Assessing Technology Gaps for the Federal Highway 

Administration Digital Highway Measurement Program 

FHWA Grant DTFH61-09-X-30011 

NISTIR 7685 

Final Report 

April 2010 

Submitted to: 

Federal Highway Administration 

Office of Safety Research and Development 

Turner-Fairbank Highway Research Center 

6300 Georgetown Pike 

McLean, VA 22101 

Submitted by: 

Geraldine Cheok, Marek Franaszek, Itai Katz, Alan Lytle, Kamel Saidi, Nicholas Scott 

Construction Metrology and Automation Group, Building and Fire Research Laboratory 

National Institute of Standards and Technology 

100 Bureau Drive 

Gaithersburg, MD 20899 

Disclaimer 

Certain commercial equipment, instruments, or materials are identified in this document. Such 

identification does not imply recommendation or endorsement by the National Institute of 

Standards and Technology, nor does it imply that the products identified are necessarily the best 

available for the purpose. 

iii

Table of Contents 

Disclaimer ........................................................................................................................................ i

Table of Contents ........................................................................................................................... iii

List of Figures ................................................................................................................................. v

List of Tables ................................................................................................................................ vii

List of Acronyms and Abbreviations ............................................................................................. ix

Executive Summary ....................................................................................................................... xi

1. Introduction ............................................................................................................................ 1

1.1 Background ..................................................................................................................... 1

1.2. Project Objectives and Tasks .......................................................................................... 4

1.3. Report Structure .............................................................................................................. 5

2. Master Element Table ............................................................................................................ 7

3. Technology Table ................................................................................................................ 10

4. Technology Gap Assessment ............................................................................................... 22

4.1 Definitions..................................................................................................................... 23

4.1.1 Demand .............................................................................................................. 23

4.1.2 Maturity Index ................................................................................................... 24

4.1.3. Impact ................................................................................................................ 27

4.1.4. Technology Readiness Level Subcategories ...................................................... 29

4.2. Technology Functional Classification Categories ........................................................ 29

4.2.1. Location Measurement....................................................................................... 29

4.2.2. Object and Feature Recognition......................................................................... 30

4.2.3 Sign Type Recognition ...................................................................................... 31

4.2.4. Character Recognition ....................................................................................... 31

4.2.5. Spatial Measurement .......................................................................................... 31

4.2.6. Material Optical Property Measurement ............................................................ 32

4.2.7. Material Property Measurement ........................................................................ 32

4.3 Determining High, Medium and Low Priority Elements.............................................. 33

4.4. Demand Calculation ...................................................................................................... 34

4.5 Maturity Index Calculation ........................................................................................... 36

4.6. Results ........................................................................................................................... 38

5. Recommendations ................................................................................................................ 41

5.1 Recommendation One ................................................................................................... 41

5.2 Recommendation Two .................................................................................................. 42

5.3 Recommendation Three ................................................................................................ 45

Acknowledgements ....................................................................................................................... 47

References ..................................................................................................................................... 47
iv

Appendix A: Digital Highway Measurement Technology Database .......................................... 49

Appendix B: Technology Search Procedure ................................................................................ 56

Appendix C: Element Priority Survey ......................................................................................... 59

Appendix D: FHWA List of Element Priorities .......................................................................... 79

D.1 Background ................................................................................................................... 79

D.2 Criteria for Defining High Priority Data Elements ....................................................... 80

D.3 Criteria for Defining Medium Priority Data Elements ................................................. 82

D.4 Criteria for Defining Low Priority Data Elements ....................................................... 84

Appendix E: Combined List of Element Priorities ...................................................................... 85

Bibliography ................................................................................................................................. 94
v

List of Figures 

Figure 3.1. Percentage of Addressed elements within each MET category. .............................. 14

Figure 3.2. Number of technologies in each of the technology functional classification 

categories. ................................................................................................................ 15

Figure 3.3. Number of identified technologies grouped by general technology category. ........ 16

Figure 3.4. Number of elements addressed (black) and potentially addressed (white) by 

technologies T001 to T072. ..................................................................................... 17

Figure 3.5. Number of elements addressed (black) and potentially addressed (white) by 

technologies T073 to T143. ..................................................................................... 18

Figure 3.6. Number of technologies within each functional classification category that provide 

real-time, offline, and unknown data processing speed. .......................................... 19

Figure 3.7. Number of technologies within each functional classification category that provide 

automated, semi-automated, manual, and unknown levels of data processing 

automation................................................................................................................ 20

Figure 4.1. Schematic of the Demand vs. Maturity Graph. ........................................................ 22

Figure 4.2. Framework for the analysis procedure. .................................................................... 23

Figure 4.3. Impact as defined by the Demand vs. Maturity Plot. ............................................... 28

Figure 4.4. Number of elements belonging to each technology functional classification 

category. ................................................................................................................... 36

Figure 4.5. Demand vs. Maturity Graph. ................................................................................... 39

Figure 5.1. The Receiver Operating Curve (ROC) characterizes the performance of a classifier.

.................................................................................................................................. 44

vii

List of Tables 

Table 1. Data Collected by DHMS and State-of-practice of Technology ............................... 3

Table 2. Description of the Data Fields in the MET ................................................................ 9

Table 3. Description of the Data Fields in the Technology Table ......................................... 12

Table 4. Number of High, Medium and Low Priority Elements and Demands .................... 35

Table 5. Maturity Index Calculation ...................................................................................... 37

Table 6. Sensitivity of Maturity Index to Additional COTS Technologies Found ................ 38

Table C.1. High Priority Elements in Survey ............................................................................ 59

Table C.2. Survey Results ......................................................................................................... 67

Table D.1. List of FHWA High Priority Elements .................................................................... 81

Table D.2. List of FHWA Medium Priority Elements............................................................... 82

ix

List of Acronyms and Abbreviations 

COTS Commercial-off-the-Shelf 

DHMS Digital Highway Measurement System 

DMI Distance Measuring Instrument 

FHWA Federal Highway Administration 

GPS Global Positioning System 

HPMS Highway Performance Monitoring System 

IHSDM Interactive Highway Safety Design Model 

INS Inertial Navigation System 

LADAR Laser Detection and Ranging 

LIDAR Light Detection and Ranging 

MET Master Element Table 

MIRE Minimum Inventory of Roadway Elements 

MMIRE Model Minimum Inventory of Roadway Elements 

MMUCC Model Minimum Uniform Crash Criteria 

MUTCD Manual on Uniform Traffic Control Devices 

NCDOT North Carolina Department of Transportation 

SHRP-2 Strategic Highway Research Program 

SIFT Scale Invariant Feature Transform 

TSIMS Transportation Safety Information Management System 

TT Technology Table 

US&R Urban Search and Rescue 

WAAS Wide Area Augmentation System 

xi

Executive Summary 

The ability to automatically collect more and better measurements of roadway assets 

(e.g., existence, condition, location, type) and to accurately map both geometry and elements of 

roadway assets is an important mitigating factor in roadway safety. This information serves 

numerous purposes including verification of road design codes, detecting degradation in roadway 

elements, detecting roadside hardware and clutter changes which impact safety (e.g., sight lines), 

creation of a roadway element inventory, analysis of accident data correlated to roadway 

inventory, and simulation of roadway conditions for safety analysis. To address the need to 

collect roadway information economically, accurately, and reliably, the Federal Highway 

Administration’s (FHWA) Office of Advanced Research initiated the development of the Digital 

Highway Measurement System (DHMS) in 2003. The DHMS is a vehicle equipped with 

multiple sensors and instrumentation to collect road environment (roadway and roadside) 

geometry, roadside inventory, and pavement condition data at highway speeds. The DHMS 

vision is to enable, using state-of-the-art sensors and analysis, the collection of roadway 

geometry at required levels of accuracy not achievable by the current state-of-practice, and to 

introduce methods and metrics for capturing the presence, location, and health of all roadside 

assets. 

To support future DHMS technology development, FHWA requires identification of 

technology gaps between currently available data collection technologies and the need for 

capabilities to efficiently collect comprehensive information about the nation’s roadway 

infrastructure. FHWA also requires a prioritization of technology gaps to future investment in 

DHMS research and development. The objectives of this project are to: 
xii

Create a master element table of roadway elements. 

The Master Element Table (MET) is a consolidation of the roadway elements 

available in the Model Minimum Inventory of Roadway Elements (MMIRE) 

(Council, Harkey, Carter, & White, 2007), the element lists generated for the SHRP-2 

(Strategic Highway Research Program) S-03 rodeo and the North Carolina Asset 

Management Workshop, the National Bridge Index, and the U.S. National HighwayRail Crossing Inventory Program (Federal Railroad Administration, 2007). The MET 

is limited to above-ground elements. Each element was reviewed to understand the 

actual physical characteristic data that required measurement in order to extract the 

relevant information needed. 

Conduct a literature review/market analysis study to identify relevant technologies. 

A literature review/market analysis survey was conducted for each of the MET elements to a) 

identify relevant technologies, b) perform an assessment of the readiness of that technology 

(where information was available), and c) document providers and researchers involved with 

the technology delivery. A Technology Table was created from the search for technologies 

in which applicable technologies were listed. These technologies were tagged with the 

following meta-data: 

• What elements would this technology address? 

• What is the estimated technology readiness? (e.g., commercial-off-the-shelf 

(COTS), research & development (R&D), Prototype/ Demonstration, Concept). 

• What is the data processing speed? (e.g., real-time, offline post-processing). 

• What is the data processing automation level? (e.g., automated, semi-automated, 

manual). 
xiii

• What general category of technology does the specific technology utilize? (e.g., 

the technology 'uses -' laser-based 3D imaging, camera-based 3D imaging, GPS, 

inertial systems, software only (mainly for research algorithms), laser-based 2D 

imaging, camera-based 2D imaging, or other). 

• What general functional capability does the specific technology provide? (e.g., the 

technology 'provides -' location measurement, object or feature recognition, signtype recognition, character recognition, spatial measurement, material property 

optical measurement, or material property measurement). 

Develop an objective process to identify and prioritize technology gaps. 

Potential technology gaps were identified by analyzing the demand and maturity of a 

technology grouping. The technologies were grouped by functional classification (i.e., what 

does the technology provide?): location measurement, object/feature recognition, sign-type 

recognition, character recognition, spatial measurement, material optical property 

measurement, and material property measurement. Demand is defined as the weighted sum 

of the high, medium and low priority elements within a technology functional classification 

category divided by the weighted sum of the total number of high, medium, and low priority 

elements in-scope. The Maturity Index of a technology functional classification category is 

defined as the weighted sum of the maturity indices for the category’s technology readiness 

level, level of automation, data processing speed, and the ability to measure an element to the 

required accuracy or resolution. Impact is defined by a region in the Demand vs. Maturity 

plot. In general, the relative locations of technology functional classification categories in 

the Demand vs. Maturity plot indicates the impact of the category relative to the other 

categories. The category with the highest demand and lowest maturity would have the 

highest impact and the category with the lowest demand and highest maturity would have the 

lowest impact. 
xiv

Using the approach described in the previous paragraph, technologies within the 

functional category of Object and Feature Recognition were rated as the highest priority and the 

functional category of Spatial Measurement as the second highest priority. The three 

recommendations were: 

• Develop performance-based standards for digital highway measurement 

• Provide standard reference data sets for DHMS algorithm development 

• Develop object/feature recognition algorithms 

Although not specifically part of the technology gap assessment, development of open, 

performance-based standards and the creation of standard reference data sets are highly 

recommended and would likely provide the highest return on investment for FHWA in 

advancing DHMS technologies. 

1

1. Introduction 

1.1 Background 

According to data compiled by the National Highway Traffic Safety Administration 

(NHTSA), there were 6,024,000 police-reported motor vehicle traffic crashes in 2007. Of those 

crashes, 29 % resulted in 2,491,000 persons injured and 41,059 deaths. The remaining 4,275,000 

accidents involved only property damage (NHTSA, 2009). Although there are no reported 

economic figures associated with these data, a comparison can be made to the year 2000, during 

which there were 6,394,000 police-reported motor vehicle crashes resulting in 2,070,000 injuries 

and 37,409 fatalities (NHTSA, 2000). The economic impact of motor vehicle crashes in 2000 

was $230.6 billion (Blincoe, et al., 2002). Accidents remain a leading cause of death in the U.S. 

(#5 in 2006), and motor vehicle accidents account for nearly 25 % of those fatalities (Heron, et 

al., 2009). 

Approximately one-third of motor vehicle traffic crashes have road geometry and 

roadway infrastructure as a contributing factor (Rumar, 1985). The ability to inventory and to 

measure the existing conditions of roadway assets and to accurately map those elements in 

relation to the geometry is important to improving roadway safety. This information serves 

numerous purposes including verifying if road design is within specifications, detecting 

degradation in roadway elements, detecting roadside hardware and clutter changes which impact 

safety (e.g., sight lines), and creation of a roadway element inventory, among others. 

To address the need to collect roadway information economically, accurately, and 

reliably, FHWA’s Office of Advanced Research initiated the development of the Digital 

Highway Measurement System (DHMS) in 2003. The DHMS is a vehicle equipped with 

multiple sensors and instrumentation to collect road environment (roadway and roadside) 
2

geometry, roadside inventory, and pavement condition data at highway speeds. The DHMS 

vision is to enable, using state-of-the-art sensors and analysis, the collection of roadway 

geometry at required levels of accuracy not achievable by the current state-of-practice, and to 

introduce methods and metrics for capturing the presence, location, and health of all roadside 

assets. The primary drivers for the DHMS are (Trentacoste, 2006): 

• Provide necessary roadway data to conduct safety analyses 

• Meet asset management requirements for road safety and condition assessment 

(GASB-341) 

• Enable the integration of safety data with infrastructure data 

• Enable cost-effective and safe data collection through automation 

• Examine next generation sensors (e.g., ground penetrating radar) for the 

collection of enhanced road data 

The central purpose of the DHMS is to support the commitment of highway agencies to 

make data-driven decisions about safety improvements. Through legislative and conventional 

educational means, state and local agencies are being encouraged to collect more data, to 

improve the quality of their data, and to better use their data in order to make the best decisions 

on how to use available funds to make road systems safer and more efficient. The DHMS is used 

to identify and demonstrate technical means of collecting and processing these data. 

