMCOG uses an automated process to estimate AADT values for 

uncounted segments. Agencies can expand this process to a statewide system to significantly 

reduce processing time and data collection requirements. 
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QA/QC PROCEDURES 

DATA COLLECTION 

Local agencies collect and report short-duration traffic count data to SEMCOG. Agencies 

conduct short-duration traffic counts on a four-year cycle and cover roughly 55 percent of the 

network in that four-year period. SEMCOG does not collect short-duration counts nor do they 

operate any permanent count stations. SEMCOG developed their own adjustment factors using 

data from the 35 Michigan DOT permanent count stations in the SEMCOG area. The 

permanent count station data are supplemented using local agency systems on traffic signals 

that collect continuous traffic data. 

Local agencies have their own data collection plans and equipment. SEMCOG ensures that the 

traffic count database manufacturer supports all the data collection systems used by local 

agencies and helps set up traffic data collection programs as needed. SEMCOG and the local 

agencies have a user’s group that meets to discuss data collection plans and locations. SEMCOG 

occasionally requests local agencies count specific locations. They will hire consultants to 

collect data when local agencies are not able to help. Most of the counts local agencies collect 

are short-duration traffic counts but may collect data for their purposes that cover a shorter 

period or fewer lanes or directions. SEMCOG’s validity checks flag invalid data so they are not 

included in analysis. 

Prior to 2001, SEMCOG received data from local agencies in over 10 different formats and 

spent too much time converting and uploading the data. As a result, SEMCOG now maintains a 

centralized traffic count database that is used by local agencies in the region so all data are 

compatible and easily transferable. SEMCOG provides the database software and login 

information to each local agency so users can deploy the software locally. SEMCOG works with 

the database system provider to ensure the traffic data collection systems that each local 

agency uses are compatible. 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 validity 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. 
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DATA PROCESSING 

SEMCOG conducts validity checks for all traffic count data to ensure quality data are used for 

analysis. Since SEMCOG does not collect traffic data itself, local agencies upload their traffic 

count data to the central database. Some of these data serve a purpose for a local agency and 

do not meet SEMCOG’s requirements for short-duration traffic count data used for AADT 

estimation. Thus, SEMCOG identifies and excludes these 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. 

Local agencies in the SEMCOG area upload short-duration traffic count data directly into the 

traffic count database. SEMCOG works with the software vendor to ensure that the software 

can support all traffic data collection systems used by the local agencies. 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 such things as 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. The validity checks are conducted in 

Microsoft Access but implemented in MySQL. The switch is necessary because the database of 

count records is too large to handle efficiently with Microsoft Access. 

SEMCOG stores short-duration count data in four tables in a relational database: 

 All Counts—This table includes the location of the count, the start and end date of the 

count, the direction, the counts for 15-minute intervals, the total count, and the 

computed AADT and average weekday daily traffic (AWDT). 

 All GIS—This table contains location information necessary to map short-duration 

traffic counts to roadway segments. This includes information such as segment ID, route 

number, milepoint, and distance. SEMCOG’s and Michigan DOT use the same linear 

referencing system (LRS). 
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 All Location—This table describes the location where short-duration traffic counts 

are taken. This includes information such as road name, direction, milepoint, and cross 

streets. 

 All Source—This table describes the agency that collected short-duration traffic 

counts. 

All tables include primary and foreign keys to identify unique features and join tables. The 

validity checks are applied to one or more tables and include the following: 

1. Is segment ID a valid primary key for the location table? Check for null and duplicate 

values. 

2. For each record in the location table, does road name contain a non-null value? 

3. For each record in the location table, does direction contain a value of either 2-Way, 

EB, NB, NEB, NWB, Ramp, SB, SEB, SWB, or WB? 

4. For each record in the location table, does location type contain a value of either 

Intersection or Link? 

5. For each Intersection location record, does At Road contain a non-null value? 

6. For each Intersection location record, does From Road contain a null value? 

7. For each Intersection location record, does To Road contain a null value? 

8. For each Intersection location record, does Approach contain a value of either East Of, 

North Of, South Of, or West Of? 

9. For each Link location record, does From Road contain a non-null value? 

10. For each Link location record, does To Road contain a non-null value? 

11. For each Link location record, does Approach contain a null value? 

12. For each Link location record, does At Road contain a null value? 

13. Is each location record unique when grouped by On Road, Direction, From Road, To 

Road, Approach, and At Road? 

14. For each Intersection location record where Direction equals any valid value other than 

2-Way or Ramp, and where Approach contains a valid value, do the directions in 

Approach in Direction run approximately parallel to each other? 
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15. Is Count ID a valid primary key for the count table? 

16. Does each record in the count table match exactly one record in the location table 

when linked via Segment ID? 

17. Does each record in the location table match at least one record in the count table? 

18. For each record in the count table, does Local ID contain a non-null value? 

19. For each record in the count table, does Start Date contain a non-null value 

corresponding to a date from between 01/01/1980 and the present? 

