MIRE MIS Lead Agency 

Data Collection Report

FHWA Safety Program

http://safety.fhwa.dot.gov
 MIRE MIS Lead Agency Data Collection Report 

ii

FOREWORD 

The Federal Highway Administration’s (FHWA’s) Highway Safety Improvement Program (HSIP) 

is a data driven program that relies on crash, roadway, and traffic data for States to conduct 

effective analyses for problem identification and evaluation. The FHWA developed the Model 

Inventory of Roadway Elements (MIRE) to provide a recommended listing and data dictionary of 

roadway and traffic data elements critical to supporting highway safety management programs. 

MIRE is intended to help support the States’ HSIPs and other safety programs. 

The MIRE Management Information System (MIRE MIS) was a project to explore better means 

of collecting MIRE data elements, using and integrating MIRE data, and identifying optimal data 

file structures. The resulting products include a report documenting potential means of 

collecting MIRE data, a MIRE Guidebook on the collection of MIRE, a suggested MIRE data file 

structure report, and a report on Performance Measures to Assess Quality that will assist the 

States in conducting a more effective safety program. The intent of the MIRE MIS project was 

the integration of MIRE into States’ safety management processes. 

The MIRE MIS Lead Agency Data Collection Report is one of the products of the MIRE MIS effort. 

This report presents the findings from an effort to assist two States to expand their roadway 

inventory data collection to include MIRE intersection data elements for use in advanced 

analytic methods. The report documents two different methods of data extraction that were 

used by the pilot states. The implications from this effort may lead to more effective and 

efficient methods of increasing the collection and use of MIRE by State and local transportation 

agencies. Further, these results may better assist States in complying with the guidance and 

requirements of the Moving Ahead for Progress in the 21st Century (MAP-21) legislation. 

 

Michael S. Griffith 

Director, Office of Safety Technologies 

Monique R. Evans 

Director, Office of Safety Research and 

Development 

 
 MIRE MIS Lead Agency Data Collection Report 

iii

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. 
 MIRE MIS Lead Agency Data Collection Report 

iv

TECHNICAL DOCUMENTATION PAGE 

1. Report No. 

FHWA-SA-13-008 

2. Government Accession 

No. 

3. Recipient's Catalog No. 

4. Title and Subtitle 

MIRE MIS Lead Agency Data Collection Report 

5. Report Date 

March 2013 

6. Performing Organization Code 

7.Author(s) 

Rebecca Fiedler, Nancy Lefler, Jagannath Mallela, Dale Abbott, David Smelser 

and Rachel Becker 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 

Vanasse Hangen Brustlin, Inc. (VHB) Applied Research Associates 

8300 Boone Blvd., Suite 700 4300 San Mateo Blvd. NE, Suite A-220

Vienna, VA 22182-2626 Albuquerque, NM 87110 

10. Work Unit No. 

11. Contract or Grant No. 

DTFH61-05-D-00024 (VHB) 

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 

Final Report, May 2010 – March 2013 

14. Sponsoring Agency Code 

FHWA 

15. Supplementary Notes 

The contract managers for this report were Dr. Carol Tan (HRDS-06) and Robert Pollack (HSA). 

16. Abstract 

The Federal Highway Administration (FHWA) developed the Model Inventory of Roadway Elements (MIRE) as a listing 

of roadway features and traffic volume elements important to safety management to help support agencies move towards 

more data-driven decision-making. The purpose of this effort was to test the feasibility of collecting MIRE data through a 

Lead Agency Program as part of the MIRE Management Information System (MIRE MIS) project. FHWA chose two lead 

agencies, New Hampshire and Washington State. Both agencies requested support for collecting intersection elements. 

This report documents the effort to develop an intersection inventory for each Lead Agency, including development of the 

data collection tools, the challenges faced, and the lessons learned. These lessons are applicable to other agencies 

interested in improving their roadway inventory data to support their safety programs through data-driven decision-making.

17. Key Words: 

MIRE, safety data, roadway inventory data, 

intersection, traffic data, data collection 

18. Distribution Statement 

No restrictions. 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this 

page) Unclassified 

21. No. of Pages 

 59 

22. Price 

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized 
 MIRE MIS Lead Agency Data Collection Report 

v
 MIRE MIS Lead Agency Data Collection Report 

vi

TABLE OF CONTENTS 

EXECUTIVE SUMMARY .......................................................................................................... X

INTRODUCTION .................................................................................................................... 1

NEW HAMPSHIRE ................................................................................................................. 3

Methodology .................................................................................................................. 5

Results .......................................................................................................................... 23

WASHINGTON STATE ........................................................................................................ 27

Methodology ................................................................................................................ 28

Results .......................................................................................................................... 48

CONCLUSION ..................................................................................................................... 52

REFERENCES ....................................................................................................................... 54

APPENDIX A—EDIT AND LOGIC CHECKS .......................................................................... 55
 MIRE MIS Lead Agency Data Collection Report 

vii

LIST OF TABLES 

Table 1. Intersection inventory elements requested by NHDOT. .............................. 4

Table 2. Elements and primary method of data collection for intersection inventory 

elements. ............................................................................................................................ 8

Table 3. Data elements included in the intersection AADT table. ............................ 20

Table 4. Data elements included in the intersection TMC table. .............................. 22

Table 5. Breakdown of hours by task and total cost for New Hampshire 

intersection inventory. .................................................................................................... 24

Table 6. Intersection inventory elements requested by WSDOT. ............................ 28

Table 7. Elements and primary method of data collection for WSDOT intersection 

inventory elements. ......................................................................................................... 32

Table 8. Calculated percent error of sample. .............................................................. 47

Table 9. Breakdown of hours by task and total cost for Washington State 

intersection inventory. .................................................................................................... 49
 MIRE MIS Lead Agency Data Collection Report 

viii

LIST OF FIGURES 

Figure 1. Data entry interface for overall intersection (left) and each leg (right). .. 15

Figure 2. Image of the data collection through the GIS-based intersection inventory 

builder. .............................................................................................................................. 17

Figure 3. Screenshot of window configuration. ............................................................ 35

Figure 4. Screenshot of Google EarthTM window with expanded control panel. ...... 35

Figure 5. Screenshot of imagery windows. ................................................................... 36

Figure 6. Tool-generated efficiency. .............................................................................. 37

Figure 7. Graphing surveyor efficiency over time. ....................................................... 37

Figure 8. Screenshot of data in survey window. ........................................................... 39

Figure 9. Imagery differences. ........................................................................................ 40

Figure 10. Clear Google Street ViewTM imagery. ........................................................ 40

Figure 11. Screenshot of intersections marked in red for survey review. ................. 41

Figure 12. Screenshot of intersections marked for QA/QC. ..................................... 46

 
 MIRE MIS Lead Agency Data Collection Report 

ix

ACRONYMS 

AADT Annual Average Daily Traffic 

AASHTO American Association of State Highway and Transportation Officials 

ADT Average Daily Traffic 

CRAB County Road Administration Board 

DMI Distance Measuring Instrument 

FAQ Frequently Asked Questions 

FHWA Federal Highway Administration 

GIS Geographic Information System 

GPS Global Positioning System 

HOV High Occupancy Vehicle 

HPMS Highway Performance Monitoring System 

HSIS Highway Safety Information System 

HSM Highway Safety Manual 

IDE Integrated Development Environment 

IHSDM Interactive Highway Safety Design Model 

KML Keyhole Markup Language 

LRS Linear Referencing System 

MIDS MIRE Intersection Data Survey 

MIRE Model Inventory of Roadway Elements 

MIS Management Information System 

NHDOT New Hampshire Department of Transportation 

QA/QC Quality Assurance/Quality Control 

RPC Regional Planning Commission 

RSDP Roadway Safety Data Program 

SIMMS Signals Maintenance Management System 

SQL Structured Query Language 

TMC Turning Movement Count 

WSDOT Washington State Department of Transportation 
 MIRE MIS Lead Agency Data Collection Report 

x

EXECUTIVE SUMMARY 

Quality data are the foundation for making important decisions regarding the design, operation, 

and safety of roadways. With the recent development of more advanced safety analysis tools, 

such as the Highway Safety Manual (HSM) (1), the Interactive Highway Safety Design Model

(IHSDM) (2), and SafetyAnalyst (3), many agencies are seeing the value of better roadway data. 

The more information a State or local agency has about its roadways, the better it can use 

resources to effectively and efficiently identify problem locations, diagnose the issues, prescribe 

appropriate countermeasures, and evaluate the effectiveness of those countermeasures. This 

can lead to a more successful safety program supported by data-driven decision-making to help 

improve the safety of roadways and ultimately save lives. 

The Federal Highway Administration (FHWA) developed the Model Inventory of Roadway 

Elements (MIRE) as a listing of roadway features and traffic volume elements important to safety 

management to help support agencies with data-driven decision-making. A critical step toward 

the acceptance and implementation of MIRE is the conversion of MIRE (which is now a listing of 

variables) into a management information system (MIS). FHWA undertook the MIRE MIS 

project to assist States in developing and integrating MIRE into an MIS structure that will 

provide greater utility in collecting, maintaining, and using MIRE data. 

The MIRE MIS project included the exploration, development, and documentation of the 

following: 

 Mechanisms for data collection. 

 An efficient process for data handling and storage. 

 Development of a data file structure. 

 Methods to assure the integration of MIRE data with crash and other data types. 

 Performance measures to assess and assure MIRE data quality and MIS performance. 

This report summarizes the MIRE MIS effort to test the feasibility of collecting MIRE data 

through a Lead Agency Program. The objective of the Lead Agency Program was to assist 

volunteer transportation agencies to collect, store, and maintain MIRE data, and to incorporate 

those data into their safety programs. Using an application process, FHWA chose the New 

Hampshire Department of Transportation (NHDOT) and the Washington State Department of 

Transportation (WSDOT) as Lead Agencies to participate in the MIRE MIS effort. A second 

objective of this effort determined the level of effort and resources necessary to achieve these 

goals. 
 MIRE MIS Lead Agency Data Collection Report 

xi

Both NHDOT and WSDOT requested an intersection inventory for use in SafetyAnalyst, but 

with slightly different variables. Having both agencies select similar elements provided the 

project team with an opportunity to compare different data collection methodologies. The 

project team developed two different tools to collect these data, one simplified tool based on a 

geographic information system (GIS) platform (for NHDOT), and one more sophisticated tool 

based on proprietary software (for WSDOT). 

Both data collection efforts presented similar lessons learned, including: 

 MIRE flexibility: The primary goal of this effort was to test the feasibility of collecting 

MIRE data elements. Both NHDOT and WSDOT requested an intersection dataset to 

import and use in SafetyAnalyst. The project team developed data collection tools to 

populate a database to meet that goal. While the data elements selected were based on 

MIRE, the data collected required deviations from the MIRE data dictionary in order to 

tailor them for SafetyAnalyst. The flexibility allowed the resulting dataset to best meet 

the needs of the individual agencies. 

 Development of the Work Plan: The work plan provided a clear vision and approach for 

conducting the data collection. Developing the work plan at the onset of the project 

helped identify clear expectations on the part of the States and the project team. 

 Constant contact/feedback between the contractor and the State DOT: Throughout the 

entire process, both transportation agencies made themselves available to answer any 

questions and to provide clarification and feedback. This constant communication was 

key to developing a dataset that best met each agency’s needs. 

 Use of the sample dataset: The project team provided a sample dataset to both agencies 

to ensure there were no problems with the data. This allowed the agencies to identify 

any potential issues and the project team time to correct them before completing the 

data collection rather than having to go back and correct the data—thus saving valuable 

time, budget, and resources. 

 Use of existing data: The project team derived many of the basic intersection inventory 

elements from existing data sources, thereby reducing the time needed for data 

collection. 

There were also differences in the two data collection efforts, primarily as a result of the data 

collection tools each agency used. The NHDOT tool took less time to develop, but did not 

have as many built-in tools. The tool developed for WSDOT featured more built-in capabilities 

for identification and extraction of elements, including tracking collection progress; however, 

the WSDOT tool took more time and resources to develop than the NHDOT tool. 
 MIRE MIS Lead Agency Data Collection Report 

xii

Ultimately, the effort to develop an intersection inventory, data collection tools, determine the 

implications of the differences in the tools, the challenges faced, and the lessons learned, are all 

important information for agencies interested in developing roadway inventories. This 

information can help improve their roadway inventories to better support data-driven decisionmaking, improve the safety of roadways, and most importantly, save lives. 
 MIRE MIS Lead Agency Data Collection Report 

1

INTRODUCTION 

Quality data are the foundation for making informed decisions regarding the design, operation, 

and safety of roadways. With the recent development of more advanced safety analysis tools, 

such as Highway Safety Manual (HSM) (1), the Interactive Highway Safety Design Model (IHSDM) 

(2), and SafetyAnalyst (3), many agencies are recognizing the value of better roadway data. The 

more information a State or local agency has about its roadways, the better it can use its 

resources to effectively and efficiently identify problem locations, diagnose issues, prescribe 

appropriate countermeasures, and evaluate the effectiveness of those countermeasures. This 

process can lead to a more successful safety program supported by data-driven decision-making 

to help improve the safety of roadways and ultimately save lives. 

To help support States improve their roadway data, the Federal Highway Administration 

(FHWA) Office of Safety created the Roadway Safety Data Program (RSDP). This program 

encompasses a variety of initiatives with the goal of improving the collection, analysis, 

management, and expansion of roadway data for safety (4). One initiative under the RSDP 

umbrella is the Model Inventory of Roadway Elements (MIRE). MIRE is a guideline that provides 

a listing of roadway features and traffic volume elements important to safety management, and 

includes standardized coding for each element. MIRE Version 1.0 currently includes 202 

elements grouped into three categories: roadway segments, roadway alignments, and roadway 

junctions (5). 

