MIRE Element Collection Mechanisms 

and Gap Analysis

FHWA Safety Program

http://safety.fhwa.dot.gov
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 the data and identifyi ng optimal data file 

structures. The resulting products include reports on the findings from the MIRE-MIS Lead 

Agency Program, a MIRE Guidebook on the collection of MIRE, a suggested MIRE data file 

structure report and a report on Metrics to Assess Quality that will assist the states in conducting 

a more effective safety program. The intent of the MIRE-MIS proj ect is the integration of MIRE 

into States' safety management processes. 

The MIRE Element Collection Mechanisms and Gap Analysis report is one of the products of the 

MIRE-MIS effort This document presents the findings of the effort to explore existing and 

emerging data collection technologies to narrow the gaps between the elements in the MIRE 

listing and the current data available from agencies' inventories and supplementa l databases. 

This report will provide data managers and collectors with potential techniques for advancing 

future data collection of roadway and traffic inventory data. 

Michael S. Griffith 

Director, Office of Safety Technologies 

Monique R. Evans 

Director, Office of Safety Research and 

Development 
ii

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. 

 
iii

TECHNICAL DOCUMENTATION PAGE

1. Report No. 

FHWA-SA-11-49 

2. Government Accession 

No. 

3. Recipient's Catalog No. 

 

4. Title and Subtitle 

MIRE Element Collection Mechanisms and Gap Analysis 

5. Report Date 

September 2012 

6. Performing Organization Code 

7.Author(s) 

Jagannath Mallela, Suri Sadasivam, and Nancy Lefler 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 

Applied Research Associates 

4300 San Mateo Blvd. NE, Suite A-220 

Albuquerque, NM 87110 

Vanasse Hangen Brustlin, Inc (VHB) 

8300 Boone Blvd., Suite 700 

Vienna, VA 22182-2626 

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, August 2009 – September 

2012 

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 

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

roadways. FHWA developed the Model Inventory of Roadway Elements (MIRE) to provide a recommended model 

of what a comprehensive roadway and traffic data inventory should be for effective safety management. A critical 

step toward acceptance and implementation of MIRE is the conversion of MIRE, which is now a listing of variables, 

into a Management Information System (MIS). This report provides an assessment of State highway agencies’ 

roadway inventory data collection practices, identifying gaps in current practices and providing recommendations for 

filling those gaps in order to support the MIRE MIS effort. 

17. Key Words: 

safety data, MIRE, MIS, roadway inventory 

data, 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
51* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO Sl UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

in2 

ft2 

yd2 

square inches 

square feet 

square yard 

AREA 

645.2 square millimeters 

0.093 square meters 

0.836 square meters 

2 mm

m2 

m2 

ac 

mi2 acres square miles 

0.405 hectares 

2.59 square kilometers 

ha 

km2 

VOLUME 

fl oz fluid ounces 29.57 milliliters ml 

gal 

ft3 

gallons 

cubic feet 

3.785 liters 

0.028 cubic meters 

L 

m3 

yd3 cubic yards 0.765 cubic meters 3 NOTE: volumes greater than 1000 L shall be shown in m

m3 

MASS oz ounces 28.35 grams g 

lb pounds 0.454 kilograms kg 

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or''t") 

OF Fahrenheit 

TEMPERATURE (exact degrees) 

5 (F-32)/9 Celsius oc or (F-32)/1.8 

ILLUMINATION 

fc 

fl 

foot-candles 10.76 lux 

foot-Lamberts 3.426 candela/m2 

lx 

cd/m2 

FORCEandPRESSUREorSTRESS 

lbf 2 lbf/in

poundforce 4.45 newtons 

poundforce per square inch 6.89 kilopascals 

N 

kPa 

APPROXIMATE CONVERSIONS FROM Sl UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

2 mm

m2 

m2 

AREA 

square millimeters 0.0016 square inches 

square meters 10.764 square feet 

square meters 1.195 square yards 

in2 

ft2 

yd2 

ha 

km2 hectares 2.47 acres square kilometers 0.386 square miles 

ac 

mi2 

VOLUME 

ml milliliters 0.034 fluid ounces fl oz 

L 

m3 

liters 0.264 gallons 

cubic meters 35.314 cubic feet 

gal 

ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz 

kg kilograms 2.202 pounds lb 

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

oc 

TEMPERATURE (exact degrees) 

Celsius 1.8C+32 Fahrenheit OF 

ILLUMINATION 

IX lux 0.0929 foot-candles fc 2 cd/m 2 candela/m 0.2919 foot-Lamberts fl 

FORCEandPRESSUREorSTRESS 

N 

kPa 

newtons 

kilopascals 

0.225 poundforce 

0.145 poundforce per square inch 

lbf 2 lbf/in

' SI IS the symbol lor the International System or Units. Appropnate round1ng should be made to comply w1th Sect1on 4 or ASTM E380. 

(ReVISed March 2003) 

iv
v

TABLE OF CONTENTS 

EXECUTIVE SUMMARY ................................................................................................ IX

CHAPTER 1: BACKGROUND......................................................................................... 1

CHAPTER 2: MODEL INVENTORY OF ROADWAY ELEMENTS ............................ 3

CHAPTER 3: MIRE MANAGEMENT INFORMATION SYSTEM ................................ 5

CHAPTER 4: REVIEW OF DATA INVENTORIES OF STATE DOTS ....................... 6

HSIS and Non-HSIS Data Inventories .....................................................................................................7

Summary of MIRE Workshop Polls ...................................................................................................... 11

NCHRP Safety Data Collection Survey .............................................................................................. 14

HPMS Data Inventory .............................................................................................................................. 14

Gap Analysis of MIRE Data Elements .................................................................................................. 18

CHAPTER 5: POTENTIAL SUPPLEMENTAL DATA SOURCES ............................ 22

Pavement Management Systems ........................................................................................................... 22

Roadway Hardware Management Systems ......................................................................................... 24

Supplemental Databases ......................................................................................................................... 27

CHAPTER 6: TECHNOLOGIES FOR COLLECTION OF ROADWAY 

INVENTORY DATA ....................................................................................................... 29

Need for Advanced Technologies ........................................................................................................ 29

Data Collection and Presentation Technologies ............................................................................... 29

Use of Automation in Roadway Inventory Data Collection .......................................................... 35

CHAPTER 7: COMPATIBILITY OF COLLECTION TECHNOLOGIES WITH MIRE 

ROADWAY ELEMENTS ................................................................................................ 38

CHAPTER 8: CONCLUSIONS AND RECOMMENDATIONS .................................. 53

Recommendations .................................................................................................................................... 55

REFERENCES .................................................................................................................. 56
vi

LIST OF TABLES 

Table 1. Number of MIRE elements in HSIS State databases. ................................................................ 9

Table 2. Number of MIRE elements in non-HSIS State databases. ..................................................... 10

Table 3. MIRE workshop poll summary for roadway segment descriptors. .................................... 12

Table 4. MIRE workshop poll summary for roadway alignment descriptors. .................................. 13

Table 5. MIRE workshop poll summary for roadway junction descriptors. ..................................... 13

Table 6. Commonality between the HPMS dataset and MIRE. ........................................................... 17

Table 7. Number of States that have an inventory and tracking system for roadway hardware 

assets. ............................................................................................................................................................... 24

Table 8. State collection of roadway hardware asset inventory. ........................................................ 26

Table 9. Inventory of selected assets. ....................................................................................................... 27

Table 10. Number of agencies using automated pavement distress data collection. ..................... 36

Table 11. Potential of technologies to collect MIRE elements. ........................................................... 40

LIST OF FIGURES 

Figure 1. Frequency of data elements in pavement management inventory. ................................... 23

Figure 2. Frequency of pavement condition data availability. ............................................................... 23

Figure 3. Methods used for original inventory of roadway hardware assets. .................................. 25

Figure 4. Schematic representation of the airborne LIDAR system. ................................................. 31

Figure 5. Caltrans DHIPP image showing roadway features. ............................................................... 35
vii

ACRONYMS 

3D Three-dimensional 

AADT Average annual daily traffic 

AASHTO American Association of State Highway and Transportation Officials 

AHMCT Advanced Highway Maintenance and Construction Technology Research Center 

C&P Condition and performance 

Caltrans California Department of Transportation 

CARMS Centre for Applied Remote Sensing, Modeling, and Simulation 

CFR Code of Federal Regulations 

DHIPP Digital Highway Inventory Photography Program 

DHMS Digital Highway Measurement System 

DMI Distance measuring instruments 

DOT Department of Transportation 

EGM96 Earth Gravitational Model of 1996 

FDE Fundamental Data Elements 

FHWA Federal Highway Administration 

G-Rail Guardrail Management System 

GIS Geographic information system 

GPS Global positioning system 

HMMS Highway Maintenance Management System 

HOV High-occupancy vehicle 

HPMS Highway Performance Monitoring System 

HSIP Highway Safety Improvement Program 

HSIS Highway Safety Information System 

HSM Highway Safety Manual 

ICAS Inventory and Condition Assessment System 

INS Inertial navigation systems 

IRI International Roughness Index 
viii

KML Keyhole Markup Language 

LIDAR Light detection and ranging 

MIRE Model Inventory of Roadway Elements 

MIS Management information system 

MMS Maintenance management system 

MMUCC Model Minimum Uniform Crash Criteria 

N.A. No additional collection needed 

NASA National Aeronautics and Space Administration 

NCHRP National Cooperative Highway Research Program 

NHS National Highway System 

OAV Oblique Aerial View 

PMS Pavement management system 

PSR Pavement Serviceability Rating 

RFI Road Feature Inventory 

RFIP Roadside Features Inventory Program 

STRAHNET Strategic Highway Network 

TIAMS Transportation infrastructure asset management system 

TMG Traffic Monitoring Guide 

TMS Traffic Monitoring System 

WGS84 World Geodetic System of 1984 

XML Extensive Markup Language 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

ix

EXECUTIVE SUMMARY 

The Highway Safety Improvement Program (HSIP) is a core program of the Federal Highway 

Administration (FHWA) and the agency’s principal safety program. The HSIP requires that 

States use a data-driven process in selecting and implementing effective countermeasures for 

reducing fatalities and serious injuries on public roads. Regulations governing the HSIP (23 Code 

of Federal Regulations [CFR] Part 924) specify that the HSIP planning process shall incorporate 

the collection and analyses of crash, roadway, and traffic data on all public roads (1). Crash data 

have long been a key component of State safety planning processes. However, fewer States 

have demonstrated an ability to integrate roadway inventory and traffic data with crash data to 

conduct more effective safety analyses for their safety programs. 

The FHWA recognizes that current trends of constrained resources, competing priorities, and 

shrinking workforces will likely be the norm for at least the near future. States will need to find 

better ways to identify, prioritize, and treat safety problems to address these issues. One 

potential solution is for States to enhance their capabilities regarding the collection, 

maintenance and use of roadway data as part of normal business practices. 

The FHWA encourages States to collect and use roadway inventory and traffic data in their 

analytical processes for safety and other transportation programs. Several tools and 

methodologies are available to conduct more rigorous approaches for traffic safety analyses and 

countermeasure selection. These include tools developed at the national level such at the 

Highway Safety Manual (HSM) (2), as well as many tools that States have developed in-house. 

These tools require detailed crash, roadway, and traffic data to achieve the most accurate 

results. 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 (3). For cases where resource limitations 

prohibit the collection of the total list of MIRE data elements on all public roads, FHWA issued 

the “Guidance Memorandum on Fundamental Roadway and Traffic Data Elements to Improve 

the Highway Safety Improvement Program” on August 1, 2011 (4). This memorandum identifies 

a subset of MIRE elements - the Fundamental Data Elements (FDE) - that FHWA recommends 

States collect to help support their safety programs. This guidance notwithstanding, FHWA still 

recommends that States collect as many of the MIRE data elements as possible on as many 

roadways as possible. 

MIRE MIS PROJECT 

To assist States in developing and integrating the MIRE into a management information system 

structure that will provide greater utility in collecting, maintaining, and using MIRE data, FHWA 

has undertaken the MIRE Management Information System (MIRE MIS) project. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

x

This report provides an assessment of State highway agencies’ roadway inventory data 

collection practices, identifying gaps in current practices and providing recommendations for 

filling those gaps in order to support States’ implementation of the MIRE MIS. The research 

team assessed State roadway inventory data collection practices by reviewing Highway Safety 

Information Systems (HSIS) data from nine States and roadway inventory databases from 14 

States. The results of this review are based on information readily available to the project team 

and not an exhaustive review of each States’ entire data systems. Most of the databases contain 

administrative information, such as location, classification, pavement cross section, and traffic 

volume, and a few contain information on alignment features. However, these databases lack 

information on elements such as intersections, interchanges, circular intersections, ramps, signs, 

rumble strips, sidewalks, pedestrians, bicycles, and motorcycles. Overall, the HSIS and non-HSIS 

databases contain only one-sixth to one-third of the MIRE data elements. The FHWA polled 

safety professionals during a series of MIRE Webinars held in 2009 and 2010 and found that the 

data elements considered most difficult to collect tend to be those that are most often missing 

from data inventories. 

