MIRE Data Collection Guidebook

FHWA Safety Program

http://safety.fhwa.dot.gov
MIRE DATA COLLECTION GUIDEBOOK

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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 identifying optimal data file

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

Agency Program, a suggested MIRE data file structure report, a report on performance

measures states can undertake to assess the quality of their roadway inventory data and a

report on collection mechanisms and gap analysis that will assist the states in conducting a

more effective safety program. The intent of the MIRE-MIS project is the integration of MIRE

into States’ safety management processes.

The MIRE Data Collection Guidebook is one of the products of the MIRE MIS effort. The

Guidebook builds upon the MIRE, Version 1.0 document to identify issues State’s should be

cognizant of when collecting information on specific MIRE elements. The guidebook discusses

methods of collecting the data and potential limitations of those methods. This document will

provide data managers and collectors with information regarding techniques for collecting MIRE

data elements that will allow them to potentially collect the elements more efficiently.

Michael S. Griffith Monique R. Evans

Director, Office of Safety Technologies Director, Office of Safety Research and 

Development
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Notice

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

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

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

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

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

objective of the document.

Quality Assurance Statement

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

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

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

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

and processes to ensure continuous quality improvement.
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TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-SA-13-009 

2. Government Accession 

No. 

3. Recipient's Catalog No.

4. Title and Subtitle

MIRE Data Collection Guidebook

5. Report Date

June 2013 

6. Performing Organization Code

7.Author(s)

Jag Mallela, Suri Sadasivam, Rachel Becker, Deepak Raghunathan, Nancy Lefler, 

Rebecca Fiedler, and Hugh McGee 

8. Performing Organization Report No.

9. Performing Organization Name and Address

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

100 Trade Centre Drive, Suite 200 8300 Boone Blvd., Suite 700 

Champaign, IL 61820 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, D.C. 20590

13. Type of Report and Period

Final Report, May 2010 – June 2013 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

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

16. Abstract

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

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

more data-driven decision-making. The MIRE Data Collection Guidebook was developed to provide agencies with the 

necessary information to facilitate the collection of MIRE data. This Guidebook was developed as part of the FHWA 

MIRE Management Information System (MIRE MIS) project to test the feasibility of converting MIRE, a listing of 

elements, into a MIS. The Guidebook is not intended to provide a complete methodology for the collection of all MIRE 

data, but instead provide State and local agencies practical means to collect MIRE data that are not already collected. 

Transportation agencies can incorporate the recommendations in the Guidebook into their current data collection methods. 

17. Key Words: 

MIRE, safety data, roadway inventory data, traffic 

data, data collection, data dictionary

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page) 

Unclassified

21. No. of Pages

 318 

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized. 
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MIRE DATA COLLECTION GUIDEBOOK

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TABLE OF CONTENTS

EXECUTIVE SUMMARY................................................................................................XI

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

Purpose ........................................................................................................................................................... 2

OVERVIEW OF COLLECTION METHODOLOGIES.................................................. 6

Data Mining.................................................................................................................................................... 6

Manual Collection (Foot on Ground)...................................................................................................... 7

Ground-based Imaging (Mobile Mapping)............................................................................................... 8

Ground-based Imaging with LIDAR...................................................................................................... 10

Aerial Imaging............................................................................................................................................. 12

Aerial Imaging with LIDAR...................................................................................................................... 13

Summary...................................................................................................................................................... 14

COLLECTION OF MIRE DATA ELEMENTS ..............................................................16

I. Roadway Segment Descriptors .......................................................................................................... 17

II. Roadway Alignment Descriptors.................................................................................................... 176

III. Roadway Junction Descriptors....................................................................................................... 193

REFERENCES ................................................................................................................309

INDEX.............................................................................................................................311
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LIST OF TABLES

Table 1. MIRE FDE for all public roads with AADT ≥ 400 vehicles per day (vpd)......4

Table 2. MIRE FDE for all public roads with AADT < 400 vpd..................................... 5

Table 3. Suggested data collection methodologies for MIRE. ....................................15

Table 4. Functional classification systems.....................................................................39

Table 5. List of relevant ASTM/AASHTO standards for IRI measurement. ............54
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LIST OF FIGURES

Figure 1. Ground-based imaging (including LiDAR)...................................................... 9

Figure 2. Route signs .......................................................................................................32

Figure 3. Route sign auxiliaries ......................................................................................34

Figure 4. Illustration of cross section, two-lane roadway............................................64

Figure 5. Illustration of cross section, multilane divided roadway inventoried in two 

directions (each direction inventoried separately)......................................................65

Figure 6. Illustration of cross section, multilane divided roadway inventoried in one 

direction (both directions inventoried together).........................................................66

Figure 7. Example of climbing lane................................................................................68

Figure 8. Example of passing lane..................................................................................69

Figure 9. Illustration of presence/types of bicycle facilities.........................................76

Figure 10. Right shoulder milled or rolled on/under edgeline....................................88

Figure 11. Left shoulder rumble strips ..........................................................................94

Figure 12. Median sideslope..........................................................................................108

Figure 13. Illustration of types of median crossover/left turn lanes.........................112

Figure 14. Illustration of roadside ratings ...................................................................121

Figure 15. FHWA vehicle classification.......................................................................142

Figure 16. School zone with mandatory and optional signs .....................................160

Figure 17. Signs for parking prohibitions ....................................................................163

Figure 18. Illustration of on-street parking type ........................................................165

Figure 19. Signs for HOT lanes ....................................................................................168

Figure 20. Centerline rumble strips (milled)..............................................................172

Figure 21. Illustration of types of curve features .......................................................177

Figure 22. Components of a simple horizontal curve ...............................................179

Figure 23. Placement of spiral curve ...........................................................................183

Figure 24. Vertical curves .............................................................................................188

Figure 25. Illustration of types of intersection/junction geometry ..........................200

Figure 26. Illustration of intersecting angle................................................................204

Figure 27. Illustration of intersection/junction offset distance ................................. 206
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Figure 28. Illustration of circular intersection............................................................214

Figure 29. Illustration of types of bicycle facilities at circular intersections ...........217

Figure 30. Illustration of unique approach identifiers................................................219

Figure 31. Illustration of left turn lane types ..............................................................227

Figure 32. Illustration of positive, negative, and zero offset distance......................232

Figure 33. Right turn channelization by raised island................................................233

Figure 34. Right turn channelization by painted island with receiving lane............234

Figure 35. Illustration of exclusive left turn lane length............................................237

Figure 36. Illustration of exclusive right turn lane length .........................................239

Figure 37. Illustration of median type .........................................................................242

Figure 38. Examples of left turn protection ...............................................................246

Figure 39. No left turn permitted during certain portions of the day.....................253

Figure 40. Signs prohibiting RTOR..............................................................................255

Figure 41. Transverse rumble strip .............................................................................261

Figure 42. Illustration of circular intersection exit and entry lanes.........................265

Figure 43. Illustration of circulatory intersection exclusive right turn lanes ..........267

Figure 44. Illustration of ramp descriptors ................................................................. 276

Figure 45. Illustration of types of interchanges ..........................................................281

Figure 46. Illustration of ramp length for freeway and intersection ramp 

connections.....................................................................................................................287

Figure 47. Illustration of acceleration lane length for tapered and parallel 

designs.............................................................................................................................289

Figure 48. Illustration of deceleration lane length for tapered and parallel 

designs.............................................................................................................................291

Figure 49. Presence of ramp metering........................................................................296

Figure 50. Ramp advisory speed limit signs ................................................................298

Figure 51. Illustration of locations of beginning ramp terminal relative to mainline 

flow ..................................................................................................................................303

Figure 52. Illustration of locations of ending ramp terminal relative to mainline 

flow ..................................................................................................................................308
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ACRONYMS

3-D Three-Dimensional 

AADT Annual Average Daily Traffic

AC Asphalt Concrete

ADT Average Daily Traffic

ASTM American Society for Testing and Materials

ATR Automatic Traffic Recorders

CRCP Continuously Reinforced Concrete Pavement

DMI Distance Measuring Instruments

DOT Department of Transportation

ETL Express Toll Lanes

FDE Fundamental Data Elements

FHWA Federal Highway Administration

FIPS Federal Information Processing Standard

FRA Federal Railroad Administration

GLC Geographic Locator Codes

GPS Global Positioning System

GSA General Service Administration

HOT High Occupancy Toll

HOV High Occupancy Vehicle

HPMS Highway Performance Monitoring System

HSIP Highway Safety Improvement Program

HSM Highway Safety Manual

INS Inertial Navigation Systems

IRI International Roughness Index

ITS Intelligent Transportation System

JPCP Jointed Plain Concrete Pavement

JRPC Jointed Reinforced Concrete Pavement
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LiDAR Light Detection and Ranging

LRS Linear Referencing System

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

MIRE Model Inventory of Roadway Elements

MMUCC Model Minimum Uniform Crash Criteria

MP Milepost

MPH Miles Per Hour

MPO Metropolitan Planning Organization

NCHRP National Cooperative Highway Research Program

NHS National Highway System

OAV Oblique Aerial View

PC Point of Curve

PCC Portland Cement Concrete

PMS Pavement Management Systems

PSR Present Serviceability Rating

PT Point of Tangent

QA/QC Quality Assurance/Quality Control

RR Railroad

RTOR Right Turn On Red

SOV Single Occupancy Vehicle

SPI Single Point Interchange

USC United States Code

USGS United States Geological Survey

VPD Vehicles Per Day
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EXECUTIVE SUMMARY

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.

A group of experts conducted an international roadway data scan and recommended 

development of a list of roadway inventory and operations data elements as a companion to the 

Model Minimum Uniform Crash Criteria (MMUCC) (1). To this end, the Federal Highway 

Administration (FHWA) developed the Model Inventory of Roadway Elements (MIRE), a data

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

safety management (2). 

The purpose of the MIRE Data Collection Guidebook is to provide agencies with information 

to facilitate the collection of MIRE data. The potential for error exists in all data collection 

techniques. Agencies collecting data must exercise care to ensure that accurate and timely data 

collection can be performed safely and in a manner that ensures quality data.

This Guidebook is not intended to serve as a substitute for sound engineering judgment or 

provide a complete methodology for the collection of all required MIRE data. The Guidebook

is intended to provide State and local jurisdictions with practical means to collect MIRE data 

that are not traditionally collected by agencies. Agencies can incorporate the Guidebook’s 

recommendations into their current data collection methods.
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INTRODUCTION

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 the those countermeasures. Comprehensive roadway safety data include 

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

Federal safety programs support this data-driven approach to decision making. The Highway 

Safety Improvement Program (HSIP) is a core Federal-aid program whose purpose is to achieve 

a significant reduction in fatalities and serious injuries on all public roads, including non-Stateowned public roads and roads on tribal land [23 United States Code (USC) 148 (b)(2)]. The 

HSIP focuses on performance and employs a data-driven, strategic approach to improving 

highway safety on all public roads. In July of 2012, the transportation legislation Moving Ahead 

for Progress in the 21st Century (MAP-21) was passed. MAP-21 calls for advancing the 

capabilities of States for safety data collection, integration, and analysis to support program 

planning and performance management and continues to support data improvement activities as 

an eligible HSIP expense [23 USC 148 (a)(4)(B)(xiv)]. Safety data means crash, roadway, and 

traffic data on a public road, and, includes, in the case of a railway-highway grade crossing, the 

characteristics of highway and train traffic, licensing, and vehicle data [23 USC 148 (a)(9)] (3). 

Even prior to MAP-21, the Federal Highway Administration (FHWA) recognized the 

importance of an integrated data system for safety. In 2003, a group of experts conducted an 

international roadway data scan and recommended development of a list of roadway inventory 

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

(MMUCC) (4, 5). To this end, the FHWA developed the Model Inventory of Roadway 

Elements (MIRE), a data dictionary of recommended roadway and traffic data elements critical 

to supporting highway safety management. The initial version of MIRE was released in 2007 (5), 

and a revised version, MIRE Version 1.0, was released in late 2010 (2). 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.

d. Roadside Descriptors.
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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.

b. Interchange and Ramp Descriptors.

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

reduced workforces will likely be the norm for 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.

PURPOSE

The purpose of the MIRE Data Collection Guidebook is to provide agencies with information 

to facilitate the collection of MIRE data. This Guidebook focuses on the MIRE elements that 

are not already traditionally collected or for which accepted data collection guidance has not 

already been developed. This Guidebook provides detailed information on these elements. 

The information will help facilitate importing the MIRE data into new or existing databases. The 

potential for error exists in all data collection techniques. Agencies collecting data must 

exercise care to ensure that accurate and timely data collection can be performed safely and in

a manner that ensures quality data.

This Guidebook is not intended to serve as a substitute for sound engineering judgment or 

provide a complete methodology for the collection of all MIRE data. The Guidebook is 

intended to provide State and local jurisdictions with practical means to collect MIRE data that 

are not traditionally collected by agencies. Agencies can incorporate the Guidebook’s 

recommendations into their current data collection methods. 

MAP-21 requires the Secretary to establish a subset of the MIRE that are useful for the 

inventory of roadway safety and ensure that States adopt and use the subset to improve data 

collection [23 USC (f)(2)]. In response, FHWA released the MAP-21 Guidance on State Safety 

Data Systems, which provides information on the set of roadway and traffic data elements States 

should be collecting on all public roads because they are fundamental to support a State’s HSIP
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(3). This set of elements is herein referred to as the MIRE Fundamental Data Elements (MIRE 

FDE). The MIRE FDE include segment, intersection, and ramp data elements and were 

determined to be the basic set of data elements that an agency would need to conduct 

enhanced safety analyses to support a State’s HSIP. This guidance supersedes the Guidance 

Memorandum on Fundamental Roadway and Traffic Data Elements to Improve the Highway Safety 

Improvement Program and the 2011 FDE (6). The MIRE FDE are based on the elements needed 

to apply the Highway Safety Manual (HSM) roadway safety management (Part B) procedures 

using network screening and analytical tools. They are a subset of MIRE and are equivalent to 

some Highway Performance Monitoring System (HPMS) full extent elements that States submit 

for Federal-aid highways. The MIRE FDE are divided into a full set of MIRE FDEs and a reduced 

set of MIRE FDEs for roads with an annual average daily traffic (AADT) less than 400 vehicles 

per day. Table 1 and Table 2 summarize the MIRE FDE.
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Table 1. MIRE FDE for all public roads with AADT ≥ 400 vehicles per day (vpd). 

FDE (MIRE Number)^

Roadway Segment Intersection

Segment Identifier (12) Unique Junction Identifier (120)

Route Number (8)0 Location Identifier for Road 1 Crossing Point (122)

Route/Street Name (9)0 Location Identifier for Road 2 Crossing Point (123)

Federal-aid/ Route Type (21)* Intersection/Junction Geometry (126)

Rural/Urban Designation (20)* Intersection/Junction Traffic Control (131)

Surface Type (23) AADT (79) [for Each Intersecting Road]

Begin Point Segment Descriptor (10) AADT Year (80) [for Each Intersecting Road]

End Point Segment Descriptor (11) Unique Approach Identifier (139)

Segment Length (13) Interchange/Ramp

Direction of Inventory (18) Unique Interchange Identifier (178)

Functional Class (19)* Location Identifier for Roadway at Beginning Ramp Terminal 

(197)

Median Type (54) Location Identifier for Roadway at Ending Ramp Terminal 

(201)

Access Control (22)† Ramp Length (187)

One/Two-Way Operations (91)* Roadway Type at Beginning Ramp Terminal (195)

Number of Through Lanes (31)* Roadway Type at Ending Ramp Terminal (199)

Average Annual Daily Traffic (AADT) 

(79)* Interchange Type (182)

AADT Year (80) Ramp AADT (191)*

Type of Government Ownership (4)* Year of Ramp AADT (192)

Functional Class (19)*

Type of Government Ownership (4)*

^ Model Inventory of Roadway Elements – MIRE Version 1.0 (2). 

* HPMS full extent elements required on all Federal-aid highways and ramps located within grade‐separated 

interchanges, i.e., National Highway System (NHS) and all functional systems excluding rural minor collectors and 

locals.

0 HPMS element required on all NHS, Interstate, Freeway & Expressways, Principal Arterials, and Minor Arterials.

† HPMS element required on all NHS, Interstate, Freeway & Expressways, and Principal Arterials
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Table 2. MIRE FDE for all public roads with AADT < 400 vpd. 

FDE (MIRE Number)^

Roadway Segment Intersection

Segment Identifier (12) Unique Junction Identifier (120)

Functional Class (19)* Location Identifier for Road 1 Crossing Point (122)

Surface Type (23) Location Identifier for Road 2 Crossing Point (123)

Type of Government Ownership (4)* Intersection/Junction Geometry (126)

Number of Through Lanes (31)* Intersection/Junction Traffic Control (131)

Average Annual Daily Traffic (AADT) (79)*

Begin Point Segment Descriptor (10)

End Point Segment Descriptor (11)

Rural/Urban Designation (20)*

^ Model Inventory of Roadway Elements – MIRE Version 1.0 (2). 

* HPMS full extent elements required on all Federal-aid highways and ramps located within grade‐separated 

interchanges, i.e., NHS and all functional systems excluding rural minor collectors and locals.

This Guidebook will indicate the elements that are part of the FDEs in the element listing under 

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

many of the MIRE data elements as possible on all public roadways.
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OVERVIEW OF COLLECTION METHODOLOGIES

This chapter will provide an overview of the methodologies to collect data related to the 

various MIRE elements. Data collection and processing technologies are continually evolving. 

As such, the information presented should be considered a snapshot of the currently available 

methodologies.

DATA MINING

Potential Data Sources

Highway agencies can use existing roadway inventories to extract information for the MIRE 

management information system. These include HPMS and State-maintained roadway 

inventories. Agencies can also use standalone information systems maintained for pavements, 

bridges, culverts, signs, signals, pavement markings, and other roadway hardware. Other 

potential sources include previously recorded data, including as-built plans, construction 

records, videologs, and other off-line data sources. The order in which to seek and utilize the 

existing information would vary by the data type and completeness and accuracy of the existing

data.

Concerns

The primary concern with the State-maintained roadway inventories and HPMS is that they do 

not include coverage of all MIRE data elements or include all public roads. These inventories

were created for other purposes. They may contain data elements for MIRE’s roadway 

location, linkage, and classification categories. These databases may also contain basic elements 

that describe surface, lanes, shoulders, and medians. However they often lack specific 

information such as surface friction, rumble strips, cross slope, and geometry of grades and 

curves. Most available data inventories lack information on roadside features, roadside 

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

and in-depth alignment features. Furthermore, these inventories are primarily designed for 

facilities serving automobile and truck traffic, and therefore, lack facility or traffic information 

pertaining to pedestrians, bicycles, and motorcycles.

While supplementary data inventories could serve as a potential source of information for MIRE 

data, each highway agency should conduct their own evaluation to assess the feasibility of 

utilizing these sources for MIRE. These supplementary sources are often collected and/or 

maintained by disparate groups within each agency.

Other key concerns while trying to use existing inventories for MIRE purposes are the accuracy 

and timeliness of available data. The validity of the data will vary based on the method of 

collection, method of data mining, and the overall age of the datasets being used.
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Nevertheless, inasmuch as possible, where existing inventories have data, it is suggested that 

they be used as appropriate.

MANUAL COLLECTION (FOOT ON GROUND)

Available Technologies and Methodologies

Backpack-based data collection method has been traditionally employed by transportation 

agencies for manual data collection. Backpack-based data collection uses lightweight, mobile 

equipment that can be hand-held or carried on the back of a person in the field. Typically, a 

differential Global Positioning System (GPS) is used for georeferencing to establish the location 

of each data element. Descriptive data for the objects of interest are recorded using image 

capturing devices or electronic devices by using hand-held, laptop, or pen-based computers.

Emerging Technologies and Methodologies

Street level imagery gathered using pedestrian platforms is an emerging technology. The 

pedestrian platforms often include a camera tower installed with multiple, high resolution 

cameras. The camera tower is equipped with an actuator that can rise up or down, as needed, 

to provide a full, un-obstructed view for the cameras. The standard camera tower in the 

system can be transferred between vehicle and pedestrian platforms, and it has a lowering 

mechanism for easy storage and transportation.

Data Collection Efficiency

Manual methods are less efficient because the data collection process is slow and labor 

intensive, often resulting in high costs and less coverage of the road system. However, the 

capital costs related to the equipment would be low. Furthermore, the data collection effort 

can be affected by adverse weather and poor lighting conditions. This method is accurate for 

data elements that involve visual identification and simple measurements such as number of 

lanes, total surface paved width, presence of rumble strips, median type, school zone indicator 

and junction type. The manual method may be suitable for data elements that involve complex 

measurements such as intersection skew angle, curves and grades. With good quality control 

at work, manual methods often do not require significant post-processing efforts.

Concerns

The major concerns with the use of manual methods are as follows: 

• Data collection by the backpack method is time-consuming because of the time involved 

in walking from one inventory element to another.

• Size and weight restrictions mean that the backpack data collection method does not 

offer many choices in terms of use of sensory equipment.

• The backpack input devices must operate on lightweight, long-life batteries and must 

tolerate rugged handling during field operations.
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GROUND-BASED IMAGING (MOBILE MAPPING)

Available Technologies

Ground-based imaging, often referred to as a mobile mapping system, is a specially 

instrumented vehicle installed with high-definition cameras for image capture (see Figure 1-a). 