Demonstrating these capabilities is a key method of technology transfer and also supports the 

future development of strong data collection performance standards. Technology transfer is 

further enabled through cooperative research and development and licensing of DHMS 

technologies through standard Federal Acquisition Regulation (FAR) provisions (Cobb, 2009). 

 

1

 GASB-34 is the Governmental Accounting Standards Board Statement No. 34, which mandated that state and local 

governments report on the value of infrastructure assets (e.g., roads, bridges, etc.), and develop methods for 

managing those assets. (DOT, 2000) 
3

Table 1, from FHWA, lists the information that the ideal data collection system would 

gather. The list is presented generally in increasing order of value, and increasing order of 

difficulty or complexity. The table also indicates FHWA’s estimate of the current state-ofpractice within the industry. FHWA’s ultimate vision of a data collection system would assess 

both “How ...” and “When to repair or upgrade ...,” with relevant information extracted and 

delivered in real-time2 as roadway measurement took place (Cobb, 2009). 

Table 1. Data Collected by DHMS and State-of-practice of Technology 

Item 

State of 

Industry/Practice 

(0=Not available; 

4=Mature, readily 

available)

Comments 

a Where it is. 4 

b What it is. 3 Optical means effective, but 

automated object extraction not robust. 

c 

Its distinguishing quantities, 

measurements and/or 

characteristic values. (How 

much, how many, what type, 

etc.) 

2 

Not all identifiable features can be 

measured directly. Some 

measurements are not accurate or 

precise. 

d How well it’s working, or meets 

standards. 1 Only subjective assessment of video. 

e 

Whether it needs 

repairs/upgrades. 1 Only subjective assessment of video. 

f How to repair or upgrade it. 0 Holy Grail! g When to repair or upgrade it. 0 

• “How well it’s working, or meets standards.” – Assesses remaining functionality 

of hardware and etc., (e.g., how much of its nominal performance does a damaged 

guardrail retain?). Alternately, determine whether current condition meets 

 

2

 Real-time is a subjective qualifier. The important aspect is that information is delivered to end-users as quickly as 

possible after roadway measurement. The less delay for post-processing the better with an ultimate vision that 

information is extracted and delivered wirelessly while the measurement platform is in the field. 
4

established standards (e.g. whether current retro-reflectivity of a sign meets 

minimum requirements). 

• “Whether it needs repairs/upgrades.” – Indicates whether the extent of damage 

reaches the threshold for intervention. 

• “How to repair or upgrade it?” – Indicates to users what methods of intervention 

are needed, including materials needed, repair protocols to be used, etc. For 

example, a moderately bent guardrail may need only one replacement w-beam 

rail, one block-out, and one set of splice fasteners. 

• “When to repair or upgrade it?” – Indicates the ideal time to make a repair based 

on lowest overall maintenance cost, or on minimizing construction time, etc. For 

example, some pavement surface distress may be a clear harbinger of worse to 

come, but may not represent an immediate hazard. On the other hand, there will 

come a time when deterioration has gone so far that repair costs increase 

dramatically. The ideal system will be capable of providing a schedule of needed 

repairs looking forward a year or more. 

The project described in this report is designed to assist the FHWA Office of Safety 

Research and Development in crafting a research roadmap for future DHMS investment. 

1.2. Project Objectives and Tasks 

FHWA requires identification of technology gaps between currently available data 

collection technologies and the need for capabilities to efficiently collect comprehensive 

information about the nation’s roadway infrastructure. Associated with this need is a 

prioritization of research gaps to guide FHWA’s future investment in Digital Highway 

Measurement System (DHMS) research and development. 

The Office of Safety Research commissioned this project to conduct a review of data 

collection technologies to provide an understanding and mapping of sensing technologies 
5

(hardware and software) to the list of roadway elements available in the Model Minimum 

Inventory of Roadway Elements (MMIRE) (Council, Harkey, Carter, & White, 2007), SHRP-2 

S-03 rodeo (Vandervalk, 2008), and the North Carolina Asset Management workshop. The 

following tasks outline the steps used to accomplish the project objectives: 

• Create a master element table of the roadway elements available in the MMIRE, 

and the element lists generated for the SHRP-2 S-03 rodeo and the North Carolina 

Asset Management Workshop. The table will be limited to above-ground 

elements. Each retained element will be reviewed to understand the actual 

physical characteristic data that need to be measured in order to extract the 

relevant information needed. 

• Conduct a literature review/market analysis study to identify relevant 

technologies, assess the readiness of those technologies, document the providers 

and researchers involved with the technology delivery, and identify potential 

technology gaps. 

• Develop and implement an objective process for prioritizing technology gaps 

identified during the literature review/market analysis study. Provide and discuss 

a prioritized list of technology gaps. 

1.3. Report Structure 

Section 2 provides a description of the generation of the Master Element Table (MET), 

including element selection criteria. Section 3 presents the results of the literature review/ 

market analysis survey conducted on each of the elements from the MET and the resulting 

Technology Table (TT). Section 4 details the methods used to identify the broad category 

technology gaps through analysis of the MET and TT. Recommendations are provided in 

Section 5. Appendix A provides a description of the database that contains the MET and TT and 

provides examples of the reports that can be generated from the database. Appendix B lists the 

procedures to conduct the technology search and Appendix C presents the survey questionnaire 
6

that was sent to subject matter experts at FHWA as well as the results of the survey. Appendix D 

includes prioritized lists of high-priority and medium priority elements provided by FHWA and 

the criteria that were used to generate them. Finally, Appendix E provides the combined lists of 

high, medium, and low priority elements from the survey (Appendix C) and the FHWA lists 

(Appendix D). 
7

2. Master Element Table 

The Master Element Table (MET) was generated by combining data elements from the 

MMIRE, SHRP-2 S-03 Rodeo, the North Carolina DOT (NCDOT) Inventory Asset Data 

Collection (NCDOT), and the NCDOT Pavement Condition Survey (NCDOT). There were 180 

data elements from the MMIRE, 120 from SHRP-2, and 134 from NCDOT, 23 data elements 

from the National Bridge Index (FHWA, 1995), and 38 data elements from the U.S. National 

Highway-Rail Crossing Inventory Program (Federal Railroad Administration, 2007) bringing the 

total number of elements in the MET to 495. All of the elements were reviewed to remove 

duplicate or similar data elements. This process reduced the number of data elements from 495 

to 392. A further sub-selection of the 392 elements was made to determine the technology gaps 

(see Section 4). 

The categorization of the data elements in the MMIRE document differed from that in the 

SHRP-2 and NCDOT documents. The decision was made to follow the MMIRE categorization 

and to add three new subcategories (I.e.1, I.e.2, and III.b.2). The structure of the MET is as 

follows: 

I. ROADWAY SEGMENT 

 I.a Segment Location Linkage 

 I.b. Segment Roadway Classification 

 I.c. Segment Cross Section 

 I.c.1. Surface Descriptors 

 I.c.2. Lane Descriptors 

 I.c.3. Shoulder Descriptors 

 I.c.4. Median Descriptors 

 I.d. Segment Roadside Descriptors 

 I.e. Other Segment Descriptors 
8

 I.e.1. Bridge Descriptors 

 I.e.2. Railroad Crossing Descriptors 

 I.f. Segment Traffic Flow Data 

 I.g. Segment Traffic Operations / Control Data 

II. Roadway Alignment Descriptors 

 II.a. Horizontal Curve Data 

 II.b. Vertical Grade Data 

III. Roadway Junction Descriptors 

 III.a. At-Grade Intersection /Junctions 

 III.a.1. General Descriptors 

 III.a.2. At Grade Intersection /Junction Descriptors-Each Approach 

 III.b. Interchange and Ramp Descriptors 

 III.b.1. General Interchange Descriptors 

 III.b.2. General Ramp Descriptors 

The MET is implemented as a database (see Appendix A) on an FHWA server3

 and 

contains several data fields as described in Table 2. The mapping of elements to the three 

primary source documents is preserved in the following three MET data fields: MMIRE 

Corresponding Elements, SHRP-2 Corresponding Elements, and NCDOT Corresponding 

Elements. 

 

 

3

 For information on how to access the database contact Lincoln Cobb, FHWA Office of Safety R&D, HRDS-2, 

(Lincoln.Cobb@dot.gov). 
9

Table 2. Description of the Data Fields in the MET 

Field Name Description 

DerivedFrom List of element IDs from which this element is derived 

ElementDescription Description of the element as given in the MMIRE, SHRP, or NCDOT 

ElementID Number of the data element 

ElementName Name of the data element 

HPMS Data element required in: U = Universe file, S = Sample section 

Note: This categorization was only done for the MMIRE elements 

IHSDM Data element required: Y = Yes, O = Optional 

Note: This categorization was only done for the MMIRE elements 

IsDerived This column indicates whether the data element can be derived from the 

knowledge obtained for other data elements. Example: “Number of 

Exclusive Right Turn Lanes” could be derived from knowing the number 

of lanes, the number of thru lanes, and the number of exclusive left turn 

lanes. 

IsNonVehicle This column indicates whether the data element can be collected from a 

moving vehicle or not. Example: “Crossing pedestrian count/exposure” 

could not be collected from a moving vehicle. 

MMIRECategory 1st level MMIRE classification 

MMIRECorrespondingElements Corresponding element in the MMIRE list 

Note: This column along with Columns X and Y provides a mapping of 

the MET elements back to the MMIRE, SHRP, and NCDOT elements. 

MMIREEaseofDataCollection “Estimate of the ease of data collection for that variable—easy (E), 

moderately difficult (M), difficult (D)” as given in the MMIRE. 

MMIREPriority Priority as given in the MMIRE: 1st priority or 2nd priority 

MMIRESubCategory 2nd level MMIRE classification 

MMIRESubSubCategory 3rd level MMIRE classification 

MMUCC This column indicates where this data element is part of the MMUCC. 

Note: This categorization was only done for the MMIRE elements 

NCDOTCorrespondingElements Correspondence of the data element to the NCDOT element. 

Note: This column along with Columns P and X provides a mapping of 

the MET elements back to the MMIRE, SHRP, and NCDOT elements. 

RequiredMeasurementUncertainties Where available, measurement uncertainties for the data element as 

required by various governmental agencies, standards, etc. 

SafetyAnalyst Data element required: M = Mandatory, O = Optional, S = Supplemental 

Note: This categorization was only done for the MMIRE elements 

SameAsElements This column indicates whether the data element is the same as another 

element. For example, the technology to identify “right shoulder type” 

would be the same as the technology to identify “left shoulder type.” 

Scope Whether the data element will be analyzed as part of this project: In / Out 

SHRP2CorrespondingElements Correspondence of the data element to the SHRP-2 element. 

Note: This column along with Columns P and Y provides a mapping of 

the MET elements back to the MMIRE, SHRP-2, and NCDOT elements. 

TechGap Preliminary gap assessment based on number of technologies found. 

“Addressed” means 5 or more COTS technologies were found; “Partial” 

means between 1 and 4 COTS technologies were found; and “Not” means 

no COTS technologies were found. 

TechnologyIDs List of technologies that would address the data element 

TechsAll Number of technologies found 

TechsCOTS Number of COTS technologies found 

TSIMS Data element required: M = Minimum, B = Basic, E = Extended 

Note: This categorization was only done for the MMIRE elements 

 
10

3. Technology Table 

A literature review was conducted for each element in the MET to identify relevant 

technologies, to perform an assessment of the readiness of those technologies (where information 

was available), and to document providers and researchers involved with the technology 

delivery. Searches were performed for elements determined to be within the scope of the 

examination. Scope was defined primarily as measurable from a moving platform (either 

terrestrial or airborne) and not derived from another source (e.g., an existing roadway database or 

through measurement of other elements within the MET). Technologies that were identified 

were tagged with the following meta-data: 

• What elements would this technology serve? 

• What is the estimated technology readiness? (e.g., COTS, R&D, 

Prototype/Demonstration, Concept). 

• What is the data processing speed? (e.g., real-time, offline post-processing). 

• What is the data processing automation level? (e.g., automated, semi-automated, 

manual). 

• What general category of technology does the specific technology incorporate? 

(e.g., the technology 'uses -' laser-based 3D imaging, camera-based 3D imaging, 

GPS, inertial systems, software only (mainly for research algorithms), laser-based 

2D imaging, camera-based 2D imaging, or other). 

• What general functional capability does the specific technology provide? (e.g., the 

technology 'provides -' location measurement, object or feature recognition, signtype recognition, character recognition, spatial measurement, material optical 

property measurement, or material property measurement). 

It was difficult to extract technology readiness level, data processing speed, and data 

processing automation level from information available during the literature 
11

review/market analysis. Most commercial vendors do not openly discuss this type of 

information. 

As stated, the 392 data elements in the MET were filtered to sub-select the elements for 

which technology searches were conducted. Table 2 shows fields which indicate elements 

considered “Not in Scope” (Field Name = Scope), “Derived” (Field Name = IsDerived), “Not 

Measured from a Moving Vehicle” (Field Name = IsNonVehicle), or “Same as another element” 

(Field Name = SameAsElement). Technology searches were not conducted for those elements. 