The research team also reviewed data elements collected for the Highway Performance 

Monitoring System (HPMS) to identify other possible sources for MIRE data. The HPMS 

requires States to collect roadway characteristic data on Federal-aid highways and report those 

data to FHWA. Federal-aid highways include all functional systems excluding rural minor 

collectors and locals. FHWA uses HPMS data for several purposes including apportioning 

Federal-aid funds back to the States. Although FHWA only requires States collect HPMS data 

on Federal-aid roads, and safety programs focus on all public roads, there often is a strong 

correlation between HPMS data requirements and State highway agency data collection 

practices. In this sense, the HPMS data can help in understanding future trends in data 

availability in State databases. The Full Extent dataset of HPMS contains data for 21 MIRE 

elements, while the Sample dataset contains data for 24 MIRE elements. This does not cover 

the entire public roadway system unless data collection practices change over time, but 

collection of these sample variables may lead to collection of the same variables on a wider 

network basis. 

The researchers conducted a literature search to identify potential supplemental databases that 

contain information on MIRE data elements. Literature on States’ pavement management 

practices confirmed that a majority of States collect and store pavement condition data (e.g. 

roughness and surface friction) in their databases. Some States also collect rumble strip 

information. Only one-third of the States use hardware management systems for signs, signals, 

guardrails, and lighting. States maintain these databases either as a separate system or as part of 

a broader management system. While the supplemental databases cannot supply all the MIRE 

information for safety analysis, they could serve as alternate sources for some categories of 

MIRE elements. Many of the elements in the MIRE listing either do not exist in current database 

systems or are contained in a series of disparate systems that are not accessed easily. Agencies 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

xi

interested in collecting MIRE elements would need to collect data not found in existing datasets 

or, in some cases, compile data that may already exist but cannot be integrated with other 

datasets. 

The research team evaluated the potential for existing and emerging data collection 

technologies to narrow the gaps between the elements in the MIRE listing and the current data 

available from agency inventory and supplemental databases. Many State agencies use semi- or 

fully automated systems for data collection. In semi-automated systems, only the data collection 

is fully automated through the use of imaging and/or sensor technologies, with the reduction of 

raw data into useful information being done manually by the operator. In fully automated 

systems, both data collection and processing are automated using software with no or minimal 

manual intervention. Among the automated systems in practice, the mobile mapping systems 

have emerged as a promising choice for automated data collection. 

While these systems have the flexibility to accommodate a custom set of geo-referencing and 

descriptive technologies, and can collect data at highway speeds, post-processing data and 

cross-referencing to other linear referencing systems still appear to be time-consuming and 

labor-intensive. Technologies such as light detection and ranging (LIDAR) and satellite imagery 

show promise. There are continual improvements in technical capabilities, such as the 

availability of high-resolution imagery that should help overcome any current limitations. The 

Keyhole Markup Language (KML) browsers, including Google Earth and Bing, could serve as an 

intuitive visualization tools for data presentation in roadway inventory applications. 

The research team evaluated the ability of four existing and emerging technologies to collect 

the required information for MIRE data elements: mobile mapping, airborne LIDAR, terrestrial 

LIDAR, and satellite imagery. While these technologies can collect most of the MIRE data 

elements, they are not without their limitations. Moreover, there are no accepted industry 

standards at this time that establish the desired accuracy of roadway measurement systems. 

Transportation agencies will need to conduct significant work in this regard. The airborne 

technologies cannot collect information on signs, whereas the terrestrial technologies have 

issues with collecting data on steep slopes. Neither can identify smaller objects, such as the 

pedestrian signalization type. The system accuracy, defined by either image resolution or point 

density of laser scans, is a critical factor in selecting an appropriate technology for inventory 

data collection. Irrespective of the technology used for data collection, significant postprocessing efforts are anticipated for data extraction. Terrestrial LIDAR systems have 

demonstrated the potential to collect many of the MIRE elements through case studies. 

Collecting additional roadway safety data can be a large undertaking. Based on the results of 

this effort, the research team developed several recommendations to help States implement a 

MIRE MIS. States should consider using available information systems as an alternative source of 

data. Integrating the available data systems and data needs within the agency will help minimize 

the data collection and processing efforts of various internal departments. States should also 

standardize data collection methods to ensure quality and simplify transfer between information 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

xii

systems. While there are multiple technologies available to assist States in data collection, 

public and private sectors should explore the development of automated techniques for 

extracting inventory feature data from advanced collection techniques into a usable database 

format. These steps can help States implement a MIRE MIS to help support their safety 

programs through data-driven decision-making. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

1

CHAPTER 1: BACKGROUND 

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

and safety of roadways. While crash data have been a consistent element of highway safety 

analysis, in recent years there has been an increased focus on the combination of crash, 

roadway, and traffic data to make more precise and prioritized safety decisions. The application 

of advanced highway safety analysis processes and tools requires a comprehensive inventory of 

roadway safety data combined with crash data to better identify and understand problems, 

prioritize locations for treatment, apply appropriate countermeasures, and evaluate the 

effectiveness of those countermeasures. Comprehensive roadway safety data include 

information on roadway and roadside features, traffic operations, traffic volumes, and crashes. 

In search of new strategies for an improved safety information system, the Federal Highway 

Administration (FHWA), the National Cooperative Highway Research Program (NCHRP), and 

the American Association of State Highway and Transportation Officials (AASHTO) sponsored 

a scanning tour to study the existing systems in Europe and Australia. The expert scan team 

identified the following strategies to improve traffic safety information in the U.S. (5): 

 Increase support for both safety programs and safety information systems from top-level 

administrators in State and local transportation agencies. 

 Improve safety data by defining good inventory data and institutionalizing continual 

improvement toward established performance measures. 

 Improve safety data by making them easier to collect, store, and use. 

 Improve safety data by increasing the use of critical safety analysis tools, which 

themselves require good data. 

 Improve and protect safety data by storage and linkage with critical non-safety data. 

To develop good safety inventory data, the expert scan team recommended a list of roadway 

inventory and operations data elements as a companion to the Model Minimum Uniform Crash 

Criteria (MMUCC) (6). Furthermore, Federal safety programs support data-driven decisionmaking. For example, the Highway Safety Improvement Program (HSIP) requires that States use 

a data-driven process in selecting and implementing effective countermeasures for reducing 

fatalities and serious injuries on public roads. Regulations governing the HSIP (23 Code of 

Federal Regulations [CFR] Part 924) specify that the HSIP planning process shall incorporate 

the collection and analyses of crash, roadway, and traffic data on all public roads (1). To this 

end, FHWA developed the Model Inventory of Roadway Elements (MIRE), a recommended 

listing and data dictionary of roadway and traffic data elements critical to supporting highway 

safety management (3). 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

2

For cases where resource limitations prohibit the collection of the total list of MIRE data 

elements on all public roads, FHWA issued the “Guidance Memorandum on Fundamental 

Roadway and Traffic Data Elements to Improve the Highway Safety Improvement Program” on 

August 1, 2011 (4). This memorandum identifies a subset of MIRE elements—the Fundamental 

Data Elements (FDE)—that FHWA recommends the States collect to help support their safety 

programs. This guidance notwithstanding, FHWA still recommends that States collect as many 

of the MIRE data elements as possible on as many roadways as possible. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

3

CHAPTER 2: MODEL INVENTORY OF ROADWAY ELEMENTS 

MIRE is a recommended listing and associated data dictionary of roadway inventory and traffic 

elements that are critical to the safety management of highways. The initial version of MIRE was 

released in 2007 (7), and a revised version, MIRE Version 1.0, was released in late 2010 (3). 

MIRE Version 1.0 consists of 202 data elements divided into the following categories: 

I. Roadway Segment Descriptors: 

a. Segment Location/Linkage Elements. 

b. Segment Roadway Classification. 

c. Segment Cross Section. 

i. Surface Descriptors. 

ii. Lane Descriptors. 

iii. Shoulder Descriptors. 

iv. Median Descriptors. 

d. Roadside Descriptors. 

e. Other Segment Descriptors. 

f. Segment Traffic Flow Data. 

g. Segment Traffic Operations/Control Data. 

h. Other Supplemental Segment Descriptors. 

II. Roadway Alignment Descriptors: 

a. Horizontal Curve Data. 

b. Vertical Grade Data. 

III. Roadway Junction Descriptors: 

a. At-Grade Intersection/Junctions. 

i. At-Grade Intersection/Junction General Descriptors. 

ii. At-Grade Intersection/Junction Descriptors (Each Approach). 

b. Interchange and Ramp Descriptors. 

i. General Interchange Descriptors. 

ii. Interchange Ramp Descriptors. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

4

FHWA recognizes that current trends of constrained resources, competing priorities, and 

shrinking workforces will likely be the norm for at the least the near future. In order to address 

these issues, States need to find better ways to identify, prioritize, and treat safety problems. 

One potential solution is for States to enhance their capabilities regarding the collection, 

maintenance, and use of roadway data as part of normal business practices. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

5

CHAPTER 3: MIRE MANAGEMENT INFORMATION SYSTEM 

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

(which is now a listing of variables) into a management information system (MIS). To assist 

States in developing and integrating the MIRE into an MIS structure that will provide greater 

utility in collecting, maintaining, and using MIRE data, FHWA has undertaken the MIRE MIS 

project. 

The proposed MIRE MIS design will include the exploration, development, and documentation 

of the following:

 Mechanisms for data collection. 

 A highly efficient process for data handling and storage. 

 Details of data file structure. 

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

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

This report represents the first step in the MIS effort—mechanisms for data collection. The 

purpose of this report it to provide an assessment of roadway inventory data collection efforts 

by State Departments of Transportation (DOTs), identify gaps in current practices, and provide 

recommendations for filling those gaps in order to support States’ implementation of the MIRE 

MIS. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

6

CHAPTER 4: REVIEW OF DATA INVENTORIES OF STATE DOTS 

Existing roadway inventory and traffic data serve as potential resources agencies could use 

when developing a data management inventory system for MIRE. Therefore, it is necessary to 

review DOT roadway data collection and inventory practices and identify whether MIRE 

elements exist in the DOT’s databases. The research team reviewed the databases of 9 States 

that have contributed to the Highway Safety Information System (HSIS) and reviewed the 

roadway inventories of 14 non-HSIS States. The team also reviewed the data requirements as 

outlined in the “Highway Performance Monitoring System (HPMS) Reassessment 2010+ Data 

Specifications” (8). In addition, the researchers conducted a literature review to obtain 

information on whether there are supplemental databases—such as pavement and other asset 

management databases—that could supply any of the MIRE data elements. Finally, the team 

compiled information on emerging automated roadway data collection methodologies to 

understand future possibilities with regard to the MIRE dataset. The research team explored 

methodologies with varying levels of automation. In fully automated systems, both data 

collection and processing are automated using software with no or minimal manual 

intervention. In semi-automated systems, the data collection is only wholly automated through 

the use of imaging and/or sensor technologies, while an operator manually reduces the raw data 

to useful information. 

The research team reviewed the roadway inventory databases of 23 DOTs to ascertain 

whether they contain the MIRE data elements. The review included only the inventories that 

were readily available to the research team, such as the FHWA’s HSIS database, and did not 

include all data systems maintained by each of the State agencies. The HSIS is a multistate safety 

database that contains crash data, roadway inventory data, traffic volume data, and other 

inventory-related elements, such as intersections, interchanges, and roadside hardware. The 

States that have provided inventory data to the HSIS database include California, Illinois, Maine, 

Michigan, Minnesota, North Carolina, Ohio, Utah, and Washington. (Michigan and Utah are no 

longer active contributors due to changes in their data systems.) The HSIS databases are known 

to have extensive roadway inventory data and should not be considered as representative 

samples. The research team also reviewed 14 non-HSIS States roadway inventory databases, 

which included Arizona, Arkansas, Florida, Indiana, Maryland, Massachusetts, Mississippi, 

Missouri, New Hampshire, Oklahoma, Pennsylvania, South Carolina, Texas, and Virginia. The 

results presented in this section reflect only an overview of the inventory information that was 

readily available to the research team as of late 2009, as this is not an exhaustive review of all 

current data systems maintained by all State agencies. 

In addition to reviewing the inventory databases of HSIS and non-HSIS States, the researchers 

investigated the following sources of information to further understand the extent of 

information that may be available to support the development of a MIRE MIS. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

7

 NCHRP 20-5/Topic 36-03, NCHRP Synthesis 367: Technologies for Improving Safety Data. 

This included a survey conducted to document the roadway data inventory practices of 

20 State DOTs (8). 

 Documentation on reporting requirements of HPMS. Although it is expected that most 

of the HPMS data pertinent to MIRE will be collected on a sample basis, the future data 

collection plans were reviewed because of the strong tendency of DOTs to collect full 

extent and sample data required by HPMS on a wider network basis (8).

HSIS AND NON-HSIS DATA INVENTORIES 

Table 1 and Table 2 summarize the MIRE data elements available in the HSIS databases of the 9 

participating States and the roadway inventories of the 14 non-HSIS States, respectively. The 

tables show that these databases contain only about one-sixth to one-third of the elements in 

the MIRE listing. The databases contain a fair amount of roadway segment elements but lack 

most of the alignment and junction descriptors. 

The key findings of the database review can be summarized as follows: 

 A majority of databases include data elements in the roadway segment location/linkage 

subcategory, except for government ownership, coinciding routes, and direction of 

inventory. Most HSIS databases have data for route signing and route signing qualifiers. 

 Most databases have roadway classification elements, such as highway functional class, 

urban/rural designation, and Federal-aid or National Highway System (NHS) route type. 