This process is synchronized with georeferencing tools that typically include a real-time 

differential GPS, distance measuring instruments (DMI), and inertial navigation systems (INS).

Multiple high-resolution cameras are typically mounted on the front and back, pointing 

downward to facilitate vertical and horizontal capture of images (see Figure 1-b). This facilitates 

precise documentation of all roadway features in all directions including the right-of-way, 

overhead signs, and roadside condition.

As the data is being collected, differential correction techniques are applied real-time to 

enhance the quality of location data gathered using the real-time differential GPS receivers. 

DMI and INS units are used in conjunction with GPS to maintain precise georeferencing 

especially when the GPS satellite signals are poor. Real-time differential correction is done 

when the base station calculates and broadcasts corrections for each satellite or land-based 

rovers as it receives the data. Using real-time differential corrections, location accuracy within 

one to two yards can be achieved. INS uses accelerometers and gyroscopes that measure 

vehicle speed, pitch, roll, and heading, which can be then used to calculate roadway geometric 

elements such as curvature, grade, and cross-slope during post-processing.

The mobile mapping system facilitates image capture and measurements at highway speeds 

ranging up to 70 miles per hour (mph). The system also allows adjustments to images for 

varying lighting and weather conditions. The system includes an image viewer to allow realtime monitoring of images for quality control purposes.
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a. Data collection vehicle

 

b. Imaging cameras c. Laser scanner. 

Figure 1. Ground-based imaging (including LiDAR). 

Images courtesy of Mandli Communications, Inc.

Emerging Technologies

There have been advancements in both data collection and post-processing capabilities of 

mobile mapping systems.

Positioning and imaging sensor technologies have rapidly improved the spatiotemporal accuracy 

of collected records. One such example is the use of real-time kinematic GPS, which has 

improved the positional accuracy to between one to two inches. Another example is the 

application of stereovision technologies in mobile mapping. Stereovision-based mobile mapping 

is an example of an emerging technology that allows the extraction of depth information from 

digital images to provide intuitive and more accurate 3-D mapping of geo-located objects (7). 

This technology is capable of consistently delivering absolute 3-D point accuracies of about 1.6 
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inches (4 cm) in horizontal and 0.8 inches (2 cm) in vertical position. Similarly, the postprocessing capabilities are continuously evolving in the areas of data storage compression, 

spatial indexing and automated feature extraction with the application of technologies such as 

cloud computing and high-performance computing. 

Data Collection Efficiency

Ground-based imagery systems provide images with high resolution texture and color that are 

reasonably accurate for most roadway inventory elements. Ground-based imagery could be 

better than Light Detection and Ranging (LiDAR) technologies in identifying boundaries and 

surface discontinuities. These systems can collect images at highway speeds; however, the 

speed of data collection can be affected by traffic congestion, lower speed limits, and large 

numbers of intersections, interchanges and ramps. The data collection effort is also adversely 

affected by unfavorable weather and poor lighting conditions, although the private vendors offer 

adjustments for captured images.

Ground-based imagery requires significant post-processing effort. Automated image processing 

algorithms and real-time monitoring of image quality are on the rise and can help minimize 

post-processing efforts.

Concerns

The major concerns with the use of ground-based imagery are as follows: 

• Higher cost of data collection and reduction.

• Skilled crew is required for both data collection and post-processing. Specialized 

software is required for automatic or semi-automatic extraction of roadway features.

• Ground-based imaging systems in general take longer time for data collection than aerial 

imaging systems. The data collection effort with ground-based imagery may be 

adversely affected by heavy traffic conditions. Ground-based imagery can also be 

obstructed or blocked by passing vehicles.

• Ground-based imagery is not suitable during unfavorable weather and poor lighting 

conditions. 

GROUND-BASED IMAGING WITH LIDAR

Available Technologies

Ground-based imaging system with LiDAR utilizes a motorized vehicle instrumented with laser

scanners and georeferencing units such as differential GPS, INS and DMI. The laser scanners 

are laser range-finding devices usually encased inside a rotating enclosure and mounted on the 

exterior of the vehicle. The enclosure spins a full 360° rotation to capture the footprint of the 

scanned environment (see Figure 1-c). These systems facilitate image capture and 
MIRE DATA COLLECTION GUIDEBOOK

11

measurements at highway speeds. When no images are needed, the LiDAR units can be 

operated during day and night times.

The laser scan transmits optical pulses and collects back the pulses that reflect off from the 

surface object using a receiver. The receiver measures the travel time of the pulse accurately 

from its start to its return. The time of travel of optical pulses returning from the surface is 

then used to build a point file of 3-D coordinates with sub-centimeter precision. The laser scan 

can measure the location of up to 8,000 surface points in one second. Often multiple laser 

scans are used to obtain denser point clouds and very high spatial resolution.

Emerging Technologies

Ground-based LiDAR systems are increasingly equipped with high-resolution imaging cameras. 

Integrating imagery with point clouds augments the user interpretation and post-processing of 

the collected data to provide a better 3-D perception. Similarly, there have been rapid 

advancements in positional and image sensing capabilities.

Another emerging technology is the potential use of spatial cloud computing concepts in 

geospatial applications (8). With rapid improvement in spatiotemporal accuracy of data, the 

demand for massive data storage, processing, and system capabilities have increased. Cloud 

computing technologies have emerged to solve data storage and computing related challenges. 

Through resource pooling, cloud computing provides massive data storage and intensive 

computing at lower life cycle costs.

Data Collection Efficiency

Ground-based LiDAR systems are capable of providing 3-D corridor scenarios with very high 

spatial resolution. At higher point cloud densities, LiDAR systems are typically accurate for 

roadway inventory purposes. Data collection effort is not hampered by unfavorable weather 

and poor lighting; both the laser scans and georeferencing tools can still operate during 

nighttime without the camera. While this may affect the color imagery of the scene, the 

positional accuracy or resolution of the point files remain unaffected.

Real-time survey capabilities facilitate high-quality data and minimize repetitions. Fewer field 

staff is often necessary during post-processing. LiDAR systems offer the ability to mine the data 

cloud of a corridor snapshot; i.e., it allows further investigation of the existing conditions. 

LiDAR measurements can also be combined with building information models for future use.

Concerns

The major concerns with the use of ground-based LiDAR are as follows: 

• Ground-based LiDAR take longer time for data collection than airborne LiDAR. The 

data collection effort with ground-based system may be adversely affected by heavy 

traffic conditions; occluding effect of passing vehicles may affect data completeness.
MIRE DATA COLLECTION GUIDEBOOK

12

• Skilled personnel are required for both data collection and post-processing. Specialized 

software is required for automatic or semi-automatic extraction of roadway features.

• User interpretation and extraction of roadway features from point clouds of corridor 

scenarios can be inefficient and less intuitive without imagery data.

AERIAL IMAGING

Available Technologies

Aerial imagery using satellites holds potential for inexpensive, real-time collection of large-area 

roadway inventory elements. The images obtained from private vendors can yield 

geopositioning accuracy of close to 4 inches in planimetry (a concept that consists of 

identification and geolocation of land areas in the X-Y plane) and 10 inches in height through 

the use of a single ground control point. Aerial imagery involves significant post-processing 

efforts using appropriate software packages to extract the required information from the 

images along with the georeferences.

Emerging Technologies

Oblique Aerial View (OAV) imagery is fast emerging as a supplement for orthoimagery (a mapquality aerial image corrected for topographic relief, lens distortion, and camera tilt). OAV is a 

continuous oblique concept that provides multiple, angled views of ground features thereby 

allowing the users to see these features in context with other surrounding features regardless 

of which angle is being viewed. The biggest difference between OAV images and normal 

oblique images used in orthoimagery is perspective, with the orthoimagery offering more of an 

overhead image of a ground feature compared to the OAV. 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 large-scale, network-level implementation is yet to be seen. 

Similarly, there are potential benefits with the emerging use of spatial cloud computing concepts 

in geospatial applications.

Data Collection Efficiency

Aerial imagery offers quick and safer data collection over large swaths at lower cost; however, 

aerial images are not as accurate as ground-based images. Since aerial imagery is incapable of 

producing 3-D effects, it is suitable for data elements that can be identified and measured from 

plan view. Aerial imagery requires significant post-processing efforts. Aerial imagery datasets 

are readily available from both private vendors and public agencies.

Concerns

The major concerns with the use of aerial imagery are as follows: 

• Coverage is adversely affected by tree cover, over passes and other canopy effects.
MIRE DATA COLLECTION GUIDEBOOK

13

• With aerial imagery, overlapping strips of images can cause accuracy related issues when 

adjusted for georeferencing.

• Measurement accuracy could be low.

AERIAL IMAGING WITH LIDAR

Available Technologies

Aerial imaging systems with LiDAR, also called airborne laser swath mapping, can collect data at 

high altitudes and thus can cover even remote locations relatively quickly. LiDAR produces 

high-resolution 3-D surfaces whose accuracy depends on the density of cloud points. The point 

cloud 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 two inches and 

point spacing (indicator of horizontal accuracy) as small as one foot. These accuracy standards 

may not be adequate for typical roadway inventory purposes. 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. 

Emerging Technologies

New generation of airborne laser scanners are fast growing in the market. These scanners 

offer high pulse rate and point cloud data density for high-accuracy roadway inventory 

applications. High resolution cameras are being used in conjunction with laser scanners during 

aerial surveys to facilitate integration of images with LiDAR data. Similarly, there are potential 

benefits with the emerging use of spatial cloud computing concepts in geospatial applications.

Airborne LiDAR datasets are readily available for several parts of the US from public forestry, 

agriculture and geological agencies. The United States Geological Survey (USGS) is currently 

coordinating efforts across multiple agencies towards a National LiDAR Dataset. However, the 

feasibility of using these datasets for roadway inventory applications remains unknown. 

Data Collection Efficiency

Like aerial imagery, airborne LiDAR systems offer quick, cheaper and safer data collection over 

large swaths albeit at lower accuracy than ground-based LiDAR. While airborne LiDAR can 

produce 3-D effects with denser point clouds, the coverage can be adversely affected by tree 

cover, over passes and other canopy effects.

Concerns

The major concerns with the use of aerial imagery are as follows: 

• Coverage is adversely affected by tree cover, over passes and other canopy effects. 
MIRE DATA COLLECTION GUIDEBOOK

14

• Significant data processing effort is required to generate digital elevation models.

SUMMARY

All the data collection methods discussed above can be used to collect most of the MIRE 

elements; however, there are advantages and concerns with each method. Airborne 

technologies may be cheaper and faster, but they do not provide information with desired 

accuracy for roadway inventory applications; these methods are not appropriate for collecting 

information on elements that require perspective from ground-level, such as signs. On the 

other hand, ground-based methods provide accurate information but are more expensive and 

time-consuming; these methods have limitations in collecting information on sideslopes and 

width if the slope is too steep. None of these methods can collect information on smaller 

objects for traffic control, such as the type of pedestrian signals. In most cases, significant postprocessing efforts, including manual intervention and quality checks, are necessary.

In summary, each system has a different set of requirements for field data collection and 

subsequent back office work to extract asset results. Several factors, including capabilities, 

accuracy, cost, operational efficiencies, quality control, and post-processing requirements 

should be taken into consideration when selecting an appropriate data collection method. 

There should be a balance between the threshold accuracy of MIRE elements needed for safety 

analysis and the dollars spent on data collection and reduction. Previous sections discuss the 

advantages and disadvantages of each data collection method. Based on this discussion, 

considering both accuracy and effort needed for both data collection and post-processing 

effort, Table 3 presents the suggested data collection methodologies for MIRE data elements.
MIRE DATA COLLECTION GUIDEBOOK

15

Table 3. Suggested data collection methodologies for MIRE. 

Data Element Type

Manual 

Methods

Groundbased 

Imagery

Groundbased 

LiDAR

Aerial 

imagery

Airborne 

LiDAR

Visual identification (e.g., surface type, 

rumble strip presence and type) LP P P MP LP

Marking (e.g., edgeline presence, 

circular intersection-pedestrian facility) LP P MP P MP

Plan view ID (e.g., sidewalk presence, 

interchange type) LP P P P P 

Plan view measurement (e.g., circular 

intersection - entry radius, grade length) LP P P P P 

Signs (e.g., speed limit, school zone 

indicator) LP P P LP LP

Shape (e.g., curb type, interchange 

lighting) LP P P LP MP

Overall LP P P MP MP

Notes: P = Preferable, MP = Moderately Preferable, LP = Less Preferable
MIRE DATA COLLECTION GUIDEBOOK

16

COLLECTION OF MIRE DATA ELEMENTS

This section presents each of the MIRE elements, along with specific guidance on how the 

elements can be collected and considerations for collection. Several elements already have 

well-accepted data collection practices. Part of the description includes references to those 

practices. Each element includes the following information:

• Definition: definition of the data element as defined in MIRE Version 1.0.

• Attributes: list of attributes (coding) as defined in MIRE Version 1.0.

• Accuracy Statement: recommended accuracy for collecting the data element. This is 

based on existing practices and research and was vetted with a technical working group 

consisting of researchers and practitioners.

• Considerations: where applicable, any special considerations regarding the definition 

or attributes. This section also indicates if the data element is an FDE.

• Existing Resources: links to resources that describe existing data collection practices. 

These include both National and State resources. The resources provided are examples 

of current practices and do not include an exhaustive list of all available resources.

• Collection Methods: list of potential collection methods. These are provided for 

elements that do not already have established data collection procedures. 

• Concerns with Collection Methods: where applicable, concerns about data 

collection methods are listed to help the user determine the appropriate method for 

collection. For example, concerns with using aerial imagery for data collection are the 

timeliness of the images and the quality of the image resolution.
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

17

I. ROADWAY SEGMENT DESCRIPTORS

I.A. Segment Location/Linkage Elements

1. County Name

Description 

Definition: The name of the county or equivalent entity where the segment is located.

Attributes:

• The county name or equivalent entity where the roadway segment is located.

Accuracy Statement: 90-100%. 

Considerations

None. 

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

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2. County Code

Description 

Definition: Census defined County Federal Information Processing Standard (FIPS) code or 

equivalent entity where the segment is located.

Attributes:

• The Census defined County FIPS code or equivalent entity where the roadway segment 

is located. If state-assigned codes are used, they should be convertible to the General 

Service Administration (GSA)/FIPS format.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

19

3. Highway District

Description 

Definition: The highway district where the segment is located.

Attributes:

• Numeric district number (as defined by the State).

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Texas Department of Transportation (DOT) Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

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4. Type of Governmental Ownership

Description 

Definition: Type of governmental ownership.

Attributes:

• State Highway Agency.

• County Highway Agency.

• Town or Township Highway Agency. 

• City or Municipal Highway Agency.

• State Park, Forest, or Reservation Agency. 

• Local Park, Forest, or Reservation Agency.

• Other State Agency.

• Other Local Agency. 

• Private (other than Railroad).

• Railroad.

• State Toll Authority. 

• Local Toll Authority.

• Other Public Instrumentality (e.g., Airport, School, University).

• Indian Tribe Nation. 

• Other Federal Agency. 

• Bureau of Indian Affairs. 

• Bureau of Fish and Wildlife. 

• U.S. Forest Service. 

• National Park Service. 

• Tennessee Valley Authority. 

• Bureau of Land Management. 

• Bureau of Reclamation. 

• Corps of Engineers. 

• Air Force. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

21

• Navy/Marines. 

• Army. 

• Other. 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd.

Existing Resources 

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

22

5. Specific Governmental Ownership

Description 

Definition: The specific governmental owner of the segment.

Attributes:

• City name or equivalent entity (e.g., tribal jurisdiction).

Accuracy Statement: 90-100%. 

Considerations

If codes are used instead of name, use the GSA Geographic Locator Codes (GLC) that can be 

found on the internet at: http://www.gsa.gov/portal/content/104507. If State-assigned codes are 

used, they should be convertible to the GSA/FIPS format.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

23

6. City/Local Jurisdiction Name

Description 

Definition: The name of the city or local jurisdiction/agency where the segment is located if 

applicable.

Attributes:

• The city name or equivalent entity (e.g., tribal jurisdiction).

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

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7. City/Local Jurisdiction Urban Code

Description 

Definition: The applicable Census urban area code of the city or local jurisdiction/agency where 

the segment is located.

Attributes:

• The Census urban code.

Accuracy Statement: 90-100%. 

Considerations

See Census website: http://www.census.gov/2010census/. 

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

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8. Route Number

Description 

Definition: The signed route number.

Attributes:

• Signed numeric value for the roadway segment.

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. Descriptive route name information should be 

included in Element 9. Route/Street Name.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

26

9. Route/Street Name

Description 

Definition: The route or street name, where different from route number (Element 8). 

Attributes:

• The alphanumeric route or street name.

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

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10. Begin Point Segment Descriptor

Description 

Definition: Location information defining the location of the beginning of the the segment.

Attributes:

• Begin point will be defined by the user agency (e.g., based on homogeneity of chosen 

attributes throughout the segment). Begin point segment descriptors can be either 

related to a Linear Reference System (LRS) (e.g., Route-beginning milepoint, Routeending milepoint) or to a spatial data system (i.e., longitude/latitude for begin and end 

points). Street address could also possibly be used for urban areas. The 

descriptortypes used must be common across all MIRE files and compatible with crash 

data location coding.

Accuracy Statement: 0.01– 0.001 mile.

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

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11. End Point Segment Descriptor

Description 

Definition: Location information defining the location of the end of the the segment.

Attributes:

• End point will be defined by the user agency (e.g., based on homogeneity of chosen 

attributes throughout the segment). End point segment descriptors can be either 

related to a Linear Reference System (e.g., Route-beginning milepoint, Route-ending 

milepoint) or to a spatial data system (i.e., longitude/latitude for begin and end points). 

Street address could also possibly be used for urban areas. The descriptortypes used 

must be common across all MIRE files and compatible with crash data location coding.

Accuracy Statement: 0.01– 0.001 mile.

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

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12. Segment Identifier

Description 

Definition: Unique segment identifier.

Attributes:

• Derived from other elements (e.g., combination of route number, county location and 

beginning and ending milepoints).

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and roads with AADT < 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
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30

13. Segment Length

Description 

Definition: The length of the segment.

Attributes:

• Miles. 

Accuracy Statement: 0.01– 0.001 mile.

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction.

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

31

14. Route Signing

Description 

Definition: The type of route signing on the segment.

Attributes:

• Not signed. 

• Interstate. 

• U.S.

• State. 

• Off-interstate business marker.

• County. 

• Township. 

• Municipal. 

• Parkway marker or forest route marker. 

• None of the above. 

Accuracy Statement: 90-100%. 

See Figure 2 for examples of route signs included in this element. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

32

Figure 2. Route signs (14). 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

33

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
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34

15. Route Signing Qualifier

Description 

Definition: The descriptive qualifier for the route sign.

Attributes:

• No qualifier or not signed. 

• Alternate. 

• Business route. 

• Bypass. 

• Spur. 

• Loop. 

• Proposed. 

• Temporary. 

• Truck route. 

• None of the above. 

See Figure 3 for examples of the types of signs for this element.

Accuracy Statement: 90-100%. 

Figure 3. Route sign auxiliaries (14). 

Considerations

None.
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

35

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
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36

16. Coinciding Route Indicator

Description 

Definition: Indication of whether the route segment is a "primary" coinciding route (i.e., the 

route to which crashes are referencedand which carries the attribute data) or a "minor"

coinciding route which is not linked to crashes and does not include attribute data. (Note that 

minor-route segments might not appear in the inventory since the primary route inventory 

information is the same for both routes.)

Attributes:

• Segment does not contain coinciding routes.

• Coinciding route – Primary (i.e., crashes linked to this route and attributes included for 

segment).

• Coinciding route – Minor (i.e., crashes not linked to this route).

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

37

17. Coinciding Route – Minor Route Information

Description 

Definition: If this segment is a primary coinciding route segment, enter the route number for 

the minor route.

Attributes:

• Signed coinciding minor route number.

Accuracy Statement: 90-100%. 

Considerations

Additional elements may be needed to handle instances of more than one coinciding minor 

route.

Existing Resources

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.A SEGMENT DESCRIPTORS

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18. Direction of Inventory

Description 

Definition: Direction of inventory if divided roads are inventoried in each direction.

Attributes:

• Reference compass direction if divided roads are inventoried in each direction usually 

due to different characteristics on each roadway.

• Both if inventory in only one direction.

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

39

I.B. Segment Classification

19. Functional Class

Description 

Definition: The functional class of the segment.

Attributes:

• Interstate. 

• Principal arterial other freeways and expressways. 

• Principal arterial other. 

• Minor arterial. 

• Major collector. 

• Minor collector. 

• Local.

See Table 4 for additional details.

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and roads with AADT < 400 vpd. Functional 

classification is the method of grouping the streets and highways into classes, or systems, 

according to the character of service they are intended to provide.

Table 4. Functional classification systems (15). 

Functional 

System Services Provided

Arterial Provides the highest level of service at the greatest speed for the longest 

uninterrupted distance, with some degree of access control.

Collector Provides a less highly developed level of service at a lower speed for shorter distances 

by collecting traffic from local roads and connecting them with arterials.