A critical step toward the acceptance and implementation of MIRE is the conversion of MIRE 

into a management information system (MIS). FHWA undertook the MIRE MIS project to assist 

States in the development and integration of MIRE into an MIS structure that will provide 

greater utility in collecting, maintaining, and using MIRE data. 

The MIRE MIS project included the exploration, development, and documentation of the 

following:

 Mechanisms for data collection. 

 An efficient process for data handling and storage. 

 Development of a data file structure. 

 Methods to assure the integration of MIRE data with crash and other data types. 

 Performance measures to assess and assure MIRE data quality and MIS performance. 

This report summarizes the MIRE MIS effort to test the feasibility of collecting MIRE data 

through a Lead Agency Program. The objective of the Lead Agency Program was to assist 

volunteer transportation agencies to collect, store, and maintain MIRE data, and to incorporate 
 MIRE MIS Lead Agency Data Collection Report 

2

those data into their safety programs. FHWA chose the New Hampshire Department of 

Transportation (NHDOT) and the Washington State Department of Transportation (WSDOT) 

through an application process to participate as lead agencies. A second objective was to 

determine the level of effort and resources necessary to achieve these goals. 

FHWA did not anticipate that one agency would collect all 202 elements but that each Lead 

Agency would collect either all critical elements in one subsection of MIRE (e.g., intersection 

elements, ramp elements, curve elements, pedestrian elements, etc.), or critical elements from 

a combination of subsections. Each Lead Agency chose the MIRE elements they wanted to 

collect through the program. Both NHDOT and WSDOT requested the collection of many 

critical intersection elements to expand their intersection inventories for use in SafetyAnalyst. 

SafetyAnalyst is a software tool used by State and local highway agencies for highway safety 

management. FHWA developed SafetyAnalyst through a transportation pooled fund study, a 

cooperative effort between States and local agencies. It is now available through the American 

Association of State Highway and Transportation Officials (AASHTO). It is important to note 

that the effort documented in this report is applicable to any agency interested in developing an 

intersection inventory, independent of the safety analysis tool(s) used by the agency. WSDOT 

also hopes to include their intersection inventory in the Highway Safety Information System 

(HSIS) database. 

Having both agencies select similar elements provided an opportunity to compare different data 

collection methodologies. The project team developed two different tools to collect 

intersection data: a simplified tool based on a geographic information system (GIS) platform for 

NHDOT, and a more sophisticated tool based on proprietary software for WSDOT. This 

report documents the effort to develop an intersection inventory, including development of the 

data collection tools, identification of the implications of the differences in the tools, the 

challenges faced, and the lessons learned that could assist other agencies interested in 

undertaking a similar effort. 
 MIRE MIS Lead Agency Data Collection Report 

3

NEW HAMPSHIRE 

The starting point for NHDOT was the MIRE listing. NHDOT reviewed the MIRE elements as 

part of the application process and provided a list of the elements they would like to have 

included in the intersection inventory. The elements chosen included all of the required, and 

some of the optional, SafetyAnalyst elements for intersections. Table 1 shows the elements 

NHDOT requested for the overall intersection and each intersection leg. These elements 

consist of location, operations, geometric, and traffic count data. 

While NHDOT would ultimately like to have detailed information on all intersections on public 

roads within the State, there was a limited budget available for the collection of data. NHDOT 

prioritized its intersections based on ownership of the intersecting roadways. NHDOT’s top 

priority was the State/State intersections (approximately 1,500), followed by the State/local 

intersections (approximately 8,800), and then local/local intersections (approximately 30,750). 

Based on the available funding, NHDOT requested that the project team focus on collecting 

data at State/State and State/local intersections. This group totaled 10,300 intersections for 

inclusion in the intersection inventory. 
 MIRE MIS Lead Agency Data Collection Report 

4

Table 1. Intersection inventory elements requested by NHDOT. 

Intersection Elements Intersection Leg Elements 

Intersection ID Intersection ID

Location System Leg ID

Route Type Type 

Route Name Location System

County Route Type

Major Road MP Route Name

Minor Road Location System County

Minor Road Route Type Milepost/Distance

Minor Road Route Name Influence Zone

Minor Route MP Direction of Leg

Agency Site Subtype Thru Lanes

GIS Identifier Left Turn Lanes

Major Road Name Right Turn Lanes

Minor Road Name Median Type

Major Road Direction Left Turn Phasing

Begin Influence Zone (Major & Minor) Speed Limit

End Influence Zone (Major & Minor) Turn Prohibitions

District Operations

City Town Approach Volume 

Jurisdiction Right Turning Movement Count 

Area Type Thru Turning Movement Count 

Intersection Type Left Turning Movement Count 

Traffic Control Type 

Offset Intersection 

Offset Distance 

Growth Factor

Date Open to Traffic 

Corridor 

Major Road Annual Average Daily Traffic 

(AADT) 

Minor Road AADT 

Comment 
 MIRE MIS Lead Agency Data Collection Report 

5

Methodology 

Once the NHDOT and the project team established the elements to include in the intersection 

inventory, the project team developed the intersection inventory using the following 12 steps: 

1. Determine what data elements are already collected and what remaining data need to 

be collected. 

2. Determine how the existing data are currently collected, the available data sources, and 

how to collect the remaining needed data. 

3. Develop a detailed work plan. 

4. Develop or expand the intersection node layer. 

5. Develop a model for extracting existing data to pre-populate the intersection inventory. 

6. Develop the data collection interface and toolbar. 

7. Collect the data. 

8. Provide a sample dataset to NHDOT. 

9. Conduct quality assurance/quality control (QA/QC) reviews. 

10. Conduct field verification of data elements. 

11. Develop the traffic volume database. 

12. Integrate the new dataset into the current system. 

The following sections provide an overview of each of these steps. 

Step 1: Determine Existing Data and Remaining Data Needs 

The project team first identified the elements NHDOT wanted included in the inventory that 

NHDOT had already collected in some form. Prior to the initiation of the Lead Agency 

Program, NHDOT participated in the FHWA Capabilities Assessment (6). The assessment 

questionnaire included a table of all of the MIRE elements, documenting which elements 

NHDOT collected and in what datasets they are stored. NHDOT gave the project team 

permission to use the information in the questionnaire as a starting point. Over a series of 

onsite meetings, the project team worked with the NHDOT safety, roadway inventory, and GIS 

staff to review their data collection practices and datasets. 

During the review of the information in the Capabilities Assessment questionnaire and in 

discussion with NHDOT staff, the project team discovered that it was not as straightforward as 
 MIRE MIS Lead Agency Data Collection Report 

6

simply having the data element, or not having the data element. The project team determined 

that there were various categories of data availability: 

 Exist: The data element exists exactly as it is defined. 

 Derive: The data element exists in another format and needs to be transformed from the 

current format or gathered from existing GIS layers. This value may need to be further 

validated. 

 Assign: The data element does not exist, but the value can be derived using guidance or 

coded values provided by NHDOT. 

 Collect: The data do not exist and will need to be collected. 

Step 2: Determine How Data are Collected or will be Collected 

Conducted concurrently with Step 1, the project team worked with NHDOT to determine 

how it currently collects and stores the existing data. The three primary sources of existing 

roadway data at NHDOT are GIS layers, roadway videolog, and the data required for the 

FHWA Highway Performance Monitoring System (HPMS). 

Currently, NHDOT stores its roadway and intersection data in a statewide road inventory 

database that is maintained using ESRI ArcGIS version 9.x software. The inventory is a nodebased model containing road centerlines and intersections for all Federal, State-maintained, 

local, and private roads. (This will be described in greater detail in subsequent sections.) The 

database evolved from NHDOT’s straight-line diagrams that it previously used to maintain the 

State’s official road mileage for use in HPMS reporting and Highway Block Grant Funding. 

In the early 2000s, NHDOT contracted with a private company to update all of the Statemaintained roadways in New Hampshire. This involved driving each State-maintained road and 

recording the mileage using a distance measuring instrument (DMI). The remaining roads (local 

and private) were inventoried by the State’s nine Regional Planning Commissions (RPCs). At 

the time of this report, NHDOT maintained the database using high-resolution aerial 

photographs with supplemental field verification. 

NHDOT has over 40 roadway attributes for each road in the database. Each road centerline 

and intersection node has a unique identifier that allows linkage between the attribute 

information and the corresponding road segment/intersection. Mileposts identify the length of 

each road segment, representing the distance between each node or intersection. The road 

centerline attribute table stores the begin and end mileposts of each road segment. In addition, 

the road centerline and intersection files use a linear referencing system (LRS) in which each 

road segment contains distance value measures along the line. 
 MIRE MIS Lead Agency Data Collection Report 

7

Within the database, the road centerlines are separated into two layers: High Order Routes 

and Road Anchorsections. The High Order Routes layer represents the entire length of 

geometry for a route, whereas the Road Anchorsections layer represents the individual road 

segments (from node to node) that make up a route. NHDOT staff update the road inventory 

database on a daily basis, with an annual release made available to the public. 

NHDOT also obtains roadway information from a videolog system that uses a data collection 

van. The van includes three cameras for the videolog—front- and rear-facing cameras with a 

110-degree field of view and a 360-degree camera mounted on the roof—much like the Google 

Street ViewTM vehicles. The van tracks global positioning system (GPS) coordinates of 

intersections and conducts real-time corrections to the GPS while linked with a GIS map. In 

addition, NHDOT collects data per the HPMS requirements. There are 27 MIRE data elements 

that are required for “full extent” collection through HPMS (i.e., data reported on all public 

roads). The MIRE Version 1.0 report includes a list of the MIRE elements that are collected 

through HPMS (5). 

Based on the information obtained from NHDOT, the project team determined how to 

populate each intersection element in the inventory based on the categories of data availability 

discussed in Step 2: 

 Exist: Use values as they currently exist. 

 Derive: Transform existing data or gather from GIS layer. These data may require 

validation during data collection. 

 Assign: Assign values that are derived using guidance or coded values. 

 Collect: Collect information that has not yet been collected or validated from GIS, HPMS, 

or visual imagery. Note the traffic data elements are discussed in further detail under 

Step 11: Develop Traffic Volume Database. 

Table 2 identifies each element included in the intersection inventory, the current data source 

(if applicable), and the method of collection based on its category of availability. Note the traffic 

data elements are discussed in further detail under Step 11: Develop Traffic Volume Database. 
 MIRE MIS Lead Agency Data Collection Report 

8

Table 2. Elements and primary method of data collection for intersection inventory 

elements. 

Intersection Elements Intersection Leg Elements 

Intersection ID (GIS – exist) Intersection ID (GIS – exist) 

Location System (GIS – assign) Leg ID (GIS – exist)

Route Type (GIS – derive) Type (GIS – derive)

Route Name (GIS – exist) Location System (GIS – assign) 

County (GIS – exist) Route Type (GIS – derive) 

Major Road MP (GIS – derive) Route Name (GIS – exist) 

Minor Road Location System (GIS – assign) County (GIS – exist)

Minor Road Route Type (GIS – derive) Milepost/Distance (GIS – derive) 

Minor Road Route Name (GIS – exist) Influence Zone (assign) 

Minor Route MP (GIS – derive) Direction of Leg (GIS – derive) 

Agency Site Subtype (GIS – assign) Thru Lanes (HPMS – collect; validate)

GIS Identifier (GIS – exist) Left Turn Lanes (HPMS – collect; validate)

Major Road Name (GIS – exist) Right Turn Lanes (HPMS – collect; validate)

Minor Road Name (GIS – exist) Median Type (HPMS – collect; validate)

Major Road Direction (GIS – derive; validate) Left Turn Phasing (collect; validate)

Begin Influence Zone (Major & Minor) (assign) Speed Limit (HPMS –collect; validate)

End Influence Zone (Major & Minor) (assign) Turn Prohibitions (collect; validate)

District (GIS – derive) Operations (collect; validate) 

City Town (GIS – exist) Approach Volume (Review existing; GIS –

assign; collect) 

Jurisdiction (GIS – derive) Right Turning Movement Count (Review 

existing; GIS – assign; collect) 

Area Type (GIS – derive) Thru Turning Movement Count (Review 

existing; GIS – assign; collect) 

Intersection Type (GIS – derive; validate) Left Turning Movement Count (Review 

existing; GIS – assign; collect) 

Traffic Control Type (validate; collect) Approach Volume (Review existing; GIS –

assign; collect) 

Offset Intersection (GIS – derive; validate)

Offset Distance (GIS – derive; validate) 

Growth Factor (NHDOT – assign) 

Date Open to Traffic (NHDOT – exist)

Corridor (NHDOT – assign) 

Major Road AADT (Review existing; GIS – assign)

Minor Road AADT (Review existing; GIS – assign)

Comment (NHDOT – assign, collect)
 MIRE MIS Lead Agency Data Collection Report 

9

Step 3: Develop Detailed Work Plan 

The project team next developed a detailed work plan that included a description of NHDOT’s 

existing data system, including sources of available data. It also provided an overview of the 

proposed effort, including methodology, timeframe, cost, and a detailed data dictionary that 

NHDOT provided to the project team. 