A Technology Table was created which contains a list of technologies that address the 

data elements in the MET. This list of technologies was compiled by conducting a search based 

on the data elements. The method used to conduct the search is given in Appendix B. The 

Technology Table is implemented as a database (see Appendix A) on a FHWA server and 

contains several data fields as described in Table 3. 

 
12

Table 3. Description of the Data Fields in the Technology Table 

Field Name Description 

AddressedElementIDs List of elements addressed by this technology 

ContactInformation Contact information for the technology 

DataProcessingAutomationLevel Is the data processing: Automated; Semi-automated; 

Manual; Unknown 

DataProcessingSpeed Is the data processed: Real time; Offline; Unknown 

Description Description of the technology. A Brief Description field is 

available in the summary view on the SharePoint server and 

contains the first 200 characters from the “Description” 

field. 

MeasurementUncertainties Uncertainties of the measurements as given by the proprietor 

OtherInformation Other information about the technology 

PotentialElementComments Comments for how this technology could address the 

potential elements 

PotentialElementIDs List of elements potentially addressed by this technology 

ProductName Name of the product 

ProprietorName Proprietor or owner of the technology 

ProvidesCharacterRecognition Does the technology recognize characters in a sign, that is, 

can it read signs? 

ProvidesLocationMeasurement Does the technology provide location measurements 

ProvidesMaterialPropertyMeasurement Does the technology provide information about the material 

of the object, i.e., is it wood, metal, concrete, metal, paint, 

polyurea? 

ProvidesMaterialPropertyOpticalMeasurement Does the technology provide the optical properties of an 

object such as the color and reflectivity? 

ProvidesObjectOrFeatureRecognition Does the technology provide object or feature recognition 

such as crack type recognition and traffic signal detection? 

ProvidesSignTypeRecognition Does the technology recognize signs and the type of signs? 

ProvidesSpatialMeasurement Does the technology provide spatial measurements such as 

the location (x, y, z) of an object or the dimensions of 

objects? 

TechID Technology identification number 

TechReadiness Level of readiness of the technology: COTS - Commercialoff-the-Shelf; Prototype or Demo; Research and 

Development; Concept 

UsesAirborneSystem Is the technology airborne-based? 

UsesCameraBased2DImaging Does the technology use camera-based 2D imaging such as 

video or camera? 

UsesCameraBased3DImaging Does the technology use camera-based 3D imaging systems 

such as stereo video or photogrammetry? 

UsesGPS Does the technology use GPS? 

UsesInertialSystem Does the technology use inertial navigation systems such as 

gyroscopes and accelerometers? 

UsesLaserBased2DImaging Does the technology use laser-based 2D imaging such as a 

laser profilometer or a line scanner? 

UsesLaserBased3DImaging Does the technology use laser-based 3D imaging systems 

such as laser scanners? 

UsesOther Does the technology use some other technology such as 

spectrometer? 

UsesSoftwareOnly Is the technology an algorithm? 

UsesTerrestrialSystem Is the technology terrestrial-based? 
13

The categories “Description of the Technology” and “Measurement Uncertainties” 

contain descriptions and uncertainties, which in most cases, were obtained from the technology 

proprietor’s literature. No attempt was made to verify those claims. 

For a given technology, the “Data Processing Speed” and the “Data Processing 

Automation Level” were rated based upon best judgment. That is, this information is often not 

indicated in the literature or it was unclear if the process was performed in real-time or 

automatically. Also, the claims of “automated” cannot be verified or assessed without a much 

more in-depth review of the applicable technology (e.g., system-level testing). In cases when 

there was no indication or if it was not clear, the entry made for these columns was “Unknown” 

(conservative). When the technologies perform several functions, some automated and some 

not, the entry made was semi-automated. The data in the Technology Table is shown graphically 

in Figures 3.1 to 3.6. 

 
14

Figure 3.1 shows a column chart of the percentage of elements within each MET 

category for which five or more commercial technologies (i.e., “Addressed” elements) were 

identified. This chart gives an indication of the maturity, in terms of commercialization, of the 

technologies within each of the MET categories. 

Figure 3.1. Percentage of Addressed elements within each MET category. 
15

Figure 3.2. Number of technologies in each of the technology functional 

classification categories. 

Figure 3.2 shows the number of technologies in each technology functional classification 

category. The black columns show the total number of technologies in each category while the 

white columns show the number of commercial technologies in each category. 

0

10

20

30

40

50

60

70

80

# of Technologies

All COTS
16

Figure 3.3. Number of identified technologies grouped by general technology category. 

Figure 3.3 shows the number of technologies within each general technology category for 

all technologies (black) and for only the commercial technologies (white). 

0

10

20

30

40

50

60

# of Technologies

All COTS
17

Figure 3.4. Number of elements addressed (black) and potentially addressed (white) by 

technologies T001 to T072. 

0 5 10 15 20 25 30 35 40 45 50

T001

T003

T005

T007

T009

T011

T013

T015

T017

T019

T021

T023

T025

T027

T029

T031

T033

T035

T037

T039

T041

T043

T045

T047

T049

T051

T053

T055

T057

T059

T061

T063

T065

T067

T069

T071

Number of Elements

Technology Number

Elements Addressed

Elements Potentially

Addressed
18

Figure 3.5. Number of elements addressed (black) and potentially addressed (white) by 

technologies T073 to T143. 

Figure 3.4 and Figure 3.5 show the number of elements addressed (black) and 

potentially addressed (white) by each of the technologies identified in this study. These figures 

0 5 10 15 20 25 30 35

T073

T075

T077

T079

T081

T083

T085

T087

T089

T091

T093

T095

T097

T099

T101

T103

T105

T107

T109

T111

T113

T115

T117

T119

T121

T123

T125

T127

T129

T131

T133

T135

T137

T139

T141

T143

Number of Elements

Technology Number

Elements Addressed

Elements Potentially

Addressed
19

give an indication of the number of elements that are impacted by or could potentially be 

impacted by a technology. 

Figure 3.6. Number of technologies within each functional classification category that provide 

real-time, offline, and unknown data processing speed. 

Figure 3.6 shows a column chart of the number of technologies within each functional 

classification category in which the technologies are divided into three groups based on their data 

processing speed (real-time, offline, and unclassified). 

0

5

10

15

20

25

30

# of Technologies

# of Realtime Technologies # of Offline Technologies # of Unclassified Technologies
20

Figure 3.7. Number of technologies within each functional classification category that provide 

automated, semi-automated, manual, and unknown levels of data processing 

automation. 

Figure 3.7 shows a column chart of the number of technologies within each functional 

classification category in which the technologies are divided into four groups based on their level 

0

5

10

15

20

25

30

35

40

45

50

# of Technologies

# of Automated Technologies # of Semi‐Automated Technologies

# of Manual Technologies # of Unclassified Technologies
21

of data processing automation (automated, semi-automated, manual, and unclassified). Of note, 

the differentiation between automated and semi-automated is subjective, and often based upon 

incomplete data. 
22

4. Technology Gap Assessment 

This section presents the procedures used to assess the technology gaps based on the 

concept of demand and maturity. The demand vs. maturity graph gives an indication of how well 

the technology demands are addressed by both the market and the research community. The 

general concept of the demand vs. maturity graph is shown in Figure 4.1. 

Some preliminary steps prior to the assessment include definitions of the terms and 

variables that will be used in the analyses. These definitions are given in Section 4.1. The 

technologies were grouped by functions (i.e., what function does the group of technologies 

provide?). These groupings and their explanations are provided in Section 4.2. The technology 

gaps were determined for these Technology Functional Classification Categories. The 

framework for the analysis is shown in Figure 4.2. 

 

Maturity 

Demand 

HIGH 

HIGH 

LOW 

Diminishing 

research returns

Figure 4.1. Schematic of the Demand vs. Maturity Graph.
23

4.1 Definitions 

4.1.1 Demand 

Demand is related to the number of elements that require a measurement capability 

provided by a particular technology functional classification category. It is defined as the 

Figure 4.2. Framework for the analysis procedure.

Determine High, 

Medium Low Priority 

Elements

Calculate 

Demand 

Calculate 

Maturity Index 

Determine weights for 

High/Medium/Low priority 

elements 

Determine weights for diff. 

levels of automation. 

Determine weights for diff. 

levels of processing speed 

Determine weights for diff. 

technology readiness levels 

Determine weights for how well the 

technology measures an element 

Plot the Demand 

vs. Maturity 

Index 

MMIRE Priorities 

MMUCC Database 

FHWA Input 

Find mid-point of 

Demand vs. Maturity 

Index plot 

High Demand 

& Low 

Maturity = 

High Impact 

Low Demand 

& High 

Maturity = 

Low Impact 

High demand & 

high maturity Æ

technology meets 

current reqm’ts. 

Low demand & 

low maturity Æ 

technology with 

no immediate 

need. 
24

weighted sum of high, medium, and low priority elements within a technology functional 

classification category divided by the weighted sum of the total number of elements in-scope: 

ൌ௜ ܦ

௜ݓ݋ܮ_ܧ כ ௪௟௢ݓ ൅ ௜݉ݑ݁݀݅ܯ_ܧ כ ௗ௠௘ݓ ൅ ௜݄݅݃ܪ_ܧ כ ௛௚௜௛ݓ

௟௔௧௢௧ݓ݋ܮ_ܧ כ ௪௟௢ݓ ൅ ௟௔௧௢௧݁݀ܯ_ܧ כ ௗ௠௘ݓ ൅ ௟௔௧௢௧݅ܪ_ܧ כ ௛௚௜௛ݓ

1 .ݍܧ 

where 

Di = the demand for technology functional classification category i. The value lies 

between [0, 1]. 

E_Highi = the number of in-scope high priority elements in functional classification 

category i

whigh = the weight for high priority elements 

E_Mediumi = the number of in-scope medium priority elements in functional classification 

category i

wmedium = the weight for medium priority elements 

E_Lowi = number of in-scope low priority elements in functional classification category i

wlow = the weight for low priority elements 

E_Hitotal = the total number of in-scope high priority elements 

E_Medtotal = the total number of in-scope medium priority elements 

E_Lowtotal = the total number of in-scope low priority elements 

4.1.2 Maturity Index 

The Maturity Index is an indication of the current state of where a technology functional 

classification category is located along a linear scale that goes from immature (0) to mature (1). 

In this report, the maturity of a technology functional classification category depends on its 

technology readiness level, level of automation, data processing speed, and its ability to measure 
25

an element to the required accuracy or resolution. A fully mature technology functional 

classification category is one where all the technologies within that category are commercially 

available and fully automated and can process data in real-time and measure elements to the 

required levels of accuracy or resolution. 

Therefore, the Maturity Index (MI) of a technology functional classification category is 

defined as the weighted sum of the maturity indices for the category’s technology readiness 

level, level of automation, data processing speed, and the ability to measure an element to the 

required accuracy or resolution (see Eq. 2). A MI of 1 would indicate that the technology 

functional classification category is fully mature. 

2 .ݍܧ ௡ܯ כ ௡ݓ൅ڮ൅ ௖௘௣௦ܯ כ ସݓ ൅ ௗ௣௘௘௦ܯ כ ଷݓ ൅ ௢௨௧௔ܯ כ ଶݓ ൅ ௅ோ்ܯ כ ଵݓ ൌ ܫܯ

Where 

w1 = weight for the technology readiness level (TRL) 

w2 = weight for level of automation of technology 

w3 = weight for processing speed of technology 

w4 = weight for how well the technology meets the accuracy or resolution 

requirement for an element 

wn = weight for Maturity index n

 MTRL is the maturity index of the TRL of the functional category, 

ܯ்ோ௅ ൌ 

௪೎כ்೎ା௪ೝ೏כ்೏ೝା௪೛כ்೛ା௪೎೚೟ೞכ்೟೚೎ೞ

்೎ା்ೝ೏ା்೛ା்೎೚೟ೞ

where 

wc, wrd, wp, wcots = weights for the technology readiness subcategories Concept, R&D, 

Prototype/Demo, COTS, respectively. See Section 4.1.4 for definitions 
26

of these subcategories. 

Tc, Trd, Tp, Tcots = number of technologies within the technology functional classification 

categories that are Concept, R&D, Prototype, and COTS, respectively. 

 Mauto is the maturity index of the level of automation of the functional category, 

Mୟ୳୲୭ ൌ ୵౗౫౪౥כA౗౫౪౥ା୵౩౛ౣ౟כA౩౛ౣ౟ା୵ౣ౗౤౫౗ౢכAౣ౗౤౫౗ౢ

A౗౫౪౥ାA౩౛ౣ౟ାAౣ౗౤౫౗ౢ

Where 

wauto, wsemi, wmanual = weights for the levels of automation fully automated, semiautomated, and manual, respectively. 

Tauto, Tsemi, Tmanual = number of technologies within a technology functional 

classification category whose level of automation is fully 

automated, semi-automated, and manual, respectively. 

 

 Mspeed is the maturity index of the data processing speed of the functional category, 

Mୱ୮ୣୣୢ ൌ ୵౨౛౗ౢ౪౟ౣ౛כS౨౛౗ౢ౪౟ౣ౛ା୵౥౜fౢ౟౤౛כS౥౜fౢ౟౤౛

S౨౛౗ౢ౪౟ౣ౛ାS౥౜fౢ౟౤౛

Where 

wreal-time, woffline = weights for the processing speeds real-time and offline, 

respectively. 

Treal-time, Toffline = number of technologies within a technology functional 

classification category whose processing speed is real-time and 

offline, respectively. 