Local Consists of all roads not defined as arterials or collectors; primarily provides access to 

land with little or no through movement.
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

40

Characteristics of principal/major and minor arterials (16): 

• Principal/major arterials have higher level of service. They have very high average daily 

traffic (ADT) and provide high speed corridors through or between large cities.

o Example: main freeway system through densely populated cities.

• Minor arterials have lower ADTs. They provide high speed corridors through or 

between medium sized population centers.

o Example: main freeway system traversing the rural area between cities.

Characteristics of major and minor collectors (16): 

• Major collectors connect arterial roadways and high traffic generators. In urban areas 

this may include education, commercial, health, and recreational centers. In rural areas 

this may include consolidated schools, shipping points, county parks, important mining 

and agricultural areas, etc. Major collectors also serve as the more important intracity 

or intracounty travel corridors that are not considered arterial.

o Example: Segment that connects to a freeway and provides access to multiple 

stores and restaurants.

• Minor collectors in urban areas penetrate residential areas to connect local roads to 

major collectors and arterial roads. Rural minor collectors serve smaller communities. 

They are spaced at intervals, consistent with population density, thereby collecting 

traffic from local roads and bringing all developed areas within a reasonable distance of a 

collector road.

o Example: Segment that connects a grid of farming roads to a nearby population 

center.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

41

• Georgia DOT HPMS Inventory Manual:

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

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20. Rural/Urban Designation

Description 

Definition: The rural or urban designation based on Census urban boundary and population.

Attributes:

• Rural. 

• Urban (population ≥ 5,000). 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. 

Rural areas are the areas outside the boundaries of small urban and urbanized areas (as 

designated by the Bureau of Census). They have a population of less than 5,000. Small urban 

areas are those urban places, as designated by the Bureau of the Census having a population of 

five thousand (5,000) or more and not within any urbanized area. Urban areas have a 

population equal to or greater than 5,000 (16). 

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

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Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

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21. Federal Aid/Route Type

Description 

Definition: Federal-aid/National Highway System (NHS) route type.

Attributes:

• Route is non Federal-aid. 

• Route is Federal-aid, but not on NHS (i.e., all non-NHS routes functionally classified as 

Interstate, Other Freeways & Expressways, Other Principal Arterials, Minor Arterials, 

Major Collectors, and Urban Minor Collectors). 

• Route is on NHS. 

• NHS connector to Major Airport. 

• NHS connector to Major Port Facility. 

• NHS connector to Major Amtrak Station. 

• NHS connector to Major Rail/Truck Terminal. 

• NHS connector to Major Inter City Bus Terminal. 

• NHS connector to Major Public Transportation or Multi-Modal Passenger Terminal. 

• NHS connector to Major Pipeline Terminal. 

• NHS connector to Major Ferry Terminal. 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

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• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

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22. Access Control

Description 

Definition: The degree of access control.

Attributes:

• Full access control – Preference given to through traffic movements by providing 

interchanges with selected public roads, and by prohibiting crossing at-grade and direct 

driveway connections (i.e., limited access to the facility).

• Partial access control – Preference given to through traffic movement. In addition to 

interchanges, there may be some crossings at-grade with public roads, but, direct private 

driveway connections have been minimized through the use of frontage roads or other 

local access restrictions. Control of curb cuts is not access control.

• No access control – No degree of access control exists (i.e., full access to the facility is 

permitted).

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 
MIRE DATA COLLECTION GUIDEBOOK I.B SEGMENT CLASSIFICATION

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Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.C SEGMENT CROSS SECTION

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I.C. Segment Cross Section

23. Surface Type

Description 

Definition: The surface type of the segment.

Attributes:

• Unpaved. 

• Bituminous. 

• JPCP – Jointed Plain Concrete Pavement. 

• JRCP – Jointed Reinforced Concrete Pavement. 

• CRCP – Continuously Reinforced Concrete Pavement.

• Asphalt-Concrete (AC) Overlay over Existing AC Pavement. 

• AC Overlay over Existing Jointed Concrete Pavement. 

• AC (Bi Overlay over Existing CRCP). 

• Unbonded Jointed Concrete Overlay on Portland Cement Concrete (PCC) Pavements. 

• Unbonded CRCP Overlay on PCC Pavements. 

• Bonded PCC Overlays on PCC Pavements. 

• Other. 

Accuracy Statement: 100% 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. Refer to 

local pavement management systems (PMS) database for determining the surface types.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 
MIRE DATA COLLECTION GUIDEBOOK I.C SEGMENT CROSS SECTION

49

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Ground-based imagery is used for collecting this data element.

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
MIRE DATA COLLECTION GUIDEBOOK I.C SEGMENT CROSS SECTION

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24. Total Paved Surface Width

Description 

Definition: The total paved surface width.

Attributes:

• Feet. 

Accuracy Statement: ± 6 in. 

Considerations

This element could be derived if all paved lane and paved shoulder widths are captured.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Airborne LiDAR with imagery.

• Ground-based imagery.

• Terrestrial LiDAR. 

This element involves measurements during post-processing of data collected. 

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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51

25. Surface Friction

Description 

Definition: The surface friction indicator for the segment.

Attributes:

• Measured skid number on the segment or general indication of wet-surface friction (e.g., 

high, medium, low).

Accuracy Statement: ± 10%. 

Considerations

Agencies will decide how to code segments with no measured number or multiple skid 

numbers and whether one number is indicative of friction on entire segment.

This information is typically collected by an agency for asset management purposes.

Existing Resources

Established data collection methodologies can be found at:

National:

• National Cooperative Highway Research Program (NCHRP) Web-Only Document 

108: Guide for Pavement Friction: 

http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w108.pdf (18). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Surface friction measurement equipment is used for collecting this data element. At the 

network level, the locked-wheel friction tester (American Society for Testing and Materials 

[ASTM] E 274) is the most appropriate. This device is installed on a trailer which is towed 

behind the measuring vehicle at a typical speed of 40 mi/hr (64 km/hr). Water (0.02 in [0.5 

mm] thick) is applied in front of the test tire, the test tire is lowered as necessary, and a braking 

system is forced to lock the tire. Then the resistive drag force is measured and averaged for 1 

to 3 seconds after the test wheel is fully locked. Measurements can be repeated after the 

wheel reaches a free rolling state again.

Concerns with Collection Methods

• Can only be used on straight segments (no curves, T-sections, or roundabouts). Can 

miss slippery spots because measurements are intermittent.
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26. Surface Friction Date

Description 

Definition: Date surface friction was last measured or assigned.

Attributes:

• mm/dd/yyyy. 

Accuracy Statement: 100%. 

Considerations

This element is recorded when an agency collects surface friction information for asset 

management purposes.

Existing Resources

None. 

Collection Methods

None.

Concerns with Collection Methods

None. 
MIRE DATA COLLECTION GUIDEBOOK I.C SEGMENT CROSS SECTION

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27. Pavement Roughness/Condition

Description 

Definition: The numeric value used to indicate pavement roughness.

Attributes:

• International Roughness Index (IRI), reported as an integer to the nearest inch per mile.

Accuracy Statement: ± 10%. 

Considerations

IRI is used to define a characteristic of the longitudinal profile of a traveled wheeltrack, and is a 

standardized pavement measurement indicating the overall smoothness of the roadway.

For a lower IRI value the pavement will ride smoother while for a higher IRI value the pavement 

will ride rougher.

This information is typically collected by an agency for asset management purposes.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

• Alabama DOT Level of Service Condition Assessment Data Collection Manual: 

http://www.dot.state.al.us/maweb/frm/ALDOT%20Condition%20Assessment%20Data%2

0Collection%20Manual.pdf (19). 

Collection Methods

Table 5 below shows the list of relevant ASTM/American Association of State Highway and 

Transportation Officials (AASHTO) standards that are used for IRI measurement.
MIRE DATA COLLECTION GUIDEBOOK I.C SEGMENT CROSS SECTION

54

Table 5. List of relevant ASTM/AASHTO standards for IRI measurement. 

Standard Title Purpose

AASHTO R43M/R43 Standard Practice for 

Determination of International 

Roughness Index (IRI) to 

Quantify Roughness of 

Pavements

• Describes a method for 

estimating roughness for a 

pavement section. 

• Proposes guidelines for a quality 

assurance plan. 

AASHTO R56 Standard Practice for

Certification of Inertial Profiling 

Systems

• Describes minimum performance 

requirements for inertial 

profilers. 

• Describes a certification 

procedure for test equipment. 

AASHTO M328 Standard Equipment 

Specification for Inertial Profiler

• Define the required attributes of 

an inertial profiling system. 

AASHTO R57 Standard Practice for Operating 

Inertial Profilers and Evaluating 

Pavement Profiles

• Describes the procedure for 

operating and verifying the 

calibration of an inertial profiler.

ASTM E 950 Standard Test Method for 

Measuring the Longitudinal 

Profile of Traveled Surfaces 

with and Accelerometer 

Established Inertial Profiling 

Reference

• Covers the measurement and 

recording of the profile of 

vehicular-traveled surfaces with 

an accelerometer established 

inertial reference on a profilemeasuring vehicle.

ASTM E 1170 Standard Practices for 

Simulating Vehicular Response 

to Longitudinal Profiles of 

Traveled Surfaces

• Covers the calculation of 

vehicular response to 

longitudinal profiles of traveled 

surface roughness.

• Presents standard vehicle 

simulations (quarter, half, and full 

car) for use in the calculations.

ASTM E 1296 Standard Practice for 

Computing International 

Roughness Index of Roads from 

Longitudinal Profile 

Measurements

• Covers the mathematical 

processing of longitudinal profile 

measurements to produce IRI. 

ASTM E 2560 Standard Specification for Data 

Format for Pavement Profile

• Describes a data file format for 

pavement profile. 

Concerns with Collection Methods

• There are apparent differences in the measurement and operational practices among 

State agencies that makes the nationwide comparability difficult.
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28. Pavement Roughness Date

Description 

Definition: Date pavement roughness (IRI) was collected.

Attributes:

• mm/dd/yyyy. 

Accuracy Statement: 100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

This element is recorded when an agency collects pavement roughness information for asset 

management purposes.

Concerns with Collection Methods

None. 
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29. Pavement Condition (Present Serviceability Rating)

Description 

Definition: Present Serviceability Rating (PSR) (descriptive scale).

Attributes:

• Code a PSR or equivalent value, to the nearest tenth (x.x), for all paved segments where 

IRI is not reported. Code "0.0" for unpaved facilities. User full range of values.

o 4.0 – 5.0: Only new (or nearly new) superior pavements are likely to be smooth 

enough and distress free (sufficiently free of cracks and patches) to qualify for 

this category. Most pavements constructed or resurfaced during the data year 

would normally be rated in this category.

o 3.0 – 4.0: Pavements in this category, although not quite as smooth as those 

described above, give a first class ride and exhibit few, if any, visible signs of 

surface deterioration. Flexible pavements may be beginning to show evidence of 

rutting and fine random cracks. Rigid pavements may be beginning to show 

evidence of slight surface deterioration, such as minor cracks and spalling.

o 2.0 – 3.0: The riding qualities of pavements in this category are noticeably 

inferior to those of new pavements, and may be barely tolerable for high-speed 

traffic. Surface defects of flexible pavements may include rutting, map cracking, 

and extensive patching. Rigid pavements in this group may have a few joint 

failures, faulting and/or cracking, and some pumping.

o 1.0 – 2.0: Pavements in this category have deteriorated to such an extent that 

they affect the speed of free-flow traffic. Flexible pavement may have large 

potholes and deep cracks. Distress includes raveling, cracking, rutting and 

occurs over 50 percent of the surface. Rigid pavement distress includes joint 

spalling, patching, cracking, scaling, and may include pumping and faulting.

o 0.1 – 1.0: Pavements in this category are in an extremely deteriorated condition. 

The facility is passable only at reduced speeds, and with considerable ride 

discomfort. Large potholes and deep cracks exist. Distress occurs over 75 

percent or more of the surface.

Accuracy Statement: ± 0.2. 

Considerations

None. 

Existing Resources

Established data collection methodologies can be found at:
MIRE DATA COLLECTION GUIDEBOOK I.C SEGMENT CROSS SECTION

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National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

• Alabama DOT Condition Assessment Data Collection Manual: 

http://www.dot.state.al.us/maweb/frm/ALDOT%20Condition%20Assessment%20Dat

a%20Collection%20Manual.pdf (19). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Airborne LiDAR with imagery.

• Terrestrial LiDAR. 

• Ground-based imagery.

This information is typically collected by an agency for asset management purposes.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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30. Pavement Condition (PSR) Date

Description 

Definition: Date PSR was last assigned.

Attributes:

• mm/dd/yyyy. 

Accuracy Statement: 100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

Collection Methods

This element is recorded when an agency collects pavement condition information for asset 

management purposes.

Concerns with Collection Methods

None. 
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31. Number of Through Lanes

Description 

Definition: The total number of through lanes on the segment. This excludes auxiliary lanes, 

such as collector-distributor lanes, weaving lanes, frontage road lanes, parking and turning lanes, 

acceleration/deceleration lanes, toll collection lanes, shoulders, and truck climbing lanes.

Attributes:

• Numeric. 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. 

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

• Aerial imagery.

• Airborne LiDAR with imagery.
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60

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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32. Outside Through Lane Width

Description 

Definition: Width of the outside (curb) through lane (i.e., not including parking area, bicycle 

lanes, gutter pan, etc.). Lane width is measured from center of edgeline to center of centerline 

or to the center of the lane line (if multilane). If edgeline striping is placed inside the edge of 

the pavement (within approximately one foot) to keep traffic from breaking the pavement edge, 

ignore the striping and measure from the pavement edge to the center of a single (or double) 

centerline stripe or to the center of the lane line (if multilane). If there is no edgeline or 

centerline, estimate a reasonable split between the actual width used by traffic and the shoulder 

or parking lane based on State/local design guides.

Attributes:

• Feet. 

See Figure 4 - Figure 6 depending on number of lanes and direction of inventory.

Accuracy Statement: ± 0.5 ft. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Ground-based imagery.

• Airborne LiDAR with imagery.

• Terrestrial LiDAR. 

This element involves measurements during post-processing of data collected.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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33. Inside Through Lane Width

Description 

Definition: Predominant lane width of all inside through lanes, not including outside through 

lane (see Element 32. Outside Through Lane Width). For a two-lane road, leave this element 

blank. 

Attributes:

• Feet.

See Figure 4 - Figure 6 depending on number of lanes and direction of inventory.

Accuracy Statement: ± 0.5 ft.

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual:

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR.

• Airborne LiDAR with imagery.

This element involves measurements during post-processing of data collected.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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Figure 4. Illustration of cross section, two-lane roadway (2). 
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Figure 5. Illustration of cross section, multilane divided roadway inventoried in two 

directions (each direction inventoried separately) (2). 
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Figure 6. Illustration of cross section, multilane divided roadway inventoried in one 

direction (both directions inventoried together) (2). 
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34. Cross Slope

Description 

Definition: The cross slope for each lane starting with the leftmost lane according to direction 

of inventory.

Attributes:

• Sign (+ or -) and percent.

Accuracy Statement: ± 12- 6 in horizontally per 1 ft. vertical. 

Considerations

The cross slope is the percent of elevation change along the cross section of highway pavement. 

It helps in providing proper water drainage facility for the pavements.

Cross slope needs to be captured for each lane individually, thus multiple elements will be 

needed. Should focus on major changes in the cross slope – average slope rather than the 

slope at every point.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

This element involves measurements during post-processing of data collected.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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35. Auxiliary Lane Presence/Type

Description 

Definition: The presence or type of auxiliary lane present on the segment. Center two-way left 

turn lanes and high occupancy vehicle (HOV) lanes are not included here. They are included 

under Element 54. Median Type and Elements 37. HOV Lane Types and 38. HOV Lanes, 

respectively.

Attributes:

• Climbing lane. 

• Passing lane. 

• Exclusive continuous right turn lane. 

• Other. 

See Figure 7 and Figure 8 for additional details.

Accuracy Statement: 90-100%. 

Figure 7. Example of climbing lane (20). 
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Passing Lane

Figure 8. Example of passing lane (21). 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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36. Auxiliary Lane Length

Description 

Definition: Length of auxiliary lane (noted in Element 35. Auxiliary Lane Presence/Type) if not 

full segment length. Length does not include taper.

Attributes:

• Feet.

Accuracy Statement: .01 - .001 mi (52.8 – 5.28 feet). 

Considerations

None.

Existing Resources

• None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

This element involves measurements during post-processing of data collected.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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37. HOV Lane Presence/Type

Description 

Definition: Presence and type of high-occupancy vehicle (HOV) lane(s) on the segment.

Attributes:

• No HOV lanes. 

• Has exclusive HOV lanes. 

• Normal through lanes used as HOV at specified times. 

• Shoulder/parking lanes used as HOV at specified times. 

Accuracy Statement: 90-100%. 

Considerations

Most HOV lanes can be located by aerial view based on pavement markings. To designate 

HOV use at specified times ground-based imagery should be used to locate signs.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11).

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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38. HOV Lanes

Description 

Definition: The maximum number of HOV lanes in both directions on the segment.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

For more information on HOV lanes see Element 37. HOV Lane Presence/Type.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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39. Reversible Lanes

Description 

Definition: Number of reversible lanes on the segment.

Attributes:

• No reversible lanes. 

• One reversible lane. 

• Two reversible lanes. 

• More than two reversible lanes. 

Accuracy Statement: 90-100%. 

Considerations

Reversible refers to the ability to change the direction of travel in a lane depending on traffic 

congestion. For example, a reversible lane may allow traffic to flow towards the center of a 

city in the morning and away from the city in the afternoon to reduce congestion for daily 

commuters.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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40. Presence/Type of Bicycle Facility

Description 

Definition: The presence and type of bicycle facility on the segment.

Attributes:

• None. 

• Wide curb lane with no bicycle markings. 

• Wide curb lane with bicycle markings (e.g., sharrows). 

• Marked bicycle lane. 

• Separate parallel bicycle path. 

• Signed bicycle route only (no designated bicycle facility). 

• Other. 

See Figure 9 for additional details.

Accuracy Statement: 90-100%. 

Considerations

None. 
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Figure 9. Illustration of presence/types of bicycle facilities (14). 
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Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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41. Width of Bicycle Facility

Description 

Definition: The width of the bicycle facility; either the width of the marked bicycle lane or 

bicycle path.

Attributes:

• Feet. 

Accuracy Statement: ± 1 ft. 

Considerations

There is not a separate code for unknowns under each element. In these cases, an agency can 

develop their own standardized means of recording this element through additional codes. For 

instance, non-numeric or negative integers can be assigned for unmarked bicycle facilities.

When there is no bicycle facility (i.e., attribute for Element No. 40 is none), “NULL” should be 

assigned.

Existing Resources

Established data collection methodologies can be found at:

National: 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

This element involves measurements during post-processing of the data collected. 

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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42. Number of Peak Period Through Lanes

Description 

Definition: The number of through lanes used in peak period in the peak direction. This 

includes reversible lanes, parking lanes, or shoulders that legally are used for through traffic 

whether for single-occupancy vehicle (SOV) or HOV operation.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

For inventory covering both directions, code the total number of through lanes. For directional 

inventory, code total number of lanes for this inventory direction. This includes the number of 

reversible lanes that are in use for that direction in the peak period.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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43. Right Shoulder Type

Description 

Definition: The predominant shoulder type on the right side of road in the direction of 

inventory. 

Attributes:

• None. 

• Surfaced shoulder exists – bituminous concrete (AC). 

• Surfaced shoulder exists – Portland Cement Concrete surface (PCC). 

• Stabilized shoulder exists (stabilized gravel or other granular material with or without 

admixture). 

• Combination shoulder exists (shoulder width has two or more surface types; e.g., part 

of the shoulder width is surfaced and a part of the width is earth). 

• Earth shoulder exists. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Ground-based imagery is used for collecting this data element.
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Concerns with Collection Methods

• Timeliness of the ground-based imagery

• Inadequate image capture and poor resolution. 
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44. Right Shoulder Total Width

Description 

Definition: The total width of the right shoulder including both paved and unpaved parts 

measured from the center of the edgeline outward. Do not include parking or bicycle lanes in 

the shoulder width measurement; code the predominant width where it changes back and forth 

along the roadway section; ensure that the total width of combination shoulders is reported. 

Include gutter pans on outside of shoulder in shoulder width.

Attributes:

• Feet. 

See Figure 4 - Figure 6 depending on number of lanes and direction of inventory.

Accuracy Statement: ± 1 ft. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 
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• Airborne LiDAR with imagery.

This element involves measurements during post-processing of data collected.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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45. Right Paved Shoulder Width

Description 

Definition: The width of paved portion of right shoulder measured from the center of the 

edgeline outward. Do not include parking or bicycle lanes in the paved shoulder width 

measurement; code the predominant width if it changes back and forth along the roadway 

section. Include gutter pans on outside of shoulder in paved shoulder width.

Attributes:

• Feet.

See Figure 4 - Figure 6 depending on number of lanes and direction of inventory.

Accuracy Statement: ± 6 in. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

This element involves measurements during post-processing of data collected.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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46. Right Shoulder Rumble Strip Presence/Type

Description 

Definition: Presence and type of rumble strips on the right shoulder.

Attributes:

• None. 