 In the surface description subcategory, information on surface type and width exists in 

the majority of the databases. Only a very few HSIS databases have data on pavement 

condition and roughness, whereas several of the non-HSIS roadway inventories have the 

same information. Among the non-HSIS State databases, only Indiana and Pennsylvania 

include surface friction in their databases. 

 In the lane description subcategory, most States include the number of through lanes; 

only a few include the presence of auxiliary lanes, reversible lanes, high-occupancy 

vehicle (HOV) lanes, and number of peak period through lanes. None of the States 

include cross slopes and lengths of auxiliary lanes. Only Washington records bike lanes 

and facilities in its HSIS database. Only 6 of the 23 States record the width of inner and 

outer lanes, while others record the average or total width of all lanes by direction. 

 In the shoulder description subcategory, most States record shoulder type and width. A 

few States record the presence of curb but not the curb type, and none of the States 

record data on rumble strips. Five non-HSIS States and Minnesota (in the HSIS group) 

record the presence of sidewalks. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

8

 Most State databases have data elements for median width and type; a few have 

information on median barrier type, but none have data elements for rumble strips, side 

slopes, inner paved shoulder width, or crossover/left lane turn types. 

 None of the States have roadside elements that include clear zone width, side slopes, 

and driveways. Washington’s Roadside Features Inventory Program (RFIP) collects 

information on roadside slopes and ditches. 

 In the other segment descriptors category, approximately half of the States’ databases 

contain roadway terrain type. Only Florida, New Hampshire, and Utah have the number 

of intersection types in a roadway segment (i.e., the number of signalized, stopcontrolled, or uncontrolled intersections in a roadway segment). 

 In the traffic flow category, the databases of most States contain traffic volume data; 

however, only a few databases contain information on traffic characteristics such as 

percent trucks, traffic growth and forecasts, and directional and K-factors. None of the 

State databases contain information on hourly traffic volumes or counts of motorcycles, 

bicycles, or pedestrians. 

 In the traffic operations/controls subcategory of roadway segments, several States 

include data on one-way/two-way operations, speed limits, and truck route designations. 

Nearly all databases lack data on the presence of edgelines, centerlines, passing zone 

percentage, 85th percentile speed, nighttime speed limit, and truck speed limit. Only a 

few databases contain information on school zone indicator, presence/type of on-street 

parking, and toll facilities. Only Mississippi includes roadway lighting in its inventory. 

 In the roadway alignment category, only four of the nine HSIS States provide data on 

horizontal curves that include the curve length, radius, and identifiers. Similarly, only 

three of the nine HSIS States have data on vertical alignment and grades. In the nonHSIS group, only Florida has data on horizontal curve radius, identifiers, deflection angle, 

and vertical alignment feature type. Most States do not include superelevation, transition 

curves, deflection angles, or length of vertical grade and horizontal curves. In general, 

the State databases lack roadway alignment elements. 

 In the roadway junction category, the majority of HSIS databases contain limited data on 

one or more of the three junction descriptors: interchanges, intersections, and ramps. 

These States have limited data on descriptors such as location identifiers, number of 

lanes, traffic volume, and traffic control. Only two have full intersection inventory files. 

None of the HSIS databases have data to describe pedestrian features, turn counts, and 

turn prohibitions, rumble strips, intersection geometry, and circular intersections. Only 

a few HSIS States have data on location identifiers and traffic volume on interchanges 

and ramps. None of the non-HSIS databases includes junction-related data elements. 
 MIRE ELEMENT COLLECTION MECHANISMS 

9

Table 1. Number of MIRE elements in HSIS State databases. 

MIRE Data Subcategories 

(total elements in a subcategory) 

Number of Elements for MIRE Inventory in 

Databases by State 

NC CA IL ME MI MN OH UT 

I. ROADWAY SEGMENT DESCRIPTORS (Total number of MIRE Elements = 106) I.a. Segment location/linkage variables (18) 12 9 12 10 8 8 16 9 I.b. Segment roadway classification (4) 4 4 4 4 3 4 4 4 

I.c. Segment cross-section (39) 13 14 16 7 12 17 15 7 

I.d. Segment roadside descriptors (13) 0 0 0 0 0 0 0 0 

I.e. Other segment descriptors (4) 1 1 0 0 1 0 0 4 

1.f. Segment traffic flow data (12) 4 1 2 2 2 3 3 4 I.g. Segment traffic operations/control data (15) 3 2 3 1 4 4 3 5 

I.h. Other supplemental descriptors (1) 0 0 0 0 0 0 0 0 

II. ROADWAY ALIGNMENT DESCRIPTORS (Total number of MIRE Elements = 13) II.a. Horizontal curve data (8) 0 0 5 0 4 0 4 0 II.b. Vertical grade data (5) 0 0 0 0 0 0 4 0 

III. ROADWAY JUNCTION DESCRIPTORS (Total number of MIRE Elements = 83) III.a. At-grade intersection/junctions (58) 0 23 1 12 2 22 4 1 

III.b. Interchange and ramp descriptors (25) 0 4 0 9 0 7 3 0 Total Number of Elements (202) 37 58 43 45 36 65 56 34 
 MIRE ELEMENT COLLECTION MECHANISMS 

10

Table 2. Number of MIRE elements in non-HSIS State databases.

MIRE Data Subcategories 

(total elements in a subcategory)

Number of Elements for MIRE Inventor

Other State Databases by State

FL MO NH OK MS SC TX IN VA AR M

I. ROADWAY SEGMENT DESCRIPTORS (Total number of MIRE Elements = 106) I.a. Segment location/linkage variables (18) 9 9 8 9 11 7 14 8 5 8 

I.b. Segment roadway classification (4) 3 2 3 3 4 2 3 2 2 4 

I.c. Segment cross-section (39) 18 8 9 10 10 2 12 8 13 13 

I.d. Segment roadside descriptors (13) 0 0 0 0 0 0 0 0 0 0 I.e. Other segment descriptors (4) 4 0 2 1 0 0 0 0 0 1 

1.f. Segment traffic flow data (12) 5 2 0 1 5 1 7 1 0 2 

I.g. Segment traffic operations/control data (15) 3 1 3 2 5 2 4 1 2 1 

I.h. Other supplemental descriptors (1) 1 1 1 0 0 0 0 0 0 1 

II. ROADWAY ALIGNMENT DESCRIPTORS (Total number of MIRE Elements = 13) II.a. Horizontal curve data (8) 3 0 0 0 0 0 0 0 0 0 

II.b. Vertical grade data (5) 1 0 0 0 0 0 0 0 0 0 III. ROADWAY JUNCTION DESCRIPTORS (Total number of MIRE Elements = 83) III.a. At-Grade intersection/junctions (58) 5 2 2 1 0 0 0 2 0 0 

III.b. Interchange and ramp descriptors (25) 3 1 0 0 0 0 0 2 0 1 Total Number of Elements (202) 55 26 28 27 35 14 40 24 22 31 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

11

SUMMARY OF MIRE WORKSHOP POLLS 

FHWA hosted a series of workshops from November 2009 through January 2010 (via 

Webinar) to present the proposed MIRE elements and receive feedback from safety 

professionals and roadway data inventory specialists in State and local highway agencies. The 

MIRE elements were organized by topic and were presented in each of the three workshops: 

roadway segment and alignment variables, intersection-related variables, and interchange and 

ramp variables. For each of the workshops, the MIRE elements were grouped into several 

categories, and the participants were asked to respond to two questions: 

1) Which elements are very important to your safety program/decisions? 

2) Which elements will be very difficult to collect? 

Table 3 through Table 5 summarizes the participant responses. Notably, most elements that the 

participants considered important to safety analyses were the ones identified as difficult to 

collect and were unavailable in the HSIS and non-HSIS data. This implies that States may lack 

critical roadway inventory data required to make effective decisions in highway safety 

management, further confirming the necessity for States to address these data gaps and 

overcome difficulties in their current data collection practices. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

12

Table 3. MIRE workshop poll summary for roadway segment descriptors.

Descriptors Category Elements Difficult to Collect Elements Important to 

Safety Programs 

Segment roadway 

classification and surface 

 Surface friction  Surface friction 

Lane  Width of marked bicycle lane 

or bike path 

 Auxiliary lane length 

Shoulder 



Sidewalk presence 

Rumble strip type 





Right/left paved shoulder width 

Rumble strip presence 

Median 





Median rumble strip type 

Median (inner) paved shoulder 

width 





Median shoulder rumble strip 

presence 

Median (inner) paved shoulder 

width 

Segment roadside 







Side slope roadside rating 

Roadside rating 

Roadside clear zone width 





Roadside clear zone width 

Driveway information 

 Driveway information 

Other segment 

descriptors 





Number of signalized 

intersections in sections 

RR grade crossing 





Number of uncontrolled / 

other intersections 

Number of stop-controlled 

intersections in segments 

Traffic flow data 





Total daily two-way pedestrian 

count/ exposure 

Bicycle count/exposure 





Hourly traffic volumes 

Percent combination trucks 

Traffic operations/ 

control data 





85th percentile speed 

Roadway lighting 







85th percentile speed 

Roadway lighting 

Edgeline presence/type 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

13

Table 4. MIRE workshop poll summary for roadway alignment descriptors. 

Descriptors Category Elements Difficult to Collect Elements Important to 

Safety Programs 

 Curve superelevation or  Curve superelevation 

superelevation adequacy  Curve identifiers and linkage 

Horizontal curve data variables 

 Curve feature type 

 Horizontal curve length 

Vertical grade data  Percent of gradient  Percent of gradient 

Table 5. MIRE workshop poll summary for roadway junction descriptors. 

Descriptors Category Elements Difficult to Collect Elements Important to 

Safety Programs 

Intersection – general 

(identifiers) 

--  Location identifiers for road 

1 & 2 crossing point 

Intersection – general 

(geometry) 

 Intersection skew angle  Intersection/junction 

geometry 

Intersection/junction – 

general (traffic control) 

 Number of quadrants with 

limited sight distance 





Traffic control 

Number of quadrants with 

limited sight distance 

Intersection/junction – 

general (circular 

intersections) 





Bicycle facility 

Circulatory width 

 Number of circulatory lanes 

Intersection approach  Length of exclusive left/right 

turn lanes 

 Approach average annual 

daily traffic (AADT) 

Intersection approach 





Crossing pedestrian count/ 

exposure 

Signal progression 

 Approach traffic control 

Intersection approach  Left/right turn counts/ percent 



Left/right turn counts/percent 

Left/right turn prohibitions 

Intersection approach 

(circular intersections) 

 Entry/exit radius  Number of entry/exit lanes 

General interchange 

descriptors 

 Interchange lighting 



Unique intersection identifier 

Interchange type 

Interchange ramp 





Ramp AADT 

Ramp posted speed limit 







Ramp AADT 

Unique ramp identifier 

Ramp length 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

14

NCHRP SAFETY DATA COLLECTION SURVEY 

NCHRP 20-5/Topic 36-03, NCHRP Synthesis 367: Technologies for Improving Safety Data appraised 

state-of-the-practice and state-of-the-art use of technologies for the efficient and effective 

collection and maintenance of roadway inventory, crash, and traffic operations data for highway 

safety analyses (8). Researchers obtained responses from 20 States, 11 of which are included in 

the list of 23 State databases reviewed for this study. The inventory data defined in the NCHRP 

Synthesis 367 survey included roadway structure elements, cross-sectional elements, geometric 

elements, traffic control devices, and pavement-related information. 

Some of the key findings of this survey are as follows: 

 Most States collect bridge and pavement elements using automated data collection 

systems. 

 Some of the key design elements that are critical to safety analyses were missing in 

roadway inventory databases. For example, fewer than half of the responding States 

collected roadway geometric elements (i.e., horizontal and vertical curvature) 

comprehensively. Most States collect this information as samples or on an as-needed 

basis. 

 While most States collected some cross-sectional elements pertaining to the lane, 

shoulder, and medians, other cross-sectional elements (e.g., barriers and cross slopes) 

were underreported. Data elements for signs, signals, and pavement markings were 

collected sporadically. 

 Generally speaking, States collect the required data more comprehensively and 

efficiently for data collection programs that are Federally mandated (8). 

The findings of this study are similar to the review of the 23 State databases discussed in the 

previous sections. 

HPMS DATA INVENTORY 

The HPMS is a national-level highway information system program that provides data on the 

extent, condition, performance, use, and operating characteristics of U.S. highways. The FHWA 

Office of Highway Policy Information manages the HPMS. Through the HPMS program, FHWA 

requires that States collect roadway characteristic data on Federal-aid highways and report 

those data to FHWA. Federal-aid highways include all functional systems excluding rural minor 

collectors and locals. Currently, the HPMS database contains over 110,000 highway sample 

segments. The major purpose of the HPMS is to support a data-driven decision-making process 

at the national, state and local level for meeting the Nation’s transportation needs. FHWA uses 

the HPMS data for highway system performance assessment, condition and performance (C&P) 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

15

reporting, apportionment of Federal-aid highway funds, reporting of highway statistics, and 

other transportation related analyses. 

Although safety programs should consider all public roads, and HPMS is only collected on a 

portion of the roadway network, there is often a strong correlation between HPMS data 

requirements and State highway agency data collection practices. In this sense, the HPMS data 

can help in understanding future trends in data availability in State databases. Additionally, the 

collection of these sample variables may lead to the collection of the same variables on a wider 

network basis. 

The current HPMS database (HPMS 2010+) contains numerous datasets sorted into six 

different catalogs (8): 

 Shapes – This catalog stores geographic data for use in geospatial analyses and includes 

such features as routes and county and state boundaries. 