The data dictionary included the intersection inventory elements, their attributes, and 

important considerations for each element. It was necessary to develop a customized data 

dictionary rather than using the MIRE data dictionary. NHDOT developed the data dictionary 

based on SafetyAnalyst requirements so the final database could be readily imported into the 

software. MIRE is guidance intended to be flexible to meet the needs of each agency. While 

FHWA developed MIRE in part to help support the implementation of SafetyAnalyst, it was not 

the solitary goal. Rather, FHWA developed MIRE to support a variety of data analysis tools. 

During the development of MIRE Version 1.0, FHWA considered not only the requirements of 

SafetyAnalyst, but also considered the requirements of HPMS requirements and other analysis 

tools, and also obtained feedback from practitioners garnered through webinars. Therefore, 

the MIRE element naming conventions and attribute listings do not align exactly with the 

SafetyAnalyst data requirements. The project team adopted the data dictionary NHDOT 

provided to ensure the resulting dataset best met the intended use of the data. 

Step 4: Develop Node Layer 

Identification of the location of the intersections proved to be a crucial step in the development 

of the intersection inventory. NHDOT already had an existing node layer that they developed 

for use by State and local law enforcement agencies for locating crashes. The State created 

nodes at intersecting roads where road names or functional classifications changed, and at town 

limits and county lines. When created, each node has a unique identifier assigned to it. The 

node layer is maintained using NHDOT’s existing road centerline file. Using this node layer as 

a base, NHDOT then undertook an extensive manual effort to review and locate the 

State/State and State/local intersections using GIS and aerial photography as part of its effort to 

implement SafetyAnalyst. 

The project team identified several issues with this methodology. Most notably, three percent 

of the nodes were not actual intersections, as defined by NHDOT. The majority of these nonintersections were locations where a Class VI road (unmaintained road subject to bars and 

gates) intersected with a State road. NHDOT did not want to include these intersections in 

the intersection inventory. 

The project team also expanded the intersection node layer to include local/local intersection 

node layer as part of this effort. Based on the issues the project team identified with the 
 MIRE MIS Lead Agency Data Collection Report 

10

existing methodology, the team took a different approach to expand the local/local node layer 

beyond what NHDOT had done to develop the State/State and State/local node layer. The 

existing node layer consisted of all the start and end points of each roadway segment in the 

road inventory database. Roads were split at town and county boundaries. To create the 

local/local intersection layer, the project team filtered the nodes down to actual intersection 

locations of local/local roads using ESRI ArcGIS 10.0 software to complete all work. The 

project team’s methodology was as follows: 

1. Extracted the local roads from the State’s road inventory centerline file using a 

definition query. 

2. Used linear referencing tools, specifically the NHDOT’s Legislative Class attribute in the 

existing roadway data was used in the ‘Locate Features Along Routes’ tool, to process development of the intersection node 

the State’s node layer and extract the local layer. The Legislative Class designates 

roads from the road inventory centerline file roadway ownership and maintenance 

to identify each local road segment that responsibility: 

 touched a node. The output was a table Class I – Primary State highways. 

 Class II – Secondary State highways. listing each roadway segment, which included  Class III – Limited access recreational 

all of the road inventory attributes, coded by roads. 

the unique identifier of each node and road  Class IV – State highways in a 

designated ‘compact section’ of cities segment. or towns (e.g., State-owned but locally 

3. Added a temporary field to the table created maintained). 

 Class V – Local roads. in Step 2 and populated based on road  Class VI – Unmaintained roads subject legislative class. Each record in the table was to bars and gates. scored based on the legislative class of the 

road. The project team created another temporary field to further select only the 

local/local intersections and exclude any node locations that represented the 

intersection of two private roads. In addition, potential intersection locations were 

screened to remove intersections of Class VI roads. 

4. Used a frequency analysis to summarize the legislative class scoring created in Step 3. 

The resulting table contained a single record for each node, with the total score of the 

legislative class information. 

5. Used a definition query to remove any potential intersections with a score of “0,” which 

represented private/private intersections and Class VI/Class VI intersections. 

6. Completed a final spatial selection using the ‘Select by Location’ feature to remove any 

potential intersections that touch a State route, which eliminated any State/local 

intersections from the database. 

This methodology provided a way of using GIS tools to screen the nodes down to local/local 

intersections without the need for manual interpretation. 
 MIRE MIS Lead Agency Data Collection Report 

11

Step 5: Develop Model for Extracting Existing Data to Pre-Populate the 

Intersection Inventory 

The project team created a model in ArcGIS to automatically extract and transform where 

necessary, the data from various existing sources within NHDOT (identified in Steps 2 and 3 of 

the overall effort). They then applied those data to each intersection to pre-populate the 

intersection inventory. For this project, the output from the model needed to be formatted 

specifically for use in SafetyAnalyst; however, the data could be formatted for use in any safety 

analysis tool. 

NHDOT had already developed a series of Structured Query Language (SQL) scripts to 

process their existing GIS road inventory files to create an intersection table for import into 

SafetyAnalyst. SQL is a programming language designed for managing data in relational database 

management systems. Due to inconsistencies in data structure between the NHDOT road 

inventory files and SafetyAnalyst, it was not possible to directly import NHDOT data into 

SafetyAnalyst. The NHDOT SQL scripts processed the road inventory files to extract existing 

roadway attribute information based on NHDOT’s roadway data dictionary and transform the 

information so that data inputs matched SafetyAnalyst’s import formats. The scripts allowed 

NHDOT to successfully import much of its State system’s inventory data into SafetyAnalyst. 

Using these imported data, NHDOT completed network analyses of the State/State and 

State/local intersections using the required SafetyAnalyst data elements. 

Although the SQL scripts helped automate the process, some limitations exist with their 

current methodology. Due to limitations in time and accuracy of some source data, several 

elements that the State could have collected from existing data were not included in their 

scripting. These data included mostly elements that the State would have to collect or verify 

using aerial imagery or roadway videolog, such as intersection offset distance, intersection type, 

and traffic control type. The State could have derived some elements, such as skew angle and 

school zones, but these were not required for analysis. In addition, the SQL scripts ran in 

Oracle SQL Developer, which does not run within the GIS environment. As with any wellmaintained GIS, the NHDOT Planning Bureau regularly updates its road inventory database, 

and, thus, the State should be able to update the SafetyAnalyst intersection tables to reflect 

those changes. Due to the limitations and time requirements of their current process, 

NHDOT was in need of a more efficient model. 

Using ESRI ModelBuilderTM software for ArcGIS 10, the project team developed an ArcGIS 

Toolbox containing a series of geoprocessing models that process the State’s roadway 

inventory files and generate additional information required by SafetyAnalyst that is currently not 

available within the roadway inventory database. The project team developed the processes 

within the models from the steps outlined in the SQL scripts originally developed by NHDOT. 
 MIRE MIS Lead Agency Data Collection Report 

12

In several instances, the project team modified the models to make use of existing GIS functions 

rather than custom coding. 

Currently, the toolbox contains an Intersection Update Model, and a New Intersection Model. The 

Update Model checks the most up-to-date road inventory database and updates the 

intersection inventory tables for any changes to the database. The New Intersection Model 

allows the GIS user to identify locations of new intersections. New intersections are 

intersections where the State has accepted a private road as a public road, a new public road 

has been constructed, or where an intersection has been realigned. The key features contained 

within the model include: 

 Identification of the major/minor road associated at each intersection based on AADT. 

Populates the road names of each major/minor road, along with its unique segment 

identifiers. 

 Population of the road route type (i.e., Interstate, U.S. route, State route, local road) of 

each road segment. 

 Calculation of the milepost location of each intersection referenced to the State’s road 

inventory database. 

 Calculation of the approach direction of each roadway segment of the intersection. 

 Identification of the number of legs present at each intersection. 

 Calculation of the intersection type (e.g., tee, four-leg, multi-leg, etc.). 

 Identification of the city/town, county, NHDOT Maintenance District, State Trooper 

District, and RPC for each intersection. 

The previous import process took several days to complete using NHDOT’s original SQL 

scripts. By using the geoprocessing models the project team developed, the team successfully 

completed this task in less than one hour. In addition to the significant time savings, the 

geoprocessing models also provided a GIS user-friendly environment and were conducive to 

more effective troubleshooting should potential issues arise. 

Step 6: Develop Data Collection Interface and Toolbar 

The project team used the model described above to pre-populate the intersection inventory 

with the existing data, and developed a tool to collect the remaining data elements. It was not 

within the scope to collect data for over 10,000 intersections in the field, so the project team 

used an alternate methodology to collect the data. The team developed an ESRI GIS-based 

system to populate the intersection inventory that employed both automated and manual 

methods. 
 MIRE MIS Lead Agency Data Collection Report 

13

GIS Database Assessment and Setup 

The project team requested and obtained from NHDOT a current version of its GIS database. 

Since NHDOT uses Oracle as the platform for its ArcSDE geodatabase, the State exported 

their data into a geodatabase file. The project team then imported the feature classes in the file 

geodatabase into a SQL Server ArcSDE® geodatabase. The processes were performed in 

ArcCatalogTM with an ArcEditorTM (standard) or ArcInfo® (advanced) software license. These 

functions would not be capable with the ArcView® (basic) license. 

Once the project team imported the data into ArcSDE®, a team analyst conducted a database 

assessment, which involved the following steps: 

1. Confirmed that all necessary fields for the data collection were present in the feature 

classes and named correctly. Created new fields, when necessary. 

2. Verified that the required fields were in the correct data type (e.g., integer, text, etc.) 

and length, referencing the SafetyAnalyst Data Import Reference document. Corrected 

field types, if necessary. 

3. Set up domains, where necessary, to make sure the data collection proceeded in a 

consistent manner and in the correct format for use with SafetyAnalyst. Used the 

SafetyAnalyst Data Import Reference document as a guide. 

4. Verified that the feature classes required for the model and the GIS interface were 

accounted for and in the proper GIS format. 

5. Created the intersection leg feature class from the existing roads layer. The length of 

the leg did not matter. 

Convert the Existing SQL Scripts into ESRI® ModelBuilderTM 

Once the model pre-populated the intersection and intersection leg feature classes from the 

roadway inventory datasets, the project team exported the files for use in populating the 

remaining required SafetyAnalyst attributes.

Develop Data Collection Interface and Toolbar 

The project team developed an interface to allow for data entry from the videolog and online 

mapping sources, such as Google and Microsoft Bing®. ESRI’s ArcGIS® 10 was the platform 

used for the interface. The project team also conducted the model and data editing (attribute 

and feature) within the ArcGIS® 10 desktop environment. 
 MIRE MIS Lead Agency Data Collection Report 

14

An overview of the GIS interface task included the following subtasks: 

 Design/review of the database: This phase included an assessment of the data as it 

currently exists so the project team could correctly set up the data for use in the GIS 

interface. 

o Exported data from the file geodatabase provided by NHDOT to ArcSDE®. 

o Added fields to feature classes (intersections and legs). 

o Set up domains. Used the SafetyAnalyst Data Import Reference document as a 

guide. 

 Development of data entry forms: The project team created data entry forms to 

enter remaining attributes not populated by Model Builder, and to allow the user to edit 

any existing attributes. 

o Created data entry form for intersections. 

o Created data entry form for legs. 

 Creation of a toolbar that includes several custom tools: This phase involved 

creating a custom toolbar that included the model and interface the project team 

developed in the previous steps. The toolbar contains buttons that perform each of the 

following functions: 

o Run Model. 

o Edit Attributes of a feature (shows custom data entry forms). 

o Export intersection and leg attribute tables for use in SafetyAnalyst. 

The project team developed a custom data entry form that allows the user to enter the 

required MIRE attributes for intersection and leg features. A third party software developer 

assisted with the development of the form. The form features built-in checks and validations to 

ensure that all attributes are accounted for. A drop-down menu includes all attributes that 

have a domain in order to collect the data in a consistent and accurate manner for use with 

SafetyAnalyst. Appendix A provides more detailed descriptions of these built-in checks. Several 

attributes that NHDOT requested to be collected were outside the scope of this project. The 

project team included these elements in the data entry interface for future use by NHDOT. 

For these attributes, a domain was assigned based on the list of attributes provided by 

NHDOT; however, the project team did not collect those data elements. 

The project team created one data entry form for intersection attributes and one form for 

intersection leg attributes, as shown in Figure 1. The elements shown in light gray text, e.g. 

Minor Road Route Type, are elements that the project team pre-populated using the model; 

these did not require any additional action. A designated list of attributes can be chosen from 
 MIRE MIS Lead Agency Data Collection Report 

15

in the drop-down menus using black text, e.g. Intersection Type 1. The empty white boxes 

shown are points that require data entry, e.g. Number of Left Turn Lanes. The gray boxes with 

no text, e.g. Lighting Presence and Pedestrian Volume, are placeholders for attributes that 

NHDOT might collect at a later date. 

Figure 1. Data entry interface for overall intersection (left) and each leg (right). 

The initial intent was to link the videolog with the data collection tool. However, the videolog 

was not compatible with the software. After working directly with the videolog vendor to find a 

solution to satisfy the needs of the tool and users, the project team determined that the 

videolog could not connect with the data collection tool automatically and the requirement of 

an automatic connection was too cumbersome for its use. 

Instead of using the videolog, the project team used the Google Street ViewTM and Microsoft 

Bing® Bird’s Eye plug-ins for ArcGIS® (7). These add-ins allowed the user to click anywhere on 

their map in ArcGIS® to bring up a small window with that location in Google or Microsoft 

Bing®. These tools aided in the data entry process by allowing the users to see fairly current 

aerial imagery and other base data to help determine the attributes of an intersection or leg. 