 ൌ௢௙௙௟௜௡௘ ݓ ,௘௠௜௧௥௘௔௟ݓ

 Mspec is the maturity index of how well the technologies within a functional category meet the 

requirements of the elements within the same functional category, 

M୰ୣ୯୫୲ ൌ ୵ౣ౛౛౪כRౣ౛౛౪ା୵౦౗౨౪౟౗ౢכR౦౗౨౪౟౗ౢା୵ౣ౟౤כRౣ౟౤

Rౣ౛౛౪ାR౦౗౨౪౟౗ౢାRౣ౟౤
27

Where 

wexceed, wmeet, wnot meet = weights for the ability to exceed, meet, not meet, respectively, 

the requirements of the elements within a functional category. 

Texceed, Tmeet, Tnot meet = number of technologies within a technology functional 

classification that exceed, meet, do not meet, respectively, the 

requirements of the elements within the same functional 

category. 

 Mn is the maturity index n. 

4.1.3. Impact 

Impact is defined as regions in the Demand vs. Maturity plot that are centered at the 

midpoint of the actual Demand and Maturity values (see Figure 4.3). 

 
28

The different regions of the Demand vs. Maturity plot represent different impacts or research 

priorities: 

• High demand and low maturity indicates a HIGH impact region 

• Low demand and high maturity indicates a LOW impact region 

• High demand and high maturity indicates a technology that meets current 

requirements. Research in this area could be beneficial if the future performance 

need is expected to increase. Because of the high initial level of maturity, 

additional research funding will experience diminishing returns and require 

additional time to obtain the benefits. However, because many elements are 

affected by this technology, the investment is justifiable. 

• Low demand and low maturity indicates a technology with no immediate need. 

Although the low initial maturity may result in rapid breakthroughs, the relatively 

few number of elements impacted by these developments may not justify an 

investment in this area. However, this area should be closely monitored as 

technological improvements could lead to new applications that address 

additional elements. 

High 

Low 

Low High 

Demand 

Maturity 

High Demand 

& Low 

Maturity = 

High Impact 

High maturity & 

high demand 

Low demand & 

high maturity = 

Low Impact 

Low maturity & 

low demand 

Mid-point of demand 

and maturity 

Figure 4.3. Impact as defined by the Demand vs. Maturity Plot.
29

4.1.4. Technology Readiness Level Subcategories 

The technology readiness level (TRL) is used to determine where a technology is in terms 

of commercialization where commercially available is the desired goal. The subcategories of 

TRL are defined as follows: 

• COTS = Commercial-off-the-shelf. Technology is commercially available. 

• Prototype/Demonstration = A technology is not yet commercially available. A 

prototype of the technology has been demonstrated under representative conditions. 

• R&D = Research and Development. The technology is at the proof-of-concept stage. 

Hardware: Component or model validation in a laboratory environment. Software: 

Development of limited functionality to validate critical properties and predictions using 

non-integrated software. 

• Concept = Idea or patent for a specific application is identified. No experimental data or 

detailed analysis is available to support the concept. 

4.2. Technology Functional Classification Categories 

4.2.1. Location Measurement 

Location measurement refers to calculating a geographic position relative to a defined 

reference frame. This measurement can be an absolute position calculation in a fixed reference 

frame (e.g. GPS, WAAS), or a dead-reckoned position (e.g., INS) relative to a known 

benchmark. Multiple location measurement technologies can be combined to overcome 

technical limitations of a single system. The accuracy of GPS receivers is dependent on a 

number of parameters including the number of satellites in view, inherent satellite signal 

accuracy, signal transmission errors, and receiver hardware and software limitations. The 

accuracy of measurements can be improved by post-processing or applying differential 

corrections. 

 
30

4.2.2. Object and Feature Recognition 

Object and feature recognition refers to the ability to identify: 

• Objects such as street lights, road signs, and barriers 

• Conditions of identified objects (e.g., damaged barrier, blockage) 

• Features such as the type of cracking 

The ideal case would be to automatically extract the information from the data in a 

reliable manner using a robust algorithm without any user intervention and while passing the 

object or feature at highway speeds. The algorithm depends on the sensor(s) being used to collect 

the data and the object itself. Every recognition algorithm consists of two parts: 1) determining 

the numerical values of the parameters characterizing the object (e.g., aspect ratio of object, 

expected size), and 2) classifying whether the candidate object belongs to a given category. 

Two common technologies used to obtain the data are 3D imaging systems4

 and video 

cameras. 3D imaging systems provide direct measurements of object position and dimensions. 

The manufacturer specified range errors for these systems are on the order of 4 mm to 8 mm5

. 

As there are no standards for these systems, it is difficult to quantify level of detail that can be 

captured by these systems as this ability is affected by factors such as beam width, object 

reflectivity, distance to the object, angle of incidence with the object, object material/texture, 

and scan speed (for scanning systems). 

 

4

 A non-contact measurement instrument used to produce a 3D representation (for example, a point cloud) of an 

object or a site. Some examples of a 3D imaging system are laser scanners (also known as LADARs or LIDARs or 

laser radars), optical range cameras (also known as flash LIDARs or 3D range cameras), triangulation-based systems 

such as those using pattern projectors or lasers, and other systems based on interferometry {ASTM, 2009, Standard 

Terminology}. 

5

 There are currently no standards for quantifying the performance of 3D imaging systems. This need is being 

addressed by the ASTM E57 3D Imaging Systems standards committee. 
31

4.2.3 Sign Type Recognition 

Sign recognition is a special case of object recognition. Standard sign dimensions, 

shapes, and colors make this technology more robust than the generalized object recognition 

technologies described in Section 4.2.2. Common failure modes for sign recognition systems 

include off-angle observations, signs with debris or damage, non-standard or partially obscured 

signs, and environmental conditions. 

4.2.4. Character Recognition 

Character recognition in this study is limited to the ability to “read” signage (e.g., speed 

limits, parking restrictions, or railroad crossing numbers). Character recognition is necessary 

when sign type recognition fails, such as in cases where signs are non-standard (e.g., in toll 

facilities) or the meaning of the sign depends solely on the text (e.g., speed limits). 

For roadside asset inventory, the data is often extracted from video images. Text 

recognition from video images is much more difficult than text recognition (optical character 

recognition or OCR) from scanned images. However, unlike commercial OCR software, text 

recognition for roadside asset inventory benefits from reduced complexity due to standardized 

fonts, font sizes, and a limited number of pre-defined text strings. The field of video OCR 

appears to be fairly mature and is growing as there is an increasing need to search for text in 

video in other sectors. 

4.2.5. Spatial Measurement 

Spatial measurement refers to obtaining the 3D coordinates of roadway structures. In 

contrast to location measurement, positions are derived from photogrammetric analysis or 3D 
32

point cloud measurements rather than the absolute position provided by GPS. Spatial 

measurement also extends to determining dimensions of structures (e.g., length of a turn lane, 

road profile). 

The two common technologies to obtain spatial measurements are 3D imaging systems 

and video cameras. See Section 4.2.2 for a discussion of these technologies. Another common 

technology is the use of a laser line scanner to obtain the road profile. 

4.2.6. Material Optical Property Measurement 

Material optical property measurement refers to the reflectivity and color of signs and 

lane markings. Potential technologies for this measurement include cameras and 

retroreflectometers. There are standard test methods for the measurement of the retroreflective 

properties of pavement markings (ASTM E1710) and signs (ASTM E1709). There is a federal 

rule which went into effect on Jan. 22, 2008 on maintaining traffic sign retroreflectivity. “The 

purpose of this final rule is to revise standards, guidance, options, and supporting information 

relating to maintaining minimum levels of retroreflectivity for traffic signs on all roads open to 

public travel.”6

 This federal rule will likely spur innovations towards more accurate and realtime measurements in this area. 

4.2.7. Material Property Measurement 

Material property measurement considers the type of material (such as concrete or 

asphalt, wood or metal) as well as material surface characteristics (e.g., friction, texture). Road 

surface materials are particularly relevant, but other structures are applicable as well, including 

 

6

 Federal Register, Vol. 72, No. 245, Dec. 21, 2007, Rules and Regulations, p. 72574-72582. 
33

lane markings and posts. Very few automated technologies for determining material type have 

been identified. There are COTS technologies available to measure roadway friction and texture 

and standards are available on how to obtain these measurements. There has been some work to 

determine if a roadway was asphalt or concrete based on analysis of the reflection and absorption 

characteristics of a light source on different surfaces (spectral analysis). 

4.3 Determining High, Medium and Low Priority Elements 

High priority elements were identified as those derived from the following: 

• Survey of subject matter experts 

• FHWA primary focus areas7

• MMIRE designated high-priority elements (those with priority 1) 

• MMUCC database8

• Communication with FHWA9

A list of 100 high priority elements (see Table C.1 in Appendix C ) was created based on 

the MMIRE priority, MMUCC database, FHWA primary focus areas, and communication with 

FHWA (elements not on this list were considered low priority). This list was then sent to subject 

matter experts in FHWA for further refinement in terms of priority and whether they were 

addressed or not. The survey responses are presented in Table C.2 in Appendix C. Based on the 

survey results, the 100 elements were reclassified as high, medium, or low priority. The 

reclassification was based on the number of “Agree” vs. the total number of “Agree” and 

 

7

 FHWA primary focus areas are intersections, pedestrian and bicyclist safety, roadside safety, run-off-road safety, 

speed management (FHWA, 2009b). Intersection and intersection-related crashes consistently make up a high 

proportion of total fatal crashes, up to 23 %, more than 50 % of the combined fatal and injury crashes occur at 

intersections (FHWA, 2009a) 

8

 See http://www.mmucc.us/dataelements/roadway-intro.aspx 

9

 Conversation with FHWA (L. Cobb, C. Tan, R. Pollack on May 12, 2009) 
34

“Disagree”10 (where R is the ratio of the number of “Agree” responses to the sum of the number 

of “Agree” and “Disagree” responses). The following criteria were then used to reclassify the 

elements (R values were based on best judgment): 

• High Priority: R ≥ 2/3 

• Medium Priority: 1/3 < R < 2/3 

• Low Priority: R ≤ 1/3 

The new list of high, medium, and low priority elements was then combined with a list of 

high, medium, low priority elements provided by FHWA (see Table D.1 in Appendix D). If the 

priority classification of an element was different in the two lists, the higher priority 

classification took precedence. The resulting prioritized list of elements is presented in 

Appendix E. 

 

4.4. Demand Calculation 

To calculate the demand, the weights used for the high, medium, and low elements were 

whigh = 1, wmed = 0.5, and wlow = 0.3, respectively. The weights were assigned based on 

discussions with FHWA. The numbers of high, medium, and low priority elements for each of 

the technology functional classification category are given in Table 4 and shown graphically in 

Figure 4.4. The demands for the technology functional classification categories were calculated 

using Eq. 1 and are also given in Table 4. 

 

10 Since there were no “Strongly Disagree” responses, that category was not used in the reclassification. Responses 

of “Don’t Know” were also not used in the reclassification because we assumed that the respondents had no opinion 

on those elements. 
35

Table 4. Number of High, Medium and Low Priority Elements and Demands 

# of 

Elements 

Location 

measurement 

Object or 

feature 

recognition 

Sign type 

recognition 

Character 

recognition 

Spatial 

measurement 

Material 

optical 

property 

measurement 

Material 

property 

measurement 

High 

Priority 26 62 7 2 38 1 5 

Medium 

Priority 7 17 8 2 8 0 3 

Low 

priority 27 84 16 3 57 4 6 

Demand 0.17 0.43 0.07 0.02 0.27 0.01 0.04 

Demand can be represented by a value between 0 and 1 where either no or all elements 

are affected by a particular functional category, respectively. The values of 0 and 1 are 

theoretical constructs and are unlikely to be achieved in practice. In our analysis, typical values 

were between 0 and 0.5. Note that due to normalization, the sum of the demand for all 

categories will equal 1.0. 
36

Figure 4.4. Number of elements belonging to each technology functional classification category. 

4.5 Maturity Index Calculation 

As shown in Eq. 2, the Maturity Index consists of several components. For this report, 

the Maturity Index is based only on the technology readiness level since the data for this aspect 

of the technology has the least uncertainty associated with it. The data collected for automation, 

data processing speeds and accuracy/resolution achievable by a technology could not be 

collected with sufficient detail or the information was not publicly available; thus, the 

classification of the subcategories for these Technology Functional Classifications in the 

Technology Table is subjective. Therefore, in this report, w1 = 1 and w2, w3, w4 = 0 in Eq. 2 until 

there are more objective means of determining levels of automation, processing speeds, and the 

levels of accuracy/resolution achievable by the technology. 

0

10

20

30

40

50

60

70

80

90

Number of Elements

High Priority

Med Priority

Low Priority
37

The weights for the subcategories (Concept, R&D, Prototype/Demonstration, COTS) of 

the technology readiness level were based on an estimate of the probability that a particular 

subcategory will become COTS in 3 to 5 years. Based on discussions with FHWA, the weights 

were wc = 0.1, wrd = 0.3, wp = 0.7 and wcots = 1.0 for Concept, R&D, Prototype/Demo, and 

COTS, respectively. That is, there is a 70 % chance that a prototype would become COTS in 3 

to 5 years. The calculation of the Maturity Index is shown in Table 5. In Table 5, the Sum of 

COTS Equivalents is the numerator of the MTRL equation and the Maturity Index obtained by 

dividing this value by N (row 2 in Table 5). 