 Sections – This catalog stores the roadway attributes submitted by the States for the 

entire network or sampled sections. 

 Summaries – This catalog contains summaries of travel, highway system length, and 

demographic data for a defined area, such as a State, county, or urban area. 

 References – This catalog stores line reference data that identifies highway sections as 

the NHS, Strategic Highway Network (STRAHNET), and National Freight Network. It 

also contains point data to identify location and type of grade-separated interchanges on 

the Federal-aid system. 

 Estimates – This catalog stores information that can be used as inputs in FHWA's 

pavement deterioration models to describe the pavement condition estimates at the 

National or State level. 

 Metadata – This catalog contains data that captures and explains variability in the 

collection and reporting of traffic and pavement data in HPMS. 

The roadway-section-level attributes reported in the Sections catalog are the primary interest 

for highway safety analysis. The section-level data in the catalog contain 63 data elements 

provided by the States describing the following attributes of a roadway section: inventory, 

route, traffic, roadway geometry, and pavement. In addition, the Sections catalog includes seven 

items calculated internally by the HPMS software, seven for sample panel identification, four 

reported by FHWA, and two coded by FHWA based on data provided by the States. 

FHWA requires States to report the section-level data for all highway functional classes within 

the Federal-aid highway program and all NHS routes. States report a limited number of data 

elements within the Sections catalog for the entire system (referred to as Full Extent data 

items). States report more detailed information for samples of roadway sections (referred to as 

Sample data items). States select the samples randomly to represent various attributes at a 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

16

system-wide level. The geographic coverage of Full Extent and Sample data items may vary with 

the functional classes within the Federal-aid Highway Program. 

Of 70 data items (63 elements collected by States and 7 software-calculated elements) in the 

Sections catalog, the Full Extent dataset contains data items ranging from a maximum of 21 

items for NHS sections to a minimum of 13 items for collector roads. The Sample dataset 

contains data items ranging from a maximum of 47 data items for rural minor arterial sections 

to a minimum of 38 items for NHS sections. 

The researchers compared the HPMS dataset to MIRE Version 1.0 to identify common data 

items between the two systems. The HPMS Sample dataset covers up to 22 elements in the 

MIRE listing; however, because this dataset contains data for only a limited number of sampled 

sections, it does not cover all public roads. The Full Extent dataset includes 19 elements in the 

MIRE listing for all functional systems and for 7 elements for certain functional systems. Table 6 

presents a list of MIRE data elements that are common to both the Full Extent and Sample 

datasets. The table indicates how many data elements these datasets can contribute to a 

potential MIRE MIS for any possible integration. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

17

Table 6. Commonality between the HPMS dataset and MIRE. 

HPMS Full Extent Data 

Items 

HPMS “Limited Full 

Extent” Data Items 

HPMS Sample Panel 

Data Items 

 Year_Record  Route_Number  At_Grade_Other 

 State_Code  Route_Signing  Curves 

 Route_ID  Route_Qualifier  Dir_Factor 

 Begin_Point  International Roughness  Grades 

 End_Point Index (IRI)  K_Factor 

 Section Length  AADT_Single  Lane_Width 

 F_System  AADT_Combination  Median_Type 

 Urban_Code  Access_Control  Median_Width 

 Urban Name  Peak_Lanes 

 County_Code  Peak_Parking 

 County Name  Shoulder_Type 

 Facility_Type  Shoulder_Width_L 

 Ownershp  Shoulder_Width_R 

 Through_Lanes  Signal_Type 

 HOV_Type  Signals 

 HOV_Lanes  Speed_Limit 

 Toll_Charged  Stop_Signs 

 Toll_Type  Surface_Type 

 AADT 







Terrain_Type 

Turn_Lanes_L 

Turn_Lanes_R 

PSR 

In this comparison, the Sample dataset was included to emphasize the capability of the States to 

collect certain data items, whether mandated or not. The review indicated the following: 

 The HPMS contains MIRE data elements for segment location, linkage, classification, and 

cross-sectional descriptors for surface, lanes, shoulders, and medians. However, it does 

not contain specific data for surface friction, cross slopes, median side slopes, rumble 

strips for medians and shoulders, curbs, sidewalks, or bicycle facilities. 

 The HPMS has route numbers, route signing, and route qualifiers in the Full Extent 

dataset for Interstates, other freeways, and principal and minor arterials; it does not 

require reporting for collector and local roads. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

18

 International Roughness Index (IRI) is reported to Full Extent only for routes classified 

as Interstates and principal arterials, whereas the Pavement Serviceability Rating (PSR) is 

reported for Sample dataset sections on all urban minor arterials, urban and rural major 

collectors, and urban minor collector routes. The HPMS does not require a PSR value 

for a sample section if a measured IRI value is reported for that section. 

 The HPMS does not include any MIRE data elements that describe roadside attributes. 

 The HPMS contains data items for location, length, number of lanes, AADT, and 

functional classification data for ramps and interchanges. 

 The HPMS contains traffic volume in the Full Extent dataset, while other traffic data 

items, such as the annual escalation based on future traffic forecasts, K-factor, 

directional factor, and truck traffic, are contained in the Sample dataset. The HPMS 

requires the State agencies to report motorcycle travel data as a percentage of total 

travel. However, it lacks information on pedestrians and bicycles. 

 Although the HPMS includes some data items for traffic operations and control (e.g., 

speed limits and toll facilities), it lacks information on the presence of school zones, 

pavement markings, and lighting. 

 In the HPMS, required Sample data include the number of horizontal curves within 

categories of degree/radius and the number of grades within categories of percent 

gradient. The States may submit the location and descriptors of each specific curve or 

grade within the sample section (as compliance with MIRE would require), but it is not 

required for the HPMS. 

 The HPMS Sample dataset contains data items for signal type and percent green time, 

but only for the “governing” or “typical” intersection within a sample section. It includes 

the number of signalized, stop-controlled, and uncontrolled intersections in a sample 

section. However, unlike MIRE, it does not include an intersection database that 

contains inventory data on each approach to each intersection. 

 The HPMS lacks information on circular intersections and pedestrian facilities. 

GAP ANALYSIS OF MIRE DATA ELEMENTS 

The review of databases from HSIS and non-HSIS States, the MIRE workshop survey results, 

and the HPMS data requirements indicated that some MIRE elements are not collected or 

planned for collection. Most State databases have MIRE elements for roadway location, linkage, 

and classification categories. These databases contain most of the elements that describe 

surface, lanes, shoulders, and medians. However, they lack information on elements such as 

surface friction, presence of curbs, rumble strips, sidewalks, and bicycle facilities. While almost 

all databases contain traffic volume, only a few databases contain data elements that describe 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

19

traffic growth, forecast, percent trucks, and directional factors. Only a few State databases 

contain information on basic alignment features, traffic operations, traffic control in 

intersections, and parking restrictions. Most databases lack information on roadside features, 

roadside hardware, pavement marking, lighting, intersections, interchanges, ramps, circular 

intersections, and in-depth alignment features. None of the databases contain information on 

traffic counts or facilities for pedestrians, bicycles, and motorcycles. 

The research team identified the following MIRE Version 1.0 elements as gaps in traditional 

roadway inventories based on the review of existing data sources: 

 Roadway Cross-Sectional Elements: 

o Cross slope. 

o Length of auxiliary lanes. 

o Presence/type of bicycle facility. 

o Width of bicycle facility. 

o Number of peak period through lanes. 

o Type of rumble strips on shoulders and medians. 

o Type of curbs on shoulders and barriers on medians. 

 Roadway Alignment Description: 

o Direction, degree, transition, and deflection angle of horizontal curves. 

o Length of vertical curves. 

o Curve and grade identifiers. 

o Percent grades. 

 Roadside Elements: 

o Clear zone width and side slopes. 

o Lighting on roadways, intersections, interchanges. 

o Driveway counts. 

o Roadside rating. 

 Traffic Flow and Operations/Control: 

o Edgeline presence. 

o Centerline presence and centerline rumble strips. 

o Speed limits trucks, night time travel, mean speed, and 85th percentile speed. 

o Passing zone percentage. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

20

 Pedestrian/Bicycle Related Information: 

o Pedestrian facilities – sidewalks & crosswalks. 

o Bicycle lanes and bike paths. 

o Pedestrian and bicycle counts on through lanes and intersections. 

o Pedestrian and bicycle facilities at intersections and circular intersections. 

o School zone indicator on through lanes and junctions. 

o Pedestrian signals. 

 Intersections and Circular Intersections: 

o Intersection identifiers (e.g., intersection milepost or coordinates). 

o Geometry of intersection – number of legs, geometry, offset distance. 

o Cross-sectional elements at junctions – medians, splitter island, rumble strips. 

o Geometry of circular intersections – number of entry, exit and circulatory lanes, entry 

and exit radius, width of entry, exit, and circulatory lanes. 

o Traffic control at intersections and circular intersections. 

o AADT at non-State crossing routes. 

In general, HPMS includes data elements that describe the administrative, cross-sectional, and 

traffic characteristics of highways and their condition. Similarly, the databases of HSIS and nonHSIS States have data categories pertaining to vehicular traffic only. This could be the reason 

why these databases have very little or no specific information on exposure and facilities for 

pedestrians, bicycles, and motorcycles. 

The coverage of MIRE elements in the States’ databases is similar to that of the HPMS. There is 

a strong correlation between the data availability in most State databases and HPMS reporting 

requirements. Although the HPMS has a limited number of samples from the entire roadway 

network, the review of HPMS gives an indication of what data the States may have in their 

databases and their capability to collect these elements. This observation is similar to a 

conclusion drawn in NCHRP Synthesis 367—that Federally mandated data programs, such as 

HPMS and those for bridge structures and railroad crossings, have a strong effect on the States’ 

comprehensive collection of data (8). This study also concluded that States have adopted 

efficient data collection methods where Federal programs required more comprehensive data. 

For instance, most State databases contain information on pavement condition and traffic 

volume, whereas they lack information on pedestrian facilities and circular intersections. The 

HPMS and similar Federally mandated programs (e.g., the Traffic Monitoring System [TMS]), 

require States to report on pavement conditions and traffic volumes but not on pedestrian 

facilities or circular intersections. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

21

The review of databases from HSIS and non-HSIS States indicated that these databases lack 

information on various safety elements, such as pavement markings (including centerlines and 

edgelines), traffic signals, and roadway lighting. There is a possibility that these elements might 

exist in other data inventories maintained by the States. In addition, technological advancements 

in data collection methodologies could aid State agencies in filling the data gaps for MIRE. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

22

CHAPTER 5: POTENTIAL SUPPLEMENTAL DATA SOURCES 

The research team explored supplemental data sources that potentially could be used to obtain 

MIRE data elements. Examples of supplementary databases include pavement management 

systems, sign management systems, and other asset inventories. This exploration focused 

primarily on roadway inventory data and does not include sources of traffic counts. FHWA 

provides information on traffic data collection in the Traffic Monitoring Guide (TMG) (10). 

FHWA is currently updating the TMG with updated methodologies and expanded count types 

(e.g., pedestrian and bicycle counts). 

PAVEMENT MANAGEMENT SYSTEMS 

Most States maintain a pavement management system (PMS) database. According to a 2004 

survey on State PMS practices, typical PMS data elements include pavement type, shoulder type, 

pavement width, shoulder width, layer thickness, and number of lanes (11); these are also MIRE 

data elements. Figure 1 presents the frequency of specific data elements that States maintain in 

PMS databases. Figure 2 presents the frequency of pavement condition data in these databases, 

which include MIRE elements such as roughness, friction, and pavement condition. 

About 80 percent of the 2004 PMS survey respondents indicated they collect surface friction 

data (11). Surface friction data, identified as a gap in the HSIS and non-HSIS databases, can thus 

be obtained from PMS. 

Several States measure pavement surface friction at the network level for a variety of reasons, 

including simple evaluations of crash rates, detailed crash analyses, restoration of friction 

performance, and friction-related design strategies. The frequency or extent of data collection 

may be determined by factors such as prioritizing testing sites based on special request from 

their maintenance unit, local concerns, crash rates, or scheduled measurements for a networklevel friction management program. States store the collected information primarily for inhouse use, and this information may not be available in the public domain. State agencies 

developing roadway inventory for MIRE can coordinate within their agency, as necessary, for 

friction data. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

23

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Pavement Type

Pavement Width

Shoulder Type

Shoulder Width

Number of Lanes in

Each Direction

Layer Thickness

Joint Spacing

Transverse Joint Load

Transfer

Subgrade Type and

Material Classification

Layer Material

Properties

Drainage

Percentof States Responding

Data Category

Figure 1. Frequency of data elements in pavement management inventory (11). 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

IRI PSR Surface

Friction

Composite

Index

Percent of States Responding

Hot Mix Asphalt Pavements

Jointed Plain/Reinforced

Concrete Pavements

Continuously Reinforced

Concrete Pavements

Composite Pavements

Note: Percentages calculatd by dividing the number of States that collect each type of data

by the number of States that have that type of pavement.

Figure 2. Frequency of pavement condition data availability (11). 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

24

ROADWAY HARDWARE MANAGEMENT SYSTEMS 

AASHTO conducted a survey in 2000 to evaluate States’ roadway safety hardware asset 

management systems. The AASHTO study, as reported by Hensing and Rowshan, included the 

following categories of assets in the survey (12): 

• Roadway signs. 

• Signals. 