• Milled beyond edgeline. 

• Rolled beyond edgeline. 

• Milled or rolled on/under edgeline (e.g., rumble stripes). 

• Edgeline-rumble strip combination (e.g., raised/inverted thermoplastic profile marker). 

See Figure 10 for illustration.

Accuracy Statement: 90-100%. 

Considerations

Rumble strips can be defined as grooves in the roadway or rows of raised pavement markers 

placed on the roadway in such a manner that, as the tires of a vehicle contact them, they 

produce sound (noise) and vibration. The noise and vibration produced by rumble strips is 

intended to alert inattentive drivers that they have drift from their lane, or to give advance 

notice of a change in the roadway, a stop or a danger spot ahead.

Shoulder rumble strips are an effective means of preventing off-road crashes and are primarily 

used to warn drivers when they have drifted from their lane. A variation on this is the edgeline

rumble stripe, which places the pavement marking within the rumble strip, improving the 

visibility of the marking. This is more commonly used on roads with narrow shoulders.
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Figure 10. Right shoulder milled or rolled on/under edgeline (22). 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

• Ground-based imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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47. Left Shoulder Type

Description 

Definition: Shoulder type on left side of roadway in direction of inventory. For undivided roads 

and divided roads with one direction of inventory, this will be the outside shoulder on the 

opposing side. Note that information on paved width of the inner (left) shoulder is included 

under median descriptors (see Element 49. Left Paved Shoulder Width).

Attributes:

• None. 

• Surfaced shoulder exists - bituminous concrete (AC). 

• Surfaced shoulder exists - Portland Cement Concrete surface (PCC). 

• Stabilized shoulder exists (stabilized gravel or other granular material with or without 

admixture). 

• Combination shoulder exists (shoulder width has two or more surface types; e.g., part 

of the shoulder width is surfaced and a part of the width is earth). 

• Earth shoulder exists. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Ground-based imagery is used for collecting this data element. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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48. Left Shoulder Total Width

Description 

Definition: Width of the left (outside) shoulder, including both paved and unpaved parts 

measured from the center of the edgeline outward. See definition of Element 47. Left Shoulder 

Type above. Do not include parking or bicycle lanes in the shoulder width measurement; code 

the predominant width where it changes back and forth along the roadway section; ensure that 

the total width of combination shoulders is reported. Include gutter pans on outside of 

shoulder in shoulder width.

Attributes:

• Feet.

See Figure 4 - Figure 6 depending on number of lanes and direction of inventory.

Accuracy Statement: ± 1 ft. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.
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• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

This element involves measurements during post-processing of data collected.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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49. Left Paved Shoulder Width

Description 

Definition: The width of the paved portion of left (outside) shoulder measured from the center 

of the edgeline outward. Do not include parking or bicycle lanes in the paved shoulder width 

measurement; code the predominant width where it changes back and forth along the roadway 

section. Include gutter pans on outside of shoulder in paved shoulder width.

Attributes:

• Feet.

See Figure 4 - Figure 6 depending on number of lanes and direction of inventory.

Accuracy Statement: ± 6 in. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

This element involves measurements during post-processing of data collected.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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50. Left Shoulder Rumble Strip Presence/Type

Description 

Definition: Presence and type of rumble strips on the left shoulder.

Attributes:

• None. 

• Milled beyond edgeline. 

• Rolled beyond edgeline. 

• Milled or rolled on/under edgeline (e.g., rumble stripes). 

• Edgeline -rumble strip combination (e.g., raised/inverted thermoplastic profile marker). 

Figure 11 shows a left shoulder rumble strip.

Accuracy Statement: 90-100%. 

Figure 11. Left shoulder rumble strips (23). 

Considerations

See Element 46. Right Shoulder Rumble Strip Presence for rumble strip definitions. 

Existing Resources

None

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.
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• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

• Ground-based imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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51. Sidewalk Presence

Description 

Definition: The presence of a paved sidewalk along the segment.

Attributes:

• None. 

• Continous left-side. 

• Discontinous left-side. 

• Continous right-side. 

• Discontinous right-side. 

• Continuous both sides. 

• Discontinous both sides. 

Accuracy Statement: 90-100%. 

Considerations

Discontinuous sidewalks have at least one gap in the paved sidewalk along the segment.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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52. Curb Presence

Description 

Definition: The presence of curb along the segment.

Attributes:

• No curb. 

• Curb on left. 

• Curb on right. 

• Curb on both sides. 

Accuracy Statement: 90-100%. 

Considerations

Curb should be predominant along the section to be considered present. For more detail on 

curbs see Element 53. Curb Type.

Existing Resources 

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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53. Curb Type

Description 

Definition: The type of curb present on the segment.

Attributes:

• No curb. 

• Sloping curb – A curb that does not exceed a 4-inch height (for a slope steeper than 

1V:1H) or a 6-inch height (for a slope equal to or flatter than 1V:1H). 

• Vertical (barrier) curb – A curb that is steeper or taller than the ranges given for a 

sloping curb. 

Accuracy Statement: 90-100%. 

Considerations

It is difficult to distinguish curb height by using overhead aerial views. Use of angled aerial or 

ground-based imagery is recommended.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

The aerial data collected depends on vegetation cover. The data collection process for this 

element is labor-intensive and involves post-processing.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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54. Median Type

Description 

Definition: The type of median present on the segment.

Attributes:

• Undivided. 

• Flush paved median (at least 4 ft in width). 

• Raised median.

• Depressed median.

• Two-way left turn lane. 

• Railroad or rapid transit. 

• Divided, separate grades without retaining wall. 

• Divided, separate grades with retaining wall. 

• Other divided. 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. 

One-way roads do not have medians. This element can be difficult to identify by aerial imagery 

because raised, depressed, and flush medians look very similar from above. An angled aerial 

image or ground-based image is often required to identify these attributes.

Undivided approaches have no separation of opposing traffic lanes. Approaches with only 

yellow pavement markings less than 4-feet wide are included in this attribute. If the median 

markings are greater than 4-feet wide and designed to keep opposing traffic apart then it is a 

flush paved median. A depressed median does not have a curb and is lower than the traffic 

lanes. The raised median attribute includes all raised barriers.

See Element 61. Median Crossover/Left Turn Lane Type and Element 153. Median Type at 

Intersection for more examples.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 
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State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

This element collected needs to be tied back to linear referencing.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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55. Median Width

Description 

Definition: The width of the median, including the inside shoulders (i.e., measured from center 

of edgeline to center of edgeline on inside edges of opposing through lanes). 

Attributes:

• Feet.

See Figure 5 and Figure 6 depending on number of lanes and direction.

Accuracy Statement: ± 1 ft. 

Considerations

This element involves measurements during post-processing of data collected.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• These methods for measurement involve post processing of data collected.
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56. Median Barrier Presence/Type

Description 

Definition: The presence and type of median barrier on the segment. 

Attributes:

• None. 

• Unprotected. 

• Curbed. 

• Rigid barrier system (i.e., concrete). 

• Semi-rigid barrier system (i.e., box beam, W-beam strong post, etc.). 

• Flexible barrier system (i.e., cable, W-beam weak post, etc.). 

• Rigidity unspecified. 

Accuracy Statement: 90-100%. 

Considerations

Another example of semi-rigid barrier systems is metal guardrail and an example of flexible 

barrier system is a cable guardrail.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:
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• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• The aerial data collected depends on vegetation cover. 

• The data collection process for this element is labor-intensive and involves postprocessing.
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57. Median (Inner) Paved Shoulder Width

Description 

Definition: The width of the paved shoulder on the median (inner) side of the roadway on a 

divided roadway measured from the center of the edgeline outward. Note that information on 

type, width, and paved width of non-median shoulders is included in section I.C.3. Shoulder 

Descriptors (See Element 47. Left Shoulder Type). If the roadway is divided AND inventoried 

in two directions, this is already captured under Element 49. Left Shoulder Paved Width.

Attributes:

• Feet. 

See Figure 6 for additional details.

Accuracy Statement: ± 6 in. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• This element involves measurements during post-processing of data collected.
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58. Median Shoulder Rumble Strip Presence/Type

Description 

Definition: Presence and type of median shoulder rumble strip. If the roadway is divided AND 

inventoried in two directions, this is already captured under Element 50. Left Shoulder Rumble 

Strip Presence/Type.

Attributes:

• None. 

• Milled beyond edgeline. 

• Rolled beyond edgeline. 

• Milled or rolled on/under edgeline (e.g., rumble stripes). 

• Edgeline -rumble strip combination (e.g., raised/inverted thermoplastic profile marker). 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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59. Median Sideslope

Description 

Definition: The sideslope in the median adjacent to the median shoulder or travel lane. If the 

sideslope varies along the segment, code the predominant sideslope. If the roadway is divided 

AND inventoried in two directions, this is already captured under Element 65. Left Sideslope.

Attributes:

• Numeric percent of the sideslope

• See 

Figure 12 for illustration of median sideslopes.

Accuracy Statement: ± 12- 6 in. horizontally per 1 ft. vertical. 

Figure 12. Median sideslope (24). 

Considerations

If using a numeric percent, this can be positive (if backslope) or negative (if foreslope).

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• This data element involves post-processing along with the use of curve-fitting and 

tracing techniques.
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60. Median Sideslope Width

Description 

Definition: The width of the median sideslope adjacent to the median shoulder or travel lane. If 

width varies along the segment, code the predominant width. If the roadway is divided AND 

inventoried in two directions, this is already captured under Element 66. Left Sideslope Width.

Attributes:

• Feet. 

See Figure 6 for additional details.

Accuracy Statement: ± 1 ft. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• This data element involves post-processing along with the use of curve-fitting and 

tracing techniques.
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61. Median Crossover/Left Turn Lane Type

Description 

Definition: The presence and type of crossover/left turn bay in the median along the segment. 

Note: This element is intended to capture the typical median characteristic along the segment 

at non-intersection locations. Information on intersection-related turn lanes will be coded in 

the Junction File.

Attributes:

• None. 

• Median crossover, no left turn bay. 

• Median crossover, left turn bay. 

• Median crossover, directional left turn lane bays (to prevent crossing traffic from 

driveways). 

• Two-way left turn lane. 

See Figure 13 for additional details.

Accuracy Statement: 90-100%. 
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Figure 13. Illustration of types of median crossover/left turn lanes (2). 

Considerations

None.
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Existing Resources 

Established data collection methodologies can be found at:

State:

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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I.D. Segment Roadside Descriptors

62. Roadside Clearzone Width

Description 

Definition: Predominate or average roadside clearzone width. Clearzone is the total roadside 

border area, starting at the edge of the traveled way, available for safe use by errant vehicles. 

This area may consist of a shoulder, a recoverable slope, a non-recoverable slope, and/or a 

clear run-out area.

Attributes:

• Feet. 

Accuracy Statement: ± 1 ft. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• This element involves measurements during post-processing of data collected.
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63. Right Sideslope

Description 

Definition: The sideslope (foreslope or backslope) on right side of roadway immediately 

adjacent to the travel lane, shoulder edge or drainage ditch in direction of inventory. If 

sideslope varies within the section, code predominant sideslope.

Attributes:

• Numeric percent (Note: This can be positive (if backslope) or negative (if foreslope)). 

• Not applicable – protected by roadside barrier. 

• Not applicable – other (e.g., city center street). 

Accuracy Statement: ± 12- 6 in. horizontally per 1 ft. vertical. 

Considerations

This data element, if not steep, is also measurable using ground based technologies.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial imagery. 

• Inadequate image capture and poor resolution. 

• This element involves measurements during post-processing of data collected.
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64. Right Sideslope Width

Description 

Definition: The width of the sideslope on right side of roadway immediately adjacent to the 

travel lane, shoulder edge or drainage ditch in direction of inventory. If the width varies along 

the segment, code the predominant width.

Attributes:

• Feet. 

See Figure 4 - Figure 6 depending on number of lanes and direction of inventory.

Accuracy Statement: ± 1 ft. 

Considerations

This data element, if not steep, is also measurable using ground based technologies.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial imagery. 

• Inadequate image capture and poor resolution. 

• This element involves measurements during post-processing of data collected.
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65. Left Sideslope

Description 

Definition: The sideslope (foreslope or backslope) on left side of roadway immediately adjacent 

to the travel lane, shoulder edge or drainage ditch in direction of inventory. If sideslope varies 

within the section, code the predominant sideslope. For undivided roads and divided roads 

with one direction of inventory, this will be the outside shoulder on the opposing side.

Attributes:

• Numeric percent of the sideslope. (Note: This can be positive (if backslope) or negative 

(if foreslope)). 

• Not applicable – protected by roadside barrier. 

• Not applicable – other (e.g., city center street). 

Accuracy Statement: ± 12- 6 in horizontally per 1 ft. vertical. 

Considerations

This data element, if not steep, is also measurable using ground based technologies.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial imagery. 

• Inadequate image capture and poor resolution. 

• This element involves measurements during post-processing of data collected.
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66. Left Sideslope Width

Description 

Definition: The width of the sideslope on left side of roadway immediately adjacent to the travel 

lane, shoulder edge, or drainage ditch in direction of inventory. If the width varies along the 

segment, code the predominant width.

Attributes:

• Feet. 

See Figure 4 - Figure 6 depending on number of lanes and direction of inventory.

Accuracy Statement: ± 1 ft. 

Considerations

This data element, if not steep, is also measurable using ground based technologies.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial imagery. 

• Inadequate image capture and poor resolution. 

• This element involves measurements during post-processing of data collected.
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67. Roadside Rating

Description 

Definition: A rating of the safety of the roadside, ranked on a seven-point categorical scale from 

1 (best) to 7 (worst).

Attributes:

• Rating — 1: 

o Wide clear zones greater than or equal to 30 ft. from the pavement edgeline. 

o Sideslope flatter than 1:4.

o Recoverable.

• Rating — 2: 

o Clear zone between 20 and 25 ft. from pavement edgeline. 

o Sideslope about 1:4.

o Recoverable.

• Rating — 3: 

o Clear zone about 10 ft. from pavement edgeline. 

o Sideslope about 1:3 or 1:4.

o Rough roadside surface.

o Marginally recoverable.

• Rating — 4: 

o Clear zone between 5 to 10 ft. from pavement edgeline. 

o Sideslope about 1:3 or 1:4.

o May have guardrail (5 to 6.5 ft. from pavement edgeline).

o May have exposed trees, poles, or other objects (about 10 ft. from pavement

edgeline).

o Marginally forgiving, but increased chance of a reportable roadside collision. 

• Rating — 5: 

o Clear zone between 5 to 10 ft. from pavement edgeline. 

o Sideslope about 1:3.

o May have guardrail (0 to 5 ft. from pavement edgeline).
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o May have rigid obstacles or embankment within 6.5 to 10 ft. of pavement

edgeline. 

o Virtually non-recoverable.

• Rating — 6: 

o Clear zone less than or equal to 5 ft.

o Sideslope about 1:2.

o No guardrail.

o Exposed rigid obstacles within 0 to 6.5 ft. of the pavement edgeline. 

o Non-recoverable.

• Rating — 7: 

o Clear zone less than or equal to 5 ft.

o Sideslope 1:2 or steeper.

o Cliff or vertical rock cut.

o No guardrail.

o Non-recoverable with high likelihood of severe injuries from roadside collision.

See Figure 14 for additional details.

Accuracy Statement: ± 1 rating level. 

Considerations

This data element, if not steep, is also measurable using ground based technologies.
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Figure 14. Illustration of roadside ratings (2). 
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Figure 14. Illustration of roadside ratings (continued) (2). 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial imagery. 

• Inadequate image capture and poor resolution. 
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68. Major Commercial Driveway Count

Description 

Definition: Count of commercial driveways in segment serving 50 or more parking spaces.

Attributes:

• Numeric.

Accuracy Statement: 90-100%. 

Considerations

Commercial driveways provide access to a facility where commercial activities happen. 

Examples include shopping centers, grocery stores, gas stations, banks, restaurants, etc. Each 

driveway in the segment is counted separately even if multiple driveways serve a single 

facility/parking lot. If a single driveway serves multiple facilities of varying category (commercial, 

residential, and/or industrial/institutional) the driveway should be counted for the facility(s) that 

generate the most traffic.

Ground-based imagery is often required to discern the nature of the facility.

Major driveways provide access to a parking lot with more than 50 parking spaces. Spaces can 

be counted in aerial imagery. In cases where lots do not have pavement markings the data 

collector should use their best judgment in categorizing a driveway as major or minor. An 

evaluation should include factors such as the purpose of the facility and degree of driveway 

usage. For example, an automobile retail facility or junkyard may have space for 50 or more 

vehicles but the degree of driveway usage may be low enough to be categorized as minor 

driveways since the parking spaces are primarily used for grounding cars. In other instances, 

such as industrial facilities (Industrial Driveways), the parking spaces may be fewer than 50 but 

the driveways handle significantly higher number of heavy trucks and can be categorized as 

major driveways.

Driveway vs. Intersection:

Some intersections are defined by an approach that appears to be a driveway in aerial and/or 

ground-based imagery. Examples are private roads, alleyways, entrances/exits to shopping 

centers, rest stops, viewing areas, and rural recreational parking lots. They often do not have 

road names and/or do not appear on road maps. Where available, a list of intersections per 

segment would help prevent errors in driveway counts (Elements 68 – 74). 

Existing Resources

None. 
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Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution.

• Classification of driveway by type of facility and size of parking lot can be subjective.
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69. Minor Commercial Driveway Count

Description 

Definition: Count of commercial driveways in segment serving fewer than 50 parking spaces.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

For the definition of a commercial driveway and directions on what should be counted as a 

driveway refer to Element 68. Major Commercial Driveway Count. Minor driveways serve 

facility parking lots with fewer than 50 spaces.

Existing Resources

None. 

Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 

Concerns with Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 
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70. Major Residential Driveway Count

Description 

Definition: Count of residential driveways in segment serving 50 or more parking spaces.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

Residential driveways serve houses and apartments. Very few residential driveways fall in the 

“Major” category.

For directions on what should be counted as a driveway refer to Element 68. Major 

Commercial Driveway Count. 

Existing Resources

None. 

Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 

Concerns with Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 
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71. Minor Residential Driveway Count

Description 

Definition: Count of residential driveways in segment serving fewer than 50 parking spaces.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

Residential driveways serve houses and apartments. Most residential driveways fall in the 

“Minor” category.

For directions on what should be counted as a driveway refer to Element 68. Major 

Commercial Driveway Count. 

Existing Resources

None. 

Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 

Concerns with Collection Methods

Refer to Element 68.Major Commercial Driveway Count. 
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72. Major Industrial/Institutional Driveway Count

Description 

Definition: Count of industrial/institutional driveways in segment serving 50 or more parking 

spaces. 

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

Industrial driveways serve facilities where goods are manufactured or stored. Examples include 

factories and warehouses. Institutional driveways serve facilities run by a government body,

organization, society, or foundation. Examples include government offices, office complex, 

schools, churches, and state parks.

For directions on what should be counted as a driveway refer to Element 68. Major 

Commercial Driveway Count. 

Existing Resources

None. 

Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 

Concerns with Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 
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73. Minor Industrial/Institutional Driveway Count

Description 

Definition: Count of industrial/institutional driveways in segment serving fewer than 50 parking 

spaces.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

For the definition and examples of industrial/institutional driveways refer to Element 72. Major 

Industrial/Institutional Driveway. 

For directions on what should be counted as a driveway refer to Element 68.Major Commercial 

Driveway Count. 

Existing Resources

None. 

Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 

Concerns with Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 
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74. Other Driveway Count

Description 

Definition: Count of “other” driveways in segment.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

“Other” driveways consist of driveways that do not fit into the categories of commercial, 

residential, or industrial/institutional. (See Element 68. Major Commercial Driveway Count, 

Element 70. Major Residential Driveway Count, and Element 72. Major Industrial/Institutional 

Driveway Count for respective definitions.)

Examples of other driveways include farm field access points and access to parking lots for 

unknown facilities.

For directions on what should be counted as a driveway refer to Element 68. Major 

Commercial Driveway Count. 

Existing Resources

None. 

Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 

Concerns with Collection Methods

Refer to Element 68. Major Commercial Driveway Count. 
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I.E. Other Segment Descriptors

75. Terrain Type

Description 

Definition: The basic terrain type for the segment. This is a (less than desirable) surrogate for 

detailed data on curvature and grade of the road and the nature of the roadside. This would be 

collected only in the absence of those elements. See roadside descriptors above and alignment 

elements below.

Attributes:

• Mountainous – Any combination of grades and horizontal or vertical alignment that 

causes heavy vehicles to operate at crawl speeds for significant distances or at frequent 

intervals.

• Rolling – Any combination of grades and horizontal or vertical alignment that causes 

heavy vehicles to reduce their speeds substantially below those of passenger cars but 

that does not cause heavy vehicles to operate at crawl speeds for any significant length 

of time.

• Level – Any combination of grades and horizontal or vertical alignment that permits 

heavy vehicles to maintain the same speed as passenger cars; this generally includes 

short grades of no more than two percent. 

Accuracy Statement: 90-100%. 