Table 5. Maturity Index Calculation Location Measurement Object / Feature Recognition Sign Type Recognition 

Character Recognition Spatial Measurement Material Property Optical Measurement Material Pro

pert

y

N = Total # of technologies in the technology 

functional classification category 34 75 34 9 38 9 13 

Concept 

(wc = 0.1)

No. technologies (Tc) 0 4 1 1 0 0 0 

COTS equivalents = wc * Tc 0 0.4 0.1 0.1 0 0 0 

R&D 

(wrd = 0.3) No. technologies (Trd) 9 50 21 6 10 3 6 

COTS equivalents = wrd * Trd 2.7 15 6.3 1.8 3 0.9 1.8 

Prototype 

(wp = 0.7) 

No. technologies (Tp) 3 6 1 0 4 0 0 

COTS equivalents = wp * Tp 2.1 4.2 0.7 0 2.8 0 0 

COTS 

(wcots = 1.0)

No. technologies (Tcots) 22 15 11 2 24 6 7 

COTS equivalents = wcots * Tcots 22 15 11 2 24 6 7 

Sum of COTS equivalents 26.8 34.6 18.1 3.9 29.8 6.9 8.8 

MATURITY INDEX (MI) 0.79 0.46 0.53 0.43 0.78 0.77 0.68 
38

An estimate of the sensitivity of the Maturity Index is ௑

ሺ௑ାேሻ where X is the number of 

additional COTS technologies found and N is as defined in Table 5. This value is the 

approximate change in the maturity index if additional COTS technologies were found in the 

literature search. If the additional technology found were other than COTS, the change would be 

less because the weight for each non-COTS technologies is less than the weight for COTS 

technologies. Table 6 lists approximate changes to the Maturity Index if additional COTS 

technologies were found. 

Table 6. Sensitivity of Maturity Index to Additional COTS Technologies Found Location Measurement Object / Feature Recognition Sign Type Recognition Character Recognition Spatial Measurement Material Property Optical Measurement Material Property 

Measurement

N = Total # of technologies in the technology 

functional classification category 34 75 34 9 38 9 13 

Change to Maturity 

Index = X / ( N + X ) 

X = + 1 COTS found 0.03 0.01 0.03 0.10 0.03 0.10 0.07 

+ 2 0.06 0.03 0.06 0.18 0.05 0.18 0.13 

+ 3 0.08 0.04 0.08 0.25 0.07 0.25 0.19 

+ 4 0.11 0.05 0.11 0.31 0.10 0.31 0.24 

+ 5 0.13 0.06 0.13 0.36 0.12 0.36 0.28 

+ 6 0.15 0.07 0.15 0.40 0.14 0.40 0.32 

+ 7 0.17 0.09 0.17 0.44 0.16 0.44 0.35 

+ 8 0.19 0.10 0.19 0.47 0.17 0.47 0.38 

+ 9 0.21 0.11 0.21 0.50 0.19 0.50 0.41 

+ 10 0.23 0.12 0.23 0.53 0.21 0.53 0.43 

4.6. Results 

Figure 4.5 shows the demand versus maturity graph derived from values calculated in 

Sections 4.4 and 4.5. 
39

Figure 4.5. Demand vs. Maturity Graph. 

From Figure 4.3 and Figure 4.5, it follows that (1) the Object/Feature Recognition 

technology functional classification category is in the high impact region, and (2) the Material 

Property Optical Measurement and the Material Property Measurement technology functional 

classification categories are in the low impact region. 

Object /Feature Recognition

Spatial Measurement

Location Measurement

Sign Type Recognition

Character Recognition

Material Property

Measurement

Material Optical

Property

Recognition

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

‐0.1

Demand

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Maturity
40

In assessing the relative priorities of each functional classification category, the following 

observations are made: 

• Object/feature recognition should be given highest priority. 

• Material property optical measurement should be given the lowest priority. 

• Spatial measurement should be given secondary priority in anticipation of increased 

accuracy requirements. 

• The remaining categories should be given third priority without explicit ranking, but 

should be closely monitored as needs evolve. 
41

5. Recommendations 

5.1 Recommendation One 

Develop Performance-Based Standards for Digital Highway Measurement

One of the challenges encountered when developing this report was determining the 

current state-of-practice and technology readiness levels from the literature review and market 

survey. While there are some standards or requirements for the elements from the MET, there 

are no standards available for assessing how well technologies measure the MET elements. 

It is well established that performance-based standards drive innovation. A case in point 

is the landmark NCHRP Report 350 (Ross Jr, Sicking, Zimmer, & Michie, 1993). This report 

was first issued in 1993 to update the crash testing of roadside hardware to address the increasing 

use of SUV’s and light trucks among the driving public. Report 350 not only improved testing 

performance requirements for these heavier and higher center-of-gravity vehicles, but 

also created a tiered approach for safety requirements based upon traffic volume and speed. 

FHWA’s mandated conformance to NCHRP Report 350 for national highways further drove 

innovation in roadside safety hardware (TRB, 2009). 

Another illustrative example of the use of standards to drive innovation is the Department 

of Homeland Security’s program to develop standards for Urban Search and Rescue (US&R) 

robots. This program, initiated in 2004, is charged with supporting the development of robotic 

technologies for US&R by creating a suite of performance-based standards relevant to US&R 

practitioners. The initial phase of the project involved a series of workshops that led to a 

robust requirements document for US&R robot technologies (Messina, et al., 2005). 

Researchers, manufacturers, and users then worked together to establish test protocols designed 

to demonstrate capabilities meeting these defined requirements. The effort is now part of the 
42

ASTM E54 committee on Homeland Security Applications. The program has been effective 

in communicating user needs to industry, demonstrating and quantifying industry and research 

capabilities to end-users, and shaping near- and long-term development particularly targeting 

weaknesses exposed during performance tests. 

As part of the SHRP-2 Highway Safety Research Projects, S-03-Roadway Measurement 

System Evaluation was charged with conducting “... an assessment of the state of the practice 

for mobile data collection of roadside and roadway characteristics and features related to 

safety analysis (TRB, 2008).” It is strongly recommended that FHWA extend this work to the 

development of open, consensus-based standards for Digital Highway Measurement. This effort, 

more than any other research investment, will help spur industry development and create a 

framework for state and local officials to specify and procure equipment and services. 

5.2 Recommendation Two 

Provide Standard Reference Data Sets for DHMS Algorithm Development

 A critical part of the evaluation process is the availability of standard datasets against 

which different algorithms can be evaluated. The creation and maintenance of standard data sets 

for roadway inventory by a neutral party should be explored. The availability of standard 

reference data sets will allow for the evaluation of the robustness of the algorithms as it will 

assess the performance of the algorithms under varying conditions - conditions differing from 

those under which they were developed. 

Standard Reference Data Sets should contain a large variety of data sets covering a broad 

range of experimental conditions (e.g., different light conditions for images acquired with 

cameras, various scanning densities of 3D imaging systems, different conditions of road signs 

and pavement markings, etc.). The Reference Data Sets should be grouped in two subsets: one 
43

exclusively for algorithm development and classifier training and one subset for testing only. In 

order to keep the training process separate from the testing process, the development/training 

subset should not contain data sets from the testing subset. Furthermore, both the training and 

testing subsets should be classified into positive examples (images containing the target) and 

negative examples (images without the target) as is required by contemporary machine learning 

algorithms. 

Additionally, performance evaluation of different classification algorithms requires the 

development of objective and meaningful metrics. For any given algorithm, a combination of 

metrics will likely be required to provide a complete performance profile, for instance, the 

Receiver Operating Characteristics (ROC) graph shown in Figure 5.1. An ideal classifier is a 

step function with a True Positive Rate (TPR) equal to 1 for all False Positive Rate (FPR) > 0. In 

Figure 5.1, the two solid lines C1 and C2 correspond to two different classifiers: the C1 curve is 

above the C2 curve for all FPRs and therefore the classifier represented by C1 is better than the 

classifier represented by C2. It is a common mistake to assess an algorithm’s performance by 

examining a single point on the ROC. For example, in Figure 5.1, the comparison of a single 

point from one curve (p1) to a single point from another curve (p2) is not the correct procedure. 

In fact, the TPR for p1 is less than the TPR for p2 and yet p1 belongs to the better classifier. 

Such a simple analysis considers only very narrow aspects of an algorithm's performance, and 

the results are unlikely to be duplicated in actual scenarios. For reference, the dashed diagonal 

line characterizes a classifier that randomly guesses the class (positive or negative). 
44

Figure 5.1. The Receiver Operating Curve (ROC) characterizes the performance of a classifier. 

Maintaining Reference Data Sets together with properly defined metrics for classifier 

evaluation will encourage and accelerate the development of new recognition/classification 

algorithms. It will also ensure a solid basis for fair and objective comparison of COTS software. 

Development and deployment of standard reference data sets provides opportunities for 

numerous entities (e.g., private companies, academia, research institutions) to simultaneously 

engage in algorithm development without the relatively high entry barrier of deploying data 

capture technologies. This strategy has proven effective in numerous science and technology 

initiatives to spur innovation and provide traceability for algorithm performance testing. 

It is recommended that FHWA examine the high-priority MET elements, and lead the 

development of data sets which provide state-of-practice measurements of those elements (e.g., 

2D and 3D camera data, laser-based 3D image data, etc.) with corresponding ground truth 

information. Ideally, this project would be an open-source endeavor where researchers 
45

worldwide could contribute new data sets to the project as new measurement technologies are 

developed. 

5.3 Recommendation Three

Development of Object/Feature Recognition Algorithms

Since the early 1980s, extensive research has been conducted in the area of object and 

feature recognition, yet the development of a robust, general-purpose algorithm has remained 

elusive. Because of the quantity of information provided by cameras, the existence of such an 

algorithm could revolutionize highway asset management by allowing cameras to replace a host 

of commonly used sensor types. Most of the algorithms currently in use, however, are only 

effective in the restricted conditions under which they were developed. Extensions of these 

algorithms beyond their expected domains seems difficult. This observation agrees with the 

observation made for Item b in Table 1, i.e., the field of object and feature recognition is 

somewhat mature but automated extraction is not robust. At present, only face recognition is 

sufficiently robust for general implementation. 

The original technique to object recognition, template matching, has shown promising 

performance for quite some time in the laboratory but several factors make this simple approach 

hard to translate to a deployment scenario. Due to minor differences between objects of the same 

type (known as "intra-class variation"), a new template would have to be trained for every 

potential object. Given the large number of objects in a roadway inventory, this is a formidable 

and uneconomical task. Viewing conditions also affect the quality of template matching: camera 

angle, viewing distance, lighting, and environmental factors all contribute to differences between 

the observed object and the equivalent object stored as a template. 
46

Searching for a stored image of an object is not sufficiently robust for general use. 

Template matching algorithms are sensitive to variations in lighting, scale, color, and view angle, 

as well as the presence of occlusions. For this reason, the predominant contemporary approach to 

object recognition is to search for representative features of an object rather than searching for 

the object directly. The nature of these features vary between algorithms but is generally limited 

to low-level characteristic such as shape, color, or intensity gradients. Feature-based approaches 

have the benefit of being invariant to viewing distance, camera angle, and to some extent, 

occlusions. Typical features include SIFT (Scale Invariant Feature Transform), shape contexts, 

and Haar-like wavelets. These newer techniques still require object-specific training but their 

robustness to varying viewing conditions make them appealing for the application in question. 

Future research in object and feature recognition should focus on adapting one or more of 

these newer techniques to the specific application. A representative training and testing set 

should be collected for each object type (see Recommendation Two). In practice, most effort in 

training classifiers is spent in collecting datasets and choosing low-level features (edges, colors, 

etc.) which best represent the target object. 
47

Acknowledgements 

Funding for this project was provided by FHWA Grant DTFH61-09-X-30011. The 

guidance and expertise provided by Mr. Lincoln Cobb of the FHWA Office of Safety R&D 

(HRDS-2) were invaluable to this study. The authors are also grateful to the other FHWA 

personnel who responded to the survey and provided feedback on this report. The support of the 

NIST Intelligent Systems Division in conducting the literature review / market analysis is greatly 

appreciated. 

References 

Blincoe, L., Seay, A., Zaloshnja, E., Miller, T., Romano, E. , Luchter, S., et al. (2002). The Economic Impact of 

Motor Vehicle Crashes, 2000. Washington, DC: National Highway Traffic Safety Administration, U.S. 

Department of Transportation. 

Cobb, L (2009). Future Vision of the DHMS - personal communication. 

Council, Forrest M., Harkey, David L., Carter, Daniel L., & White, Bryon (2007). Model Minimum Inventory of 

Roadway Elements - MMIRE (No. FHWA-HRT-07-046): Federal Highway Administration. 

DOT (2000). Primer: GASB 34. 

Federal Railroad Administration (2007). U. S. DOT National Highway-Rail Crossing Inventory, Policy, Procedures 

and Instructions for States and Railroads. 

FHWA (1995). Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation's Bridges (No. 

FHWA-PD-96-001). Washington D.C. 

FHWA (2009a). TFHRC - Intersection Safety Retrieved June 30, 2009, from 

http://www.tfhrc.gov/safety/intersect.htm 

FHWA (2009b). TFHRC - Safety Research Retrieved June 30, 2009, from 

http://www.tfhrc.gov/safety/index.htm#Intersections 

Heron, M.P., Hoyert, D.L., Murphy, B.S., Xu, J., Kochaneck, M.A., & Tejada-Vera, B. (2009). Deaths: Final data 

for 2006 (No. DHHS Publication No. (PHS) 2009-1120 ): Centers for Disease Control and Prevention, U.S. 

Department of Health and Human Services. 