Using these tools also reduced the data entry time since manually searching for the visual image 

of the intersection was no longer required. This process was the primary substitute for the 
 MIRE MIS Lead Agency Data Collection Report 

16

videolog, but the videolog could still be used as a resource if the imagery from Google or 

Microsoft Bing® did not provide the information needed. However, it was not an automatic 

connection and required manually locating the intersections in the videolog. 

Step 7: Collect Data 

Collecting the data required the installation of the data collection tool on each work station 

and completion of a two-day training session for the data entry clerks. As part of the training, 

the project team developed a data entry manual that provided explicit instructions for data 

entry clerks. 

Once the project team installed the tool and completed the training, the data entry effort 

began. The interface allowed the data entry clerks to enter the attributes for overall 

intersection and individual leg features using existing satellite and aerial images, including Google 

Street ViewTM and Microsoft Bing® Bird’s Eye, as well as web map service imagery from a 2011 

flyover provided by the University of New Hampshire. The NHDOT GIS database was 

connected to the user interface and the imagery sources. When the users clicked on the 

intersection on the GIS map that they wanted to populate, the data entry form for that location 

automatically appeared with the user interface pre-populated. The user then keyed in the 

remaining items. The project team developed the interface to have the pre-populated items 

“grayed” out so they could not be edited by the data entry clerk. These pre-populated data 

elements were primarily moved to the bottom of the form so as not to confuse or slow down 

the data entry process. Only the data elements that were being collected could be changed. 

There were drop-down menus and built in error checks that prevented the user from entering 

erroneous data. Figure 2 shows a data entry clerk using the user interface. Appendix A 

includes a description of these error checks. 
 MIRE MIS Lead Agency Data Collection Report 

17

Figure 2. Image of the data collection through the GIS-based intersection inventory 

builder. 

The GIS data were stored in an ArcSDE® geodatabase. This format of geodatabase allowed for 

multi-user editing and multiple versions. All users had their own version of the database, which 

helped with the QA/QC process described below in Step 9.

Step 8: Provide Sample Dataset to NHDOT 

In order to ensure that there were no issues with the data, the project team provided NHDOT 

with a sample of the dataset to test the process of incorporating it into its system. . Once 

NHDOT approved the sample, the project team completed the data collection. Had there 

been any issues, the project team could have rectified them early on in the data collection 

process rather than having to go back and fix any issues after all the data had been collected. 

Step 9: Conduct Quality Assurance/Quality Control (QA/QC) Reviews 

All data entry clerks posted their individual database versions to a “Quality” version of the 

database once a week. An independent reviewer checked a sample of intersections from each 

data entry clerk and noted any inconsistencies. The independent reviewer then reported the 

errors back to each data entry clerk, who was then responsible for fixing those errors and for 

reviewing their data to ensure similar errors did not exist at other intersections. Once each 

dataset was corrected, it was then posted to a “Master” database. 
 MIRE MIS Lead Agency Data Collection Report 

18

Step 10: Field Verification 

The project team conducted a field verification of the data elements to compare the differences 

between data collected in the field and data collected remotely in the office. The team 

collected data for 200 intersections which included a mixture of urban and rural intersections, 

3-leg and 4-leg intersections, and signalized and unsignalized intersections. The field survey 

crew used the same data entry interface that the office data entry clerks used loaded on a 

portable tablet, and started with a blank database so as not to be influenced by what was 

previously collected in the office for the same location. They were also given the same 

instructions and training as the in-office data entry clerks. Upon completion, the project team 

analyzed and compared the field data to the data collected in-office. These results are 

discussed in the New Hampshire Results section below. 

Step 11: Develop the Traffic Volume Database 

NHDOT currently conducts traffic counts on Federal-aid highways for the HPMS every three 

years (1/3 of their system per year). At the end of every year, NHDOT sends the counts to 

the Planning Department to be incorporated into the GIS. NHDOT has a GIS snapshot of each 

years’ counts, but does not have one electronic database of historic traffic counts. 

As part of this effort, the project team developed an intersection traffic volume database for 

NHDOT. This electronic inventory of historic traffic count data is stored in a Microsoft Access 

database format that contains two tables of traffic volume data for the State/State and 

State/local intersections—one table for Annual Average Daily Traffic (AADT) and another table 

for turning movement counts (TMCs). The following sections describe the data contained in 

each table and the methodology behind the table development. 

Intersection Annual Average Daily Traffic (AADT) 

The project team obtained AADTs from NHDOT for years 2006 through 2010. NHDOT has 

approximately 5,800 counter stations collecting AADT throughout the State. Each year the 

Planning Department assigns the counter stations (and its data) to the surrounding roads. The 

project team used NHDOT’s 2011 Asset Roads layer to associate the corresponding road data 

(and thus counter station ID) to each State/State and State/local intersection leg. Not every 

road in the State has an assigned counter station, so therefore not every intersection leg had a 

counter ID assigned to it. 

The project team used Microsoft Access to link the counter station AADT data to the 

intersection legs by way of the associated counter ID. For the legs that did not have a counter 

ID, the project team assigned an AADT value based on the functional class and county. This is 
 MIRE MIS Lead Agency Data Collection Report 

19

the same methodology the project team used during the development of the intersection 

inventory and is based on guidance from NHDOT. 

Instead of having AADT volumes for each leg of an intersection, the project team consolidated 

the data into major and minor volumes for each intersection. The team used the major/minor 

designations assigned to the intersection legs during the development of the intersection 

inventory. For the intersections that had different volume data for the major (or minor) leg 

pairs, the project team used the following criteria: 

1. If one of the legs had an assigned counter ID and the other leg did not, the data from 

the leg with the counter ID was kept. 

2. If both legs had an assigned (but different) counter ID, the data from the leg with the 

closest (in distance) counter station was kept. The project team calculated the distance 

to the counter stations in ArcGIS® using the counter station shapefile provided by 

NHDOT. 

Four percent, or 367 intersections, did not have both major and minor AADT volumes. 

Approximately 70 percent of these were the nodes the project team identified during the 

intersection data collection as having errors (e.g., not actual intersections, intersections with 

missing legs, etc.). The project team left these intersections in the database, but provided them 

to NHDOT in a separate list. 

The database table with the intersection AADT data includes roadway identification data 

elements (e.g., road name, city, functional class, etc.) in addition to the AADT volumes. Table 3 

lists each data element and its definition. 
 MIRE MIS Lead Agency Data Collection Report 

20

Table 3. Data elements included in the intersection AADT table. 

Field Name Description 

AGENCY_ID Unique intersection ID 

MAJOR_MINOR Indicates if roadway is the major or minor road for the intersection 

SRI Statewide route identifier 

ROAD_NAME Roadway name 

CITY City or town name 

COUNTY County name 

FUNCT_CLASS Functional classification of roadway 

LEGIS_CLASS Legislative classification of roadway 

LC_LEGEND Legislative classification legend 

COUNTER_ID Identification number of the traffic volume counter associated with 

that roadway 

AADT AADT based on functional classification and county (if no counter 

assigned to road) 

AADT_2010 2010 AADT 

AADT_2009 2009 AADT 

AADT_2008 2008 AADT 

AADT_2007 2007 AADT 

AADT_2006 2006 AADT 

Intersection Turning Movement Counts (TMC) 

The project team obtained TMC data from NHDOT and the State’s nine RPCs. The RPCs 

involved in this effort included: 

1. North Country Council. 

2. Lakes Region Planning Commission. 

3. Upper Valley Lake Sunapee Regional Planning Commission. 

4. Southwest Region Planning Commission. 

5. Central New Hampshire Regional Planning Commission. 

6. Southern New Hampshire Planning Commission. 

7. Nashua Regional Planning Commission. 

8. Rockingham Planning Commission. 

9. Strafford Regional Planning Commission. 
 MIRE MIS Lead Agency Data Collection Report 

21

The project team identified a primary contact person for each of the organizations. This 

person was contacted through email and given a brief overview of the project and the data 

request, which was for intersection counts that were not already in the State system, along 

with a brief overview of the data collection procedures. 

Since many of the counts conducted by the RPCs are submitted to the State, three 

organizations did not have additional data to provide. These organizations were the North 

Country Council, the Lakes Region Planning Commission, and the Upper Valley Lake Sunapee 

Regional Planning Commission. The six remaining RPCs were able to provide data. The TMC 

data came in various formats (e.g., PETRAPro Software files, Microsoft Excel, PDFs, etc.,), which 

the project team exported or manually entered into Microsoft Excel as needed. 

Once the project team imported all the traffic count data into Excel, the next step was to 

identify the appropriate intersection and leg IDs. The project team used the available 

information provided with the TMC data (e.g., road names, city, county, etc.) and the roadway 

data associated with each intersection and leg from the intersection inventory. If the city name 

or county name was included in the TMC file, the search was narrowed down to that specific 

city or county. If not, the project team searched in the intersection list directly for the road 

name or route number from the TMC file. The intersection ID was considered a match when 

all legs in the TMC file matched the data in the legs file. If the TMC file did not provide enough 

identifying information, or the information did not match any of the associated roadway data at 

the intersections (e.g., it was a count at a local/local intersection), the project team considered 

it a non-match and did not include it in the database. The project team matched 242 TMC files 

to 197 intersections. There were approximately 115 files that the project team considered a 

non-match. 

With the intersection ID assigned to the TMC, the project team next identified the leg IDs. In 

a similar manner as before, the project team used the road information provided in the TMC 

file to match the leg IDs to the appropriate leg. Aware of the importance to match the correct 

leg in the TMC file to the correct leg ID, the team used the GIS data files to double check the 

leg ID. The project team also used Google MapsTM to visually check the intersections and legs. 

Upon completion of assigning intersection and leg IDs to the TMC files, a member of the 

project team performed a QA/QC check on the data. The reviewer assessed a sample of 

intersections and corrected any errors. 

This TMC table contains a row for each State/State and State/local intersection, along with 

some intersection identification information (e.g., major and minor road name, city, county 

etc.). Table 4 lists each data element and its definition. 
 MIRE MIS Lead Agency Data Collection Report 

22

Table 4. Data elements included in the intersection TMC table. 

Field Name Description 

AGENCY_ID Unique intersection ID 

SRI_MAJOR Statewide route identifier for major road 

SRI_MINOR Statewide route identifier for minor road 

MAJOR_NAME Major road name 

MINOR_NAME Minor road name 

CITY City or town name 

COUNTY County name 

RPC Regional Planning Commission 

TMC Link1 Hyperlink to spreadsheet of turning movement counts (if available). 

TMC Link2 Hyperlink to spreadsheet of turning movement counts (if available). 

TMC Link3 Hyperlink to spreadsheet of turning movement counts (if available). 

TMC Link4 Hyperlink to spreadsheet of turning movement counts (if available). 

The project team linked the TMC data to the Access database via a hyperlink. The 

intersections that have an associated TMC file show the hyperlinks in those fields. The 

hyperlink text displays the date of the count. If the user hovers their mouse over the hyperlink, 

the time of the count will be displayed. This way, the user does not have to open the file to 

check the time or date of the count. Clicking on the hyperlink will open the TMC data in an 

Excel file. 

Step 12: Integrate the New Dataset into the Current System 

For the final step, the project team delivered the database to NHDOT and installed the data 

collection tool and model on its system. The project team conducted a site visit to NHDOT to 

deliver the intersection inventory database, GIS models, data forms, and the custom GIS 

toolbar developed under this project. The project team developed all of the deliverables in 

ESRI ArcGIS® 10 format. Based on conversations with the NHDOT, the project team 

anticipated that NHDOT would be operating on ESRI ArcGIS® 10 by the time the project 

concluded. However, due to internal software conflicts at NHDOT, the department had not 

yet migrated to ArcGIS® 10 before the project team presented the deliverables. NHDOT was 

able to load a version of ArcGIS® 10 onto a standalone laptop that was connected to the 

NHDOT network, allowing the project team to copy over all the project deliverables and 

demonstrate how the models, data forms, and toolbars worked. 
 MIRE MIS Lead Agency Data Collection Report 

23

The final deliverables for the project consisted of an ESRI ArcGIS® 10 file geodatabase 

containing the entire updated intersection inventory for State/State and State/local 

intersections. In addition, the project team delivered a local/local intersection layer populated 

with the intersection attributes derived from the GIS models. The project team delivered the 

GIS models in an ArcGIS® 10 toolbox containing the two models that were developed for the 

project: (1) New Intersection/Leg Model, and (2) Update (future year) Intersection Model. The 

project team developed both models using ArcGIS® 10 ModelBuilderTM software. The project 

team developed the source code for the custom data collection forms and custom toolbar 

using Visual Studio 2010 (VB.NET) and ArcObjects 10.0 and compiled it all into an ESRI® Add-In 

with an Extensible Markup Language (XML) configuration file. 

The project team loaded the final deliverables onto the NHDOT laptop running ArcGIS® 10. 

The team demonstrated how to setup the configurations files, install the custom toolbar, and 

execute each of the GIS models to ensure that the deliverables were functioning correctly on a 

local NHDOT system. Once NHDOT completely migrates to ArcGIS® 10, NHDOT will be 

able to fully integrate the new data and tools into the NHDOT enterprise GIS and share the 

information across their network. 

As discussed, the project team delivered a sample dataset to NHDOT to import into their 

system, which was evaluated successfully for use in SafetyAnalyst. Because there were no issues 

with the sample data, it is not anticipated that there will be any significant issues with integrating 

the completed intersection inventory into NHDOT’s GIS. 