Considerations

 Refer to section I.D. Segment Roadside Descriptors above and section II. Roadway Alignment 

Descriptors below.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 
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• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• The data collection for this element involves operator judgment.
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76. Number of Signalized Intersections in Segment

Description 

Definition: The number of at-grade intersections with a signal controlling traffic on the 

inventory route within the segment. A signal that cycles through red, yellow, and green for all 

or a portion of the day should be counted as a signalized intersection. Include at-grade 

intersections at entrances to shopping centers, industrial parks, and other large traffic 

generating enterprises.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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77. Number of Stop-Controlled Intersections in Segment

Description 

Definition: The number of at-grade intersections with a stop sign controlling traffic on the 

inventory route within the segment. A continuously operating, flashing red signal should be 

counted as a stop sign control. Include at-grade intersections at entrances to shopping centers, 

industrial parks, and other large traffic generating enterprises.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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78. Number of Uncontrolled/Other Intersections in Segment

Description 

Definition: Number of at-grade intersections where traffic on the inventory route is not 

controlled by either a signal or a stop sign; or is controlled by other types of signing; or has no 

controls within the segment. A continuously operating, flashing yellow signal should be 

considered as “other or no control.” Include at-grade intersections at entrances to shopping 

centers, industrial parks, and other large traffic generating enterprises.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.
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Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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I.F. Segment Traffic Flow Data

79. Annual Average Daily Traffic (AADT)

Description 

Definition: AADT value to represent the current data year. For two-way facilities, provide the 

AADT for both directions; provide the directional AADT if part of a one-way couplet or for 

one-way streets.

Attributes:

• Vehicles per day. 

Accuracy Statement: ± 5 - 10%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. 

AADT can be defined as the annualized average 24-hour volume of vehicles at a given point or 

section of highway. It is normally calculated by determining the volume of vehicles during a 

given period, and dividing that number by the number of days in that period. In general terms it 

is also known as "Traffic Count". 

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Each State highway agency maintains a traffic counting program. To comply with HPMS 

requirements, State highway agencies are required to take traffic volume counts on all 

Interstate, principal arterials and HPMS sample sections on a minimum three-year cycle. On 

non-HPMS roadways, traffic counts should be taken at least every 6 years. Traffic information 

can be obtained from State’s traffic monitoring office and other monitoring sources including 

Metropolitan Planning Organizations (MPOs), cities, counties, Intelligent Transportation 

Systems (ITS), and other third-party sources. A typical traffic counting program includes both 
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continuous measurements using Automatic Traffic Recorders (ATRs) and short counts for 

roadways respectively with high and low priority of safety consequences. 

Concerns with Collection Methods

• Traffic count data may not meet the FHWA Traffic Monitoring Guide quality 

requirements.

• Sample traffic count data may not be representative geographically and temporally.
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80. AADT Year

Description 

Definition: Year of AADT.

Attributes:

• Year.

Accuracy Statement: 100%.

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources 

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13).

Collection Methods

This element is collected in conjunction with AADT or traffic count measurement. Refer to 

Element 79. Annual Average Daily Traffic (AADT) for more information on collection methods.

Concerns with Collection Methods

None. 
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81. AADT Annual Escalation Percentage

Description 

Definition: Expected annual percent growth in AADT, with “AADT Year” as base year. This 

will allow calculation of current year’s AADT if “AADT Year” differs from current year.

Attributes:

• Percent. 

Accuracy Statement: ± 1%. 

Considerations

None.

Existing Resources

None

Collection Methods

This element is calculated from multi-year AADT values. Refer to Element 79. Annual Average 

Daily Traffic (AADT) for more information on collection methods.

Concerns with Collection Methods

• Available AADT values may not be adequate to determine this element.
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82. Percent Single Unit Trucks or Single Truck AADT

Description 

Definition: Percentage combination truck or combination truck AADT (Classes 4-7).

Attributes:

• Percent or numeric count. 

See Figure 15 for additional details on FHWA vehicle classes to be considered for AADT 

determination.

Figure 15. FHWA vehicle classification (25). 

Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:
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• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

Collection Methods

This element is calculated from AADT values. Refer to Element 79. Annual Average Daily 

Traffic (AADT) for more information on collection methods.

Concerns with Collection Methods

• Traffic count data may not meet the FHWA Traffic Monitoring Guide quality 

requirements.

• Sample traffic count data may not be representative geographically and temporally. For 

example, percent single unit trucks derived from peak-hour sample data may not 

represent daily values.
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83. Percent Combination Trucks or Combination Truck AADT

Description 

Definition: Percentage combination truck or combination truck AADT (Classes 8-13). 

Attributes:

• Percent or numeric count. 

See Figure 15 for additional details on FHWA vehicle classes to be considered for AADT 

determination.

Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources 

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

Collection Methods

This element is calculated from AADT values. Refer to Element 79. Annual Average Daily 

Traffic (AADT) for more information on collection methods.

Concerns with Collection Methods

• Traffic count data may not meet the FHWA Traffic Monitoring Guide quality 

requirements.

• Sample traffic count data may not be representative geographically and temporally. For 

example, percent combination trucks derived from peak-hour sample data may not 

represent daily values.
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84. Percentage Trucks or Truck AADT

Description 

Definition: Percentage truck or truck AADT (includes tractor-semis and trucks with 6+ 

wheels). Note that this can be derived if both Element 82. Percent Single Unit Trucks Or Single 

Truck AADT and Element 83. Percent Combination Trucks Or Combination Truck AADT are 

captured. 

Attributes:

• Percent or numeric count. 

See Figure 15 for additional details on FHWA vehicle classes to be considered for AADT 

determination.

Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

This element is calculated from AADT values. Refer to Element 79. Annual Average Daily 

Traffic (AADT) for more information on collection methods. 

Concerns with Collection Methods

• Traffic count data may not meet the FHWA Traffic Monitoring Guide quality 

requirements.

• Sample traffic count data may not be representative geographically and temporally. For 

example, truck counts taken during peak hours may not represent daily values.
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85. Total Daily Two-Way Pedestrian Count/Exposure

Description 

Definition: Total daily pedestrian flow along roadway in both directions (unless directional 

segment). This is a (less than desirable) surrogate for crossing pedestrian counts.

Attributes:

• Average daily count (numeric). 

Accuracy Statement: 10%.

Considerations

None.

Existing Resources

Established data collection methodology can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

Collection Methods

For high-priority roadway segments, site-specific statistical sampling using short term counting 

is recommended. For low-priority roadway segments, pedestrian volume adjustment factors, 

determined based on statistical short term counting, can be applied.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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86. Bicycle Count/Exposure

Description 

Definition: The total daily bicycle flow in both directions along the roadway (unless directional 

segment).

Attributes:

• Average daily count (numeric). 

Accuracy Statement: 10%.

Considerations

None.

Existing Resources

Established data collection methodology can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

Collection Methods

For high-priority roadway segments, site-specific statistical sampling using short term counting 

is recommended. For low-priority roadway segments, bicycle volume adjustment factors, 

determined based on statistical short term counting, can be applied.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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87. Motorcycle Count or Percentage

Description 

Definition: Motorcycle daily count or percentage of AADT.

Attributes:

• Percent or numeric count. 

Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

Collection Methods

Refer to Element 79. Annual Average Daily Traffic (AADT) for more information on collection 

methods. Motorcycle adjustment factors, determined based on statistical short term counting, 

can be applied to AADT.

Concerns with Collection Methods

Refer to Element 79. Annual Average Daily Traffic (AADT) for more information on concerns 

with collection methods.
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88. Hourly Traffic Volumes (or Peak and Offpeak AADT)

Description 

Definition: Hourly traffic volumes (or peak and offpeak AADT).

Attributes:

• Numeric count. 

Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

Collection Methods

Continuous traffic monitoring sites are the best sources of hourly traffic volume. If continuous 

data is not available, short term traffic counts can be used. If either traffic count is not available, 

hourly counts on the same route or a similar route with similar traffic characteristics in the 

same area can be used.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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89. K-Factor

Description 

Definition: The K-factor is the 30th highest hourly volume (i.e., the design hour volume) for a 

year, as a percentage of the annual average daily traffic.

Attributes:

• Percent. 

Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

• Highway Capacity Manual, http://books.trbbookstore.org/hcm10.aspx (26). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Continuous traffic monitoring sites are the best sources of hourly traffic volume. If continuous 

data are not available, short term traffic counts can be used. If either traffic count is not 

available, hourly counts on the same route or a similar route with similar traffic characteristics 

in the same area can be used.

The Highway Capacity Manual provides additional guidance on how to collect and calculate this 

element if sufficient traffic data is not available.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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90. Directional Factor

Description 

Definition: Proportion of peak hour traffic in the predominant direction of flow.

Attributes:

Numeric Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Continuous traffic monitoring sites are the best sources of hourly traffic volume. If continuous 

data are not available, short term traffic counts can be used. If either traffic count is not 

available, hourly counts on the same route or a similar route with similar traffic characteristics 

in the same area can be used.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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I.G. Segment Traffic Operations/Control Data

91. One/Two-Way Operations

Description 

Definition: Indication of whether the segment operates as a one- or two-way roadway.

Attributes:

• One-way. 

• Two-way. 

• One direction of travel for divided roadways. 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. 

In aerial and ground-based imagery a segment is determined to be one-way or two-way based 

on pavement markings and signs. Roadway pavement striping between lanes is white when 

both lanes are traveling the same direction (one-way) and yellow when they are opposing (twoway). Other pavement markings may include arrows painted on the lane to specify direction of 

traffic. Signs at intersections that say “One Way” or “Do Not Enter” clearly define the road as 

one-way. Also, road maps often show directional indicators if a roadway is one-way. 

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.
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• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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92. Speed Limit

Description 

Definition: The daytime regulatory speed limit for automobiles posted or legally mandated on 

the greater part of the section.

Attributes:

• mph. 

• No posted or legally mandated speed limit. 

Accuracy Statement: 90-100%. 

Considerations

This information may already be available with an agency for roadways under its jurisdiction.

If multiple speed limits are posted within the segment choose the speed limit that covers the 

greater portion of the segment.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 
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Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution.
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93. Truck Speed Limit

Description 

Definition: The regulatory speed limit for trucks posted or legally mandated on the greater part 

of the section (i.e., differential speed limit).

Attributes:

• mph. 

Accuracy Statement: 90-100%. 

Considerations

If multiple truck speed limits are posted within the segment choose the truck speed limit that

covers the greater portion of the segment. Only code if the speed limit for trucks is different 

than the limit for automobiles.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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94. Nighttime Speed Limit

Description 

Definition: The regulatory speed limit for vehicles at night posted or legally mandated on the 

greater part of the section (i.e., differential speed limit).

Attributes:

• mph. 

Accuracy Statement: 90-100%. 

Considerations

Only code if the speed limit is different at night than during the day.

If multiple nighttime speed limits are posted within the segment choose the nighttime speed 

limit that covers the greater portion of the segment.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution.
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95. 85th Percentile Speed

Description 

Definition: Traffic speed exceeded by 15 percent of the vehicles in the flow for this section.

Attributes:

• mph. 

Accuracy Statement: ± 5-10%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

• Spot Speed Manual: 

http://www.ecs.umass.edu/masssafe/PDFS%20for%20Site/Spot%20Speed%20Manual.p

df (28). 

Collection Methods

Continuous traffic monitoring sites using automatic traffic recorders are the best sources of 

hourly traffic volume. If continuous data are not available, short term traffic counts can be 

used. If either traffic count is not available, hourly counts on the same route or a similar route 

with similar traffic characteristics in the same area can be used. In addition, speed detection 

radars can be used to sample vehicular spot speeds.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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96. Mean Speed

Description 

Definition: The arithmetic mean (average) of all observed vehicle speeds in the segment (i.e., 

the sum of all spot speeds divided by the number of recorded speeds).

Attributes:

• mph. 

Accuracy Statement: ± 5-10%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

State:

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

• Spot Speed Manual: 

http://www.ecs.umass.edu/masssafe/PDFS%20for%20Site/Spot%20Speed%20Manual.p

df (28). 

Collection Methods

Continuous traffic monitoring sites using automatic traffic recorders are the best sources of 

hourly traffic volume. If continuous data are not available, short term traffic counts can be 

used. If either traffic count is not available, hourly counts on the same route or a similar route 

with similar traffic characteristics in the same area can be used. In addition, speed detection 

radars can be used to sample vehicular spot speeds.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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97. School Zone Indicator

Description 

Definition: Indication of whether the segment contains a school zone.

Attributes:

• Yes. 

• No. 

Accuracy Statement: 90-100%. 

Considerations

A school zone is a designated roadway segment approaching, adjacent to, and beyond school 

buildings or grounds, or along which school related activities occur (14). 

The optional signs shown in Figure 16 may be used with the School Zone sign to provide 

additional information.

School zones can also be identified by the reduced speed limit signs paired with the S4-3P sign 

shown in Figure 16 found along a roadway.

Figure 16. School zone with mandatory and optional signs (14). 
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Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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98. On-Street Parking Presence

Description 

Definition: Time-based parking restrictions.

Attributes:

• Permitted 24 hrs/day. 

• Prohibited 24 hrs/day. 

• Permitted during specified times. 

See Figure 17 for examples of parking restriction signs.

Accuracy Statement: 90-100%. 
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Figure 17. Signs for parking prohibitions (14). 

Considerations

On-street parking that is permitted 24 hrs/day does not require signing. “No Parking” signs are 

present where on-street parking is prohibited at all times. These signs have time restrictions if 

parking is permitted (or prohibited) during specific times of the day.
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Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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99. On-Street Parking Type

Description 

Definition: Type of on-street parking present on the segment.

Attributes:

• No parking allowed or none available. 

• Head-in/back-out angle parking on one side. 

• Head-in/back-out angle parking on both sides. 

• Back-in/head-out angle parking on one side. 

• Back-in/head-out angle parking on both sides. 

• Parallel parking on one side. 

• Parallel parking on both sides. 

See Figure 18 for additional details.

Accuracy Statement: 90-100%. 

Figure 18. Illustration of on-street parking type (14). 

Considerations

Perpendicular parking must be covered as a part of ‘angle parking’. 
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Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12).

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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100. Roadway Lighting

Description 

Definition: The type of roadway lighting present on the segment.

Attributes:

• None. 

• Spot on one side. 

• Spot on both sides. 

• Continuous on one side. 

• Continuous on both sides. 

Accuracy Statement: 90-100%. 

Considerations

Spot lighting refers to isolated locations within the segment being lighted such as single 

intersections or crosswalks. Continuous lighting requires a longer section of roadway to have 

lighting such as a series of intersections, stretches of roadway with high ADT, and sections of 

roadway that are enclosed for an extended period such as tunnels.

The data collection process for this element is labor-intensive and involves post-processing. 

This element can also be derived based on rules.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• The aerial data collected depends on vegetation cover
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101. Toll Facility

Description 

Definition: Presence and typed of toll facility on the segment.

Attributes:

• No toll. 

• Toll paid in one direction only, non-high-occupancy toll (non-HOT) lanes. 

• Toll paid in both directions, non-HOT lanes. 

• Toll paid in one direction, HOT lanes. 

• Toll paid in both directions, HOT lanes. 

See Figure 19 for additional details.

Figure 19. Signs for HOT lanes (14). 

Accuracy Statement: 90-100%. 

Considerations

Note that HOT lanes are different than express toll lanes (ETLs). HOT lanes give SOVs access 

to HOV lanes for a price. HOVs can use HOT lanes without paying the toll.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 
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State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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102. Edgeline Presence/Width

Description 

Definition: Presence and width of edgeline. 

Attributes:

• No marked edgeline. 

• 4 inch marked edgeline. 

• 6 inch marked edgeline. 

• 8 inch marked edgeline. 

• Greater than 8 inch marked edgeline. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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103. Centerline Presence/Width

Description 

Definition: Presence and width of centerline.

Attributes:

• No marked centerline.

• 4 inch marked centerline.

• 6 inch marked centerline.

• 8 inch marked centerline.

• Greater than 8 inch marked centerline.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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104. Centerline Rumble Strip Presence/Type

Description 

Definition: Presence and type of centerline rumble strips on the segment.

Attributes:

• None. 

• Milled adjacent to centerline.

• Rolled adjacent to centerline.

• Milled or rolled on/under centerline (e.g., rumble stripes). 

• Centerline-rumble strip combination (e.g., raised/inverted thermoplastic profile marker). 

See Figure 20 for center line rumble strip images.

Figure 20. Centerline rumble strips (milled) (23). 

Accuracy Statement: 90-100%. 

Considerations

See Element 46. Right Shoulder Rumble Strip Presence/Type for rumble strip definitions. 

Center line Rumble Strips are an effective countermeasure to prevent head-on collisions and 

opposite-direction sideswipes, often referred to as cross-over or cross-center line crashes. 

They are primarily used to warn drivers whose vehicles are crossing center lines of two-lane, 

two-way roadways.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:
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• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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105. Passing Zone Percentage

Description 

Definition: Percent of segment length striped for passing.

Attributes:

• Percent. 

Accuracy Statement: ± 5 - 10%. 

Considerations

Where there is a discernible difference in striped passing zones between the two directions of 

roadway, code the more restrive direction, i.e., the direction with the smallest percentage of 

striped passing zones. 

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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I.H. Other Supplemental Segment Descriptors

106. Bridge Numbers for Bridges in Segment

Description 

Definition: Bridge numbers from bridge file for bridges in segment. 

Attributes:

• Bridge number for each bridge in the segment.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

• National Bridge Inventory, http://www.fhwa.dot.gov/bridge/nbi.cfm (29). 

Collection Methods

This information is readily available in the FHWA National Bridge Inventory. See Element 6. 

Structure.

Concerns with Collection Methods

None. 
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II. ROADWAY ALIGNMENT DESCRIPTORS

II.A. Horizontal Curve Data

107. Curve Identifiers and Linkage Elements

Description 

Definition: All elements needed to define location of each curve record and all elements 

necessary to link with other safety files.

Attributes:

• Route and location descriptors (e.g., route and beginning and ending milepoints or route 

and beginning and ending spatial coordinates). Must be consistent with other MIRE files 

for linkage.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• This data element requires manual intervention, significant post-processing effort, and 

quality assurance/quality control.

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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108. Curve Feature Type

Description 

Definition: Type of horizontal alignment feature being described in the data record.

Attributes:

• Horizontal angle point (i.e., joining of two tangents without a horizontal curve). 

• Independent horizontal curve. 

• Component of compound curve (i.e., one curve in compound curve). 

• Component of reverse curve (i.e., one curve in a reverse curve). 

See Figure 21 for additional details.

Accuracy Statement: 90-100%. 

Figure 21. Illustration of types of curve features (2). 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.
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• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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109. Horizontal Curve Degree or Radius

Description 

Definition: Degree or radius of curve.

Attributes:

• Numeric, feet if radius. 

Accuracy Statement: .01 - .001 mi. or 52.8 – 5.28 ft. 

See Figure 22 for additional details where ‘R’ is the Radius of circular curve measured in feet. 

‘D’ is the degree of curvature (or the angle subtended by an arc of 100 feet radius).

Intersection/Deflection Angle 

Figure 22. Components of a simple horizontal curve. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 
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State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• This data element requires post-processing along with the use of curve fitting or tracing 

techniques. 

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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110. Horizontal Curve Length

Description 

Definition: Length of curve including spiral.

Attributes:

• Feet.

See Figure 22 for additional details where length of the horizontal curve, L, is the total length in 

feet of the circular curve from point of curve (PC) to point of tangent (PT) measured along its 

arc.

Accuracy Statement: .01 - .001 mi. or 52.8 – 5.28 ft. 

Considerations

None. 

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• This data element requires post-processing along with the use of curve fitting or tracing 

techniques. 

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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111. Curve Superelevation

Description 

Definition: Measured superelevation rate or percent.

Attributes:

• Rate/percent. 

Accuracy Statement: ± 0.5 - 0.25%. 

Considerations

Superelevation can be defined as the banking of the roadway along a horizontal curve so drivers 

can safely and comfortably maneuver the curve at reasonable speeds. A steeper superelevation 

rate is required when speeds increase and horizontal curves become tighter.

Superelevation is expressed as a decimal, representing the ratio of the pavement slope to width, 

ranging from 0 to 0.12 feet/foot. The adopted criteria allow for the use of maximum 

superelevation rates from 0.04 to 0.12. Maximum superelevation rates for design are 

established by policy by each State.

Existing Resources

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• This data element requires post-processing along with the use of curve fitting or tracing 

techniques. 

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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112. Horizontal Transition/Spiral Curve Presence

Description 

Definition: Presence and type of transition from tangent to curve and curve to tangent.

Attributes:

• No transition. 

• Spiral transition. 

• Other transition. 

See Figure 23 for additional details.

Intersection/Deflection Angle

Figure 23. Placement of spiral curve. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.
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• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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113. Horizontal Curve Intersection/Deflection Angle

Description 

Definition: The angle between the two intersecting tangents in the direction of inventory 

(sometimes called the "deflection angle"). Code only for Horizontal Angle Points; not 

applicable if there is a curve joining the tangents.

Attributes:

• Degrees (absolute value). 

See Figure 22 and Figure 23 for additional details where delta (Δ) is the intersection angle.

Accuracy Statement: 1.0 degree.

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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114. Horizontal Curve Direction

Description 

Definition: Direction of curve in direction of inventory.

Attributes:

• Right. 

• Left. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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II.B. Vertical Grade Data

115. Grade Identifiers and Linkage Elements

Description 

Definition: All elements needed to define location of each vertical feature and all elements 

necessary to link with other safety files.