20. For each record in the count table, does End Date contain a non-null value 

corresponding to a date from between 01/01/1980 and the present? 

21. For each record in the count table, is End Date exactly one day after Start Date? 

22. For each record in the count table, does Interval contain a value of either 15 or 60? If 

Interval is 24, delete all those records. 

23. For each record in the count table, does each hourly field (HR01, HR02, HR03, etc.) 

contain a non-negative integer? 

24. For each record in the count table, does the value in Total Count exactly equal the sum 

of the hourly values? 

25. For each count record where Interval contains a value of 60, does each 15-minute field 

(HR01_1, HR01_2, HR01_3, HR01_4, HR02_1, etc.) contain a null value? 

26. For each count record where Interval contains a value of 15, does each 15-minute field 

contain a non-negative integer? 

27. For each count record where Interval contains a value of 15, does each hourly field 

exactly equal the sum of its four 15-minute fields? 

28. For each record in the count table, if AWDT is not a positive integer, is there a value of 

X in Action? 

29. For each record in the count table, if AADT is not a positive integer, is there a value of 

X in Action? 

30. For each record in the count table, does Source ID contain a positive integer? 

31. Is each count record unique when grouped by Source ID, Local ID, and Start Date? 
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32. Is each count record unique when grouped by Segment ID, Source ID, and Start Date? 

33. Is GIS ID a valid primary key for the GIS table? 

34. Does each record in the GIS table match exactly one record in the location table when 

linked via Segment ID? Note: All GIS records must match location records, but not all 

location records have corresponding GIS records. 

35. For each record in the GIS table, does Primary Route Number contain a positive 

integer? 

36. For each record in the GIS table, does Begin Milepoint contain a non-negative value? 

37. For each record in the GIS table, does End Milepoint contain a positive value? 

38. For each record in the GIS table, is End Milepoint ≥ Begin Milepoint?

39. For each record in the GIS table where End Milepoint = Begin Milepoint, does the 

record correspond with a location that is associated solely with Michigan DOT counts? 

40. For each record in the GIS table where End Milepoint = Begin Milepoint, is that record 

the only GIS record associated with the corresponding record in the location table? 

41. For each record in the GIS table, does the field Michigan Geographic Framework Value 

contain a reference to the appropriate Michigan Geographic Framework version 

number? 

42. Is each GIS record unique when grouped by Segment ID and Primary Route Number? 

43. Does each Primary Route Number/Begin Milepoint combination plot without error as a 

point? 

44. Does each Primary Route Number/End Milepoint combination plot without error as a 

point? 

45. For each location record without a corresponding GIS record, does the field GIS 

Comment contain an appropriate non-null comment? 

46. For each location record with at least one corresponding GIS record, does the field GIS 

Comment contain either a null value or the value Ramp? 
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AADT ESTIMATION 

OBJECTIVE 

SEMCOG developed automated procedures to expedite high-quality AADT estimation without 

requiring additional data collection. SEMCOG estimates AADT in three ways: 

 Observed—SEMCOG collects these counts in the field and annualizes them using 

adjustment factors. 

 Interpolated—For segments that do not have an observed count but do have observed 

counts on either side of them, SEMCOG calculates the weighted average value of the 

adjoining segments. A Python script in ArcGIS interpolates the adjoining segment 

counts. 

 Default—SEMCOG estimates an average value by grouping observed counts by county, 

number of lanes, and functional class, and then assigns this value to the remaining 

segments. 

SEMCOG uses the interpolation and default procedures based on observed counts to provide 

additional coverage of AADT values without significantly increasing data collection costs. 

DATA ANALYSIS 

SEMCOG developed Python scripts in a GIS environment to automate the generation of AADT 

estimates for uncounted segments. The scripts interpolate traffic volumes from counted 

segments and produce AADT estimates for adjacent uncounted segments. SEMCOG counts 

roughly 48 percent of the network segments and uses an interpolated AADT values for about 

40 percent of the network. The scripts assign a default AADT value to the remaining 12 

percent of segments. 

Python scripts optimize the interpolation method and quickly determine AADT values. These 

scripts systematically work through several steps. First, the scripts search for observed counts 

on links with the same primary route number. If no links of the same primary route have 

observed counts, then interpolation is not possible. If all links of the same primary route have 

observed counts, then interpolation is not required. For primary routes that have at least one 

link with an observed count, the scripts conduct interpolation as follows: 

 The beginning and ending segments are assigned the nearest AADT value for a route of 

the same functional class. 
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 If only one observed count is found on the primary route, all links are given that AADT 

value. 

 If multiple observed counts are found on the primary route, the inverse distance 

weighted average AADT is calculated for the links without an AADT value. 

For example, the primary route shown in Figure 1 has known AADT values for roadway links 

(or roadway segments) a (AADTa=10,000) and d (AADTd=30,000). The AADT of links b and c 

is unknown. 

Figure 1. Sample AADT Interpolation. 