Results 

The development of the data collection tools and model began in January 2012 and took 

approximately three months to complete. The project team completed the data collection for 

all 10,300 intersections in five months (March 2012 - July 2012). The project team initially 

estimated the data collection to take six months to complete, but it only took five months. The 

team estimated the data collection to take approximately 2,000 person-hours but it only took 

1,600 person-hours. At any given time over the data collection period, there were three to five 

data collection stations that were manned almost full-time. The management and QA/QC time 

took slightly more hours than initially planned, but was more than offset by the reduction in the 

data collection time. The initial estimates were based on 10-12 minutes per intersection, but it 

took only an average of 9 minutes per intersection. 

Throughout the data collection process, the number of intersections completed per hour 

improved for each data entry clerk. At the start of the data collection it took almost 12 

minutes per intersection; however, by the end, it took approximately seven minutes per 

intersection. Error rates also improved through the data collection period. Since this process 
 MIRE MIS Lead Agency Data Collection Report 

24

is repetitive in nature, the more familiar the clerks became with the process and the data 

elements, the more efficient they became. 

As discussed in Step 10, the project team collected data in the field from a sample set of 

intersections. Collecting data in the field provided an opportunity to compare the differences 

between in-office (remote) data collection and field data collection. The project team analyzed 

the two datasets with some surprising results. In many cases, especially for geometric 

elements, the in-office data were more accurate. This was because the bird’s eye view of aerial 

imagery allowed the data collectors to see the geometry better than when on the ground. An 

example of this is T- versus Y-intersections. In other cases, the field data proved more 

accurate than the in-office data, especially for the signal timing elements since the technicians in 

the field could observe the timing, whereas in the office they had to rely on a frozen snapshot. 

Overall, the field collection took almost twice as long per intersection as the in-office data 

collection. 

The entire effort, including the development of the intersection inventory and the traffic 

dataset, cost approximately $210,000, which FHWA funded through the MIRE MIS Lead Agency 

Program. Table 5 lists the hours spent on each task, rounded to the nearest five hours, and the 

total cost. 

Table 5. Breakdown of hours by task and total cost for New Hampshire 

intersection inventory. 

Activity Hours 

Coordination with NHDOT and development of a Work Plan 455 

Development of model that pre-populated the inventory 75 

Development of the data collection tool/interface to collect the remaining data 175 

Develop node layer for the 24,000 local/local intersections 30 

Hiring and training of data collection clerks including development of 

collection manual 135 

Collection of intersection data: 

In-office collection for 10,300 intersections 1,600 

In-field data collection for 200 intersections 60 

Management and QA/QC 360 

Development and delivery of a dataset of existing intersection traffic volumes 375 

Providing the dataset, model, and tool to NHDOT and setting-up and training 25 

Total Cost $210,000 

*Note: Total cost is in 2012 dollars and may vary by agency. 
 MIRE MIS Lead Agency Data Collection Report 

25

Challenges 

The largest obstacle during data collection involved determining posted speed limits, as it 

required the most time of any data element. NHDOT does not have a speed limit database, so 

the project team needed to collect posted speed limits for each approach by visually identifying 

speed limit signs. However, the signs were often not right at the approach and the data entry 

clerks had to “drive” down the street using Google Street ViewTM to find the speed limit sign. 

The data entry clerks were challenged with developing an efficient method to collect this 

information. The method adopted by the majority of the data collectors was to print out a map 

of the corridor and then “drive” the corridors using Google Street ViewTM, noting on the map 

the location of the speed limit signs and the posted speed limit. Then, when entering the data 

on the approach leg, they could quickly reference the map. 

Lessons Learned 

Many factors contributed to the success of this effort, such as: 

1. Development of the Work Plan: The work plan provided a clear vision and 

approach for conducting the data collection. This helped to lay out clear expectations 

on the part of NHDOT and the project team. 

2. MIRE flexibility: The primary goal of this effort was to test the feasibility of collecting 

MIRE data elements. Both NHDOT and WSDOT requested an intersection dataset to 

import and use in SafetyAnalyst. The project team developed data collection tools to 

create a database to be able to meet that goal. While the data elements selected by the 

States were based on MIRE, the data collected required deviations from the MIRE data 

dictionary in order to tailor them for SafetyAnalyst. 

3. Constant contact/feedback between the contractor and the State DOT:

Throughout the entire process, NHDOT was available to answer questions and to 

provide clarification and feedback. This constant communication was key to developing 

a dataset that best met their needs. 

4. Development of a “Frequently Asked Questions” document: Since there were 

multiple data entry clerks simultaneously entering data, there were several similar 

questions that came up in the beginning of the data collection effort. The project team 

developed a Frequently Asked Questions (FAQ) document. Each time a data entry 

clerk asked a question, the project team added that question and its response to the 

FAQ document. The data entry clerks were instructed to review the document every 

morning. This helped to provide a level of consistency among the various staff 

members. 
 MIRE MIS Lead Agency Data Collection Report 

26

5. Data collection flexibility: Each data entry clerk had the flexibility to collect the data 

in the manner that was most efficient for them. Some collected all of the speed limits 

first within a corridor; some did all of the intersections, then all of the legs. By allowing 

this flexibility, each data entry clerk was able to maximize his or her efficiency. 

6. Use of the sample dataset: The project team provided NHDOT a sample dataset to 

ensure there were no problems with the data. Although none were found, if there had 

been issues, they could have been resolved before completing the data collection rather 

than having to go back and correct the data—thus saving valuable time, budget, and 

resources. 

7. Use of GIS tools: The tool was completely GIS-based using ESRI® products and did 

not require any proprietary software. This allowed the project team to install it on 

NHDOT’s system, allowing NHDOT to continue the data collection effort in the future. 

8. Use of existing data: The project team was able to derive many of the basic 

intersection inventory elements from existing data sources. Out of the 31 elements for 

the overall intersection, the project team only needed to collect four elements; the 

remaining 27 either already existed or were derived from existing sources. Out of the 

23 elements for each intersection leg, the project team only needed to collect eight; the 

remaining 27 either already existed or were derived from existing sources. 

9. Temporality of the collected data: In order to better ascertain how current the 

roadway inventory data are, the date of visual imagery from which the data are 

extracted should be recorded. This provides information regarding the currency of the 

data. This information could be recorded as metadata. 
 MIRE MIS Lead Agency Data Collection Report 

27

WASHINGTON STATE 

Similar to the effort conducted with NHDOT, the starting point for WSDOT was the MIRE 

listing. As part of the application process, WSDOT reviewed the MIRE elements and provided 

a list of the elements they would like to have collected. WSDOT organized their selected 

elements into three priority categories—high, medium, and low. Taking into account the 

funding available to complete the work, WSDOT requested the project team collect the high 

and medium priority elements. However, the project team also collected one low priority 

element, circular intersection data, at the request of WSDOT. Table 6 provides the list of 

requested elements, which include identification, location, operations, geometric, and traffic 

data. 

Approximately 76,000 centerline miles of roadway exist in the State of Washington. The State 

owns and maintains only about 7,000 centerline miles of this roadway, and WSDOT collects 

and maintains roadway data only on the State-owned roadways. Given the vast road network 

and the limited funding available for this effort, the project team and WSDOT acknowledged 

that it might not be feasible to develop an inventory and collect the data elements for all public 

roadway intersections in the State. Since WSDOT has a base GIS layer of State/State 

intersections (approximately 320) and State/local intersections (approximately 17,200), the 

State prioritized these intersections over other intersection types (such as, local/local and 

local/ramps or interchanges) to maximize the benefit from this work. The State also requested 

that all circular intersections be included in the data collection effort. In addition, the project 

team was responsible for assigning existing traffic volumes to all intersection types within the 

dataset. Ultimately, the project team collected data for approximately 15,820 spatially distinct 

State/State and State/local intersections in the intersections geodatabase. 
 MIRE MIS Lead Agency Data Collection Report 

28

Table 6. Intersection inventory elements requested by WSDOT. 

Intersection Elements Intersection Leg Elements 

Unique Junction Identifier 

County Name 

Rural/Urban Designation 

AADT Annual Escalation Percentage 

Type of Intersection/Junction 

Intersection/Junction Geometry 

Intersecting Angle 

Intersection/Junction Offset Flag 

Intersection/Junction Offset Distance 

Intersection/Junction Traffic Control 

Signalization Presence/Type 

Route Number, Route/Street Name 

Circular Intersection – Circulatory Lane 

Width 

Circular Intersection – Inscribed Diameter 

Circular Intersection – Entry Width 

Circular Intersection – Presence/Type of 

Exclusive Right Turn Lane 

Circular Intersection – Entry Radius 

Circular Intersection – Exit Width 

Circular Intersection – Number of Exit Lanes

Circular Intersection – Exit Radius 

Circular Intersection – Crosswalk Location 

Circular Intersection – Island Width 

Unique Approach Identifier 

Major Commercial Driveway Count 

Minor Commercial Driveway Count 

Major Residential Driveway Count 

Minor Residential Driveway Count 

Major Industrial/Institutional Driveway Count 

Minor Industrial/Institutional Driveway Count 

Other Driveway Count 

Number of Approach Through Lanes 

Number of Exclusive Left Turn Lanes 

Number of Exclusive Right Turn Lanes 

Speed Limit 

Approach AADT 

Approach AADT Year 

Approach Directional Flow 

Approach Traffic Control 

Approach Left Turn Protection 

Left/Right Turn Prohibitions 

Right Turn-On-Red Prohibitions 

Left Turn Counts 

Year of Left Turn Counts 

Right Turn Counts 

Year of Right Turn Counts 

Right Turn Channelization 

Methodology 

The project team followed the following general steps to develop the WSDOT intersection 

inventory: 

1. Determine what data are already collected and what remaining data need to be 

collected. 

2. Determine how the data are currently collected, the available data sources, and how to 

collect the new data. 
 MIRE MIS Lead Agency Data Collection Report 

29

3. Develop a detailed work plan. 

4. Develop the data collection tool and interface. 

5. Collect the data – manual survey. 

6. Develop automated import of existing data. 

7. Conduct quality assurance/quality control (QA/QC) reviews. 

8. Provide a sample dataset to WSDOT. 

Step 1: Determine Existing Data and Remaining Data Needs 

The project team first determined which of the WSDOT-requested elements were already 

being collected. Over a series of onsite meetings, the project team worked with the WSDOT 

staff and reviewed their data collection practices and datasets. The project team identified 

several existing data sources that provided coverage for the data collection needs of this 

project. The sources, and the data contained in them, included: 

1. WSDOT Roadway Datamart – A collection of geospatially-referenced datasets broken 

into multiple tables. The project team made use of the following tables: 

a. Intersections. 

b. Lanes. 

c. LegalSpeedLimits. 

d. UrbanRural. 

2. Signs, Signals, and Intersections Geodatabase: 

a. Traffic Signs. 

b. Signals_FlashingBeacons. 

c. Signal_Beacons_OpsInfo. 

d. TurnLanes. 

3. County Road Administration Board (CRAB) Mobility Database: 

a. Roadlog. 

b. RoadMaster. 

4. Functional Class Geodatabase: 

a. FunctionalClassStateRoute. 

b. FunctionalClassNonStateRoute. 

5. Traffic Geodatabase (developed for WSDOT by third party vendor): 
 MIRE MIS Lead Agency Data Collection Report 

30

a. TRAFCWA – A database of projected traffic counts for the State of Washington. 

b. Growthrateswa – A database of projected traffic growth rate zones for the State 

of Washington. 

These datasets often contained overlapping information. Due to its design, the collection 

database represents all sources of data. The final data transformation step aggregated 

overlapping data points (for a given intersection or leg element and data field) according to 

WSDOT data source preferences. 

Step 2: Determine How Data Are/Will be Collected 

Conducted concurrently with Step 1, the project team worked with WSDOT to determine 

how it currently collects and stores the data. The State roadway inventory system is based on 

an LRS that feeds into WSDOT’s Datamart, which can link to other datasets such as traffic 

counts and crash data. The data contained in the State Highway Log are collected and updated 

using contract plans, field reviews, and information provided by numerous WSDOT regional 

and headquarters offices, as well as other county and city sources. Video of the roadway is 

collected via a digital imagery van. Washington uses GIS for mapping their data. Included in 

WSDOT’s Datamart is a geodatabase of State/State intersections. When two State routes 

meet at an intersection, they are represented by two spatially-coincident records in the 

intersections geodatabase. 

Traffic data are also a critical piece of information for safety analysis, particularly for use of the 

SafetyAnalyst software. WSDOT collects and stores data on the State-maintained highway 

network. There are a number of local organizations responsible for collecting and storing traffic 

data on local roads. The data collection processes, data sampling, data interpretation, data 

storage, and extent of available historical information vary between WSDOT and the various 

local agencies. There is no single database and data format to easily access this traffic data and 

not all data are available for a given current year (a current year estimate is essential for safety 

analysis). The process to gather, interpret using a consistent set of tools, and compile the data 

into a single database with a common geospatially-referenced data format for use under this 

effort would have taken considerable effort. As part of this project, the project team sought 

third-party vendors to provide this information and supply it in a readily usable format with the 

intent to reduce the data collection, interpretation, and formatting costs. 

The project team selected a vendor who had 32,856 traffic counts in Washington State. The 

traffic counts the vendor provided are those published by the various city, State, and Federal 

organizations. The vendor developed a methodology using the raw published counts to derive 

the best feasible current year traffic volume estimates in terms of Annual Average Daily Traffic 

(AADT). A total of 18,315 such estimates are available in the database; all data are geospatially 
 MIRE MIS Lead Agency Data Collection Report 

31

referenced. The project team determined this was a cost effective way to obtain the necessary 

traffic data for this project. 