• Roadway lighting. 

• Supports and structures for signs, signals, and lighting. 

• Guardrails, barriers, and crash cushions. 

• Pavement markings and treatments. 

• Detectors. 

Of the 40 State agencies that responded to the AASHTO survey, only one-third use hardware 

management systems. Table 7 presents the number of States that have an inventory system for 

the seven roadway hardware assets listed above. The table also provides the number of States 

that track the location of all or some or none of the inventoried items. For instance, of the 25 

States that have an inventory for signs, 15 track the location of all inventoried items, while 7 

others track the location of only some items, and the remaining 3 do not track any items at all. 

Similarly, of the 14 States that have an inventory for lighting assets, 9 track the location of all 

items, 3 track some items, and 2 do not track any items. 

Table 7. Number of States that have an inventory and tracking system for 

roadway hardware assets (12). 

Inventory 

States 

Having an 

Inventory 

States 

Tracking 

Location of All 

Inventory 

Items 

States Tracking 

Location of 

Some Inventory 

Items 

States Not 

Tracking 

Location of Any 

Items 

Signs 25 15 7 3

Signals 26 24 2 0 

Lighting 14 9 3 2 

Supports 19 9 9 1 

Guardrails 19 13 2 4 

Pavement 

Markings 21 9 9 2

Detectors 17 12 3 2 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

25

Figure 3 shows the data collection methods the States reported that they used to develop their 

hardware asset inventories. As shown, States collect an overwhelming majority of the data 

through “manual surveys.” This study selected eight States for case studies on roadway safety 

hardware management systems to provide information to other State agencies that would help 

increase their use of state-of-the-practice techniques. It reviewed the agency practices that use 

fully or semi-integrated asset management systems leading to higher-level databases and 

presented two State models—New Mexico and Virginia—in detail. 

0 5 10 15 20 25 30 35 40 45 50

Signs

Signals

Lighting

Supports

Guardrails

Pvmt Markings

Detectors

Manual Survey

Semi‐Automated

Automated

Other

Figure 3. Methods used for original inventory of roadway hardware assets (12). 

Table 8 summarizes the roadway hardware asset data collected in States included in the 

AASHTO study (12). For example, Virginia uses the Inventory and Condition Assessment 

System (ICAS) to collect inventory and condition data for all roadway assets that exist within 

the State’s highway fenceline boundaries. Virginia captures a global positioning system (GPS)-

referenced digital videolog along every segment of highway for ICAS and uses these data to 

develop centerline coordinates of the roadway network. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

26

Table 8. State collection of roadway hardware asset inventory (12).

Inventory NM 

RFI 

VA 

ICAS 

CA 

IMMS 

FL 

RCI 

GA 

HSMS 

MD 

GIS 

MD 

TSIIM 

MN 

AFMS 

TN 

TRIMS 

Signs     

Signals      

Lighting     

Supports     

Guardrails    

Pavement 

Markings    

Detectors    

Note: 

RFI- Road Feature Inventory 

ICAS-Inventory and Condition Assessment System 

IMMS-Inventory Maintenance Management System 

RCI -Roadway Characteristics Inventory

HSMS-Highway Sign Management System 

TSIIM-Traffic Structure Inventory Inspection and Maintenance 

AFMS-Automated Facilities Management System 

TRIMS-Tennessee Road Information Management System

The New Mexico State Highway and Transportation Department has two primary asset 

management systems that are fully integrated into a single asset management system: the Road 

Feature Inventory (RFI) and the Highway Maintenance Management System (HMMS). The RFI 

system includes an extensive database on the entire New Mexico roadway system, while the 

HMMS includes information on highway labor, equipment, commodities, and maintenance 

management processes. New Mexico uses a video system to capture roadway assets and 

manually extracts detailed data for each asset from the video image. The data collection van is 

fully equipped with cameras, computer equipment, and lasers to capture pavement conditions 

and road geometry. 

Markow explored the state of the practice for managing transportation infrastructure assets 

other than pavements and bridges for the NCHRP Project 20-05/Topic 37-03, NCHRP Synthesis 

371: Managing Selected Transportation Assets: Signals, Lighting, Signs, Pavement Markings, Culverts, 

and Sidewalks (13). This study collected and synthesized information on managing selected 

infrastructure assets, such as traffic signals, lighting, signs, pavement markings, drainage culverts 

and pipes, and sidewalks. Table 9 summarizes survey responses from State and city agencies, 

indicating whether the respondents have a separate management system for a given asset type 

or an inventory that is part of a broader system covering several assets. Examples of such 

broader systems include the maintenance management system (MMS) or the transportation 

infrastructure asset management system (TIAMS). For example, some States have a separate 

roadway lighting management system, while other States have a broader asset management 

system that includes an inventory of roadway lighting. This study indicated that many agencies, if 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

27

not all, have location information in their inventories that could be useful in any possible data 

integration for MIRE. 

Table 9. Inventory of selected assets (13). 

States/Cities that Have States/Cities that Include Their 

Inventory Separate Management Maintenance/Asset Management 

Systems System 

AR, MI, SC, VT, CO (Region 2), FL, NM, NC, ND, UT, CO(Region 5), 

Signs Dakota County, NE, City of Cape Coral, FL 

City of Tampa, FL City of Portland, OR 

Signals MI, MN, NC, OH, OR, CO 

(Region 4) 

MD, NM, OH, OR, VA, 

CO (Regions 1 and 5), City of Portland, OR 

FL, IO, MN, OH, OR, 

Lighting CO (Region 4) CO (Regions 1, 3 and 5), City of Portland, 

OR 

Pavement 

Markings 

IA, KS, MN, OH, CO (Region 4), 

City of Tampa, FL 

FL, MD, NM, NC, OH, TX, UT, 

Dakota County, CO (Regions 2 and 5), NE, 

City of Portland, OR 

The Idaho Transportation Department has developed a new system called Guard Rail 

Management System (G-Rail). The G-Rail system uses Microsoft Access Visual BasicTM as an 

interface between the photo log data sources and the guardrail inventory database. The system 

uses a van that is capable of collecting high-quality images, GPS location data, and the distance 

traveled. Idaho is in the process of implementing an enterprise-wide data system that connects 

all of its business functions with available information. Roadside features included in this system 

are guardrail, signs, sign structures, culverts, railroad crossings, bridges, and approaches (14). 

SUPPLEMENTAL DATABASES 

Overall, the review of supplemental databases indicates that these could serve as an excellent 

source of information for MIRE data. For instance, a majority of DOTs collect surface condition 

data that are not present in the HSIS and non-HSIS databases. Some agencies also store rumble 

strip information in their databases. Similarly, some DOTs maintain separate databases for 

other roadway elements like signs, signals, lighting, and pavement markings. The AASHTO 

survey conducted in 2005 indicated that only one-third of responding DOTs use hardware 

management systems (12). 

It should be noted that the information on supplemental databases, as extracted from available 

published literature, is limited. Future research should focus on how many States collect and 

maintain inventories of supplemental information (e.g., physical assets). Key details of these 
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28

databases, such as their attributes, structure, and the ability to link with other data inventories, 

remain largely unknown. An extensive survey of such supplemental databases would be 

required to ascertain what data are being collected and how they are stored. 

While large volumes of data may exist at State and other agencies, the data managers may store 

these data in a wide variety of databases, reports, and other documents. Furthermore, disparate 

groups within each agency may be collecting or maintaining these various data sets. There are 

significant technological and institutional barriers to the cost-effective mining of these data 

sources for information applicable to safety. Additionally, the accuracy of the data will vary 

widely based on the method of collection, method of data mining, and the overall age of the 

datasets being used. Field data collection will be required to collect data not found in existing 

datasets and may be more effective in some cases where data already exist. 
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29

CHAPTER 6: TECHNOLOGIES FOR COLLECTION OF ROADWAY 

INVENTORY DATA 

NEED FOR ADVANCED TECHNOLOGIES 

As noted previously, many of the data gaps that were identified correspond to data elements 

that the MIRE workshop participants considered difficult to collect. The use of advanced 

technologies could help fill the data gaps for safety analyses. The expert scan team that 

proposed the need for MIRE also recommended the increased use of validated automated 

technologies that can collect roadway and roadside data at highway speeds to improve the 

quality of safety data. 

The use of automated technologies can be cost-effective over time by minimizing the need for 

labor-intensive and time-consuming processes associated with manual methodologies. 

Considering the fact that data collection is expensive, the automated technologies can help 

integrate the data needs of various internal business units within an agency and minimize 

replication of the process. For instance, a mobile mapping system can be used to collect 

information for pavement management and roadside inventory effort with a single data 

collection operation. The use of these technologies could also help manage some inadequacies 

in existing practices, such as the lack of precision measurement and reporting, lack of 

automated tools, inadequate coverage of the roadway network, incomplete data or missing data 

elements, and integration issues among databases (15). 

DATA COLLECTION AND PRESENTATION TECHNOLOGIES 

Highway agencies invest a significant amount of resources to collect, maintain, and update 

roadway inventories, and they employ a host of methods and technologies for data collection to 

suit their inventory requirements. Many agencies currently deploy semi-or fully automated 

systems—such as the mobile mapping systems or light detection and ranging (LIDAR)—for 

roadway inventory data collection. 

With technological advancements, the use of high-resolution satellite imagery in transportation 

applications has gained widespread attention. Though not a data collection technology, Keyhole 

Markup Language (KML)-based geobrowsers are increasingly being used as visualization tools 

for better presentation in roadway inventory applications. 

While there is an array of technologies available for roadway inventory data collection and 

processing, the usefulness of inventory data significantly depends on the data accuracy of both 

georeference and descriptive information obtained from these technologies, the economic 

viability of data collection, and post-processing efforts. The conventional wisdom from the 

comparative studies reported in the literature indicates that no single technology can collect all 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

30

roadway inventory elements efficiently (8, 16, 17). For instance, digital imaging appears to be 

promising for filling the MIRE data gaps; however, it still requires labor-intensive methods to 

identify false positives and missed assets and produce good results. Furthermore, the level of 

precision for MIRE data elements is largely undefined at this point. In other words, the 

sensitivity of data precision levels (as collected using various techniques) to safety analysis is 

unknown. 

The following section provides a discussion on various existing and emerging data collection 

technologies. 

Mobile Mapping Systems 

A mobile mapping system is a specially instrumented vehicle that has the flexibility to 

accommodate a wide array of sensors to collect georeferenced and descriptive data, as well as 

computers to synchronize the data collection activities and store the data. These vehicles can 

collect data at near highway speeds with a high degree of accuracy. They are usually configured 

with a combination of technologies for georeferencing that include differential GPS, distance 

measuring instruments (DMI), and inertial navigation systems (INS). Differential correction 

techniques are also applied to enhance the quality of location data gathered using GPS receivers 

in real time or during post-processing. INS uses accelerometers and gyroscopes that measure 

vehicle pitch, roll, and heading, which are then converted into roadway alignment and 

superelevation data. 

Some systems are equipped with additional sensors to collect pavement condition and roadside 

inventories at highway speeds. Typical technologies include laser-based road profilers (for 

measuring pavement roughness), pavement imaging systems (for measuring pavement distress), 

scanning lasers (for measuring transverse profile/rutting), GPS, and macro-texture sensors. 

Agencies can also use optical character recognition for the classification of features like 

guardrails and signs. FHWA’s Digital Highway Measurement System (DHMS) involved an 

instrumented vehicle that used this approach (18). 

The mobile mapping systems are typically configured with digital video cameras to collect highresolution stereo images for roadway inventory. The use of digital video cameras or videologs 

allows an agency to store and analyze captured images with ease on computers with no further 

need for digitization. Users can enter descriptive data into the system by viewing the images, 

with a lower risk of errors caused by digitization. 

The collected data are processed using photogrammetric software packages to obtain the 

desired georeferencing and descriptive data elements for the items being inventoried. Image 

processing packages are also used to detect and extract the selected inventory data 

automatically from the digital images. These packages have been used successfully in processing 

selected pavement distresses, traffic monitoring images, and traffic signs with a high level of 

accuracy (19-22).
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

31

LIDAR 

LIDAR is an optical remote sensing system that utilizes an airborne or terrestrial platform 

integrated with GPS, precision INS, laser range finders, and high-speed computing for data 

collection. The integrated system is capable of acquiring data to produce accurate digital 

elevation models. In a LIDAR system, a laser scans the footprint of an object using optical 

pulses that are transmitted, reflected off the object, and returned to a receiver. The receiver 

measures the travel time of the pulse accurately from its start to its return. The GPS measures 

the range, angle, and position of the laser, and the INS measures the laser orientation (23). 

Figure 4 presents the schematic representation of an airborne LIDAR system. 

Figure 4. Schematic representation of the airborne LIDAR system. 

Airborne LIDAR 

The airborne LIDAR systems, also called airborne laser swath mapping, can collect data at high 

altitudes and, thus, can cover even remote locations relatively quickly. Applications like flood 

mapping, coastal zone mapping, and atmospheric studies use airborne LIDAR systems, but the 

applications in highway engineering are limited. 

LIDAR produces high-resolution three-dimensional (3D) surfaces whose accuracy depends on 

the density of cloud points. The cloud point collected from LIDAR can be as dense as millions 

of points per square mile, depending on the point spacing (distance between two points), pulse 

repetition rate, flying height, and flying speed. The airborne systems can produce elevation 

models with an accuracy of 2 inches and point spacing (indicator of horizontal accuracy) as 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

32

small as 1 foot. These accuracy standards may not be adequate for typical roadway inventory 

purposes. A higher cloud density may have useful applications but involves considerable time 

and cost for data collection and processing. 