2 miles 4 miles

a b c d

10,000 30,000

SEMCOG uses the inverse distance weighted average formula to determine the unknown 

AADT value. It takes the difference between the higher and lower AADT values and multiplies 

it by the distance from the endpoint of the link with the lower AADT value to the mid-point of 

the link with an unknown AADT. Then it divides it by the distance of the links with observed 

AADT values, all added to the lowest AADT. This can be expressed as Equation (1). 

AADTunc =

(AADThigh − AADTlow) ∗ dEnd to MP

dtot

+ AADTlow (1) 

Where: 

AADTunc = the AADT being calculated for the uncounted link. 

AADThigh = the higher AADT value of the nearest link on either side of the uncounted link. 

AADTlow = the lower AADT value of the nearest link on either side of the uncounted link. 

dEnd to MP = the distance from the end of the link with the lower AADT value to the 

mid-point of the uncounted link. 

dtot = the distance between links with observed AADT values on both sides of the 

uncounted link.
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Solving Equation (1) for links b and c in Figure 1 results in the following: 

AADTb =

(30,000 − 10,000) ∗ 1

6

+ 10,000 = 13,333

AADTc =

(30,000 − 10,000) ∗ 4

6

+ 10,000 = 23,333

SEMCOG validated its interpolated and default data estimates by excluding observed counts 

and using the interpolation and default methods to estimate the AADT. The estimates 

compared closely with the observed value for the segment. As a result of the data validation, 

SEMCOG feels confident in its estimation procedures. 

After SEMCOG staff receive data, conduct validity checks, and estimate AADT, they upload 

short-duration traffic counts to a web viewer. This viewer shows AADT values on roads in the 

SEMCOG area (Figure 2) and shows whether the AADT value is observed, interpolated, or 

default (Figure 3). 
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Figure 2. SEMCOG Traffic Volume Data Viewer. 

Source: http://maps.semcog.org/TrafficVolume/ 
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Figure 3. Traffic Volume Information for Individual Link. 

Source: http://maps.semcog.org/TrafficVolume/ 
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APPLICABILITY TO OTHER STATES 

SEMCOG has implemented several innovative practices for its AADT data collection program, 

including 46 validity checks to ensure short-duration count accuracy and automated Python 

scripts to estimate and assign AADT values to uncounted roadway sections. Other agencies can 

adopt SEMCOG’s validity checks and apply the Python scripts to interpolate AADT values to 

make certain elements of their AADT estimation practices more efficient. SEMCOG regularly 

helps other agencies develop similar validity checks and AADT interpolation code. To adopt 

similar validity check and AADT estimation systems, agencies would need personnel familiar 

with database management and programming. If an agency uses a third-party software vendor 

for its AADT data, the vendor may be able to implement additional specific validity checks. 

QA/QC PROCEDURES 

Lessons learned from SEMCOG regarding 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. Having a member of the team who is competent in SQL helps with 

implementing and maintaining the validity checks. 

 Gathering and using data collected by local agencies can increase the number of shortduration 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 

may be necessary. These checks will allow agencies 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 types of analyses.
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AADT ESTIMATION 

Lessons learned from SEMCOG regarding AADT estimation include the following: 

 Keep the initial program simple. Start with a clear goal of what is important and what 

system can be implemented and maintained currently. Create the system to satisfy the 

immediate need and then expand upon it. 

 Using interpolation can greatly reduce data collection needs for estimating AADT 

values. For example, SEMCOG estimates the AADT using interpolation on 40 percent 

of the roadway links in its area. 

 Developing automated scripts to interpolate AADTs can make the interpolation process 

more efficient, resulting in time and money savings. 

 By automating the interpolation process, agencies can easily validate the results by 

randomly selecting some counted segments and interpolating those segments as if they 

had not been counted. The interpolated AADT value can be compared to the actual 

observed AADT value. SEMCOG found the interpolated values to be accurate 

compared to observed values. 
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REFERENCES 

Phone interviews and email conversations with Chade Saghir, Transportation Planner, 

SEMCOG. 

SEMCOG, Traffic Volume Maps. http://maps.semcog.org/TrafficVolume/. Accessed December 

19, 2016. 

FHWA, Office of Safety 

Stuart Thompson, P.E. 

Federal Highway Administration 

202-366-8090 

Stuart.Thompson@dot.gov
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ACKNOWLEDGEMENTS 

The FHWA Office of Safety Data Management Systems and Processes Project Team would like to 

thank the following individuals, who graciously provided information needed to develop this case 

study. 

Name Title Department Email/Phone #

Chade Saghir Transportation Planner Southeast 

Michigan Council 

of Governments

saghir@semcog.org

313-324-3342

Delores Muller Applications Developer Southeast 

Michigan Council 

of Governments

muller@semcog.org

313-324-3412

Brian Mohr Transportation Engineer Southeast 

Michigan Council 

of Governments

mohr@semcog.org

313-324-3337