Messina, E., Jacoff, A., Scholtz, J., Schlenoff, C., Huang, H.-M., Lytle, A., and Blitch, J. (2005), Statement of 

Requirements for Urban Search and Rescue Robot Performance Standards, Preliminary Report, May 13, 

2005, National Institute of Standards and Technology, Gaithersburg, MD. 

NCDOT. Inventory Asset Data Collection Elements. National Conference: Highway Asset Inventory and Data 
48

Collection, from http://www.itre.ncsu.edu/ncassetmgmtconf/downloads/RoadsideDataElementTable.pdf 

NCDOT. NCDOT Pavement Condition Survey. National Conference: Highway Asset Inventory & Data Collection, 

2009, from http://www.itre.ncsu.edu/ncassetmgmtconf/downloads/PavementsDataElementTable.pdf 

NHTSA (2000). Traffic Safety Facts 2000. Washington, DC: National Highway Traffic Safety Administration, U.S. 

Department of Transportation. 

NHTSA (2009). Traffic Safety Facts 2007. Washington, DC: National Highway Traffic Safety Administration, U.S. 

Department of Transportation. 

Ross Jr, H.E., Sicking, D.L., Zimmer, R.A., & Michie, J.D. (1993). NCHRP Report 350: Recommended Procedures 

for the Safety Performance Evaluation of Highway Features. 

Rumar, K. (1985). The role of perceptual and cognitive filters in observed behavior. Human behavior and traffic 

safety, 151-165. 

TRB (2008). SHRP-2 Safety Program Brief: Transportation Research Board, National Academy of Sciences. 

TRB (2009). Crash standards save lives, spur innovation: Transportation Research Board, National Academy of 

Sciences. 

Trentacoste, M.F. (2006). Digital Highway Measurement System (Presentation to the 5th International Visualization 

In Transportation Symposium). McLean, VA: Office of Safety Research and Development, Turner 

Fairbank Highway Research Center, FHWA. 

Vandervalk, Anita (2008, July 17, 2008). SHRP 2 Project S-03, Task 2, Determination & Prioritization of Data 

Elements. Paper presented at the Fourth SHRP Safety Symposium. 
49

Appendix A: Digital Highway Measurement Technology 

Database 

A Digital Highway Measurement Technology Database was developed during this study. 

The two primary objectives of this effort were: 1) to enable FHWA and their research partners 

to interactively analyze the data from both the Master Element Table (MET) and Technology 

Table (TT), and 2) to create a collaborative environment for continuous maintenance and 

updates. The main functionalities of the database are: 

• Maintain records 

o Collaboratively add, delete, and edit records 

• Browse the element and technology records 

• Identify technologies that address a particular element 

• Identify elements that are addressed by a particular technology 

• Sort by element and/or technology attribute(s): 

o An example of an attribute is technology readiness level (e.g., COTS, Prototype, 

R&D, Concept). 

• Filter by element and/or technology attribute(s): 

o For example, within an attribute, filter to show only the technologies for a given 

technology readiness level (e.g., COTS only). 

• Generate formatted reports (see sample outputs below) for each element and technology 

• Provide a mechanism to generate custom reports 

Sample information from the database for one element (Element Number 22) and one 

Technology (T0001) follows. 

 
50

Element Number: 22

Element Name: Surface Friction

Source of Element: 

MMIRE (Council, Harkey, Carter, & White, 2007) 

Level 1 Descriptor: 

I. ROADWAY SEGMENT 

Level 2 Descriptor: 

I.c. Segment Cross Section 

Level 3 Descriptor: 

I.c.1. Surface Descriptors 

Description: 

None given in MMIRE. 

Surface friction has two major components (Noyce, Bahia, Yambó, & Kim, 2005): 

1. Microtexture refers to irregularities in the surfaces of the stone particles (fine-scale 

texture) that affect adhesion (molecular bond shearing). 

2. Macrotexture refers to the larger irregularities in the road surface (coarse-scale texture) 

that affects hysteresis (energy dissipation from tire deformation) 

Additional secondary components (Noyce, et al., 2005): 

1. Megatexture describes irregularities that can result from rutting, potholes, patching, 

surface stone loss, and major joints and cracks 

2. Roughness refers to surface irregularities larger than megatexture that also affects rolling 

resistance, in addition to ride quality and vehicle operating costs. 

There are three predominant measurement techniques (WSDOT, 2009 Module 9: Pavement 

Evaluation, Section 4: Skid Resistance): 

1. Locked wheel tester (AASHTO T 242 or ASTM E-274) 

Consists of a locked wheel skidding along a wet surface to measure friction resistance. 

This is the most common method. 

2. Spin up tester 

With a similar setup as the locked wheel tester, an unlocked wheel is lowered onto the 

pavement and the spin up time is measured. This requires no force sensors and reduces 

wear on the test wheel. 

3. Surface texture measurement 

Surface texture is measured (see Element 23) and a mathematical model correlates 

surface texture to surface friction. 

What to record: 

Surface friction indicator 

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

high, medium, low)” (Council, et al., 2007) 
51

Lock Wheeled Skid Tester (WSDOT, 2009 Module 9: Pavement Evaluation, Section 4: Skid Resistance)

Out of Scope?

No

Derived from Other Elements? 

No

Measured from a Moving Vehicle?

Yes

Similar to Other Element(s)? 

No 

Ease of Data Collection (from MMIRE): 

Difficult 

Levels of Uncertainty Required: 

ASTM E274-06 states that acceptable precision of Skid Number (SN) units can be stated in the 

form of repeatability. Acceptable standard deviation of 2 SN units was obtained from ASTM 

tests with varying instruments at different speeds (ASTM, 2006b). 

ASTM E274 (ASTM, 2006b), E1337 (ASTM, 2008), and E1859 (ASTM, 2006a) require the 

following specifications for the force-measuring transducer for a locked wheel test setup. The 

transducer shall measure the tire-road interface force with minimal inertial effects. It shall 

provide an output directly proportional to the force with hysteresis less than 1 % of the applied 

load, nonlinearity less than 1 % of the applied load up to the max expected loading, and 

sensitivity to any expected cross-axis loading or torque loading less than 1 % of the applied load. 

ASTM E274 (ASTM, 2006b) requires the torque-measuring transducer in a locked wheel setup to 

provide an output directly proportional to the torque with hysteresis less than 1 % of the applied 

load, nonlinearity less than 1 % of the applied load up to the max expected loading, and 

sensitivity to any expected cross-axis loading or torque loading less that 1 % of the applied load. 
52

ASTM E274 (ASTM, 2006b) and E1337 (ASTM, 2008) require the vehicle speed-measuring 

transducers to have a speed resolution accuracy of +/- 1.5 % of the indicated speed or +/- 

0.8km/h, whichever is greater. 

ASTM E274 (ASTM, 2006b) requires an overall system accuracy of +/- 1.5 % of the applied load 

from 900 N to full scale. ASTM E1337 (ASTM, 2008) requires an overall system accuracy of +/- 

1.5 % of the applied load from 890 N to full scale.

Applicable Measurement Technologies: 

T002, T008, T010, T013, T019, T028, T056 

Potential Measurement Technologies: 

T009 – if method to determine surface friction based on macro- and microtexture parameters is 

implemented. 

MMIRE Priority:

1

MMUCC Element? 

No

Intersection Element? 

No

Roadway Alignment Element? 

No

References:

ASTM (2006a). Standard Test Method for Friction Coefficient Measurements Between Tire and 

Pavement Using a Variable Slip Technique, E1859-97. 

ASTM (2006b). Standard Test Method for Skid Resistance of Paved Surfaces Using a Full-Scale Tire, 

E274-06. 

ASTM (2008). Standard Test Method for Determining Longitudinal Peak Braking Coefficient of Paved 

Surfaces Using Standard Reference Test Tire, E1337-90. 

Council, Forrest M., Harkey, David L., Carter, Daniel L., & White, Bryon (2007). Model Minimum 

Inventory of Roadway Elements - MMIRE (No. FHWA-HRT-07-046): Federal Highway 

Administration. 

Noyce, DA, Bahia, HU, Yambó, JM, & Kim, G (2005). Incorporating road safety into pavement 

management: Maximizing asphalt pavement surface friction for road safety improvements. 

Midwest Regional University Transportation Center Traffic Operations and Safety (TOPS) 

Laboratory, Draft Literature Review and State Surveys (April 2005). 

WSDOT (2009). Washington State Department of Transportation Pavement Guide, Module 9: Pavement 

Evaluation, Section 4: Skid Resistance Retrieved 05/08/2009, 2009, from 

http://training.ce.washington.edu/wsdot/Modules/09_pavement_evaluation/09-4_body.htm 

 
53

Technology Number: T0001

Technology Proprietor Name: Fugro-Roadware 

Product Name: ARAN 

General Technology Category(s): 

Terrestrial System

GPS 

Inertial System 

Camera-based 2D Imaging 

Laser-based 2D Imaging 

Other

Functional Classification(s): 

Location measurement 

Object/feature recognition 

Spatial measurement 

Information: 

Vehicle measures longitudinal and transverse road profiles (IRI, ride number, rut), velocity and 

distance traveled (DMI). Profiler conforms to: AASHTO PP 38-00, AASHTO PP 49-03, 

AASHTO PP 50-03, AASHTO MP 11, ASTM E950-98, ASTM E1705/E1705M 

Vehicle (Wisecrack) has automated crack detection - reports crack type, severity and location - 

compliant with AASHTO and LTPP (SHRP) Protocols. 

Smart Geometrics is a vehicle-mounted subsystem that utilizes a patented control algorithm and a 

combination of gyroscopes and software to measure the crossfall, transverse profile, vertical 

alignment (grade) and horizontal alignment (curve radius) of the roadway. 

Vehicle (Surveyor 2) asset surveying - determine the linear position, measurements, X, Y and Z 

location and other user-defined attributes of roadside assets from geo-referenced digital images. 

Source: (Roadware, 2009) 

Achievable/Potential Level of Measurement Uncertainty: 

Cracks >= 1 mm, Rut depth accuracy of 1.5 mm, GPS of 1 m or 5 m

Technology Readiness:

Commercial-off-the-shelf 

Addresses Elements: 

5, 7, 23, 26, 32, 33, 35, 37, 42, 47, 59, 68, 70, 138, 149, 177, 197, 198, 204, 230, 299, 302, 303, 

304, 305, 311 

Elements that Could be Addressed: 

28 - The system can report crack type and severity, IRI, and rut depth - depending on the 

definition of pavement condition, the system can be used to evaluate this element. 
54

34 - Polished aggregates may be detected (manually or automatically if an algorithm were 

developed) from digital images. 

36 - Water bleeding may be detected (manually or automatically if an algorithm were developed) 

from digital images. 

39 - uncertain if the results of oxidation is detectable from digital images or not, or if mechanical 

properties of the pavement need to be measured 

40 - Bleeding may be detected (manually or automatically if an algorithm were developed) from 

the digital images. 

41 - Pumping may be detected (manually or automatically if an algorithm were developed) from 

the digital images. 

53 - Blowups can be detected from digital images (pavement module). 

54 - Element can be detected from digital images and the length can be measured using the 

profiling system or from a calibrated image. Will need good resolution to measure separation to 

the millimeter level. 

55 - Element can be detected from digital images and distances/lengths can be obtained from 

calibrated images or profiling system. 

143, 144 - 2D images may be needed for initial inlet identification, 3D LADAR for more 

accurate; difficult task for software recognition due to expected large variation in shape, 

geometry, etc. of different inlets 

149 - Based on 2008 Highway Asset Inventory and Data Collection NCDOT wkshop, this 

element was obtained with this technology. 

150 - Driveways and driveway type may be detected from digital images 

161 - Barrier condition may be assessed from video. Recognition algorithm will need to be 

developed. 

174, 175, 176 - obstacles can be identified from video and offset from road can be measured - 

(Surveyor 2 vehicle), and the location can be obtained from GPS 

178 - element can be identified from digital images which is tied to GPS 

194 - curb/sidewalk width may be obtained from their Surveyor 2 module 

300 - curve length could be determined with DMI & gyroscopes; 

313, 314, 318 - can be derived from the raw sensor data, given the appropriate algorithms

Data Processing Speed: 

Offline 
55

Data Processing Level of Automation: 

Unknown 

Contact Information for Technology:

Fugro Roadware Inc. (USA) 

3104 Northside Avenue 

Richmond, Virginia 23228 

USA 

Toll Free: +1 800 828-ARAN (2726) 

Other information: 

1. 2003 report (Javidi, et al., 2003) on the performance of WiseCrack and proposed 

improvements. 

2. Roadware was one of the vehicles evaluated in (Mullis, 2005). 

3. 2008 Highway Asset Inventory & Data Collection National Workshop ("2008 Highway Asset 

Inventory & Data Collection, Roadside Manual and Vendor Data Sets," Roadside Manual) - 

Roadware was one of the vehicles that collected data. 

4. 2003 report (Sokolic, 2003) - Early version of ARAN was evaluated. 

References:

2008 Highway Asset Inventory & Data Collection, Roadside Manual and Vendor Data Sets Retrieved 

June 25, 2009, from http://www.itre.ncsu.edu/NCassetMgmtConf/vendordatasets.html 

Javidi, B., Stephens, J., Kishk, S., Naughton, T., McDonald, J., & Isaac, A. (2003). Pilot for Automated 

Detection and Classification of Road Surface Degradation Features (No. JHR 03- 293): Univ. of 

Connecticut. 

Mullis, C., Reid, J. Brooks, E., Shippen, N. (2005). Automated Data Collection Equipment for Monitoring 

Highway Condition (No. SPR 332 and FHWA-OR-RD-05-10): Oregon DOT and FHWA. 