With LIDAR, a significant data processing effort is required to generate digital elevation models 

using automated processes. Several software packages are available in the market that process 

LIDAR cloud point data. Fernandez et al. provide a comparison of the processing capabilities of 

various software packages available in the market (24). Typical operations performed on LIDAR 

data include georeferencing, gridding, data visualization, measurement of distances and angles, 

primitive fitting of geometric shapes, segmentation, classification, merging of several point 

clouds of the same object, and filtering. Inventory applications require some manual editing and 

quality control. 

In its current form, the true potential of airborne LIDAR in highway applications appears to be 

a supplemental form of traditional mapping practices. Unable to completely replace existing 

methods, the airborne systems only find increasing utility in roadway inventory applications 

when combined with traditional photogrammetry (25-29). 

Terrestrial LIDAR 

Terrestrial LIDAR systems use a motorized vehicle instrumented with lasers, GPS, and INS. 

The Centre for Applied Remote Sensing, Modeling, and Simulation (CARMS) at the University 

of Victoria, British Columbia, released a white paper on the various applications of LIDAR data 

and the general state of the LIDAR market and commercial vendors (30). 

There have been several demonstrations on the use of terrestrial LIDAR for inventory data 

collection (31). Oregon tested a system on approximately 77 miles of highway on Interstate 5 

to collect an inventory of roadside features to support various safety improvements and 

pavement projects. Tennessee has implemented a terrestrial LIDAR-based survey for its entire 

State-maintained network. A commercial vendor scanned a 6.6-mile stretch of an urban 

Interstate in Kansas. Similar use of terrestrial systems has been demonstrated in the U.K. and 

Denmark (32). Washington State DOT conducted a field pilot study to collect empirical data on 

the feasibility of collecting inventory information using terrestrial LIDAR. The pilot study 

demonstrated the potential positive impacts on business processes, and it also highlighted the 

need for best practices documentation for using mobile LIDAR to ensure consistent and 

accurate results (33). 

The Minnesota DOT conducted a survey to find out which U.S. and Canadian agencies were 

seriously considering an investment in mobile laser systems, as well as those that already have 

purchased mobile scanner technology (34). The survey indicated that only Florida has 

purchased a terrestrial LIDAR system; Oregon and the Province of British Columbia have used 

this system for surveying or highway inventory purposes. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

33

The literature provided by terrestrial LIDAR vendors indicates that these systems can collect 

typical MIRE elements, such as signs, pavement markings, and lighting (35-38). However, there is 

no peer-reviewed literature or proven track record to demonstrate the capability or evaluate 

the accuracy of terrestrial LIDAR. The utility of terrestrial LIDAR systems in roadway inventory 

collection is expected to be seen in the near future. 

Satellite Imagery 

High-resolution satellite imagery holds potential for inexpensive, real-time collection of largearea roadway inventory elements. The digital images captured using satellite imagery can be 

used for collecting data involving plan view measurement and feature recognition. 

Hallmark et al. conducted a pilot study on a 4.1-mile corridor with four panchromatic1

 imagery 

datasets with resolutions of 2, 6, 24, and 40 inches for feature recognition, accuracy of linear 

measurements, and positional accuracy of roadway inventory elements (39). The feature 

recognition elements included signs, traffic signals, geometric design of intersections, lane 

elements, roadside hardware, cross-sectional elements, pedestrian facilities, and bicycle 

facilities. This study included roadway elements similar to those included in the MIRE listing. 

The elements identified from imagery sets of varying resolutions were then compared with 

those identified in the field. The study found that the elements identified in the high-resolution 

datasets (2 and 6 inches) were consistent with those in the field, while there was a significant 

reduction in the number of features identified and higher measurement errors in the lowresolution datasets (24 and 40 inches). This study gives an indication as to the requirements of 

image resolution appropriate for roadway inventory data collection. 

The positional accuracy of satellite images obtained from IKONOS, a commercial earth 

observation satellite launched in 1999, provided a spatial resolution of 40-inch panchromatic 

and 160-inch multispectral imagery2 in 7-mile swaths. The resolution of these images provided 

only a handful of data elements; thus, these images are inadequate for inventory applications

(17). Launched in 2008, the GeoEye-1 satellite provides a spatial resolution of 16.14-inch 

panchromatic and 65-inch multispectral imagery in 9.44-mile swaths. These images can yield 

geopositioning accuracy of close to 4 inches in planimetry (i.e., the measurement of plane areas) 

and 10 inches in height through the use of a single ground control point (40). However, the 

feasibility of using GeoEye-1 imagery for roadway inventory purposes is yet to be established. 

 

1

 An image collected in the broad visual wavelength spectrum range (i.e., wavelengths from about 390 to 750 nm) 

but rendered in black and white. 

2

 A multi-spectral image is one that captures image data at specific frequencies across the electromagnetic 

spectrum. The electromagnetic spectrum extends from low frequencies used for modern radio to gamma 

radiation at the short-wavelength end, covering wavelengths from thousands of kilometers down to a fraction of 

the size of an atom. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

34

The positional accuracy of satellite imaging is expected to improve in the foreseeable future. 

Planned to launch in 2013, the GeoEye-2 satellite is expected to provide better images with a 

panchromatic resolution of 10 inches. The resolution of GeoEye-2 imagery may still be 

inadequate for routine inventory data collection purposes, but it can be used in conjunction 

with aerial or LIDAR imagery to produce better terrain models with minimal editing. 

Oblique Aerial View (OAV) imagery is fast emerging as a supplement for orthoimagery. 

Orthoimagery is a map-quality aerial image corrected for topographic relief, lens distortion, and 

camera tilt. The biggest difference between OAV images and normal oblique images used in 

orthoimagery is perspective, with the latter offering more of an overhead image of a ground 

feature compared to the former. OAV finds applications in image analysis by providing multiple 

angled views of ground features and helps mitigate some of the limitations of traditional 

orthoimagery. While several commercial vendors offer this technology, information on largescale, network-level implementation is yet to be seen. 

While the satellite imagery provides an economic advantage to users, the available image 

resolution capacities often are inadequate for roadway inventory applications. However, the 

potential for satellite imagery applications is expected to improve as the technology improves. 

Nevertheless, these applications require significant post-processing and manual manipulation of 

data. 

Applications of Keyhole Markup Language (KML) and Geobrowsers 

KML is a file format that is used to display geographic data in a geobrowser. A geobrowser is a 

3D virtual representation of the Earth that allows the superimposition of images obtained from 

satellite imagery, aerial photography, and geographic information system (GIS) 3D globe. 

Examples of geobrowsers include Google Earth, ESRI ArcGIS Explorer, Microsoft Virtual Earth, 

and the National Aeronautics and Space Administration (NASA) World Wind. 

Geobrowsers use a coordinate system based on the World Geodetic System of 1984 (WGS84) 

for latitudinal and longitudinal components and the Earth Gravitational Model of 1996 (EGM96) 

Geoid vertical datum for altitude. Many browsers offer free services to view surface terrain, 

satellite imagery, and maps, and their commercial editions include expanded functionalities to 

host information internally on an agency’s own servers and datasets, as well as to use advanced 

geocoding capabilities with greater volume and speed in a secure domain. 

The application of KML and geobrowsers would serve primarily as a visualization tool for 

elements in the MIRE listing. Once an agency completes data collection efforts, the agency can 

use these tools to enable geographic annotation and intuitive visualization of geospatial data. 

Agencies can also use these applications for overlaying different data sources, including the 

roadway network, pavement condition, sign and signal inventories, bridge inventory, traffic data, 

etc. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

35

The California DOT’s (Caltrans’) Digital Highway Inventory Photography Program (DHIPP) 

uses Google Earth with the imagery and elevation data for a wide variety of applications, such as 

an intuitive visualization of survey control points, asset management, right-of-way record maps, 

and real-time traffic conditions (41). Figure 5 shows an image from Caltrans’ DHIPP showing 

roadway features of interest to MIRE. The Advanced Highway Maintenance and Construction 

Technology Research Center (AHMCT) at the University of California, Davis, is working on 

identifying and recommending best practices in roadway inventory for Caltrans. As part of this 

study, researchers are expected to develop a software-based asset management tool for 

tracking inventory of roadside features using a KML client (42). Similarly, HPMS is developing a 

data visualization using Microsoft Bing maps. 

Figure 5. Caltrans DHIPP image showing roadway features (41).

USE OF AUTOMATION IN ROADWAY INVENTORY DATA COLLECTION 

Prior to promoting the use of automated systems or other advanced technologies for collecting 

MIRE data elements, it is important to assess the extent of use and application of automated 

data collection by various State agencies. Most State agencies employ some form of automation 

to serve a particular purpose but have not yet extended it to other objectives. For example, 

many State agencies use automated data collection systems for pavement management 

purposes. This project did not determine whether these agencies use such systems for 

collecting inventory of roadside hardware or signs. 

Under NCHRP Synthesis 20-05/Topic 34-04, NCHRP Synthesis 334: Automated Pavement Distress 

Collection Techniques, survey responses were collected from 43 State highway agencies, 2 FHWA 

offices, 10 Canadian provinces or territories, and Transport Canada regarding their automated 

data collection processes (19). 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

36

Table 10 presents the number of respondents utilizing automation for data collection using 

either images or sensors for different pavement distresses. As shown in the table, the 

respondents overwhelmingly use automation techniques for collecting pavement distress and 

condition data. 

Table 10. Number of agencies using automated pavement distress data collection. 

Distress Agency Vendor Total 

Cracking 10 20 30

Roughness 31 23 54

Rutting 30 21 51

Joint Faulting 21 12 33 

NCHRP Synthesis 367 found a similar trend (8). Over half of the responding States reported the 

use of specialized vans to collect pavement data. However, the States used the collected data 

only for pavement management applications. Although several States use automation techniques 

for collecting pavement distress or condition data, fewer use these advanced technologies for 

collecting other roadway inventory data. NCHRP Synthesis 367 found that only four States use 

videologging for collecting cross-sectional elements (e.g., barriers and clear zone maintenance), 

while others conduct field surveys using pen and paper, and still others do not collect these 

data elements at all (8).

Similarly, of the States who collect sign and pavement marking inventories, NCHRP Synthesis 367 

found that most use as-built plans or videologs to obtain this information (8). Colorado and 

Washington use GPS technology for their sign inventories, while four other States collect 

retroreflectivity data for pavement markings and inventoried signs. Seven of the responding 

States indicated that they use sensor technology to collect geometric data elements (e.g., 

roadway grades and cross slope), while seven others extract these data from as-built or 

construction plans. Collection of geometric alignment data involves post-processing raw spatial 

data using specialized software packages. For instance, Florida estimates horizontal curvature 

information by processing centerline data using its GIS application, and Massachusetts estimates 

vertical alignment data using its GIS application. Five of the responding States use videologging 

for geometry data, two States use pen and paper, and two States do not collect this information 

at all. 

DeGray and Hancock synthesized the practice of ground-based imaging technologies in the six 

New England States (43). These States collect and extract several roadway elements (typical of 

those in the MIRE list) from imagery for inventory purposes. This study discussed the economic 

viability issues in implementing these systems. As every State is concerned with a cost-benefit 

ratio, small and predominantly rural States, such as New Hampshire, will typically see the least 

“bang for their buck” and may be inclined to keep the current data collection systems in place. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

37

This study also addressed the issue of underutilizing systems like videologging and the need to 

demonstrate its potential to users. The authors observed that the maximum effectiveness of 

advanced systems could be realized in a single, well-managed, well-promoted data collection 

system that provides nearly all of the data collection needs of the State at a relatively low cost. 

While these systems can generate large volumes of data in relatively short lengths of time with 

limited field exposure, they also require fairly large volumes of labor to extract useful data from 

the raw field files. Even well-researched systems that perform automated pavement distress 

ratings require significant operator supervision to resolve issues that are not addressed by the 

processing algorithms. This situation is far more pronounced as the data extraction moves from 

pavements to 3D environment assets, such as signs and guardrails. While the effort to collect 

data by automated means is far safer and less time-consuming than manual field surveys, it is still 

a major undertaking. 

In summary, the literature review indicates that the application of advanced technologies is 

limited to pavement management and videologs. However, some States have demonstrated the 

use of these systems for collecting other roadway elements, such as alignment features, signs, 

pavement markings, and guardrails. The obstacles to using these technologies are both technical 

and institutional. The technical difficulties include data accuracy issues and automatic 

information extraction from images, and the institutional challenges include a mediocre costbenefit ratio and under-utilization. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

38

CHAPTER 7: COMPATIBILITY OF COLLECTION TECHNOLOGIES 

WITH MIRE ROADWAY ELEMENTS 

There a variety of existing and emerging technologies that an agency can employ to collect data 

elements that are compatible with those included in the MIRE listing. Multiple factors come into 

play in the selection of these data collection technologies, including cost, availability of skilled 

personnel, institutional challenges, and policies. To help identify appropriate technologies for 

MIRE, the project team categorized the data elements into different groups based on the 

characteristics/type of data: 

 Administrative (route number, functional class, etc.). 

 Surface measurement elements (surface friction, roughness and pavement condition). 

 Shape elements (curb type, hardware descriptors, lighting, etc.). 

 Sign elements (school zone indicator, speed limit, on-street parking presence, etc.). 