Attributes:

• Route/linear reference system descriptors (e.g., route and beginning and ending 

milepoints or route and beginning and ending spatial coordinates). Must be consistent 

with other MIRE files for linkage.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources 

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• This data element requires manual intervention, significant post-processing effort, and 

quality assurance/quality control.

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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116. Vertical Alignment Feature Type

Description 

Definition: Type of vertical alignment feature being described in the data record.

Attributes:

• Vertical angle point (i.e., joining of two vertical gradients without a verticle curve). 

• Vertical gradient. 

• Sag vertical curve (i.e., vertical curve that connects a segment of roadway with a 

segment of roadway that has a more positive grade). 

• Crest vertical curve (i.e., vertical curve that connects a segment of roadway with a 

segment of roadway that has a more negative grade). 

See Figure 24 for additional details.

Figure 24. Vertical curves. 

Accuracy Statement: 90-100%. 

Considerations

None. 

Existing Resources

None. 
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Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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117. Percent of Gradient

Description 

Definition: Percent of gradient. Leave blank if record concerns a sag or crest vertical curve.

Attributes:

• Percent. 

Accuracy Statement: ± 0.5 - 0.25%. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• HPMS Field Manual:

http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/hpms2013.pdf (9). 

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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118. Grade Length

Description 

Definition: Grade length if Element 116. Vertical Alignment Feature Type is “Vertical gradient.” 

Length does not include any portion of a vertical curve. Leave blank if record concerns a sag or 

crest vertical curve.

Attributes:

• Feet. 

Accuracy Statement: .01 - .001 mi. or 52.8 – 5.28 ft. 

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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119. Vertical Curve Length

Description 

Definition: Vertical curve length if 116. Vertical Alignment Feature Type is “Sag vertical curve” 

or Crest vertical curve.”

Attributes:

• Feet. 

See Figure 24 where ‘L’ = Total length of vertical curve.

Accuracy Statement: .01 - .001 mi. or 52.8 – 5.28 ft. 

Considerations

None.

Existing Resources

None

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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III. ROADWAY JUNCTION DESCRIPTORS

III.A. At-Grade Intersection/Junctions

120. Unique Junction Identifier

Description 

Definition: A unique junction identifier.

Attributes:

• User defined (e.g., node number, LRS of primary route, etc.). 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. This is 

often referred to as Intersection ID.

Existing Resources 

None. 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. If this element is not available, an agency can 

generate a unique alpha-numeric identifier for a given junction based on its geographical 

location. 

Concerns with Collection Methods

None. 
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121. Type of Intersection/Junction

Description 

Definition: Type of Junction being described in the data record.

Attributes:

• Roadway/roadway (not interchange related). 

• Roadway/roadway (interchange ramp terminal). 

• Roadway/pedestrian crossing (e.g., midblock crossing, pedestrian path or trail). 

• Roadway/bicycle path or trail. 

• Roadway/railroad grade crossing. 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

If the type of intersection/junction cannot be distinguished between roadway/pedestrian 

crossing and roadway/bicycle path then choose the roadway/pedestrian crossing attribute.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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122. Location Identifier for Road 1 Crossing Point

Description 

Definition: Location of the center of the junction on the first intersecting route (e.g. routemilepost). Note that if the Junction File is a spatial data file, this would be the coordinates and 

would be the same for all crossing roads.

Attributes:

• Route and location descriptors (e.g., route and milepoint or route and spatial 

coordinates).

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. 

The location identifier format must be consistent with other MIRE files for linkage.

Manual intervention along with a significant post-processing effort and quality control/quality 

assurance (QA/QC) are necessary for collecting this data element.

Existing Resources

Established data collection methodologies can be found at:

State:

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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123. Location Identifier for Road 2 Crossing Point

Description 

Definition: Location of the center of the junction on the second intersecting route (e.g. routemilepost). Note that in a spatial data system, this would be the same as Element 122. Location 

Identifier For Road 1 Crossing Point. Not applicable if intersecting route is not an inventoried 

road (i.e., a railroad or bicycle path).

Attributes:

• Route and location descriptors (e.g., route and milepoint or route and spatial 

coordinates).

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. 

Manual intervention along with a significant post-processing effort and quality control/quality 

assurance (QA/QC) is necessary for collecting this data element.

Must be consistent with other MIRE files for linkage.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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124. Location Identifier for Additional Road Crossing Points

Description 

Definition: Location of the center of the junction on the third and subsequent intersecting 

route (e.g. route-milepost). Note that in a spatial data system, this would be the same as 

Element 122. Location Identifier For Road 1 Crossing Point. Not applicable if intersecting route 

is not an inventoried road (i.e., a railroad or bicycle path).

Attributes:

• Route and location descriptors (e.g., route and milepoint or route and spatial 

coordinates). Must be consistent with other MIRE files for linkage.

Accuracy Statement: 90-100%. 

Considerations

Manual intervention along with a significant post-processing effort and quality control/quality 

assurance (QA/QC) is necessary for collecting this data element.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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125. Intersection/Junction Number of Legs

Description 

Definition: The number of legs entering an at-grade intersection/junction.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

The intersection legs are the approach segments to a junction (See Element 121.Type Of 

Intersection/Junction ). 

The approach segments could be at an offset from the point of intersection (See Element 

130.Intersection/Junction Offset Distance). 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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126. Intersection/Junction Geometry

Description 

Definition: The type of geometric configuration that best describes the intersection/junction.

Attributes:

• T-Intersection. 

• Y-Intersection. 

• Cross-Intersection (four legs).

• Five or more legs and not circular roundabout. 

• Other circular intersection (e.g., rotaries, neighborhood traffic circles). 

• Non-conventional intersection (e.g., superstreet, median U-turn, displaced left turn). 

• Midblock pedestrian crossing. 

See Figure 25 for additional details.

Accuracy Statement: 100%. 
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Figure 25. Illustration of types of intersection/junction geometry (2).

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. 

Attribute Descriptions

• T-Intersection – An intersection with three legs and an intersection angle of 90 degrees.

o Skewed T-Intersection – The intersection angle is skewed. 
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• Y-Intersection – An intersection with three legs that does not meet the T-intersection 

definition. 

• Four-Leg Intersection – Consists of 4 legs/approaches in the following variations:

o Approaches intersect at right angles. 

o Approaches intersect at non-right angles and the opposite angles are equal. 

o Approaches intersect with non-right and different angles. 

o Approaches to the intersection are offset from the point of intersection. 

• Roundabout – An intersection in which traffic merges from multiple roadways into a 

counter-clockwise circle where normally all turns are right-turns. 

• Circular Intersection – An intersection with a circular island where all turns are rightturns. It includes roundabout, rotaries, and traffic circles. Traffic circles, or rotaries are 

much larger than modern roundabouts. The traffic circles or rotaries differ from 

modern roundabouts in their control or entry/yield patterns, operational characteristics 

such as weaving patterns, deflection or intersection entry angles, speeds, circle 

diameters, pedestrian crossing, presence of splitter islands, and parking characteristics.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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127. School Zone Indicator

Description 

Definition: An indication of whether the intersection/junction is in a school zone.

Attributes:

• Yes. 

• No. 

See Figure 16 for additional details.

Accuracy Statement: 90-100%. 

Considerations

Refer to Element 97. School Zone Indicator for more details.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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128. Railroad Crossing Number

Description 

Definition: Railroad (RR) crossing number if a RR grade crossing (for linkage to National 

Highway-Rail Crossing Inventory).

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

A unique identifier assigned to each highway-railroad crossing. It is a United States DOT 

Crossing Inventory number, normally referred to as an FRA (Federal Railroad Administration) 

number. Additional information can be obtained from Federal Railroad Administration Office of 

Safety Analysis.

Existing Resources

Established data collection methodologies can be found at:

State:

• Texas DOT Traffic Data and Analysis Manual: 

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm (12). 

Collection Methods

This information is readily available in the Highway-Rail Crossing Inventory maintained by the 

FRA Office of Safety Analysis, 

http://safetydata.fra.dot.gov/OfficeofSafety/publicsite/query/HRCrossinginv.aspx 

Concerns with Collection Methods

None. 
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129. Intersection Angle

Description 

Definition: The measurement in degrees of the smallest angle between any two legs of the 

intersection. This value will always be within a range of 0 to 90 degrees (i.e., for non-zero 

angles, always measure the acute rather than the obtuse angle).

Attributes:

• Degrees. 

See Figure 26 for additional details.

Accuracy Statement: 1-5 degrees. 

Figure 26. Illustration of intersecting angle (2). 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.
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• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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130. Intersection/Junction Offset Distance

Description 

Definition: Offset distance between the centerlines of the intersecting legs (minor road) at the 

intersection. When the intersection legs are not offset, the value of this data item should be 

zero.

Attributes:

• Numeric (Note that “zero” should be entered if crossing road centerlines are not offset). 

See Figure 27 for additional details.

Accuracy Statement: .01-.001 mile.

Offset

Figure 27. Illustration of intersection/junction offset distance.

Considerations

A maximum offset distance is required to distinguish which offset legs belong to a given 

intersection.

Existing Resources

None. 
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Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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131. Intersection/Junction Traffic Control

Description 

Definition: Traffic control present at intersection/junction.

Attributes:

• Uncontrolled. 

• Two-way stop.

• All-way stop. 

• Yield sign. 

• Signalized (with ped signal). 

• Signalized (w/o ped signal). 

• Railroad crossing, gates and flashing lights. 

• Railroad crossing, flashing lights only. 

• Railroad crossing, stop-sign controlled. 

• Railroad crossing, crossbucks only.

• Other. 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd and for roads with AADT < 400 vpd. 

Possible ‘Other’ attributes includes roundabouts and fire signals. Amber/red flashing 

intersection control beacons can be considered two-way stop controlled, and red/red flashing 

intersection control beacons can be considered all-way stop controlled. Agencies can consider 

adding a comment field to indicate whether a flasher is present.

Signalized traffic control consists of the typical red, yellow, and green light combination. 

Signalized with pedestrian signal means there is a traffic light and a Walk/No Walk signal at the 

intersection. For more examples of pedestrian signals at signalized intersections see Element 

158. Pedestrian Signalization Type and Element 159. Pedestrian Signal Special Features. 

Existing Resources

• Sign and/or signal inventories if available.

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:
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• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Inadequate image capture and poor resolution may cause difficulties in identifying the 

correct type of traffic control method used, especially for pedestrian signals.

• Poorly maintained pavement markings or signs may result in incorrect identification of 

the traffic control method.

• Timeliness of the aerial and ground-based imagery.
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132. Signalization Presence/Type

Description 

Definition: Presence and type of signalization at intersection/junction.

Attributes:

• No signal. 

• Uncoordinated fixed time. 

• Uncoordinated traffic actuated. 

• Progressive coordination (with several signals along either road). 

• System coordination (e.g., real-time adapative systemwide). 

• Railroad crossing signal (includes signal-only and signal and gates). 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

This element describes how the intersection traffic signals change over time. An uncoordinated 

signal is not affected by any other signal and changes using a timer (fixed time) or is triggered by 

traffic approaching the intersection (traffic actuated). The progressive coordination attribute 

describes a timed signal that is part of a sequence of other timed signals along the roadway. 

Similarly, the system coordination attribute describes a traffic actuated signal that also receives 

feedback from other intersections in the area. The railroad crossing signal describes a signal 

that is activated by the approach of a train.

Existing Resources 

Established data collection methodologies can be found at:

State:

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

This information is typically available from the highway agency’s traffic operations department 

that handles traffic signal inventory including type, timing and coordination.

Concerns with Collection Methods

None. 
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133. Intersection/Junction Lighting

Description 

Definition: Presence of lighting at intersection/junction. 

Attributes:

• Yes. 

• No. 

Accuracy Statement: 90-100%. 

Considerations

This element includes overhead lights and street lamps that illuminate the intersection to 

improve visibility.

This does not refer to traffic signal lights. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• The aerial data obtained will depend on vegetation cover. This data element collection 

is labor-intensive and involves manual post-processing. 
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134. Circular Intersection – Number of Circulatory Lanes

Description 

Definition: Number of circulatory lanes in circular intersection.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

This element captures the number of lanes that continue all the way around the intersection. 

Figure 28is an example of a circular intersection with one circulatory lane.

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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135. Circular Intersection – Circulatory Lane Width

Description 

Definition: Width of the roadway between the central island and the outer edge of the 

circulatory lane in a circular intersection.

Attributes:

• Feet. 

See Figure 28 for additional details.

Accuracy Statement: ± 1 ft.

Considerations

None. 

Existing Resources 

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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136. Circular Intersection – Inscribed Diameter

Description 

Definition: Distance between the outer edges of the circulatory roadway of a circular 

intersection.

Attributes:

• Feet. 

See Figure 28 for additional details.

Accuracy Statement: ± 1 ft.

Figure 28. Illustration of circular intersection (2). 
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Considerations

None. 

Existing Resources 

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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137. Circular Intersection – Bicycle Facility

Description 

Definition: Presence and type of bicycle facility at circular intersection.

Attributes:

• None. 

• Separate cycle path. 

• Circulatory bike lane. 

See Figure 29 for additional details.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR.

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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Figure 29. Illustration of types of bicycle facilities at circular intersections (2). 
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138. Intersection Identifier for this Approach

Description 

Definition: The unique numeric identifier assigned to the intersection that includes this 

approach is (see Element 120. Unique Junction Identifier). This element provides linkage to the 

basic intersection information and to all other approaches.

Attributes:

• The intersection identifier entered in Element 120. Unique Junction Identifier. 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

See Element 120. Unique Junction Identifier. 

Concerns with Collection Methods

See Element 120. Unique Junction Identifier. 
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139. Unique Approach Identifier

Description 

Definition: A unique identifier for each approach of an intersection.

Attributes:

• Any identifier that is unique for each approach within a single intersection (e.g., 

sequential numbers or letters, compass directions, “clock hours”). See Figure 30 for 

additional details.

Accuracy Statement: 100%. 

A, 1, North, 12:00, 0 ̊

B, 2, East, 3:00, 90 ̊

N 

D, 4, West, 9:00, 270 ̊

C, 3, South, 6:00, 180 ̊

Figure 30. Illustration of unique approach identifiers.

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. 

It is important for each leg/approach in an intersection to have a unique ID. A similar option to 

those listed above is to set North/12:00 as 0 ̊ and identify each approach by the angle at which 

it enters the intersection.

Existing Resources

None. 
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Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. If this element is not available, an agency can 

generate a unique alpha-numeric identifier for a given approach based on its geographical 

location. 

Concerns with Collection Methods

None. 
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140. Approach AADT

Description 

Definition: The AADT on the approach leg of the intersection/junction.

Attributes:

• Vehicles per day. 

Accuracy Statement: ± 5 - 10%. 

Considerations

AADT can be defined as the annualized average 24-hour volume of vehicles at a given point or 

section of highway. It is normally calculated by determining the volume of vehicles during a 

given period, and dividing that number by the number of days in that period. In general terms it 

is also known as "Traffic Count".

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

Collection Methods

Refer to Element 79. Annual Average Daily Traffic (AADT) for more information on collection 

methods. 

Concerns with Collection Methods

Refer to Element 79. Annual Average Daily Traffic (AADT) for more information of concerns 

with collection methods.
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141. Approach AADT Year

Description 

Definition: The year of the AADT on the approach leg of the intersection/junction.

Attributes:

• Year. 

Accuracy Statement: 100%. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Refer to Element 80. AADT Year for more information on collection methods.

Concerns with Collection Methods

None. 
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142. Approach Mode

Description 

Definition: Intended modes for the approach.

Attributes:

• Vehicles only or shared use (e.g., vehicles, peds, bikes). 

• Pedestrians only. 

• Bicycles only. 

• Pedestrians and bicycles. 

• Railroad. 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

This element captures the type of traffic using the approach. Vehicle roadways and railroad 

tracks are fairly easy to distinguish using aerial imagery. Pedestrian and bicycle crossings may 

require the use of ground-based imagery to identify signs or pavement markings distinguishing 

the mode of travel.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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143. Approach Directional Flow

Description 

Definition: Indication of one-way or two-way flow on approach.

Attributes:

• One-way. 

• Two-way. 

Accuracy Statement: 90-100%. 

Considerations

See Element 91.One/Two Way Operations. 

Existing Resources 

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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144. Number of Approach Through Lanes

Description 

Definition: Total number of through lanes on approach (both directions if two-way, one 

direction if one-way).

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

Existing Resources

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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145. Left Turn Lane Type

Description 

Definition: Type of left turn lane(s) that accommodate left turns from this approach.

Attributes:

• No left turn lanes. 

• Conventional left turn lane(s). 

• U-turn followed by right turn. 

• Right turn followed by U-turn. 

• Right turn followed by left turn (e.g., jughandle near side). 

• Right turn followed by right turn (e.g., jughandle far side). 

• Left turn crossover prior to intersection (e.g., displaced left turn). 

• Other. 

See Figure 31 for additional details.

Accuracy Statement: 90-100%.
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Figure 31. Illustration of left turn lane types (2). 
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Figure 31. Illustration of left turn lane types (continued) (2). 

Considerations

None.
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Existing Resources

Established data collection methodologies can be found at:

State:

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution.
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146. Number of Exclusive Left Turn Lanes

Description 

Definition: Number of exclusive left turn lanes that accommodate left turns from this approach.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

Only left turns can be made from an exclusive left turn lane. These lanes usually have a leftfacing turn arrow painted on the pavement or on a sign at the intersection.

Existing Resources

Established data collection methodologies can be found at:

State:

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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147. Amount of Left Turn Lane Offset

Description 

Definition: Amount of offset between conventional left turn lane(s) on this approach and 

opposing approach. Offset refers to direction (plus or minus) and distance between the 

centerline of the left turn lane on this approach and the centerline of the left turn lane on the 

opposing approach. The direction is positive if the left turn lane on this approach is to the left 

of the opposing left turn lane and negative if vice versa. If the opposing left turn lanes are 

aligned, enter “0”.

Attributes:

• Sign (+ or -) and distance (feet).

Accuracy Statement: ± 6 in. 

See Figure 32 for additional details.

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR.

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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Figure 32. Illustration of positive, negative, and zero offset distance (2). 
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148. Right Turn Channelization

Description 

Definition: Right turn channelization on approach.

Attributes:

• None. 

• Painted island with receiving lane. 

• Painted island without receiving lane. 

• Raised island with receiving lane. 

• Raised island without receiving lane. 

Figure 33 shows examples of raised island with a receiving lane and without a receiving lane.

Accuracy Statement: 90-100%. 

Figure 33. Right turn channelization by raised island (21). 

Considerations

Right turn channelization exists when exclusive right turn lanes (see Element 150.Number Of 

Exclusive Right Turn Lanes) are separated from through or exclusive left turn lanes by an island. 
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This island can be painted (pavement markings on a flush surface) or raised (raised surface with 

curbs). 

The channelization islands serve the purpose of delineating the area in which vehicles can 

operate, reducing the area of vehicle conflict, incorporating a smaller (safer) angle of merge for 

vehicles, and providing pedestrian refuge.

This element can be difficult to identify by aerial imagery because raised and painted islands look 

very similar from above. Figure 34 shows an example of a painted channelization island that 

might not be distinguishable by aerial imagery, an angled aerial image, or ground-based image is 

often required to identify these attributes.

Figure 34. Right turn channelization by painted island with receiving lane (27). 

Google MapsTM

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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149. Traffic Control of Exclusive Right Turn Lanes

Description 

Definition: Traffic control of exclusive right turn lanes on approach.

Attributes:

• Signal. 

• Yield sign. 

• Stop sign. 

• No control (e.g., free flow). 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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150. Number of Exclusive Right Turn Lanes

Description 

Definition: Number of exclusive right turn lanes on approach.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

Only right turns can be made from an exclusive right turn lane. These lanes usually have a 

right-facing turn arrow painted on the pavement or on a sign at the intersection.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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151. Length of Exclusive Left Turn Lanes

Description 

Definition: Storage length of exclusive left turn lane(s) (not including taper).

Attributes:

• Feet. 

Figure 35 illustrates the length measurement.

Accuracy Statement: .01 - .001 mi or 52.8 – 5.28 ft.

Figure 35. Illustration of exclusive left turn lane length (2). 

Considerations

None. 

Existing Resources

None. 
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Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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152. Length of Exclusive Right Turn Lanes

Description 

Definition: Storage length of exclusive right turn lane(s) (not including taper).

Attributes:

• Feet. 

Figure 36 illustrates the measurement for the right turn lane length.

Accuracy Statement: 0.01 - 0.001 mi or 52.8 – 5.28 ft.

Figure 36. Illustration of exclusive right turn lane length (2). 

Considerations

None. 

Existing Resources

None. 
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Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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153. Median Type at Intersection

Description 

Definition: Median type at intersection separating opposing traffic lanes on this approach. 

Pedestrian refuge islands are also captured in Element 157. Crosswalk Presence/Type.

Attributes:

• Undivided. 

• Flush paved median (at least 4 feet in width). 

• Raised median with curb. 

• Depressed median. 

• Two-way left turn lane. 

• Railroad or rapid transit. 

• Other divided. 

Figure 37 shows example of median types.

Accuracy Statement: 90-100%. 