Roadware (2009). Fugro Roadware - ARAN, from http://www.roadware.com/products_services/aran/ 

Sokolic, Ivan (2003). Criteria to Evaluate the Quality of Pavement Camera Systems in Automated 

Evaluation Vehicles. Unpublished Masters Thesis.
56

Appendix B: Technology Search Procedure 

Assumptions: 

• Applicable technologies and relevant research is assumed to be published in some form 

on the internet and reachable through a Google search. 

Instructions: 

• Search time is generally limited to two hours per element. 

• Use google.com as the search engine and set the following Google search preferences: 

o Language: English 

o Safe Search Filtering: Moderate 

o Results displayed per page: 10 

• Use the file “Elements_to_search.xls” to determine which elements you have been 

assigned. 

• Use the file template “element_technology_search.xlt” to document your search 

o Save the file with the element number as the filename (e.g., if you’re working on 

element number 171, the filename would be “171.xls”) 

• Do not include papers that are published before 2004 as they are likely out-dated due to 

the rapid advance in technology (this is especially true in the image processing field) - 

unless there is a reason to. 

• Record the following with each new search term in the above Excel file: 

o The exact search terms used for each search 

o The number of results for each search 

• The information recorded for each new technology is shown on the last page of these 

instructions. 

Procedure: 

1. Look at the “Technology Table” and try to find the element # in column S. If the element 

exists, then go to Step 4 (quick search for other technologies). 

2. If the element is not listed, identify the technology classification(s) required for the 

element under investigation and search “Technology Table” on Google Docs (columns X 

through AD). If a technology already exists in the database that meets the requirements of 

the element, then add your element number to it and go to Step 5 (moderate search for 

other technologies). 
57

3. If Steps 1 and 2 do not turn up any applicable technologies, then do a thorough search 

(six searches for each search term). This consists of: 

a. Google search on the search term11 in quotes (i.e., “search term”) plus the words 

“road” then “highway” with the qualifiers identified in the spreadsheet (e.g., 

(detect OR measure)) with each search and the acronym “DOT” for the searches 

that do not include phrases (i.e., words in quotation marks). The six searches 

become more general as you progress. 

E.g., if the element 171 is Sign Type, then you would type the following six 

searches in Google exactly as they appear (without the roman numerals of 

course): 

i. “sign type” road (detect OR recognize) 

ii. “sign type” highway (detect OR recognize) 

iii. sign type road (detect OR recognize) DOT 

iv. sign type highway (detect OR recognize) DOT 

v. sign type (detect OR recognize) DOT 

vi. sign (detect OR recognize) DOT 

b. For each search, look through the first three pages of results. If no applicable 

technologies are found, look through pages 4 and 5. 

c. Use scholar.google.com and go through three pages of results using the most 

appropriate term based on your own judgment and record the term used. 

4. For a quick search, use two search terms (one general and one specific if possible) and 

follow Steps 3b and 3c. 

5. For a moderate search, use four search terms (two general and two specific if possible) 

and follow Steps 3b and 3c. 

 

11 See Table 1. Also, the search term in many cases is equivalent to the element name. 
58

Information that should be recorded for each technology identified

• General Technology Category: 

o Terrestrial system 

o Airborne system 

o Laser-based 3D imaging 

o Camera-based 3D imaging 

o Global positioning system 

o Inertial system 

o Software only 

o Camera-based 2D digital imaging 

o Laser-based 2D imaging 

o Other 

• Technology proprietor name: (Company/researcher name)

• Product name (if applicable): (E.g., Bentley’s “InRoads” software)

• Description: (Brief description of what the technology is)

• Achievable (or potential) level of measurement uncertainty: 

• Technology readiness: 

o Concept 

o R&D 

o Prototype/Demonstration 

o Commercial-off-the-shelf 

• Element #: 

• Data processing speed: (Offline/Real-time/Unknown) 

• Data processing level of automation: (Manual/Automatic/Semi-automatic/Unknown) 

• Contact information for technology: (Company contact information) 

• Other information: 

• Functional classification: 

o Location measurement 

o Object/feature recognition 

o Sign-type recognition 

o Character recognition (words and numbers on signs, etc.) 

o Spatial measurement 

o Material optical property measurement (color, retro-reflectivity, etc.) 

o Material property recognition (concrete, asphalt, wood, metal, paint, thermoplastic, 

etc.) 
59

Appendix C: Element Priority Survey 

The elements in Table C.1 were identified based on the criteria discussed in Section 4.3 and were include

FHWA. The survey results are summarized in Table C.2. The numbers in columns A and B are the numbers 

choice. In the comments column (C) in Table C.2, the numbers at the beginning of the comments corresponumbers. 

Table C.1. High Priority Elements in Survey 

Data Elements and Categories A. This element has a high safety priority. B. Technologies exi

to measure this element. 

I. Pavement Condition Descriptors 

1 Polished Aggregate - [Record square feet of affected surface 

area] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

2 Water Bleeding - [Record the number of occurrences of water 

bleeding and the length in meters of affected pavement with a minimum length of 3.28 feet] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

3 Oxidation - [Only plant mix surfaces are rated for oxidation. 

Determine presence of oxidation] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

4 Bleeding - [Record square feet of surface area at each severity 

level] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

5 Pumping - [Record the number of occurrences of pumping and 

the length in feet of affected pavement] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

6 Shoving - [Record number of occurrences and square feet of 

affected surface area] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

7 Scaling - [Record the number of occurrences and the square 

meters of affected area] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N
60

Data Elements and Categories A. This element has a high safety priority. B. Technologies exi

to measure this element. 

8 Lane-to-Shoulder Separation - [Record to the nearest 

millimeter at intervals of 50 feet along the lane-to-shoulder joint. Indicate whether the joint is well-sealed (yes or no) at each location] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

9 Transverse Construction Joint Deterioration - [Record number 

of construction joints at each severity level] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

10 Lane Add Point - [GPS or Reference Post of Start of a FULL 

lane width] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

11 Exclusive Left Turn Lane Length - [Exclusive left turn lane 

length] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

12 Auxiliary Lane Presence/Type - [Presence or type of auxiliary 

lane] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

13 Auxiliary Lane Length - [Length of auxiliary lane] Agree Disagree Strongly Disagree Don't Know Yes Possible N14 HOV Lanes - [Presence of HOV lanes in segment] Agree Disagree Strongly Disagree Don't Know Yes Possible N15 HOV Lane Types - [HOV lane types] Agree Disagree Strongly Disagree Don't Know Yes Possible N16 Reversible Lanes - [Number of reversible lanes present on 

segment] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

17 Presence/Type of Bicycle Facility - [Presence or type of 

bicycle facility on segment] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

18 Number of Peak Hour Lanes - [Number of through lanes used 

in peak period in the peak direction] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

II. Shoulder, Median, and Roadside Descriptors 

19 Right Shoulder Type - [Shoulder type on right side of road in 

direction of inventory] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

20 Right Paved Shoulder Width - [Width of paved portion of 

right shoulder] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

21 Shoulder Rumble Strip Presence - [Presence of shoulder 

rumble strip] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

22 Rumble Strip Type - [Rumble strip type if present] Agree Disagree Strongly Disagree Don't Know Yes Possible N23 Location of Rumble Strip - Begin - [GPS or reference post of 

start] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

24 Rumble Strip Offset - [From edge of lane] Agree Disagree Strongly Disagree Don't Know Yes Possible N
61

Data Elements and Categories A. This element has a high safety priority. B. Technologies exi

to measure this element. 

25 Sidewalk is separated from edge of road - [Yes/No] Agree Disagree Strongly Disagree Don't Know Yes Possible N26 Curb Type - [Curb type] Agree Disagree Strongly Disagree Don't Know Yes Possible N27 Curb Location - [GPS coordinates of beginning and end point] Agree Disagree Strongly Disagree Don't Know Yes Possible N28 Curb Blockage Agree Disagree Strongly Disagree Don't Know Yes Possible N29 Curb Damage Agree Disagree Strongly Disagree Don't Know Yes Possible N30 Median Opening Location - [GPS of first corner in direction 

of travel] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

31 Median Barrier Type - [Soil, paved (striped), paved (barrier), 

raised curb, None, Other] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

32 Barrier beginning point location - [GPS or Reference Post of 

Beginning of entire barrier system] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

33 Barrier offset - beginning - [From edge of lane] Agree Disagree Strongly Disagree Don't Know Yes Possible N34 Barrier height - [Height from ground surface in inches] Agree Disagree Strongly Disagree Don't Know Yes Possible N35 Barrier post type - [Strong Post (Metal), Weak Post (Metal), 

Wooden Post, N/A, Other] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

36 Barrier offset bracket - [Yes/No] Agree Disagree Strongly Disagree Don't Know Yes Possible N37 Barrier rub rail - [Yes/No] Agree Disagree Strongly Disagree Don't Know Yes Possible N38 Barrier end treatment type - beginning - [Impact Attenuator, 

Buried End, Terminal End, Fist, Bridge Connection, None, Other] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

39 Median Left Turn Lane Type - [Type of left turn lane in 

median] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

40 Median Left Turn Lane Width - [Width of median left turn 

lane] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

41 Drop Inlet Location - [GPS coordinates] Agree Disagree Strongly Disagree Don't Know Yes Possible N42 Drop Inlet Blockage Agree Disagree Strongly Disagree Don't Know Yes Possible N43 Roadside Clearzone Width - [Roadside clearzone width] Agree Disagree Strongly Disagree Don't Know Yes Possible N
62

Data Elements and Categories A. This element has a high safety priority. B. Technologies exi

to measure this element. 

44 Roadside Clearzone Cross Slope (Assume Sideslope = 

Clearzone Cross Slope) - [Sideslope] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

45 Driveway Type (MMIRE = Driveway Information) - 

[Residential, Farm, Retail/Commercial, Industrial] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

46 Barrier Condition - [Functional or not] Agree Disagree Strongly Disagree Don't Know Yes Possible N47 Attenuator present (barrier beginning) - [Yes/No] Agree Disagree Strongly Disagree Don't Know Yes Possible N48 Attenuator type - Agree Disagree Strongly Disagree Don't Know Yes Possible N49 Attenuator Condition - [Functional or not] Agree Disagree Strongly Disagree Don't Know Yes Possible N50 Offset of roadside obstacle - [From edge of lane] Agree Disagree Strongly Disagree Don't Know Yes Possible N51 Location of roadside obstacle - [GPS or reference post of each 

obstacle] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

III. Bridge Descriptors 

52 Bridge Begin Location (MMIRE: Bridge Descriptors for 

Bridges in Segment) - [GPS coordinates] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

53 Bridge approach Slab Settlement Exists - [Yes/No] Agree Disagree Strongly Disagree Don't Know Yes Possible N54 Offset of bridge rail - [Distance from edge of lane] Agree Disagree Strongly Disagree Don't Know Yes Possible N55 Bridge Median - [Whether the median is non-existent, open or 

closed. The median is closed when the area between the 2 roadways at the structure is bridged over and is capable of supporting traffic. All bridges that carry either 1-way traffic or 2-way traffic separated only by a centerline have no median] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

56 Bridge transitions - [The transition from approach guardrail to 

bridge railing requires that the approach guardrail be firmly attached to the bridge railing. It also requires that the approach guardrail be gradually stiffened as it comes closer to the bridge railing. The ends of curbs and safety walks need to be gradually tapered out or shielded] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N
63

Data Elements and Categories A. This element has a high safety priority. B. Technologies exi

to measure this element. 

57 Type of Service on Bridge - [Classification: Highway, 

Railroad, Pedestrian-bicycle, Highway-railroad, Highwaypedestrian, Overpass structure at an interchange or second level of a multilevel interchange, Third level (Interchange), Fourth level (Interchange), Building or plaza, Other] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

58 Left Curb/Sidewalk Width - [Widths of the left and right curbs 

or sidewalks to nearest tenth of a meter. "Left" and "Right" should be determined on the basis of direction of the inventory] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

IV. Railroad Crossing Descriptors 

59 Number of Tracks - [Number of railroad tracks] Agree Disagree Strongly Disagree Don't Know Yes Possible N60 Railroad Crossing Control Type - [Crossbucks, gates, flashing 

lights, signal] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

61 Railroad crossing number - [U.S. DOT inventory crossing 

number] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

62 Railroad crossing Four-quadrant (or Full Barrier) Gates - 

[Whether or not four-quadrant (or full barrier) gates are present at the crossing. Full barrier gates apply in the case of 1-way streets or where the gate arms reach across the entire roadway] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

63 Railroad crossing Cantilevered (or Bridged) Flashing Lights - 

[The number of cantilevered (or bridged) flashing lights in the appropriate block. Separate cantilevered flashers from those over traffic lanes and those not reaching the roadway (over only parking lanes, turnout lanes, or shoulders). Count individual cantilever units; do not count the flasher head pairs mounted on the units] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

64 Railroad crossing Wigwags - [The number of wigwag signals] Agree Disagree Strongly Disagree Don't Know Yes Possible N65 Railroad crossing Bells - [The number of all bells, if present, 

that are either alone or in conjunction with other train activated warning devices] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N
64

Data Elements and Categories A. This element has a high safety priority. B. Technologies exi

to measure this element. 