 Visual identification elements (surface type, rumble strip type). 

 Marking elements (edgeline, crosswalk presence, median left-turn lane type, etc.). 

 Plan view identification (curve identifiers, number of through lanes). 

 Plan view measurement elements (interchange type, shoulder width, etc.). 

 Traffic elements (AADT, bicycle count, etc.). 

o FHWA provides information on traffic data collection in the TMG (10) and is 

currently revising it with updated methodologies and expanded count types (e.g., 

pedestrian and bicycle counts); traffic count collection methodologies will not be 

covered in this section. 

These categories define the type of information needed for an element (e.g., visual 

identification, shape, measurement, etc.) and the functional requirements for a technology to 

collect such information. For example, a sign indicates the presence of on-street parking, and 

the technology used should be capable of recognizing the location and description (i.e., the 

content) of the sign. Similarly, for collecting the on-street parking type, the surface marking or 

striping defines the attributes (i.e., angle or parallel). Surveyors should identify the surface type 

(e.g., asphalt or concrete) and the rumble strip type (e.g., milled or rolled) from either highresolution imagery or an onsite assessment. The technology that is used for collecting such 

elements should be able to identify surface markings. Aerial imagery could be capable of 

identifying striping but not the content of a sign. 

It should be noted that the elements in the list above are not mutually exclusive. Some 

elements can be defined by two or more categories, depending on the required information. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

39

For instance, location identifiers can be obtained from spatial coordinates, as well as signs. 

Similarly, the presence of HOV lanes can be obtained from pavement striping, signs, and plan 

view measurements. 

The following four existing and emerging technologies were evaluated for their ability to collect 

the required information for MIRE: 

 Ground-based imagery (e.g., mobile mapping systems). 

 Aerial imagery. 

 Airborne LIDAR with imagery. 

 Terrestrial LIDAR. 

Table 11 shows whether a particular technology has the potential to collect a certain type of 

data. Note that there is no additional data collection (indicated by N.A.) needed for 

administrative and pavement condition data items, as these elements are either already available 

within agencies that manage the roadway network or already being collected by the same 

agency for other purposes. For example, an agency can use information on pavement condition 

for roadways within its jurisdiction on which it already routinely collects the same information 

for asset management purposes. 

For the shape category, where the attributes of the elements can be recognized from their 

shape, elements like curb type, barrier type, roadside hardware, and lighting can be recognized 

by all four technologies. However, there is a significant amount of post-processing involved in 

using these technologies where these elements have to be manually tied with imaging/LIDAR 

data. Automation may help to reduce the labor required in these efforts, but the problem is not 

a trivial one to solve, given the number of variables involved. Because the raw data must be 

collected from a moving platform, the imaging angle will vary greatly. Additional variables 

induced by weather conditions, sun glare, material variability, partial obstruction due to 

vegetation, passing traffic, etc. create major obstacles to automation. The trained human eye is 

able to manage these conditions with relative ease. 

The primary problem of collecting sufficient data to locate and classify assets has largely been 

solved. The cost-effective reduction of these data into a database of classified and attributed 

assets remains a work in progress. Using these technologies requires a laborious and timeconsuming effort to collect data on smaller “shape” elements, such as signalization type or 

pedestrian signal special features. Information on median type can be obtained from their shape 

and plan view. 
MIRE ELEMENT COLLECTION MECHANISMS 

40

Table 11. Potential of technologies to collect MIRE elements.

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

1 County Name Administrative N.A. N.A. N.A. N2 County Code Administrative N.A. N.A. N.A. N

3 Highway District Administrative N.A. N.A. N.A. N4 Type of Governmental Ownership Administrative N.A. N.A. N.A. N5 Governmental Ownership Administrative N.A. N.A. N.A. N

6 City/Local Jurisdiction Name Administrative N.A. N.A. N.A. N7 City/Local Jurisdiction Urban Code Administrative N.A. N.A. N.A. N

8 Route Number Administrative N.A. N.A. N.A. N

9 Route/Street Name Administrative N.A. N.A. N.A. N

10 Begin Point Segment Descriptor Administrative N.A. N.A. N.A. N11 End point Segment Descriptors Administrative N.A. N.A. N.A. N12 Segment Identifier Administrative N.A. N.A. N.A. N

13 Segment Length Administrative N.A. N.A. N.A. N

14 Route Signing Administrative N.A. N.A. N.A. N

15 Route Signing Qualifier Administrative N.A. N.A. N.A. N

16 Coinciding Route Indicator Administrative N.A. N.A. N.A. N

17 Coinciding Route – Minor Route Info. Administrative N.A. N.A. N.A. N

18 Direction of Inventory Administrative N.A. N.A. N.A. N

19 Functional Class Administrative N.A. N.A. N.A. N

20 Rural/Urban Designation Administrative N.A. N.A. N.A. N
MIRE ELEMENT COLLECTION MECHANISMS 

41

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

21 Federal Aid/ Route Type Administrative N.A. N.A. N.A. N

22 Access Control Administrative N.A. N.A. N.A. N26 Surface Friction Date Administrative N.A. N.A. N.A. N28 Pavement Roughness Date Administrative N.A. N.A. N.A. N

30 Pavement Condition (PSR) Date Administrative N.A. N.A. N.A. N67 Roadside Rating Administrative N.A. N.A. N.A. N

101 Toll Facility Administrative N.A. N.A. N.A. N106 Bridge Numbers for Bridges in Segment Administrative National Bridge Inventory 128 Railroad Crossing Number Administrative USDOT Railroad Crossing Directory 

23 Surface Type Visual Yes No No N

43 Right Shoulder Type Visual Yes No No N

47 Left Shoulder Type Visual Yes No No N46 Right Shoulder Rumble Strip Presence and Type Visual Yes Yes Yes Y

58 Median Shoulder Rumble Strip Presence and Type Visual Yes Yes Yes Y

50 Left Shoulder Rumble Strip Presence and Type Visual Yes Yes Yes Y

104 Centerline Rumble Strip Presence and Type Visual Yes Yes Yes Y167 Transverse Rumble Strip Presence Visual Yes Yes Yes Y

99 On-Street Parking Type Marking Yes Yes Yes Y
MIRE ELEMENT COLLECTION MECHANISMS 

42

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

102 Edge Line Presence Marking Yes Yes Yes Y

103 Centerline Presence Marking Yes Yes Yes Y

105 Passing Zone Percentage Marking Yes Yes Yes Y

157 Crosswalk Presence/Type Marking Yes Yes Yes Y

175 Circular Intersection - Pedestrian Facility Marking Yes Yes Yes Y

61 Median Crossover/Left Turn Lane Type Marking Yes Yes Yes Y

37 HOV Lane Types Plan view ID Yes Yes Yes Y

51 Sidewalk Presence Plan view ID Yes Yes Yes Y

52 Curb Presence Plan view ID Yes Yes Yes Y75 Terrain Type Plan view ID Yes Yes Yes Y

108 Curve Feature Type Plan view ID Yes Yes Yes Y

112 Horizontal Transition/Spiral Curve Presence Plan view ID Yes Yes Yes Y

114 Horizontal Curve Direction Plan view ID Yes Yes Yes Y

116 Vertical Alignment Feature Type Plan view ID Yes Yes Yes Y

121 Type of Intersection/Junction Plan view ID Yes Yes Yes Y125 Intersection/Junction No of legs Plan view ID Yes Yes Yes Y126 Intersection/Junction Geometry Plan view ID Yes Yes Yes Y137 Circular Intersection - Bicycle Facility Plan view ID Yes Yes Yes Y144 Number of Approach Through Lanes Plan view ID Yes Yes Yes Y
MIRE ELEMENT COLLECTION MECHANISMS 

43

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

145 Left turn Lane Type Plan view ID Yes Yes Yes Y

146 Number of Exclusive Left Turn Lanes Plan view ID Yes Yes Yes Y

148 Right Turn Channelization Plan view ID Yes Yes Yes Y

150 Number of Exclusive Right Turn Lanes Plan view ID Yes Yes Yes Y182 Interchange Type Plan view ID Yes Yes Yes Y

190 Ramp Number of Lanes Plan view ID Yes Yes Yes Y

31 Number Of Through Lanes Plan view ID / Marking Yes Yes Yes Y

134 Circular Intersection - Number of Circulatory Lanes Plan view ID / Marking Yes Yes Yes Y

170 Circular Intersection 

– Presence and 

Type of Exclusive Right turn Lane 

Plan view ID / Marking Yes Yes Yes Y

24 Total Paved Surface Width Plan view 

measurement

Yes Yes Yes Y

32 Outside Through Lane Width Plan view measurement Yes Yes Yes Y33 Inside Through Lane Width Plan view measurement Yes Yes Yes Y

34 Cross Slope Plan view measurement Yes Yes Yes Y

59 Median Side Slope Plan view measurement Yes Yes Yes Y

60 Median Side Slope Width Plan view measurement Yes Yes Yes Y36 Auxiliary Lane Length Plan view measurement Yes Yes Yes Y
MIRE ELEMENT COLLECTION MECHANISMS 

44

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

41 Width of Bicycle facility Plan view measurement Yes Yes Yes Y

44 Right Shoulder Total Width Plan view measurement Yes Yes Yes Y45 Right Paved Shoulder Width Plan view measurement Yes Yes Yes Y48 Left Shoulder Total Width Plan view 

measurement

Yes Yes Yes Y

49 Left Paved Shoulder Width Plan view 

measurement

Yes Yes Yes Y

55 Median Width Plan view 

measurement

Yes Yes Yes Y

57 Median (Inner) Paved Shoulder Width Plan view measurement Yes Yes Yes Y62 Roadside Clear Zone Width Plan view 

measurement

Yes Yes Yes Y

63 Right Side Slope Plan view measurement No Yes Yes Y

64 Right Side Slope Width Plan view measurement No Yes Yes Y65 Left Side Slope Plan view measurement No Yes Yes Y

109 Horizontal Curve Degree or Radius Plan view measurement Yes Yes Yes Y

110 Horizontal Curve Length Plan view measurement Yes Yes Yes Y

111 Curve Superelevation Plan view measurement Yes Yes Yes Y
MIRE ELEMENT COLLECTION MECHANISMS 

45

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

113 Horizontal Curve Intersection/Deflection Angle Plan view measurement Yes Yes Yes Y

117 Percent Of Gradient Plan view 

measurement

Yes Yes Yes Y

118 Grade Length Plan view measurement Yes Yes Yes Y119 Vertical Curve Length Plan view measurement Yes Yes Yes Y129 Intersection Skew Angle Plan view measurement Yes Yes Yes Y

130 Intersection/Junction Offset Distance Plan view measurement Yes Yes Yes Y135 Circular Intersection - Circulatory Width Plan view measurement Yes Yes Yes Y136 Circular Intersection - Inscribed Diameter Plan view measurement Yes Yes Yes Y147 Amount of Left turn Lane Offset Plan view 

measurement

Yes Yes Yes Y

151 Length of Exclusive Left Turn Lanes Plan view measurement Yes Yes Yes Y152 Length of Exclusive Right Turn Lanes Plan view measurement Yes Yes Yes Y168 Circular Intersection - Entry Width Plan view measurement Yes Yes Yes Y

169 Circular Intersection - Number of Entry lanes Plan view measurement Yes Yes Yes Y171 Circular Intersection - Entry Radius Plan view measurement Yes Yes Yes Y
MIRE ELEMENT COLLECTION MECHANISMS 

46

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

172 Circular Intersection - Exit Width Plan view 

measurement

Yes Yes Yes Y

173 Circular Intersection - Number of Exit lanes Plan view measurement Yes Yes Yes Y174 Circular Intersection - Exit Radius Plan view 

measurement

Yes Yes Yes Y

176 Circular Intersection - Crosswalk Location (distance from yield line) Plan view measurement Yes Yes Yes Y177 Circular Intersection – Island Width Plan view 

measurement

Yes Yes Yes Y

187 Ramp Length Plan view measurement Yes Yes Yes Y188 Ramp Acceleration Lane Length Plan view measurement Yes Yes Yes Y189 Ramp Deceleration Lane Length Plan view measurement Yes Yes Yes Y

53 Curb Type Shape Yes Yes Yes Y

56 Median Barrier Type Shape Yes Yes Yes Y

100 Roadway Lighting Shape Yes Yes Yes Y132 Signalization Type Shape No No No N133 Intersection/Junction Lighting Shape Yes Yes Yes Y