Considerations

Based on the definition, median type only applies to two-way approaches. This element can be 

difficult to identify by aerial imagery because raised, depressed, and flush medians look very 

similar from above. An angled aerial image or ground-based image is often required to identify 

these attributes.

Undivided approaches have no separation of opposing traffic lanes. Approaches with only 

yellow pavement markings less than 4 feet wide are included in this attribute. If the median 

markings are greater than 4 feet wide and designed to keep opposing traffic apart then it is a 

flush paved median. A depressed median does not have a curb and is lower than the traffic 

lanes.
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Figure 37. Illustration of median type (20, 27). 

Existing Resources

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17).

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.
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• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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154. Approach Traffic Control

Description 

Definition: Traffic control present on approach. Pedestrian signalization is captured in Element 

158. Pedestrian Signalization Type.

Attributes:

• Uncontrolled. 

• Stop sign. 

• Yield sign. 

• Signalized. 

• Railroad crossing, gates and flashing lights. 

• Railroad crossing, flashing lights only. 

• Railroad crossing, stop-sign controlled. 

• Railroad crossing, crossbucks only. 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

See Element 131. Intersection/Junction Traffic Control for additional attribute descriptions.

Existing Resources

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 
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Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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155. Approach Left Turn Protection

Description 

Definition: Presence and type of left turn protection on the approach.

Attributes:

• Unsignalized. 

• Signalized with no left turn protection (i.e., permissive). 

• Protected, all day. 

• Protected, peak hour only. 

• Protected permissive, all day. 

• Protected permissive, peak hour only. 

• Other. 

Figure 38 provides examples of left turn protection.

Accuracy Statement: 90-100%. 

Figure 38. Examples of left turn protection (27). 

Google MapsTM
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Considerations

• Permitted – Approaches that are signalized with no left turn protection are called 

permissive because traffic is permitted to turn left after yielding to opposing through 

traffic and pedestrians. This attribute is recognized in ground-based imagery by a 

standard red, yellow, and green traffic signal.

• Protected – Traffic turning left is protected from opposing traffic. This is often denoted 

by a red, yellow, and green turn arrow signal or a turn only sign posted on a standard 

traffic signal.

• Protected-Permitted – One phase of the traffic signal allows protected left turns and 

another phase allows permitted left turns. Signals consist of a standard traffic signal with 

an added green turn arrow. Some signals also have a yellow turn arrow.

These aspects of the element can be determined by ground-based imagery. The signal timing 

can be collected from agency signal management databases.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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156. Signal Progression

Description 

Definition: Signal progression on approach.

Attributes:

• No signal. 

• Uncoordinated fixed time. 

• Uncoordinated traffic actuated. 

• Progressive coordination (with several signals along either road). 

• System coordination (e.g., real-time adapative systemwide). 

• Railroad crossing signal (includes signal-only and signal and gates). 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

See Element 132. Signalization Presence/Type.

Existing Resources

Established data collection methodologies can be found at:

State:

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

This information is typically available from the highway agency’s traffic operations department 

that handles traffic signal inventory including type, timing and coordination.

Concerns with Collection Methods

None. 
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157. Crosswalk Presence/Type

Description 

Definition: Presence and type of crosswalk crossing this approach leg.

Attributes:

• Unmarked crosswalk. 

• Marked crosswalk. 

• Marked crosswalk with supplemental devices (e.g., in-street yield signs, in-pavement 

warning lights, pedestrian bulb outs, etc.). 

• Marked crosswalk with refuge island. 

• Marked with refuge island and supplemental devices (e.g., in-street yield signs, inpavement warning lights, pedestrian bulb outs, etc.). 

• Pedestrian crossing prohibited at this approach. 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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158. Pedestrian Signalization Type

Description 

Definition: Type of pedestrian signalization for crossing this approach.

Attributes:

• None. 

• Activated by traffic signal (e.g., recall). 

• Pushbutton actuated. 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

The presence of pushbuttons can be identified with ground-based imagery.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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159. Pedestrian Signal Special Features

Description 

Definition: Special features for pedestrian signals for crossing this approach. 

Attributes:

• None. 

• Accessible pedestrian signal (i.e., audible tones/messages for blind or low-vision 

pedestrians). 

• Countdown pedestrian signal. 

• Both accessible and countdown features. 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

It is possible to recognize a countdown pedestrian signal with ground-based imagery as a signal 

will be present to show the countdown sequence. However, signals with audible tones must be 

collected from agency signal management databases.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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160. Crossing Pedestrian Count/Exposure

Description 

Definition: Count or estimate of average daily pedestrian flow crossing this approach (Note: 

only applicable to approaches with vehicular traffic).

Attributes:

• Numeric. 

Accuracy Statement: 10%.

Considerations

None.

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10).

Collection Methods

For high-priority roadway segments, site-specific statistical sampling using short term counting 

is recommended. For low-priority roadway segments, pedestrian volume adjustment factors

based on statistical short-term counting can be applied.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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161. Left/Right Turn Prohibitions

Description 

Definition: Signed left or right turn prohibitions on this approach.

Attributes:

• No left turns permitted at any time. 

• No left turn permitted during certain portions of the day. 

• No right turns permitted at any time. 

• No right turns permitted during certain portions of the day. 

• No right or left turns permitted at any time. 

• No right or left turns permitted during certain portions of the day. 

• No U-turns. 

Figure 39 provides an example of no turns permitted during certain portions of the day. 

Figure 39. No left turn permitted during certain portions of the day (30). 

Accuracy Statement: 90-100%. 

Considerations

None.
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Existing Resources

Established data collection methodologies can be found at:

State:

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution.
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162. Right Turn-On-Red Prohibitions

Description 

Definition: Prohibition of right turns-on-red (RTOR) from this approach.

Attributes:

• RTOR allowed at all times. 

• RTOR prohibited at all times. 

• RTOR prohibited during certain portions of the day. 

See Figure 40 for examples of RTOR prohibition signs.

Accuracy Statement: 90-100%. 

Figure 40. Signs prohibiting RTOR (27). 

Google MapsTM

Considerations

This element only applies to approaches with signalized traffic control. Appropriate signs must 

be present on the approach to use the RTOR prohibited attributes. 

Existing Resources

Established data collection methodologies can be found at:

State:

• Georgia DOT HPMS Inventory Manual: 

http://www.dot.ga.gov/informationcenter/statistics/Documents/GDOT_HPMS_Data

CollectionGuide.pdf (17). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:
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• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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163. Left Turn Counts/Percent

Description 

Definition: Count or estimate of average daily left turns, or percent of total approach traffic

turning left. (Note: This could also be captured for peak-periods only or by hour of day.)

Attributes:

• Count or percent. 

Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

For high-priority roadway segments, site-specific statistical sampling using short term counting 

is recommended. For low-priority roadway segments, left turn adjustment factors, determined 

based on statistical short term counting, can be applied to AADT.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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164. Year of Left Turn Counts/Percent

Description 

Definition: Year of count or estimate of average daily left turns or percent of total approach 

traffic turning left.

Attributes:

• Year. 

Accuracy Statement: 100%. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

This element is collected in conjunction with Element 163. Left Turn Counts/Percent.

Concerns with Collection Methods

None. 
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165. Right Turn Counts/Percent

Description 

Definition: Count or estimate of average daily right turns, or percent of total approach traffic 

turning right. (Note: This could also be captured for peak-periods only or by hour of day.)

Attributes:

• Count or percent. 

Accuracy Statement: ± 5 - 10%. 

Considerations

None.Existing Resources

None. 

Collection Methods

For high-priority roadway segments, site-specific statistical sampling using short term counting 

is recommended. For low-priority roadway segments, right turn adjustment factors, 

determined based on statistical short term counting, can be applied to AADT.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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166. Year of Right Turn Counts/Percent

Description 

Definition: Year of count or estimate of average daily right turns or percent of total approach 

traffic turning right.

Attributes:

• Year. 

Accuracy Statement: 100%. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

This element is collected in conjunction with Element 165 Right Turn Counts/Percent.

Concerns with Collection Methods

None. 
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167. Transverse Rumble Strip Presence

Description 

Definition: Presence of transverse rumble strips on approach.

Attributes:

• Yes. 

• No. 

See Figure 41 for one example of a transverse rumble strip.

Accuracy Statement: 90-100%. 

Figure 41. Transverse rumble strip (31). 

Considerations

Transverse rumble strips consist of intermittent narrow, transverse areas of rough-textured, or 

slightly raised or depressed road surface that extend across the travel lanes to alert drivers to 
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unusual vehicular traffic conditions. Through noise and vibration, they attract the attention of 

road users to features such as unexpected changes in alignment and conditions requiring a 

reduction in speed or a stop.

When the color of a transverse rumble strip used within a travel lane is not the color of the 

pavement, the color of the rumble strip shall be white, black, or orange.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

• Ground-based imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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168. Circular Intersection – Entry Width

Description 

Definition: Full width of entry on this approach where it meets the inscribed circle.

Attributes:

• Feet. 

See Figure 28 for additional details.

Accuracy Statement: ± 1 ft. 

Considerations

None. 

Existing Resources 

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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169. Circular Intersection – Number of Entry lanes

Description 

Definition: Number of entry lanes into circular intersection on this approach, not including right 

turn only or auxiliary lanes.

Attributes:

• Numeric. 

See Figure 42 for an illustration a one and two-lane entry lane. 

Accuracy Statement: 90-100%. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR.

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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One Lane

Two Lanes

Figure 42. Illustration of circular intersection exit and entry lanes (2). 
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170. Circular Intersection – Presence/Type of Exclusive Right Turn Lane

Description 

Definition: Presence and type of exclusive right turn lane(s) on this approach.

Attributes:

• None. 

• Exclusive right turn bypass/slip lane with separating island. 

• Exclusive right turn bypass/slip lane without separating island. 

See Figure 43 for examples of exclusive right turn lane with and without a separating island.

Accuracy Statement: 90-100%. 

Considerations

The separating island can be painted or raised. 

Existing Resources

None.

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR.

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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With Island

Without Island 

Figure 43. Illustration of circulatory intersection exclusive right turn lanes (2). 
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171. Circular Intersection – Entry Radius

Description 

Definition: Minimum radius of curvature of the curb on the right side of the entry.

Attributes:

• Feet. 

See Figure 28 for additional details.

Accuracy Statement: 0.01 - 0.001 mi or 52.8 – 5.28 ft. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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172. Circular Intersection – Exit Width

Description 

Definition: Full width of exit on this approach where it meets the inscribed circle.

Attributes:

• Feet. 

See Figure 28 for additional details.

Accuracy Statement: ± 1 ft. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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173. Circular Intersection – Number of Exit Lanes

Description 

Definition: Number of exit lanes from roundabout on this approach leg.

Attributes:

• Numeric. 

See Figure 42 for additional details.

Accuracy Statement: 90-100%. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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174. Circular Intersection – Exit Radius

Description 

Definition: Minimum radius of curvature of the curb on the left side of the approach, when 

facing the intersection.

Attributes:

• Feet. 

See Figure 28 for additional details.

Accuracy Statement: 0.01 - 0.001 mi or 52.8 – 5.28 ft. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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175. Circular Intersection – Pedestrian Facility

Description 

Definition: Type of facility for pedestrians crossing this approach.

Attributes:

• Marked crosswalk with raised island. 

• Marked crosswalk with flush island. 

• Marked crosswalk with no island. 

• Unmarked crosswalk with raised island. 

• Unmarked crosswalk with flush island. 

• Unmarked crosswalk with no island. 

• Pedestrian crossing prohibited at this approach. 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

For more details on crosswalks see Element 157.Crosswalk Presence/Type.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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176. Circular Intersection – Crosswalk Location

Description 

Definition: Location of marked pedestrian crosswalk line (measured as the distance between 

the yield line and crosswalk markings closest to the intersection).

Attributes:

• Feet. 

See Figure 28 for additional details.

Accuracy Statement: 0.01 - 0.001 mi or 52.8 – 5.28 ft. 

Considerations

None. 

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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177. Circular Intersection – Island Width

Description 

Definition: Width of raised or painted island separating entry and exit legs (measured at the 

inscribed circle). If no island is present, record width as zero.

Attributes:

• Feet. 

See Figure 28 for additional details.

Accuracy Statement: ± 1 ft. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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III.B. Interchange and Ramp Descriptors

178. Unique Interchange Identifier

Description 

Definition: A unique identifier for each interchange.

Attributes:

• User defined (e.g., node number, LRS of primary route, exit numbers, etc.). 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. If this element is not available, an agency can 

generate a unique alpha-numeric identifier for a given interchange based on its geographical 

location. 

Concerns with Collection Methods

None. 
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179. Location Identifier for Road 1 Crossing Point

Description 

Definition: Location of midpoint of interchange (e.g., crossing route) on the first intersecting 

route (e.g. route-milepost, spatial coordinates). Note that if the Junction File is a spatial data file, 

this would be the coordinates and would be the same for all crossing roads.

Attributes:

• Route and location descriptors (e.g., route and milepoint or spatial coordinates). Must 

be consistent with other MIRE files for linkage.

See point A in Figure 44 for additional details.

Figure 44. Illustration of ramp descriptors (2). 

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 
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Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
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180. Location Identifier for Road 2 Crossing Point

Description 

Definition: Location of midpoint of interchange (e.g., crossing route) on the second intersecting 

route (e.g. route-milepost). Note that if the Interchange File is a spatial data file, this would be 

the same coordinates as in 179. Location Identifier For Road 1 Crossing Point, the previous 

element.

Attributes:

• Route and location descriptors (e.g., route and milepoint orspatial coordinates). Must 

be consistent with other MIRE files for linkage.

See point A in Figure 44 for additional details.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources 

None. 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
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181. Location Identifier for Additional Crossing Points

Description 

Definition: Location on the third and subsequent intersecting route (e.g. route-milepost). Note 

that if the Interchange File is a spatial data file, this would be the same coordinates as in 

Element 180 for all additional routes.

Attributes:

• Route and location descriptors (e.g., route and milepoint or spatial coordinates). Must 

be consistent with other MIRE files for linkage.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources 

None. 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. 

Concerns with Collection Methods

None. 
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182. Interchange Type

Description 

Definition: Type of interchange.

Attributes:

• Diamond. 

• Full cloverleaf. 

• Partial cloverleaf. 

• Trumpet. 

• Three-leg directional. 

• Four-leg all-directional. 

• Semi-directional. 

• Single entrances and/or exits (partial interchange). 

• Single point interchange (SPI). 

• Other (e.g., double crossover diamond, displaced left turn, diverging diamond). 

See Figure 45 for additional details.

Accuracy Statement: 100%. 
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Figure 45. Illustration of types of interchanges (2). 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. 
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Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

• Oklahoma Road Inventory Manual: 

http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf (11). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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183. Interchange Lighting

Description 

Definition: Type of interchange lighting.

Attributes:

• None. 

• Full interchange-area lighting (high mast). 

• Full interchange-area lighting (other). 

• Partial interchange lighting. 

• Other. 

Accuracy Statement: 90-100%. 

Considerations

High mast full interchange-area lighting means that the entire interchange has overhead lighting. 

Other full interchange-area lighting means that there are multiple types of lighting for the 

interchange such as ground or wall lighting in enclosed roadways.

This data element can also be derived based on rules.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• The aerial data collected depends on vegetation cover.

• The data collection process for this element is labor-intensive and involves postprocessing
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184. Interchange Entering Volume

Description 

Definition: Sum of entering volumes for all routes entering interchange. For each entering 

route, this would be counted at a point prior to the first exit ramp.

Attributes:

• Average daily volume. 

Accuracy Statement: ± 5 - 10%. 

Considerations

None.

Existing Resources 

None. 

Collection Methods

For high-priority roadway segments, site-specific statistical sampling using short term counting 

is recommended. For low-priority roadway segments, volume adjustment factors, determined 

based on statistical short term counting, can be applied to AADT.

Concerns with Collection Methods

• Inadequate duration and frequency of data collection.
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185. Interchange Identifier for this Ramp

Description 

Definition: The unique numeric identifier assigned to the interchange that this ramp is part of. 

See Element 178. Unique Interchange Identifier above. This provides linkage to the basic 

interchange information and to all other ramps.

Attributes:

• The interchange identifier entered in Element 178. Unique Interchange Identifier.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• The data collection process for this element requires manual intervention, significant 

post-processing effort, and QA/QC.
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186. Unique Ramp Identifier

Description 

Definition: An identifier for each ramp that is part of a given interchange. This defines which 

ramp the following elements are describing.

Attributes:

• Alphanumeric (e.g., each set of interchange ramps could begin with “1” or “A”, each 

ramp could be indentified by it’s route and exit number, etc.).

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

There is no additional collection needed for this element as this information is already available 

with an agency for roadways under its jurisdiction. If this element is not available, an agency can

generate a unique alpha-numeric identifier for a given ramp based on its geographical location. 

Concerns with Collection Methods

None. 
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187. Ramp Length

Description 

Definition: Length of ramp. In the case of ramp connecting to an at-grade intersection, this 

would be measured from painted nose of gore to intersection curb line. In the case of ramp 

connecting to another ramp or a freeway, this would be measured from painted nose of gore 

to painted nose of gore.

Attributes:

• Feet. 

See Figure 46 for additional details.

Accuracy Statement: 0.01 - 0.001 mi or 52.8 – 5.28 ft. 

Figure 46. Illustration of ramp length for freeway and intersection ramp 

connections (2). 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. 

A gore is defined as the area downstream from the intersection of the shoulders of the main 

line and exit ramp. The painted nose refers to the point where the main line and ramp lanes 

separate.

Existing Resources

None. 
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Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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188. Ramp Acceleration Lane Length

Description 

Definition: Length of acceleration lane, not including taper. For tapered ramps, this would be 

measured from point of tangency of the last ramp curve to the point where the ramp lane 

width becomes less than 12 feet. For parallel ramps, this would be measured from nose of 

painted gore to beginning of taper.

Attributes:

• Feet. 

See Figure 47 for additional details.

Accuracy Statement: 0.01 - 0.001 mi or 52.8 – 5.28 ft. 

Figure 47. Illustration of acceleration lane length for tapered and parallel designs

(2). 

Considerations

Note that this element is for on-ramps only.

Existing Resources

None. 
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Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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189. Ramp Deceleration Lane Length

Description 

Definition: Length of deceleration lane, not including taper. For tapered ramps, this would be 

measured from the point where the ramp lane width becomes 12 feet to the point of curvature 

of the initial ramp curve. For parallel ramps, this would be measured from end of taper to nose 

of painted gore.

Attributes:

• Feet. 

See Figure 48 for additional details.

Accuracy Statement: 0.01 - 0.001 mi or 52.8 – 5.28 ft. 

Figure 48. Illustration of deceleration lane length for tapered and parallel designs

(2). 

Considerations

Note that this element is for off-ramps only.

Existing Resources

None. 
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Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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190. Ramp Number of Lanes

Description 

Definition: Maximum number of lanes on ramp.

Attributes:

• Numeric. 

Accuracy Statement: 90-100%. 

Considerations

None. 

Existing Resources

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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191. Ramp AADT

Description 

Definition: AADT on ramp.

Attributes:

• Numeric. 

Accuracy Statement: ± 5 - 10%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. 

Refer to Element 79. Annual Average Daily Traffic (AADT) for AADT definition.

Existing Resources 

Established data collection methodologies can be found at:

National:

• Traffic Monitoring Guide: http://www.fhwa.dot.gov/ohim/tmguide/index.htm (10). 

Collection Methods

Refer to Element 79. Annual Average Daily Traffic (AADT) for more information on collection 

methods. Ramp volume adjustment factors, determined based on statistical short term 

counting, can be applied to AADT.

Concerns with Collection Methods

Refer to Element 79. Annual Average Daily Traffic (AADT) for more information on concerns 

with collection methods.
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192. Year of Ramp AADT

Description 

Definition: Year of AADT on ramp.

Attributes:

• Year. 

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources

None. 

Collection Methods

This element is collected in conjunction with Element 191. Ramp AADT.

Concerns with Collection Methods

None. 
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193. Ramp Metering

Description 

Definition: The presence and type of any metering of traffic entering mainline.

Attributes:

• Pretimed. 

• Traffic actuated. 

• No metering. 

• Not applicable (i.e., ramp does not feed into mainline). 

See Figure 49 for additional details.

Accuracy Statement: 90-100%. 

Figure 49. Presence of ramp metering (27). 

Google MapsTM
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Considerations

The presence of ramp metering can be determined with ground-based imagery. If metering is 

present a sign with an amber signal will be posted at the ramp entrance to prevent traffic 

acceleration. A red and green (or red, yellow, and green) signal located near the mainline will 

control the traffic flow during times of mainline congestion. The signal is regulated by a timer 

(pretimed) or by the presence of traffic (traffic actuated). This difference is not noticeable in 

imagery so another source is required to determine these attributes.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution.
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194. Ramp Advisory Speed Limit

Description 

Definition: The advisory speed limit on the ramp.

Attributes:

• Numeric. 

• No advisory limit (i.e., limit will be the same as on the connecting roadways). 

See Figure 50 for additional details.

Accuracy Statement: 90-100%. 

Figure 50. Ramp advisory speed limit signs (14). 

Considerations

Advisory speed limit signs are yellow in color and are labeled “RAMP” or “EXIT” above the 

speed limit number.