Based on the information obtained from WSDOT, the third party vendor, and the data 

currently available, the project team established two primary methods of data collection: 

manual collection and automated import. Table 7 presents the data items that were collected 

manually, imported automatically, or both. For the manual collection phase, the project team 

developed a tool that presented aerial and ground photography and any associated GIS layers 

to the user, as well as the Google MapsTM and Microsoft Bing® map of the intersection. For the 

data import phase, the project team created source-specific importers that mapped geospatially 

referenced data to the intersection and leg inventory according to the relevant geometries. 
 MIRE MIS Lead Agency Data Collection Report 

32

Table 7. Elements and primary method of data collection for WSDOT intersection 

inventory elements. 

Intersection Elements Intersection Leg Elements

Unique Junction Identifier (Imported) 

Unique Approach Identifier (Imported) 

Number of Approach Through Lanes 

(Manual/Imported) 

County Name (Imported) Number of Exclusive Left Turn Lanes (Manual)

Number of Exclusive Right Turn Lanes (Manual)

Rural/Urban Designation (Manual/Imported) Speed Limit (Imported)

Approach AADT (Imported)

AADT Annual Escalation Percentage 

(Imported) 

Approach AADT Year (Imported) 

Approach Directional Flow (Manual) 

Type of Intersection/Junction (Manual) 

Approach Traffic Control (Manual/Imported)

Approach Left Turn Protection (Manual/Imported)

Intersection/Junction Geometry 

(Manual/Imported) 

Left/Right Turn Prohibitions (Manual) 

Right Turn-On-Red Prohibitions (Manual) 

Intersecting Angle (Imported) Left Turn Counts (Imported) 

Intersection/Junction Offset Flag (Imported) 

Year of Left Turn Counts (Imported) 

Right Turn Counts (Imported) 

Intersection/Junction Offset Distance 

(Imported) 

Year of Right Turn Counts (Imported) 

Right Turn Channelization (Manual) 

Intersection/Junction Traffic Control 

(Manual/Imported) Circular Intersection – Entry Width (Manual) 

Signalization Presence/Type (Imported) Circular Intersection – Presence/Type of Exclusive 

Right Turn Lane (Manual) 

Route Number, Route/Street Name 

(Imported) 

Circular Intersection – Entry Radius – (Manual)

Circular Intersection – Exit Width – (Manual)

Circular Intersection – Circulatory Lane 

Width (Manual) 

Circular Intersection – Number of Exit Lanes 

(Manual) 

Circular Intersection – Exit Radius (Manual) 

Circular Intersection – Inscribed Diameter 

(Manual) 

Circular Intersection – Crosswalk Location (Manual) 

Circular Intersection – Island Width (Manual)
 MIRE MIS Lead Agency Data Collection Report 

33

Step 3: Develop Detailed Work Plan 

The project team developed a detailed work plan. The work plan provided an overview of the 

proposed effort, including timeframe and cost, and included a description of WSDOT’s existing 

data system, including sources of available data. 

Following the development of the work plan, the project team developed a detailed data 

dictionary. The data dictionary included the attributes and important considerations for each 

element. Since WSDOT intends to use the dataset for SafetyAnalyst, the project team created a 

model that mapped the MIRE elements to the corresponding SafetyAnalyst fields (where 

applicable). Higher order discrepancies between the two models (MIRE and SafetyAnalyst) 

ultimately led the project team to abandon this method. Instead, the team identified each 

element, defined the element using both the MIRE and SafetyAnalyst definitions, and assigned 

allowable values. For the data elements with numeric fields, the project team identified specific 

values. The creation of the data dictionary also involved adding proposed data sources and 

technical field information (, data type and size). 

Step 4: Develop Data Collection Tool and Interface 

The project team designed a data collection tool called the MIRE Intersection Data Survey 

(MIDS) tool, which provides a platform for integrated data sources used to conduct manual 

data collection (i.e., GIS maps, third-party databases, and State databases). The MIDS tool 

builds on concepts that members of the project team used in a previous data collection project 

(the FHWA Indian Roads Functional Classification). The goal of the tool was to present 

multiple and complimentary data sources to the users so they could accurately determine the 

required input fields. 

The project team developed the MIDS tool in the C# programming language using the Visual 

Studio® 2010 Integrated Development Environment (IDE) and utilizes the .NET 4.0 Framework. 

The tool works on any Microsoft operating system later than Windows® XP Service Pack 3 

(i.e., Vista®, Windows® 7, and Windows® 8). The project team used Microsoft SQL Server®

2008 R2 database for the tool, which is forwardly compatible with Microsoft SQL Server® 2012. 

The MIDS tool provides many different data sources used for data entry, data viewing, tracking, 

and visual imagery, which are accessed via specially designed windows: 

 Explorer window: displays interface for navigating between intersections. 

 Google EarthTM window: displays aerial imagery from Google EarthTM with associated 

layers and options. Allows import/creation of keyhole markup language (KML) files. 

Includes drawing tools for collecting measurement data fields. 

 Microsoft Bing® Map window: displays Microsoft Bing® aerial and Bird’s Eye Imagery. 
 MIRE MIS Lead Agency Data Collection Report 

34

 Google Street ViewTM window: displays Google MapsTM and Google Street ViewTM 

imagery. 

 SR View window: displays WSDOT videolog imagery. 

 Output window: provides feedback to the users. 

 Survey window: displays the data entry interface. 

 Changes window: provides summary of manual data entry. 

 Efficiency window: tracks user’s data entry progress. 

Figure 3 is an example of the various data windows that are part of the MIDS tool. Notice that 

some windows are fully visible and some are shown as tabs. Each window can be moved based 

on the user’s preferences. Tabs allow for larger windows and fast transition between the 

different windows. 

The Explorer window shows the intersections grouped by county and route. At the route 

level, intersections are ordered by milepost to provide a logical survey progression. Progress 

bars provide visual representation of survey progress. It is possible to search for intersections 

by name within a route. If a letter is typed on the keyboard, the next intersection in the list 

starting with that letter will be highlighted. This window is also connects to the imagery and 

survey windows. When the user clicks on an intersection in the explorer list, Google EarthTM, 

Google Street ViewTM, and Microsoft Bing® Map automatically center on that location while 

keeping the zoom level constant. The Survey window automatically shows only the data for 

that intersection. 

The Google EarthTM window provides aerial imagery of the intersection, along with a set of GIS 

tools for annotating the intersection. It is possible to load existing files and create new KML 

files. Linear and circular measurement tools are provided within the interface so that measured 

fields (e.g., entry width, circulatory width, and inscribed diameter, etc.), can be surveyed and 

saved for later checks and audits. These options are organized in a collapsible control panel. 

Intersection approach angles can also be determined using the compass which is automatically 

centered on the active intersection. Figure 4 is a screenshot of the Google EarthTM window. 
 MIRE MIS Lead Agency Data Collection Report 

35

Tabs allow for larger window size.

Figure 3. Screenshot of window configuration. 

Figure 4. Screenshot of Google EarthTM window with expanded control panel. 
 MIRE MIS Lead Agency Data Collection Report 

36

The Bing® Map window provides information similar to the Google EarthTM window, but with a 

different set of aerial photographs, dates, and resolutions. The Bing® Map window does not 

contain the KML editing tools. The purpose of this window is to supplement the aerial imagery 

provided by the Google EarthTM window. 

The Google Street ViewTM window provides ground-level imagery. Each intersection can be 

viewed from multiple directions so the user can identify signs and traffic signals. 

SR View is WSDOT’s videolog database. It acts as a supplement to Google Street ViewTM as it 

also provides ground-level imagery. While Google data are available for most of the United 

States, SR View is only available for routes maintained by WSDOT. It is an example of custom 

integration of agency specific tools. The project team developed MIDS to allow the addition of 

custom tools with minimal additional coding. 

Having multiple sources for both aerial and ground imagery is helpful in locations where 

vegetation and/or traffic block the data elements. Figure 5 shows an example of the Bing® Maps 

(top left), SR View window (bottom left), and Google Street ViewTM (right) imagery arranged as 

separate, floating windows. 

Figure 5. Screenshot of imagery windows. 
 MIRE MIS Lead Agency Data Collection Report 

37

The Output window provides feedback to the user including error messages, user hints, and 

other information. The user can choose to dock the Output window behind other windows to 

provide additional space for the other survey information. 

The Survey window is the main interface data entry clerks use to input data. The Survey 

window is configurable by changing the FIELD_MAP table in the tool’s primary database. 

Surveyors enter the numerical data by typing and text data are chosen from dropdown menus. 

The Survey window is connected to the Changes and Efficiency windows so that each change to 

the database is logged and processed. 

The Changes window is a summary of all the data entered for each intersection. Each data 

entry clerk had a unique username to allow the project team to track their data entry. This 

also allows the tool to calculate the efficiency of each surveyor. Figure 6 is a screenshot of the 

Efficiency window in the MIDS tool. Graphing efficiency on a daily basis, as shown in Figure 7, 

allows the project team to track progress and set data collection goals. Another benefit of 

usernames is that each surveyor can personalize the window locations on their workstation 

based on the number of monitors being used and personal preference. Upon each login to that 

computer, the tool windows open in the same place as when the program was last exited. 

Figure 6. Tool-generated efficiency. 

Figure 7. Graphing surveyor efficiency over time. 
 MIRE MIS Lead Agency Data Collection Report 

38

Step 5: Collect Data – Manual Survey 

Before the data collection began, the project team selected a team of data entry clerks and set 

up the appropriate number of workstations to accommodate them. The majority of the data 

entry clerks worked in a single room so that they could exchange questions and data collection 

tips very quickly. The project team held a group training session to familiarize the clerks with 

the tool. The team also provided individual training sessions to each data entry clerk to clarify 

the process. This training included working through multiple examples and pointing out subtle 

clues in the aerial imagery, such as lane alignment and shadow recognition, to prepare them for 

solo data collection. As part of the training, the team developed a survey manual that provided 

detailed descriptions of data fields and instructions. 

At the start of the data collection effort, the project team assigned each data entry clerk a 

county. Each clerk was responsible for completing all intersections in their county before 

moving on to another county. The MIDS tool allowed the users to enter data into the database 

without manually saving any changes. This reduced the required motions of the mouse and 

number of keystrokes, and safe-guarded data in the case of unexpected shutdown. In addition, 

a user could view recent changes made by other users without disrupting their data entry or 

restarting the program. The design of the tool makes accidental survey overlap nearly 

impossible. 

After logging in, the data entry clerks could quickly find where to start collecting data by 

navigating through the tree in the Explorer window. The progress boxes next to each level in 

the tree made it easy to see which intersections were complete. The built-in automation of 

zooming to the correct location when selecting intersections prevents users from having to 

search for the correct location and saves valuable time. 

The Survey window contains the data fields that are arranged vertically and grouped by 

intersection and intersection leg. This method of organization helped the user to first 

recognize the intersection as a whole before narrowing their attention to the specific details of 

each leg. This feature also allowed the data entry clerks to become familiar with common 

intersection geometries and traffic patterns which increased data collection efficiency. Text 

field drop-down menus prevented the user from entering erroneous data. To further reduce 

the amount of data entry, the project team pre-populated some fields with values from the 

WSDOT database such as intersection geometry and intersection traffic control. Figure 8 

shows the data layout within the Survey window. Note that surveyors can minimize or expand 

the set of data for each approach with the ease of a double click. 
 MIRE MIS Lead Agency Data Collection Report 

39

Figure 8. Screenshot of data in survey window. 

The SR View, Google EarthTM, Bing® satellite view, Bing® Bird’s Eye View, and Google Street 

ViewTM windows give the user a lot of view options for collecting data. The primary window 

used for finding field information was usually Google EarthTM, but it depended on the image 

quality and image date. An example of image quality difference is shown in Figure 9. Some 

intersections that appear clearly on the Bing® map can appear as blurry construction sites on 

Google EarthTM (or vice versa). The different view options are also important for determining 

fields that are dependent on non-durable paint marks and for those that could be blocked by 

vegetation. 
 MIRE MIS Lead Agency Data Collection Report 

40

Google EarthTM image (left) and Microsoft Bing® (right). 

Figure 9. Imagery differences. 

The surveyors used Google Street ViewTM heavily for data fields that are defined by signs, such 

as turn prohibitions. Figure 10 is an example of ideal imagery. This single view allowed the 

user to collect Traffic Control, Left/Right Turn Prohibitions, and Left Turn Protection. 

Familiarity with common sign appearance and locations allowed for more accurate data 

collection, even with unclear imagery. Surveyors used SR View less frequently, typically only 

when Google Street ViewTM did not offer acceptable visibility. SR View is the only imagery 

window that does not automatically locate the intersection. 

Figure 10. Clear Google Street ViewTM imagery. 

When a data entry clerk encountered a question that could not be answered expediently, they 

marked the intersection for review. As shown in Figure 11, a red box is drawn next to the 
 MIRE MIS Lead Agency Data Collection Report 

41

intersection name so it can be easily found and reviewed at a later time. Often, a user would 

survey an entire route and then review any marked in red. A different surveyor reviewed the 

remaining marked intersections prior to QA/QC. 

Figure 11. Screenshot of intersections marked in red for survey review. 