183 Interchange Lighting Shape Yes Yes Yes Y

159 Pedestrian Signal Special Features Shape No No No N
MIRE ELEMENT COLLECTION MECHANISMS 

47

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

158 Pedestrian Signalization Type Shape No No No N

54 Median Type Shape/Plan view Yes Yes Yes Y

153 Median Type at Intersection Shape/Plan view Yes Yes Yes Y

93 Truck Speed Limit Signs Yes No Yes N94 Nighttime Speed Limit Signs Yes No Yes N

97 School Zone Indicator (Segment) Signs Yes No Yes N

98 On-Street Parking Presence Signs Yes No Yes N

127 School Zone Indicator (Intersection/Junction) Signs Yes No Yes N

131 Intersection/Junction Traffic Control Signs Yes No Yes N

149 Traffic Control of Exclusive Right Turn Lanes Signs Yes No Yes N

154 Approach Traffic Control Signs Yes No Yes N

155 Approach Left Turn Protection Signs Yes No Yes N

161 Left/Right Turn Prohibitions Signs Yes No Yes N

162 Right Turn-On-Red Prohibitions Signs Yes No Yes N

193 Ramp Metering Signs Yes No Yes N

194 Ramp Advisory Speed Limit Signs Yes No Yes N

195 Feature at Beginning Ramp Terminal Signs Yes Yes Yes Y

199 Feature at Ending Ramp Terminal Signs Yes Yes Yes Y
MIRE ELEMENT COLLECTION MECHANISMS 

48

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

68 Major Commercial Driveway Count Signs /Plan ID view Yes Yes Yes Y

69 Minor Commercial Driveway Count Signs /Plan view ID Yes Yes Yes Y

70 Major Residential Driveway Count Signs /Plan view ID Yes Yes Yes Y71 Minor Residential Driveway Count Signs /Plan view ID Yes Yes Yes Y72 Major Industrial/Institutional Driveway Count Signs /Plan view ID Yes Yes Yes Y73 Minor Industrial/Institutional Driveway Count Signs / Plan view ID Yes Yes Yes Y74 Other Driveway Count Signs / Plan view ID Yes Yes Yes Y

107 Curve Identifiers and Linkage Elements Signs / Plan view ID Yes Yes Yes Y115 Grade Identifiers and Linkage Elements Signs / Plan view ID Yes Yes Yes Y

120 Unique Junction Identifier Signs / Plan view ID Yes Yes Yes Y122 Location Identifier for Road 1 Crossing Point Signs / Plan view ID Yes Yes Yes Y123 Location Identifier for Road 2 Crossing Point Signs / Plan view ID Yes Yes Yes Y124 Location Identifier for Road 3, 4, etc Signs / Plan view ID Yes Yes Yes Y
MIRE ELEMENT COLLECTION MECHANISMS 

49

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

138 Intersection Identifier for this Approach Signs / Plan view ID Yes Yes Yes Y139 Unique Approach Identifier Signs / Plan view ID Yes Yes Yes Y142 Approach Mode Signs / Plan view ID Yes Yes Yes Y143 Approach is Two-Way, One-Way Signs / Plan view ID Yes Yes Yes Y178 Unique Interchange Identifier Signs / Plan view ID Yes Yes Yes Y179 Location Identifier for Road 1 Crossing Point Signs / Plan view ID Yes Yes Yes Y180 Location Identifier for Road 2 Crossing Point Signs / Plan view ID Yes Yes Yes Y181 Location Identifier for Road 3, 4, etc Signs / Plan view ID Yes Yes Yes Y185 Interchange Identifier for this Ramp Signs / Plan view ID Yes Yes Yes Y186 Unique Ramp Identifier Signs / Plan view ID Yes Yes Yes Y197 Location Identifier for Roadway At Beginning Ramp Terminal Signs / Plan view ID Yes Yes Yes Y201 Location Identifier For Roadway At Ending Ramp Terminal Signs / Plan view ID Yes Yes Yes Y196 Ramp Descriptor at Beginning Ramp Terminal Signs / Plan view ID Yes No Yes N198 Location of Beginning Ramp Terminal Relative to Mainline Flow Signs / Plan view ID Yes Yes Yes Y
MIRE ELEMENT COLLECTION MECHANISMS 

50

Table 11. Potential of technologies to collect MIRE elements (cont.).

MIRE 

Element 

No. 

MIRE Data Elements Type 

Groundbased 

Imaging 

Aerial 

Imaging 

Groundbased 

LIDAR 

Airb

LID

w

Ima

200 Ramp Descriptor at Ending Ramp Terminal Signs / Plan view ID Yes No Yes N

202 Location of ending Ramp Terminal Relative to Mainline Flow Signs / Plan view ID Yes Yes Yes Y

76 Number of Signalized Intersections in Segment Signs & Plan view ID Yes Yes Yes Y77 Number of Stop-Controlled Intersections in Segment Signs & Plan view ID Yes Yes Yes Y

78 Number of Uncontrolled/Other Intersections Signs & Plan view ID Yes Yes Yes Y

35 Auxiliary Lane Presence/Type Signs/Plan view ID/ Marking Yes Yes Yes Y

38 HOV Lanes Signs/Plan view ID/ Marking Yes Yes Yes Y39 Reversible Lanes Signs/Plan view ID/ Marking Yes Yes Yes Y40 Presence/Type of Bicycle Facility Signs/Plan view ID/ Marking Yes Yes Yes Y25 Surface Friction Surface N.A. N.A. N.A. N

27 Pavement Roughness/Condition Surface N.A. N.A. N.A. N

29 Pavement Condition (Present Serviceability Rating) Surface Yes Yes Yes Y
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

51

The required information for “sign” elements includes the location, shape, and content of the 

signs. The ground-based imaging and LIDAR technologies are capable of collecting this 

information. FHWA’s DHMS uses optical character recognition to read the content of signs, 

while the terrestrial LIDAR can recognize sign content from the reflectivity of laser pulses. An 

agency can derive certain elements, such as the number of signalized or stop-controlled or 

uncontrolled intersections in a segment, through a GIS-based query if information is available on 

the presence/type of signs and the number of intersections in a segment obtained from plan 

views. An agency can obtain location identifiers for curves, grades, intersecting roads, and 

ramps from signs or plan views, but it requires significant post-processing efforts and quality 

checks. 

The reviewed technologies can collect all elements in the pavement markings category. 

However, only terrestrial imaging can collect visual data, such as surface type, shoulder type, 

and the presence and type of rumble strips. An agency would need high-resolution cameras to 

identify lane elements (e.g., auxiliary, HOV, and reversible lanes) using signs, pavement striping, 

or plan views, or some combination thereof. An agency can identify the presence and type of 

bicycle facilities by signs, marking, or plan views. 

Plan views obtained from imagery or LIDAR techniques with GPS data can identify as many as 

18 elements. The spatial data serve to identify attributes for elements like the type of 

interchanges and alignment features and the number of different lane types. Similarly, plan views 

can provide the means to measure as many as 40 elements, provided accurate spatial data are 

available. All four technologies can provide x-, y-, and z-coordinates. Using the spatial data 

coordinates, such as length, width, and height, an agency can measure directly or indirectly in 

computation of features. An agency can obtain elements like the length of ramps, curves, and 

turn lanes directly from the longitudinal measurements. 

Similarly, elements such as the surface width, shoulder width, and entry and exit widths of 

circular intersections can be measured from transverse measurements. Side slopes and 

roadside clear zone widths can also be obtained; however, there are limitations with groundbased techniques if the sides are too steep beyond the reach of the camera or lasers. Terrain 

information, whether flat, mountainous, or rolling, can be gathered using the operator’s 

judgment. 

Some elements (e.g., intersection skew angles or the entry and exit radii of circular 

intersections) must be derived from further computations of spatial coordinates. Roadway 

alignment elements (e.g., the radius of a horizontal curve or percent vertical gradient) need 

further computations and post-processing work such as tracing and curve fitting to obtain more 

accurate information. 

In summary, these technologies can collect most of the data elements in MIRE. However, 

significant post-processing efforts, including manual intervention and quality checks, are 

necessary in most cases. These technologies have limitations too. The airborne technologies are 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

52

not appropriate for collecting information on signs, whereas the ground-based technologies 

have limitations in collecting information on side slope and width if the slope is too steep. Only 

terrestrial imaging can collect information on image elements where high-resolution is 

necessary. None of these technologies can collect information on smaller objects for traffic 

control, such as the type of pedestrian signals. It also should be noted that the scope of 

information provided in Table 11 is restricted to the capabilities of these four technologies. 

However, other important factors, including the accuracy of information and cost 

considerations, were not taken into account. 

The fact that a system is capable of extracting an asset with accurate attributes is not a direct 

indication that the system is capable of producing these results in a cost-effective manner. Each 

system has a different set of requirements for field data collection and subsequent office work 

to extract asset results. The efforts required to produce high-quality results in a timely manner 

are significant but difficult to quantify. The true time and cost to extract assets is proprietary 

for most data collection vendors. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

53

CHAPTER 8: CONCLUSIONS AND RECOMMENDATIONS 

The following conclusions are drawn from this study: 

 The HSIS and non-HSIS State databases reviewed in this study do not contain all of the 

inventory data needed for conducting sophisticated safety analyses. The HSIS and nonHSIS databases include a reasonable number of data elements for MIRE’s roadway 

segment location/linkage, classification, cross section (lane, shoulders, and medians), 

surface condition, and traffic volume subcategories. However, most of these databases 

lack some specific data elements (e.g., surface friction, rumble strips, curbs, and 

sidewalks) in these subcategories. 

 None of the databases reviewed herein included any information on traffic volumes, 

facilities, and other features for pedestrians and bicycles. HPMS provides only statewide 

summaries of motorcycle data (percent of total travel related to motorcycles) for each 

functional classification. 

 Only a few State databases have information on basic alignment features for horizontal 

curves and vertical grades. Often these databases lack geometric features that are 

critical to safety analyses. 

 While only a few of the HSIS State databases contain limited information on location 

and traffic volumes, supplementary analyses of available traffic and spatial data may help 

establish information for intersections, interchanges, and ramps. 

 Among the HSIS states, only California and Minnesota have intersection files that 

contain information on both intersection geometry and intersection traffic control. 

None of the non-HSIS inventories contain data to describe traffic control at 

intersections, turn counts and prohibitions, rumble strips, intersection geometry, and 

circular intersections. 

 Most of the State databases reviewed in this study do not include information on 

roadside elements, safety hardware, signs, signals, pavement markings, or lighting. 

 Per the results of the polls conducted as part of the MIRE workshops, the data elements 

that are considered difficult to collect tend to be the data elements that are missing 

from existing data inventories. 

 The HPMS inventory contains 21 Full Extent and 24 Sample data items in MIRE. While 

HPMS only requires a large number of data items for sample segments rather than for 

the Full Extent file, the review of HPMS could indicate what data the States may have in 

their databases. There appears to be a strong correlation between the data available in 

State databases and HPMS reporting requirements. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

54

 Automated data collection techniques seem to have an edge over manual surveys in 

terms of improved efficiency and accuracy. Adoption of these techniques for data 

collection would help to ensure crew safety and cost-efficiency, which might make 

agencies more likely to collect the data elements necessary for MIRE. However, most of 

the automated techniques require further development before specific MIRE elements 

can be collected. 

 Most States have used automated data collection technologies successfully for pavement 

management purposes. However, only a few have used these techniques for collecting 

other roadway inventory data, such as alignment features. Some States still extract data 

from as-built plans, and others do not collect the data at all. 

 The specialized instrumented vehicles and the mobile mapping system appear to be the 

most popular and most promising choice for States’ inventory data collection. These 

systems are well-equipped to collect MIRE data elements and for other inventory 

purposes. 

 Agencies are increasingly using technologies (e.g., LIDAR and satellite imagery) for data 

collection purposes. However, they require a concentrated effort for automated data 

extraction and post-processing. The increased use of these technologies in both 

research and production-level projects, as well as technological advancements, will help 

move these issues forward. 

 Agencies can use the KML-based browsers, such as Google Earth, as a visualization tool 

for presenting roadway inventory data. 

 Agencies can collect most of the elements in the MIRE listing using the technologies 

evaluated in this study; however, there are limitations in their capabilities. Significant 

post-processing will be required to extract the desired MIRE data elements using 

manual, fully automated, or semi-automated methods. Since agencies do not routinely 

collect many of the MIRE data elements during the automated data collection surveys, 

agencies may need to develop standard formulas or analysis methods to ensure that the 

data collected meet MIRE requirements. 

 While much of the data required for MIRE exists in the record sets of agencies, 

particularly larger agencies, often the agency has the data scattered among disparate 

databases and paper record sets. The data may be inaccurate due to either poor data 

collection or data aging. An up-to-date, accurate, and comprehensive dataset is needed. 

 This research effort includes a thorough review of all readily available resources; 

however, it was not an exhaustive examination of each State’s collection practices. 

Additional potential research could include a more comprehensive review of all State 

data inventories and systems. 
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

55

RECOMMENDATIONS

The key recommendations for addressing the MIRE data gaps are as follows: 

 Agencies should consider using available information systems, such as asset management 

systems, to serve as alternate sources of data for MIRE. 

 Other supplemental systems that contain inventories of physical assets (e.g., roadside 

hardware, signs, and signals) can also be used for filling the data gaps. Future research 

should focus on exploring other supplemental databases to determine their value for 

collecting MIRE data. 

 States should consider integrating the information collected for various internal 

purposes (e.g., planning, asset management, etc.) across the entire agency to create 

linkages and export data between systems. In this vein, States should explore cloud 

computing solutions and data warehousing techniques to achieve better integration. 

 States should also consider integrating their data needs to save time and resources. 

 States should develop data standards for MIRE collection to ensure data quality and 

facilitate transfer between information systems. 

 The public and private sector should explore the development of automated techniques 

for extracting inventory feature data from advanced collection techniques into a usable 

database format. 

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

safety of roadways. However, collecting additional roadway safety data can be a large 

undertaking. Integrating existing data systems, standardizing data collection and storage, and 

utilizing automated technologies are just some of the recommendations for States to move 

forward in expanding their data capabilities. These steps can help States implement a roadway 

inventory system, such as MIRE MIS, to help support their safety programs through data-driven 

decision-making.
 MIRE ELEMENT COLLECTION MECHANISMS AND GAP ANALYSIS 

56

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Robert Pollack

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