Existing Resources 

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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195. Roadway Type at Beginning Ramp Terminal

Description 

Definition: A ramp is described by a beginning and ending ramp terminal in the direction of 

ramp traffic flow or the direction of inventory. This element describes the type of roadway 

intersecting with the ramp at the beginning terminal.

Attributes:

• Freeway. 

• Non-freeway (surface street). 

• Other Ramp. 

• Frontage road. 

• Other. 

See Figure 44 for additional details. For point B, Ramp 005 P5 12754, Roadway Type at 

Beginning Ramp Terminal = Freeway.

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. 

Existing Resources

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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196. Roadway Feature at Beginning Ramp Terminal

Description 

Definition: The feature found at the beginning terminal of the ramp.

Attributes:

• Acceleration Lane. 

• Deceleration Lane. 

• Weaving lane (e.g., the weaving area joining two ramps under an overpass in a cloverleaf 

interchange). 

• Signalized intersection. 

• Stop/yield controlled intersection. 

• Uncontrolled intersection. 

• Another ramp. 

• Other. 

See Figure 44 for additional details. For point B, Ramp 005 P5 12754, Roadway Feature at 

Beginning Ramp Terminal = Weaving Lane.

Accuracy Statement: 90-100%. 

Considerations

For descriptions of acceleration or deceleration lane see Element 188. Ramp Acceleration Lane 

Length or Element 189.Ramp Deceleration Lane Length, respectively.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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197. Location Identifier for Roadway at Beginning Ramp Terminal

Description 

Definition: Location on the roadway at the beginning ramp terminal (e.g. route-milepost for that 

roadway) if the ramp connects with a roadway at that point.

Attributes:

• Route and location descriptors (e.g., route and milepoint or spatial coordinates) for the 

roadway intersected at the beginning ramp terminal. Must be consistent with other 

MIRE files for linkage.

See Figure 44 for additional details. Point D has a Location Identifier for Roadway at Beginning 

Ramp Terminal = Route 005 MP 128.06.

Accuracy Statement: 100%. 

Considerations

None.

Existing Resources

This is an FDE for roads with AADT ≥ 400 vpd.

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 

• The data collection process for this element requires manual intervention, significant 

post-processing effort, and QA/QC.
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198. Location of Beginning Ramp Terminal Relative to Mainline Flow

Description 

Definition: Ramps can intersect with the traffic flow of a divided or undivided roadway on 

either of two sides. This defines the side of the roadway flow intersected by the ramp.

Attributes:

• Right side with respect to mainline traffic flow at intersecting point. 

• Left side with respect to mainline traffic flow at intersection point. 

• Ramp does not intersect mainline at this point (e.g., ramp intersects another ramp). 

See Figure 51 for additional details.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources 

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR.

• Airborne LiDAR with imagery.

Concerns with Collection Methods 

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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Figure 51. Illustration of locations of beginning ramp terminal relative to mainline 

flow (2). 
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304

199. Roadway Type at Ending Ramp Terminal

Description 

Definition: A ramp is described by a beginning and ending ramp terminal in the direction of 

inventory. This element describes the type of roadway intersecting with the ramp at the ending 

terminal.

Attributes:

• Freeway. 

• Non-freeway (surface street). 

• Other Ramp. 

• Frontage road. 

• Other. 

See Figure 44 for additional details. For point C, Ramp 005 R1 12806, Roadway Type at Ending 

Ramp Terminal = Non-freeway.

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd. 

Existing Resources 

Established data collection methodologies can be found at:

State:

• Florida Roadway Characteristic Inventory Field Handbook: 

http://www.dot.state.fl.us/planning/statistics/rci/fieldhandbook/fullrpt.pdf (13). 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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200. Roadway Feature at Ending Ramp Terminal

Description 

Definition: The feature found at the ending terminal of the ramp.

Attributes:

• Acceleration Lane. 

• Deceleration Lane. 

• Weaving lane (e.g., the weaving area joining two ramps under an overpass in a cloverleaf 

interchange). 

• Signalized intersection. 

• Stop/yield controlled intersection. 

• Uncontrolled intersection. 

• Another ramp. 

• Other. 

See Figure 44 for additional details. For point C, Ramp 005 R1 12806, Roadway Feature at 

Ending Ramp Terminal = Signalized Intersection.

Accuracy Statement: 90-100%. 

Considerations

For descriptions of acceleration or deceleration lane see Element 188. Ramp Acceleration Lane 

Length or Element 189.Ramp Deceleration Lane Length , respectively.

Existing Resources

None

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Ground-based imagery.

• Terrestrial LiDAR. 

Concerns with Collection Methods

• Timeliness of the ground-based imagery. 

• Inadequate image capture and poor resolution. 
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201. Location Identifier for Roadway at Ending Ramp Terminal

 Description 

Definition: Location on the roadway at the ending ramp terminal (e.g. route-milepost for that 

roadway) if the ramp connects with a roadway at that point.

Attributes:

• Route and location descriptors (e.g., route and milepoint or spatial coordinates) for the 

roadway intersected at the ending ramp terminal. Must be consistent with other MIRE 

files for linkage.

See Figure 44 for additional details. Point E has a Location Identifier for Roadway at Ending 

Ramp Terminal = Route 005 MP 126.77.

Accuracy Statement: 100%. 

Considerations

This is an FDE for roads with AADT ≥ 400 vpd.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR. 

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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202. Location of Ending Ramp Terminal Relative to Mainline Flow

Description 

Definition: Ramps can intersect with the traffic flow of a divided or undivided roadway on 

either of two sides. This defines the side of the roadway flow intersected by the ramp.

Attributes:

• Right side with respect to mainline traffic flow at intersecting point. 

• Left side with respect to mainline traffic flow at intersection point. 

• Ramp does not intersect mainline at this point (e.g., ramp intersects another ramp). 

See for Figure 52 for additional details.

Accuracy Statement: 90-100%. 

Considerations

None.

Existing Resources

None. 

Collection Methods

Resources for collecting this data element in the order of increasing accuracy are as follows:

• Aerial imagery.

• Aerial imagery in conjunction with ground-based imagery.

• Terrestrial LiDAR.

• Airborne LiDAR with imagery.

Concerns with Collection Methods

• Timeliness of the aerial and ground-based imagery.

• Inadequate image capture and poor resolution. 
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Figure 52. Illustration of locations of ending ramp terminal relative to mainline flow

(2). 
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309

REFERENCES

1. National Highway Traffic Safety Association. Model Minimum Uniform Crash Criteria - 

MMUCC. Washington, DC, http://www.mmucc.us/. 

2. Lefler, N., F. Council; D. Harkey; D. Carter, H. McGee, and M. Daul. Model Inventory of 

Roadway Elements – MIRE Version 1.0. Federal Highway Administration, FHWA-HRT-10-

048, Washington, DC, 2010.

3. Federal Highway Administration, Office of Safety. MAP-21 Moving Ahead for Progress in 

the 21st Century – Guidance on State Safety Data Systems. December 2012, 

http://www.fhwa.dot.gov/map21/guidance/guidesafetydata.cfm. 

4. Council, F. and D. Harkey. Traffic Safety Information Systems International Scan: Strategy 

Implementation White Paper. Federal Highway Administration, FHWA-HRT-06-099, 

McLean, VA, 2006. 

5. Council, F.; D. Harkey; D. Carter; and B. White. Model Minimum Inventory of Roadway 

Elements—MMIRE. Federal Highway Administration, FHWA-HRT-07-046, McLean, VA, 

2007. 

6. Furst, A. Guidance Memorandum on Fundamental Roadway and Traffic Data Elements to 

Improve the Highway Safety Improvement. Federal Highway Administration, August 1, 

2011. 

7. Burkhard, J.; S. Cagegn; A. Barmettler; and S. Nebiker. Stereovision Mobile Mapping: 

System Design and Performance Evaluation. The XXII Congress of the International Society 

for Photogrammetry and Remote Sensing, Melbourne, Australia, 2012.

8. Yang, C; M. Goodchild; Q. Huang; D. Nebert; R. Raskin; Y. Xu; M. Bambacus; and D. 

Fay. Spatial Cloud Computing: How can the geospatial sciences use and help shape cloud 

computing? International Journal of Digital Earth, Vol. 4, No. 4, 2011.

9. Highway Performance Monitoring System Field Manual. Office of Highway Policy 

Information, Federal Highway Administration, Washington, DC, March 2013.

10. Traffic Monitoring Guide. Office of Highway Policy Information, Federal Highway 

Administration, Washington, DC, 2013, 

http://www.fhwa.dot.gov/ohim/tmguide/index.htm. 

11. Road Inventory Manual. Oklahoma Department of Transportation, Oklahoma City, OK, 

2010, http://192.149.244.31/griplite/help/2010_RoadInvManual.pdf. 

12. Traffic Data and Analysis Manual. Transportation Planning and Programing Division, Texas 

Department of Transportation, Austin, TX, 2001,

http://onlinemanuals.txdot.gov/txdotmanuals/tda/index.htm. 

13. Transportation Statistics Roadway Characteristics Inventory Field Handbook. Transportation 

Statistics Office, Florida Department of Transportation, Tallahassee, FL, 2012. 
MIRE DATA COLLECTION GUIDEBOOK 

310

14. Manual on Uniform Traffic Control Devices. Federal Highway Administration, Washington, 

DC, 2009.

15. Flexibility in Highway Design. Federal Highway Administration, Washington, DC, 1997.

16. Functional Classification Guidelines. Federal Highway Administration, Washington, DC, 

1989. 

17. GDOT HPMS Inventory Manual. Office of Transportation Data, Georgia Department of 

Transportation, Chamblee, GA, 2010. 

18. Hall, J. W.; K. L. Smith; L. Titus-Glover; J. C. Wambold; T. J. Yager; and Z. Rado. Guide 

for Pavement Friction. NCHRP Web-Only Document 108, National Cooperative Highway 

Research Program, Washington, DC, 2009. 

19. Level of Service Condition Assessments Data Collection Manual. Alabama Department of 

Transportation, 

http://www.dot.state.al.us/maweb/frm/ALDOT%20Condition%20Assessment%20Data%2

0Collection%20Manual.pdf. 

20. “Section 232.1: Climbing Lanes,” Engineering Policy Guide, Missouri Department of 

Transportation, 2010, http://epg.modot.org/index.php?title=232.1_Climbing_Lanes. 

21. Microsoft Bing®, 2013. 

22. North Dakota Department of Transportation, Bismark, ND, http://www.dot.nd.gov/. 

23. Washington State Department of Transportation, Olympia, WA, 

http://www.wsdot.wa.gov/. 

24. Roadside Design Guide. American Association of State Highway and Transportation 

Officials, 4th Edition, Washington, DC, 2011.

25. Image courtesy of Texas DOT. 

26. Highway Capacity Manual, Transportation Research Board, Washington, DC, 2010. 

27. Google MapsTM, 2013. 

28. Spot Speed Study Workshop Instruction Manual. Governor’s Highway Safety Bureau, 

Boston, MA, 2005, 

http://www.ecs.umass.edu/masssafe/PDFS%20for%20Site/Spot%20Speed%20Manual.pdf. 

29. National Bridge Inventory. Federal Highway Administration, Washington, DC, 

http://www.fhwa.dot.gov/bridge/nbi.cfm. 

30. Traffic Signal Timing Manual. Federal Highway Administration, FHWA-HOP-08-021, 

Washington, DC, 2008.

31. Srinivasas, R.; J. Baek; and F. Council. Safety Evaluation of Transverse Rumble Strips on 

Approaches to Stop-Controlled Intersections in Rural Areas. Federal Highway Administration, 

FHWA-HRT-12-047, Washington, DC, 2012. 
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311

INDEX

This Index provides an alphabetical listing of the MIRE Elements, along with the corresponding 

page number.

95. 85th Percentile Speed, 161 

81. AADT Annual Escalation Percentage, 144 

80. AADT Year, 143 

22. Access Control, 46 

147. Amount of Left Turn Lane Offset, 234 

79. Annual Average Daily Traffic (AADT), 141 

140. Approach AADT, 224

141. Approach AADT Year, 225 

143. Approach Directional Flow, 227

155. Approach Left Turn Protection, 249 

142. Approach Mode, 226

154. Approach Traffic Control, 247 

36. Auxiliary Lane Length, 70 

35. Auxiliary Lane Presence/Type, 68 

10. Begin Point Segment Descriptor, 27 

86. Bicycle Count/Exposure, 150 

106. Bridge Numbers for Bridges in Segment, 178 

103. Centerline Presence/Width, 174 

104. Centerline Rumble Strip Presence/Type, 175 

137. Circular Intersection - Bicycle Facility, 219 

135. Circular Intersection - Circulatory Lane Width, 216 

176. Circular Intersection - Crosswalk Location, 276 

171. Circular Intersection - Entry Radius, 271 

168. Circular Intersection - Entry Width, 266 

174. Circular Intersection - Exit Radius, 274 

172. Circular Intersection - Exit Width, 272 
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136. Circular Intersection - Inscribed Diameter, 217

177. Circular Intersection - Island Width, 277 

134. Circular Intersection - Number of Circulatory Lanes, 215 

169. Circular Intersection - Number of Entry lanes, 267 

173. Circular Intersection - Number of Exit lanes, 273 

175. Circular Intersection - Pedestrian Facility, 275

170. Circular Intersection - Presence/ Type of Exclusive Right Turn Lane, 269 

6. City/Local Jurisdiction Name, 23 

7. City/Local Jurisdiction Urban Code, 24 

17. Coinciding Route - Minor Route Information, 37 

16. Coinciding Route Indicator, 36 

2. County Code, 18 

1. County Name, 17 

34. Cross Slope, 67 

160. Crossing Pedestrian Count/Exposure, 255 

157. Crosswalk Presence/Type, 252 

52. Curb Presence, 99 

53. Curb Type, 100 

108. Curve Feature Type, 180 

107. Curve Identifiers and Linkage Elements, 179 

111. Curve Superelevation, 185 

18. Direction of Inventory, 38 

90. Directional Factor, 154

102. Edgeline Presence, 173 

11. End Point Segment Descriptor, 28 

21. Federal Aid/Route Type, 44 

19. Functional Class, 39 

115. Grade Identifiers and Linkage Elements, 190 

118. Grade Length, 194 
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3. Highway District, 19 

109. Horizontal Curve Degree or Radius, 182 

114. Horizontal Curve Direction, 189 

113. Horizontal Curve Intersection/Deflection Angle, 188 

110. Horizontal Curve Length, 184 

112. Horizontal Transition/Spiral Curve Presence, 186 

88. Hourly Traffic Volumes (or Peak and Offpeak AADT), 152 

37. HOV Lane Presence/ Types, 71 

38. HOV Lanes, 73 

33. Inside Through Lane Width, 63 

184. Interchange Entering Volume, 287 

185. Interchange Identifier for this Ramp, 288 

183. Interchange Lighting, 286 

182. Interchange Type, 283

129. Intersection Angle, 207 

138. Intersection Identifier for this Approach, 221

126. Intersection/Junction Geometry, 202 

133. Intersection/Junction Lighting, 214 

125. Intersection/Junction Number of Legs, 201 

130. Intersection/Junction Offset Distance, 209 

131. Intersection/Junction Traffic Control, 211 

89. K-Factor, 153 

49. Left Paved Shoulder Width, 94 

50. Left Shoulder Rumble Strip Presence/Type, 96

48. Left Shoulder Total Width, 92 

47. Left Shoulder Type, 90 

65. Left Sideslope, 119 

66. Left Sideslope Width, 120 

163. Left Turn Counts/Percent, 260 
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145. Left Turn Lane Type, 229 

161. Left/Right Turn Prohibitions, 256 

151. Length of Exclusive Left Turn Lanes, 240 

152. Length of Exclusive Right Turn Lanes, 242 

179. Location Identifier for Road 1 Crossing Point, 279 

122. Location Identifier for Road 1 Crossing Point, 198 

180. Location Identifier for Road 2 Crossing Point, 281 

123. Location Identifier for Road 2 Crossing Point, 199 

124. Location Identifier for Additional Road Crossing Point, 200 

181. Location Identifier for Additional Road Crossing Point, 282 

197. Location Identifier for Roadway at Beginning Ramp Terminal, 306 

201. Location Identifier for Roadway at Ending Ramp Terminal, 312 

198. Location of Beginning Ramp Terminal Relative to Mainline Flow, 307 

202. Location of Ending Ramp Terminal Relative to Mainline Flow, 313 

68. Major Commercial Driveway Count, 125 

72. Major Industrial/Institutional Driveway Count, 130 

70. Major Residential Driveway Count, 128 

96. Mean Speed, 162 

57. Median (Inner) Paved Shoulder Width, 107 

56. Median Barrier Presence/Type, 105 

61. Median Crossover/Left Turn Lane Type, 113 

58. Median Shoulder Rumble Strip Presence/ Type, 109 

59. Median Sideslope, 110

60. Median Sideslope Width, 112 

54. Median Type, 101 

153. Median Type at Intersection, 244 

55. Median Width, 103 

69. Minor Commercial Driveway Count, 127 

73. Minor Industrial/Institutional Driveway Count, 131 
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71. Minor Residential Driveway Count, 129 

87. Motorcycle Count or Percentage, 151 

94. Nighttime Speed Limit, 160 

144. Number of Approach Through Lanes, 228 

146. Number of Exclusive Left Turn Lanes, 233 

150. Number of Exclusive Right Turn Lanes, 239 

42. Number of Peak Period Through Lanes, 80 

76. Number of Signalized Intersections in Segment, 135 

77. Number of Stop-Controlled Intersections in Segment, 137 

31. Number of Through Lanes, 59 

78. Number of Uncontrolled/Other Intersections, 139 

91. One/Two-Way Operations, 155 

98. On-Street Parking Presence, 165 

99. On-Street Parking Type, 168 

74. Other Driveway Count, 132 

32. Outside Through Lane Width, 61 

105. Passing Zone Percentage, 177 

29. Pavement Condition (Present Serviceability Rating), 56 

30. Pavement Condition (PSR) Date, 58 

28. Pavement Roughness Date, 55 

27. Pavement Roughness/Condition, 53 

159. Pedestrian Signal Special Features, 254 

158. Pedestrian Signalization Type, 253 

83. Percent Combination Trucks or Combination Truck AADT, 147 

117. Percent of Gradient, 193 

82. Percent Single Unit Trucks or Single Truck AADT, 145 

84. Percentage Trucks or Truck AADT, 148 

40. Presence/Type of Bicycle Facility, 75 

128. Railroad Crossing Number, 206 
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191. Ramp AADT, 297 

188. Ramp Acceleration Lane Length, 292 

194. Ramp Advisory Speed Limit, 301 

189. Ramp Deceleration Lane Length, 294 

187. Ramp Length, 290 

193. Ramp Metering, 299 

190. Ramp Number of Lanes, 296 

39. Reversible Lanes, 74 

45. Right Paved Shoulder Width, 86 

46. Right Shoulder Rumble Strip Presence/ Type, 88 

44. Right Shoulder Total Width, 84 

43. Right Shoulder Type, 82 

63. Right Sideslope, 117 

64. Right Sideslope Width, 118 

148. Right Turn Channelization, 236 

165. Right Turn Counts/Percent, 262 

162. Right Turn-On-Red Prohibitions, 258 

62. Roadside Clearzone Width, 116 

67. Roadside Rating, 121 

196. Roadway Feature at Beginning Ramp Terminal, 304 

200. Roadway Feature at Ending Ramp Terminal, 311 

100. Roadway Lighting, 170

195. Roadway Type at Beginning Ramp Terminal, 302 

199. Roadway Type at Ending Ramp Terminal, 309

8. Route Number, 25 

14. Route Signing, 31 

15. Route Signing Qualifier, 34 

9. Route/Street Name, 26 

20. Rural/Urban Designation, 42 
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97. School Zone Indicator, 163 

127. School Zone Indicator, 205 

12. Segment Identifier, 29

13. Segment Length, 30 

51. Sidewalk Presence, 98

156. Signal Progression, 251 

132. Signalization Presence/ Type, 213

5. Specific Governmental Ownership, 22 

92. Speed Limit, 157 

25. Surface Friction, 51 

26. Surface Friction Date, 52 

23. Surface Type, 48 

75. Terrain Type, 133 

101. Toll Facility, 171 

85. Total Daily Two-Way Pedestrian Count/Exposure, 149 

24. Total Paved Surface Width, 50 

149. Traffic Control of Exclusive Right Turn Lanes, 238 

167. Transverse Rumble Strip Presence, 264 

93. Truck Speed Limit, 159

4. Type of Governmental Ownership, 20 

121. Type of Intersection/Junction, 197 

139. Unique Approach Identifier, 222 

178. Unique Interchange Identifier, 278 

120. Unique Junction Identifier, 196 

186. Unique Ramp Identifier, 289 

116. Vertical Alignment Feature Type, 191 

119. Vertical Curve Length, 195 

41. Width of Bicycle Facility, 78 

164. Year of Left Turn Counts/Percent, 261 
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192. Year of Ramp AADT, 298 

166. Year of Right Turn Counts/Percent, 263 
For More Information:

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

FHWA, Office of Safety

Robert Pollack Carol Tan, PhD

Robert.Pollack@dot.gov Carol.Tan@dot.gov

202-366-5019 202-493-3315

FHWA-SA-13-009