The intersection dataset from WSDOT contained many intersections that were not included in 

the survey. These included duplicate intersections, intersections that were combined with 

others (e.g., offsets), locations where roadways come together without traffic intersecting, and 

locations where channelized exclusive turn lanes met the roadway. Surveyors flagged these 

intersections to show they were skipped. Intersections under construction in the imagery 

were also flagged and skipped. These intersections were included in the QA/QC process to 

determine if they were correctly skipped or if they needed to be fully surveyed. Approximately 

2,680 intersections (14.5 percent) were flagged and skipped. 

Some intersections in the dataset were reversible, meaning that traffic directions for each leg 

could be different depending on time of day. These intersections were given a unique flag to 

show that the surveyors collected data for one possible traffic configuration for the 

intersection. Not all intersections with the word “reversible” in their description were truly 

reversible intersections by the above definition. Some of these include entrances or exits to 

reversible lanes but are only in use for one direction. These cases were surveyed as normal 

intersections for the direction in which they were used and did not receive a flag. 

The survey administrator was able to manually enter a limited number of comments into the 

database to record survey decisions on complicated intersections; however, future efforts 

should incorporate a way to enter and view survey notes through the MIDS tool. 

Step 6: Automated Import of Existing Data 

The second part of the data collection phase involved the automated import of existing data. 

For this phase of data collection, the project team created source-specific importers that 

mapped geospatially-referenced data to the intersection and leg inventory according to the 

relevant geometries. 
 MIRE MIS Lead Agency Data Collection Report 

42

The import of the intersection inventory was relatively straightforward. The project team took 

data directly from the intersection geodatabase and keyed to a geospatially-generated ID. The 

team generated the ID using the Bing® Maps tile numbering scheme to identify and eliminate 

coincident records (8). 

The import of the intersection leg data was more challenging. The project team developed a 

custom model to extract geometry data from the State and non-State functional class 

geodatabases and determine intersection associations by lowest-distance parameters. The 

development of the import model started with linestring matching algorithm, which operated as 

follows: 

1. Partitioned the data into county-sized chunks to reduce the number of pairings that are 

not possible. 

2. Wrapped leg geometries in a convex hull (see examples below) and calculated their area 

as projected onto an Earth-sized sphere. 

3. Wrapped linestrings for the data to be imported in a convex hull and calculated their 

area as projected onto an Earth-sized sphere. 

4. For each county, checked each pair of legs/data linestrings by wrapping a convex hull 

around both the leg and data linestrings and computing that area. 

5. Produced an error metric by subtracting each of the individual areas from two times the 

combined area and dividing by two times the summed length of the leg and data 

geometries. 

Example of the Linestring Algorithm Process 

The following example provides a general description of the linestring algorithm process: 

Difficult case: overlapping roadway geometry. 
 MIRE MIS Lead Agency Data Collection Report 

43

Position of attribute data: 

Good Match: Bad Match: 

This matching is obvious to human eyes, but needs to be quantifiable for a computer to match it. 

For each pairing of inventory and attribute geometries, a simple scalar value that represents how well 

the data match is needed. Step 1: Wrap a convex hull around the inventory geometry and calculate the 

area as projected on a great-sphere approximation of the earth. The original inventory is shown as a 

dotted line inside the hull. 

A

1

A

2
 MIRE MIS Lead Agency Data Collection Report 

44

Step 2: Do the same for the attribute geometry. 

Step 3: Now wrap a convex hull around the inventory AND attribute geometries. 

A

3

A

4

If the inventory and attribute geometries overlap exactly, the sum of their convex hull areas will be 

equal to twice the area of the combined convex hull (since the points will lie on top of one another). If 

they are close, the left-over area will be small (and will simply need to be normalized by the lengths of 

the shapes). 

This error metric closely approximates the distance between the linestrings that served as an 

excellent matching heuristic for line-line matching problems. Point data were handled in a 

simpler way by looking at straight line distances (either directly to the checked intersection 

point or to the closest point on the relevant leg) to determine best matches. 

A

3
 MIRE MIS Lead Agency Data Collection Report 

45

Matching Data from the Import and Manual Collection 

Upon completion of the two data collection phases, the next step required matching the 

manually collected data and the imported data. The matching of intersection inventory datasets 

was simplified since both datasets (i.e., manual and imported) had the same data elements. The 

leg inventory featured different elements, and as such the matching of data proved more 

complicated. 

The project team assigned each manual/imported leg pair an error value according to the 

difference in degrees of their bearing. For pairs of intersections with the same number of legs, 

matching simply involved choosing the consistent pairing (meaning all legs are matched once and 

only once) with the lowest total error rate. For pairs of intersections with a disparate number 

of legs, leg matching proceeded in a similar way, but with the constraint of complete matching 

relaxed (while still looking for the lowest total error). Import of any leftover legs simply copied 

whatever data were available and flagged the intersection for QA/QC. 

Step 7: Conduct Quality Assurance/Quality Control (QA/QC) Reviews 

The QA/QC reviews required that the project team scan the manual survey data for logic 

errors using a series of SQL queries. (Appendix A includes the list of the SQL queries used 

during this process.) Any errors found were manually checked and edited. It was important to 

implement this step multiple times during the survey process because it helped identify 

reoccurring errors made by specific surveyors. These errors can be corrected early to reduce 

the amount of required edits later in the survey. 

The manual survey data also underwent a quality check to determine the accuracy of the data 

collection. A random sample of five percent of the intersections was chosen for QA/QC. 

These intersections were automatically marked with a blue box in the Explorer window for 

administrative users only. An olive-colored box, for example SR 5 NB in Figure 12, indicated 

the completion of QC for an intersection, which provided to visible confirmation of the QC 

survey progress. Figure 12 shows a screenshot of the marked intersections. The designated 

quality inspector logged into the tool using an administrator user name and reviewed the data 

for the marked intersections. If an error was found, it was immediately corrected and the 

change automatically logged by the tool. 
 MIRE MIS Lead Agency Data Collection Report 

46

Figure 12. Screenshot of intersections marked for QA/QC. 

The project team could use the MIDS data log to identify all of the errors corrected by the 

quality inspector and calculate the survey accuracy for individual data fields. Table 8 shows the 

percent of errors found in each manually surveyed field in the QA/QC dataset. It is important 

to note that the WSDOT QA/QC effort began before some of the logic queries were 

developed. These queries were very important to improving the accuracy of surveyed data. 

Some errors caught by the quality inspector may have been corrected by queries and not 

included in the final accuracy. The survey team is confident that no data element has an actual 

percent error higher than 10 percent. Appendix A lists the logic queries used for the QA/QC 

effort. 
 MIRE MIS Lead Agency Data Collection Report 

47

Table 8. Calculated percent error of sample. 

Elements Percent Error

Intersection Elements:

Type of Intersection/Junction 2% 

Intersection/Junction Geometry 7% 

Intersection/Junction Traffic Control 5% 

Rural/Urban Designation 2% 

Intersection Leg Elements:

Approach Traffic Control 9% 

Approach Left Turn Protection 4% 

Left/Right Turn Prohibitions 10% * 

Right Turn-On-Red Prohibitions 1% * 

Approach Directional Flow 9% 

Number of Approach Through Lanes 11% 

Number of Exclusive Left Turn Lanes 9% 

Number of Exclusive Right Turn Lanes 9% 

Right Turn Channelization 3% * 

*See description below. 

An additional complication affected text fields where NULL was an acceptable entry due to 

software complexities in MIDS. The data collection clerk and the quality inspector were not 

able to change an entered text field back to NULL. The solution for data collection clerks was 

to delete all leg data for the intersection and resurvey. As this was not acceptable for a 

QA/QC solution, these fields were edited using SQL update statements that were not 

recorded in the log. The missing changes cause the percent error to be too low. For the 

Left/Right Turn Prohibitions element, this was possible but not a likely error because surveyors 

were much more likely to miss turn prohibition signs than misinterpret them. The survey team 

believes the listed percent error for this field is accurate. Based on the quality administrator’s 

familiarity with the data, the Right Turn-On-Red Prohibitions and Right Turn Channelization 

should be higher by approximately one percent. 

Errors in manually collected data occur for a number of reasons. Misinterpretation of data field 

definitions, misinterpretation of traffic patterns, and surveyor fatigue/inattentiveness are 

common sources of error. Common errors found in the surveyed data include the following: 

 Failure to recognize high occupancy vehicle (HOV) or bus-only lanes as traffic lanes. 

Many lane counts were missing most likely due to the lack of traffic in imagery and the 

solid line that usually divides these lanes from normal traffic lanes. 
 MIRE MIS Lead Agency Data Collection Report 

48

 Failure to recognize exit only lanes as exclusive turn lanes. Many off ramp intersections 

had GPS locations far from the gore point and given the relatively high zoom level 

preferred by surveyors, the gore points were often outside the viewing area. Also, 

pavement marking symbols are not as widely used on the high-speed mainline lanes. 

The combination of surveying errors led to the perception that all lanes were through 

lanes. 

 Incorrect left turn protection due to definition misinterpretation. Some surveyors 

focused more on the recognition of traffic signals than the actual definition. They failed 

to take traffic patterns into consideration at signalized intersections and entered 

“Permitted” or “Protected-permitted” when there was no opposing traffic. When left 

turns do not have to yield to opposing traffic the correct option is “Protected.” 

Step 8: Provide Sample Dataset 

The project team provided WSDOT with a series of sample datasets to verify the data 

structure and the intersection and leg fields were sufficient for their needs. WSDOT team 

members found errors in surveyed data as well as the data import process and alerted the 

project team. This process proved to have a very positive effect on the project as it improved 

data accuracy and communication between the teams. 

Results 

The focus on user productivity and the intelligent application of geometric mapping heuristics 

made this project successful despite the complications described above. By the end of the 

project, user entry rates decreased to 5.2 seconds per question (with the number of questions 

per intersection varying based on the number of legs, but averaging just over three minutes per 

intersection). In addition, the abstraction of the geospatial referencing problems allowed for 

significant reuse of code, even in the face of disparate data sources. This reuse also allowed 

improvements in the matching heuristics to automatically propagate to all of the specific 

importers, greatly speeding up the QA/QC cycle for the automated importers. 

The entire effort for the Washington State data collection, including the development of the 

tool and interface, cost approximately $340,000, which FHWA funded through the MIRE MIS 

Lead Agency Program. Table 9 lists the hours spent on each task, rounded to the nearest five 

hours. 
 MIRE MIS Lead Agency Data Collection Report 

49

Table 9. Breakdown of hours by task and total cost for Washington State 

intersection inventory. 

Activity Hours 

Develop detailed Work Plan 425 

Develop data collection tool and interface 815 

Collect data – manual survey (15,820 spatially distinct State/State and 

State/local) 2,105 

Acquire and incorporate third-party traffic volume data 140 

Automated import of existing data from WSDOT source and data 

aggregation 445 

Conduct QA/QC 590 

Exporting the final dataset 250 

Total Cost* $340,000 

*Note: Total cost is in 2012 dollars and may vary by agency. 

Challenges 

Definitional Challenges 

While the goal of this project was to produce a dataset that followed MIRE guidelines as closely 

as possible, its intended immediate use as input into SafetyAnalyst created some inconsistencies 

in the way certain elements of the system were defined. Perhaps most glaringly, the 

SafetyAnalyst data model does not contain a concept of interchanges (as distinct from 

intersections). This created a certain tension on two levels. First, it raised the question of 

whether or not to represent interchange elements at all. The project team resolved this issue 

as the absence of this data would represent unacceptably large gaps in the final deliverable. 

Second, the presence of these elements raised the issue of the applicability of a large portion of 

the MIRE intersection fields. While these issues were resolvable, that resolution often involved 

relaxing the MIRE definition by the requisite addition of certain enumerated values.

Inventory Development 

The initial creation of an intersection and leg inventory represented a significant hurdle in the 

early phases of this project. While WSDOT does possess an intersection geodatabase as part 

of its Roadway Datamart, the use of this dataset as a basis for the intersection inventory proved 

to be somewhat problematic. This dataset suffers from three significant issues: 
 MIRE MIS Lead Agency Data Collection Report 

50

1. Spatially coincident records – When two State routes meet at an intersection, they are 

represented by two spatially coincident records in the WSDOT intersection 

geodatabase. While reasonable as a matter of bookkeeping (particularly in the absence 

of a separate leg/approach table), this over-coverage fails to be useful from a data 

collection perspective. As the data collection process is GIS driven, such records fail to 

be functionally distinct from a user perspective. 

2. Diffuse interchange definition – The distinction between intersections and interchanges 

presented a challenge with respect to this dataset. Even accounting for the presence of 

spatially coincident records, the representation of interchanges as multiple intersection 

records for each road junction produced additional difficulties. The post-processing 

aggregation of these spatially distinct, but logically joined, records necessitated a simple 

first pass that aggregated nearby records and then a QA/QC pass that involved manually 

checking and removing incorrect associations from the aggregated intersection objects. 

3. Irrelevant junction entries – The rather late discovery of certain issues with the 

presence of intersection records that were simply right turn channelization junctions 

meant that an entire class of records became unusable. 

Automated Data Import 

Although the presence of multiple data sources helped the effort in terms of coverage, the 

format and consistency of WSDOT’s spatial referencing created some difficult challenges in 

matching the data to the existing intersection and leg inventory. These challenges necessitated 

the creation of customized import applications for data source in order to spatially match both 

point and linestring data to both intersections and legs (while using the attribute matching 

available to shore up gaps in the spatial matching methodology). 

Lessons Learned 

1. Development of a data dictionary: The process of refining the fields to be collected 

and the allowable values for those fields created the need for a data dictionary. This 

method of explicit field definition provided a consistent and traceable medium of 

information exchange between, and within, the institutions involved in this process, 

which allowed for the resolution of many questions raised by the data collection team. 

2. Institutional cooperation: The active involvement of WSDOT in this process was 

invaluable. The identification of existing data sources, as well as the dedicated work to 

convert existing LRS-referenced data sources into geospatially referenced data sources 

saved an immense amount of time and effort. 
 MIRE MIS Lead Agency Data Collection Report 

51

3. Design of the database: The early anticipation of the potential for multiple data 

sources allowed for a database design that made their representation trivial. While this 

created questions about the aggregation of these data points in the final deliverable, it 

allowed all data to be represented in the system at the time those decisions needed to 

be made. 

4. Use of third party data: The use of a third party to supply traffic data simplified the 

issue of traffic data collection. The available resources for this project precluded the 

possibility of manual collection. Moreover, the dataset provided by the third party 

contained a projected 2011 average daily traffic (ADT) value that was computed from 

disparate traffic measures. The decision to use a third party vendor for this data made 

its collection possible at minimal cost. 

5. Conversion of spatially referenced data: As mentioned above, some of the best 

data available were originally not spatially referenced. The project team initially 

assumed references to an LRS system to be problematic, given the high potential for 

naming differences to produce mismatch problems. During the assessment phase, 

WSDOT converted their Signal Maintenance Management System (SIMMS) database 

into a geospatially referenced dataset, greatly simplifying the process of associating data 

with our inventory using the same set of tools. 
 MIRE MIS Lead Agency Data Collection Report 

52

CONCLUSION 

The purpose of this effort was to test the feasibility of collecting MIRE data through a Lead 

Agency Program. Both NHDOT and WSDOT requested an intersection inventory for use in 

SafetyAnalyst, but requested slightly different variables. Having both agencies select similar 

elements provided the project team an opportunity to compare different data collection 

methodologies. The project team developed two different tools to collect these data, one 

simplified tool based on a GIS platform (for NHDOT) and one more sophisticated tool based 

on proprietary software (for WSDOT). 

There were similar lessons learned from both efforts, including: 

 MIRE flexibility: The primary goal of this effort was to test the feasibility of collecting 

MIRE data elements. Both NHDOT and WSDOT requested an intersection dataset to 

import and use in SafetyAnalyst. The project team developed data collection tools to 

populate a database to meet that goal. While the data elements selected were based on 

MIRE, the data collected required deviations from the MIRE data dictionary in order to 

tailor them for SafetyAnalyst. The flexibility allowed the resulting dataset to best meet 

the needs of the individual agencies. 

 Development of the Work Plan: The work plan provided a clear vision and 

approach for conducting the data collection. Developing the work plan at the onset of 

the project helped identify clear expectations on the part of the States and the project 

team. 

 Constant contact/feedback between the contractor and the State DOT:

Throughout the entire process, both transportation agencies were available to answer 

any questions and to provide clarification and feedback. This constant communication 

was key to developing a dataset that best met their needs. 

 Use of the sample dataset: The project team provided a sample dataset to both 

agencies to ensure there were no problems with the data. This allowed the agencies to 

identify any potential issues and the project team time to correct them before 

completing the data collection rather than having to go back and correct the data—thus 

saving valuable time, budget, and resources. 

 Use of existing data: The project team was able to derive many of the basic 

intersection inventory elements from existing data sources; utilizing existing data 

reduced the time needed for data collection. 
 MIRE MIS Lead Agency Data Collection Report 

53

There were also differences in the two data collection efforts. The largest differences arose due 

to the data collection tools themselves. The tool used for NHDOT (based on a GIS platform) 

took less time to develop, but did not have as many built-in analytical capabilities. The tool 

developed for WSDOT (based on proprietary software) had more built-in analytical capabilities, 

including tracking collection; however, it required more time and resources to develop. 

There were also differences in the approach. For the NH effort, the project team first 

imported all of the existing data into the intersection inventory, and then began collecting the 

needed remaining data. For the WSDOT effort, the project team imported the data into one 

version of the inventory and collected the data into another version, and then combined them 

together into one master version of the inventory. The benefit of this approach was that it 

allowed the project team to conduct the two data collection efforts concurrently (in New 

Hampshire they had to be done first one and then the other). However, there was more effort 

spent on the back-end trying to combine the two datasets. Agencies could employ either 

approach, depending on which method best fit their existing data, needs, resources, and 

schedule. 

The effort to develop an intersection inventory and the data collection tools, as well as 

determine the implications of the differences in the tools, challenges faced, and lessons learned, 

provides information that will be of critical importance to agencies when developing roadway 

inventories in the future. This information will help improve their roadway inventories to 

better support data-driven decision-making, improve the safety of roadways, and ultimately save 

lives. 
 MIRE MIS Lead Agency Data Collection Report 

54

REFERENCES 

1. American Associate of State Highway and Transportation Officials, Highway Safety Manual, 

http://www.highwaysafetymanual.org/. 

2. Federal Highway Administration, Interactive Highway Safety Design Model, 

http://www.fhwa.dot.gov/research/tfhrc/projects/safety/comprehensive/ihsdm/index.cfm. 

3. American Associate of State Highway and Transportation Officials, SafetyAnalyst, 

http://www.safetyanalyst.org/. 

4. Federal Highway Administration, Roadway Safety Data Partnership, Washington, D.C., 

accessed online August 2012, http://safety.fhwa.dot.gov/rsdp/. 

5. Lefler, N.; F. Council; D. Harkey; D. Carter; H. McGee; and M. Daul. Model Inventory of 

Roadway Elements – MIRE, Version 1.0. Federal Highway Administration, FHWA-HRT-10-048, 

Washington, D.C., October 2010, http://safety.fhwa.dot.gov/tools/data_tools/mirereport/. 

6. Sawyer, M.; N. Lefler; J. Soika; D. Carter; R. Scopatz; F. Gross; H. Rothenberg; J. Miller; G. 

Bahar; and K. Eccles. “United States Roadway Safety Data Capabilities Assessment,” Report No. 

FHWA-SA-12-028, Federal Highway Administration, Washington, D.C., July 2012, 

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

7. ESRI, “ArcGIS Desktop Street View AddIn,” ArcGIS Resource Center, accessed online 

August 2012, http://resources.arcgis.com/gallery/file/arcobjects-netapi/details?entryID=48F2BB6C-1422-2418-8822-C06E828584E8. 

8. “Building Your Own Tile Server”, Microsoft Developer Network Library, accessed online 

October 2012, http://msdn.microsoft.com/en-us/library/bb545006.aspx. 
 MIRE MIS Lead Agency Data Collection Report 

55

APPENDIX A—EDIT AND LOGIC CHECKS 

New Hampshire Intersection Inventory Edit/Logic Checks 

To ensure entry of appropriate values in the data fields, the project team prepared domains 

based on the SafetyAnalyst documentation to provide the user with a drop down menu of 

acceptable values, as opposed to manual entry that might not have been acceptable for use in 

SafetyAnalyst. The project team did not have any checks on open text box fields except for 

requiring a number value only (not text) where appropriate. The following sections describe 

the edit and logic checks built into the attributes, data entry form, and tools. 

Attributes/Data Entry Form 

1. All fields with a drop down box have set attributes to keep data collection consistent 

and accurate. 

2. Validation to ensure that numeric values are set for open text boxes that require a 

number. For instance, for Offset Distance, if a text value is entered the user will be 

alerted once they press OK on the form to save edits that the value is not allowed and 

a numeric value must be entered before the edits will be saved. 

3. Only the fields to be entered or verified by the data entry clerks were enabled; all other 

fields were disabled. Most of the fields that were disabled were determined from 

running the model. 

4. If the “X” in the top right corner of the attribute form is clicked to close the form 

before saving edits that were made, the user is prompted if they want to save the 

existing edits. If they select “Yes,” the edits are saved and the form is closed. If they 

select “No,” the form is closed without saving the edits. If “Cancel” is selected the form 

stays open and the edits are not saved. 

5. The attributes that are blank and editable are not required for SafetyAnalyst so there are 

no checks to ensure that those are populated. 

6. When a feature has been edited/verified, a field in the database is tagged as such so the 

project team could keep track of which records had been checked. 

7. When a new intersection is created a field in the database is tagged so when the model 

is run to update the attributes it will know which fields to update. 
 MIRE MIS Lead Agency Data Collection Report 

56

Tools 

1. Only one intersection form can be open at once so the user can focus on one 

intersection/leg area. If the user attempts to open a second intersection form they are 

asked if they want to save the edits to the first form before the second form will open. 

2. A feature (intersection or leg) is highlighted on the map when its associated form is the 

active form so the user knows which feature they are assigning attributes for. 

3. The layers must be added to the map document and sourced correctly in the XML file 

in order to edit intersections or legs; otherwise, an error message will pop up. 

4. When an intersection is deleted, all associated legs are deleted so that there are no legs 

without associated intersections. 

5. A node must be selected in order to create a new intersection. 

6. The update intersection tool will only update a specific set of attributes that may change 

from year to year. 

7. The update new intersection tool will only run against the new records in the database 

so as not to disturb the records that have already been edited and field verified. 

 
 MIRE MIS Lead Agency Data Collection Report 

57

Washington State Intersection Inventory Edit/Logic Checks 

The user interface of the MIDS tool had error checks built into the various data entry controls. 

The project team also conducted additional checks on the data collected through this tool to 

ensure that the data passed basic logic checks. These additional checks were performed as 

queries written against the SQL database where the data were stored. The objective of these 

queries was to isolate bogus data that were not detected at the time of collection. It was most 

efficient to batch process the queries and review the resulting errors all at once rather than 

process one query at a time. Most errors found with these logic checks were corrected 

through the MIDS tool. Some errors were corrected with SQL update statements. A list of 

the queries and corrective actions taken is as follows: 

1) Automatic corrections made with SQL update statements: 

a. If the number of exclusive right turn lanes was equal to zero then right turn 

channelization has to be NULL. 

b. If the approach has only one lane and it is an exclusive right turn lane then right turn 

channelization has to be ‘No’. 

c. Right Turn-on-Red prohibition was flagged by surveyors signaling that it should be 

NULL. 

d. Right/left turn prohibitions were flagged by surveyors signaling that it should be 

NULL. 

e. Once flagged fields were corrected the flags were erased to prevent confusion. 

2) Finding NULL records for fields that do not allow NULL values 

a. All valid intersection fields must not be NULL. 

b. The following approach fields must not be NULL. 

i. Traffic control. 

ii. Left turn phasing. 

iii. Directional flow. 

iv. Number of through lanes. 

v. Number of left exclusive turn lanes. 
 MIRE MIS Lead Agency Data Collection Report 

58

vi. Number of right exclusive turn lanes. 

3) Intersection field logic 

a. Urban/rural field checked if equals ‘Unknown’. 

b. Intersection type of ‘Ramp/ramp’ almost always has intersection geometry of ‘on 

ramp’ or ‘off ramp’. 

c. If intersection geometry is ‘on ramp’ or ‘off ramp’ then intersection type has to be 

either ‘Roadway/ramp’ or ‘Ramp/ramp’. 

4) Approach field logic 

a. Approach traffic control if NOT ‘Signalized’ requires: 

i. Right Turn-on-Red prohibitions is NULL. 

ii. Left turn protection is ‘N/A’. 

b. Approach traffic control is ‘Signalized’ requires: 

i. Left turn protection is not ‘N/A’. 

ii. Total number of lanes is greater than zero. 

c. If right/left turn prohibitions equals ‘No left allowed’ or ‘No right/left allowed’ then: 

i. Number of left exclusive turn lanes has to be zero. 

ii. Left turn protection equals ‘No protection’ for signalized approaches. 

d. If right/left turn prohibitions equals ‘No right allowed’ or ‘No right/left allowed’ then: 

i. Number of right exclusive turn lanes has to be zero. 

ii. Right Turn-on-Red prohibitions is NULL. 

e. If an approach has no through lanes and only exclusive left turn lanes then right/left 

turn prohibitions is ‘No right allowed’. 

f. If an approach has no through lanes and only exclusive right turn lanes then right/left 

turn prohibitions is ‘No left allowed’. 
 MIRE MIS Lead Agency Data Collection Report 

59

g. If number of right exclusive turn lanes is greater than zero then right channelization 

is NOT NULL. 

h. Intersections with a total number of lanes equal to zero has a directional flow of 

‘one-way’. 

i. Intersections should be checked if they have two approaches. Also check those with 

six or more approaches. 

j. No intersections should have one approach. 

k. Approach angle (bearing) should be between 0 and 360. 

5) Crossing tables to combine intersection and approach field logic: 

a. Intersection traffic control has to agree with the approach traffic control of at least 

one of the associated approaches. 

i. The intersection traffic control should be the highest form of control from 

the approaches. Examples: signalized, red flashing, and emergency signals 

take precedence over stop signs and uncontrolled. 

b. If total number of legs is three then the intersection geometry cannot be ‘four leg’ 

or ‘multi-leg’. 

c. If total number of legs is four then the intersection geometry cannot be ‘Tee’ or ‘Y’ 

i. Also unlikely to be ‘on ramp’ or ‘off ramp’. 

d. If total number of legs is greater than four then intersection geometry cannot be 

‘Tee’ or ‘Y’ or ‘Four-leg’. 

i. Also very unlikely to be ‘on ramp’ or ‘off ramp’. 
For More Information:

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

FHWA, Office of Safety

Robert Pollack Carol Tan, PhD

Robert.Pollack@dot.gov Carol.Tan@dot.gov

202-366-5019 202-493-3315

FHWA-SA-13-008