66 Crossing Surface (on Main Line) - [Type of crossing surface 

(if there are multiple tracks which have different types of surfaces, indicate the lower grade surface material): Timber, Sectional Treated Timber, Full Wood Plank, Asphalt, Asphalt and Flange, Concrete, Concrete Slab, Concrete and Rubber, Rubber, Metal, Metal Sections, Other Metal, Unconsolidated, Other] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

V. Traffic Operations/Control Descriptors 

67 One/Two-Way Operations - [Whether the segment operates as 

a one- or two-way roadway] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

68 On-Street Parking Type - [On-street parking type] Agree Disagree Strongly Disagree Don't Know Yes Possible N69 Beginning of on-street parking - [GPS or Reference of start] Agree Disagree Strongly Disagree Don't Know Yes Possible N70 Roadway Lighting Presence - [Yes/No] Agree Disagree Strongly Disagree Don't Know Yes Possible N71 Location of street lighting - [GPS or reference post of pole] Agree Disagree Strongly Disagree Don't Know Yes Possible N72 Toll Facility? - [Toll facility indicator] Agree Disagree Strongly Disagree Don't Know Yes Possible N73 Edgeline Marking Width - [To the nearest inch] Agree Disagree Strongly Disagree Don't Know Yes Possible N74 Edgeline Marking Color - Agree Disagree Strongly Disagree Don't Know Yes Possible N75 Edgeline Location of marking - begin - [GPS or Reference of 

start] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

76 Edgeline Marking Material Type - [Paint, Thermoplastic, 

Polyurethane] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

77 Edgeline Marking offset - [Offset of each type of line (center, 

lane & edge) from right edge of pavement] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

78 Edgeline marking type Agree Disagree Strongly Disagree Don't Know Yes Possible N

VI. Horizontal and Vertical Curve Descriptors 

79 Curve Feature Type - [Type of horizontal alignment feature 

being described in the data record] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

80 Sight Distance (Stopping) - [Report sight distance at 0.01-mile 

intervals for Horizontal Curves from PC to PT] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N
65

Data Elements and Categories A. This element has a high safety priority. B. Technologies exi

to measure this element. 

81 Sight Distance (Stopping) - [Report sight distance at 0.01-mile 

intervals for Vertical Curves from PC to PT] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

VII. Intersection and Interchange Descriptors 

82 Type of Intersection/Junction - [Type of junction being 

described in the data record] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

83 Location Identifier for Road 1 Crossing Point - [Location on 

the first intersecting route (e.g., route-milepost)] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

84 Intersection/Junction No. of Legs - [Intersection/junction no. 

of legs] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

85 Intersection/Junction Geometry - [Intersection/junction 

geometry] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

86 Intersection Skew Angle - [Angle from perpendicular of 

intersection of the roads] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

87 Intersection/Junction Offset - [Whether crossroad approach 

centerlines are directly opposed or offset by some distance] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

88 Intersection/Junction Offset Distance - [Distance that 

approach centerlines are offset] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

89 Type of signalized intersection - [Standard, Protected Turn, 

Permitted Turn] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

90 Flashing beacon present [at stop-controlled intersection] - 

[Yes/No (Flashing yellow/red beacon)] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

91 Number of Intersection/Junction Quadrants With Limited 

Sight Distance - [Number of intersection/junction quadrants with limited sight distance] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

92 Roundabout-Inscribed Diameter - [Distance between the outer 

edges of the circulatory roadway] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

93 Channelization exists on approach - [Yes/No] Agree Disagree Strongly Disagree Don't Know Yes Possible N94 Pedestrian Signalization Type - [Type of pedestrian 

signalization on approach] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

95 Roundabout-Entry Radius - [Minimum radius of curvature of 

the curb on the right side of the entry] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

96 Roundabout-Pedestrian Facility - [Type of pedestrian crossing 

facility on this approach to roundabout] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N
66

Data Elements and Categories A. This element has a high safety priority. B. Technologies exi

to measure this element. 

97 Roundabout-Splitter Island Width - [Width of the splitter 

island separating entry and exit legs (measured at the inscribed circle)] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

98 Ramp Location - [GPS Coordinates or Reference Post of gore 

area] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N

99 Ramp Length - [Length of ramp] Agree Disagree Strongly Disagree Don't Know Yes Possible N

100 Location Identifier For Roadway at Beginning Ramp Terminal 

- [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] 

Agree Disagree Strongly 

Disagree 

Don't 

Know 

Yes Possible N
67

Table C.2. Survey Results 

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

I. Pavement Condition Descriptors 

1 Polished Aggregate - [Record square feet of 

affected surface area] 

5 0 0 5 2 3 3 

1. It is possible that a reflectance from a linprovide a reasonable e 8. Skid trailers; 10. There are several their digital imagery tcollecting the roadwaThe difficulty is automdata collection compoequipment operation. http://www.itre.ncsu.eentations.html 

2 Water Bleeding - [Record the number of 

occurrences of water bleeding and the length in meters of affected pavement with a minimum length of 3.28 feet] 

3 2 0 5 0 3 4 

4. Optical scanning.; 11. Not sure what this 

3 Oxidation - [Only plant mix surfaces are rated 

for oxidation. Determine presence of oxidation] 2 4 0 6 1 2 5 

4. Optical scanning.; 11. Not clear what the 

4 Bleeding - [Record square feet of surface area at 

each severity level] 4 1 0 7 1 3 5 4. Optical scanning. 

5 Pumping - [Record the number of occurrences of 

pumping and the length in feet of affected pavement] 2 2 0 7 0 3 5 

4. Optical scanning.; 9. Severity and PREVfor impact on safety fproblems listed 

6 Shoving - [Record number of occurrences and 

square feet of affected surface area] 3 1 0 8 0 3 5 4. Optical scanning. 

7 Scaling - [Record the number of occurrences and 

the square meters of affected area] 3 3 0 6 0 2 6 4. Optical scanning. 
68

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

8 Lane-to-Shoulder Separation - [Record to the 

nearest millimeter at intervals of 50 feet along the lane-to-shoulder joint. Indicate whether the joint is well-sealed (yes or no) at each location] 

7 2 0 3 1 3 4 

1. Line scanning laser4. LIDAR 

9 Transverse Construction Joint Deterioration - 

[Record number of construction joints at each severity level] 

5 2 0 4 1 3 4 

1. Line scanning laser

II. Lane Descriptors 

10 Lane Add Point - [GPS or Reference Post of 

Start of a FULL lane width] 5 4 0 3 2 3 4 

4. LIDAR and optical appropriate software. 

11 Exclusive Left Turn Lane Length - [Exclusive 

left turn lane length] 

8 3 0 1 3 3 3 

1. Local DMI or GPS4. LIDAR and optical appropriate software. 5. Depends on capacit7. Highway Driving SResearch Vehicle 

12 Auxiliary Lane Presence/Type - [Presence or 

type of auxiliary lane] 8 1 0 3 1 2 4 

4. LIDAR and optical appropriate software. 

13 Auxiliary Lane Length - [Length of auxiliary 

lane] 6 2 0 4 1 2 4 

4. LIDAR and optical appropriate software. 

14 HOV Lanes - [Presence of HOV lanes in 

segment] 6 4 0 2 0 3 5 

4. Optical scanning on7. Highway Driving SResearch Vehicle 

15 HOV Lane Types - [HOV lane types] 

5 5 0 2 0 3 5 

4. Optical scanning on7. Highway Driving SResearch Vehicle 
69

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

16 Reversible Lanes - [Number of reversible lanes 

present on segment] 

7 2 0 3 0 2 6 

4. No current distinguand non-rev. lanes; 7. Highway Driving SResearch Vehicle 

17 Presence/Type of Bicycle Facility - [Presence or 

type of bicycle facility on segment] 

11 1 0 0 1 2 5 

4. LIDAR and optical appropriate software. 7. Highway Driving SResearch Vehicle 

18 Number of Peak Hour Lanes - [Number of 

through lanes used in peak period in the peak direction] 

8 3 0 1 1 2 5 

1. But still good infor4. No current distingu5. More impact on mo7. Highway Driving SResearch Vehicle 

III. Shoulder, Median, and Roadside Descriptors 

19 Right Shoulder Type - [Shoulder type on right 

side of road in direction of inventory] 

11 1 0 0 1 1 6 

4. LIDAR and optical appropriate software. 7. Highway Driving SResearch Vehicle 

20 Right Paved Shoulder Width - [Width of paved 

portion of right shoulder] 12 0 0 0 1 2 5 

4. LIDAR; 7. Highway Driving SResearch Vehicle 

21 Shoulder Rumble Strip Presence - [Presence of 

shoulder rumble strip] 

11 0 0 1 1 1 6 

4. LIDAR and optical appropriate software. 7. Highway Driving SResearch Vehicle 

22 Rumble Strip Type - [Rumble strip type if 

present] 6 3 0 3 1 1 6 

4. LIDAR and optical appropriate software. 
70

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

7. Field Research Veh

23 Location of Rumble Strip - Begin - [GPS or 

reference post of start] 

9 1 0 2 1 1 6 

4. LIDAR and optical appropriate software. 7. Highway Driving SResearch Vehicle 

24 Rumble Strip Offset - [From edge of lane] 

9 1 0 2 1 1 6 

4. LIDAR and optical appropriate software. 7. Highway Driving SResearch Vehicle 

25 Sidewalk is separated from edge of road - 

[Yes/No] 

11 1 0 0 1 2 5 

1. Video; 4. LIDAR aappropriate software. 7. Highway Driving SResearch Vehicle 

26 Curb Type - [Curb type] 

6 2 0 4 1 2 5 

4. LIDAR and optical appropriate software. 

27 Curb Location - [GPS coordinates of beginning 

and end point] 6 4 0 2 2 1 5 

4. LIDAR and optical appropriate software. 

28 Curb Blockage 

3 3 0 5 0 2 5 

3. I don't know what i4. Optical scanning. Lscans. 

29 Curb Damage 

4 5 0 3 0 3 5 

1. Agency may wish tas part of asset manag4. Optical scanning. Lscans. 

30 Median Opening Location - [GPS of first corner 

in direction of travel] 9 1 0 2 1 2 5 

4. LIDAR and optical appropriate software. 
71

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

31 Median Barrier Type - [Soil, paved (striped), 

paved (barrier), raised curb, None, Other] 12 0 0 0 1 2 5 

4. LIDAR and optical appropriate software. 7. Federal Outdoor Im

32 Barrier beginning point location - [GPS or 

Reference Post of Beginning of entire barrier system] 9 2 0 1 1 2 5 

4. LIDAR and optical appropriate software. 7. Federal Outdoor Im

33 Barrier offset - beginning - [From edge of lane] 

9 2 0 1 1 2 5 

4. LIDAR and optical appropriate software. 

34 Barrier height - [Height from ground surface in 

inches] 

11 1 0 0 0 3 5 

1. LIDAR; 4. LIDAR. Limitation surface through veget7. Federal Outdoor Im

35 Barrier post type - [Strong Post (Metal), Weak 

Post (Metal), Wooden Post, N/A, Other] 11 1 0 0 1 1 6 

4. Optical. LIDAR wposts.; 7. Federal Out

36 Barrier offset bracket - [Yes/No] 

4 2 0 6 1 1 5 

4. LIDAR and optical appropriate software. 

37 Barrier rub rail - [Yes/No] 

5 1 0 6 1 1 5 

4. LIDAR and optical appropriate software. 

38 Barrier end treatment type - beginning - [Impact 

Attenuator, Buried End, Terminal End, Fist, Bridge Connection, None, Other] 

10 1 0 1 1 1 6 

39 Median Left Turn Lane Type - [Type of left turn 

lane in median] 10 1 0 1 1 3 4 

1. Video?; 7. Highway Driving SResearch Vehicle 

40 Median Left Turn Lane Width - [Width of 

median left turn lane] 10 1 0 1 2 2 4 7. Highway Driving S

Research Vehicle 
72

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

41 Drop Inlet Location - [GPS coordinates] 4 2 0 6 1 2 5 42 Drop Inlet Blockage 4 2 0 6 0 2 5 4. Optical if inlet in v43 Roadside Clearzone Width - [Roadside clearzone 

width] 11 0 0 0 1 2 5 

1. LIDAR and Video?4. LIDAR. (With app

44 Roadside Clearzone Cross Slope (Assume 

Sideslope = Clearzone Cross Slope) - [Sideslope] 8 2 0 2 1 2 5 

1. LIDAR and Video?4. LIDAR 

45 Driveway Type (MMIRE = Driveway 

Information) - [Residential, Farm, Retail/Commercial, Industrial] 

8 3 0 1 0 2 6 

46 Barrier Condition - [Functional or not] 10 0 0 2 0 1 6 47 Attenuator present (barrier beginning) - [Yes/No] 9 1 0 2 2 1 5 48 Attenuator type - 7 2 0 3 1 1 6 4. Optical. LIDAR w

posts. 

49 Attenuator Condition - [Functional or not] 

9 0 0 3 0 1 5 

4. Optical based entirLIDAR can generate but no protocols or teevaluate damage. 

50 Offset of roadside obstacle - [From edge of lane] 10 0 0 2 1 3 3 1. LIDAR; 4. LIDAR 51 Location of roadside obstacle - [GPS or 

reference post of each obstacle] 

10 1 0 1 1 3 3 

1. LIDAR coupled to 4. LIDAR or optical.; 7. Highway Driving SResearch Vehicle 

IV. Bridge Descriptors 

52 Bridge Begin Location (MMIRE: Bridge 

Descriptors for Bridges in Segment) - [GPS coordinates] 

3 4 0 4 1 3 3 

1. GPS 

53 Bridge approach Slab Settlement Exists - 

[Yes/No] 6 1 0 4 1 3 3 1. LIDAR; 4. LIDAR

54 Offset of bridge rail - [Distance from edge of 

lane] 7 1 0 3 1 3 3 

1. Line scanning laser4. LIDAR 
73

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

55 Bridge Median - [Whether the median is nonexistent, open or closed. The median is closed 

when the area between the 2 roadways at the structure is bridged over and is capable of supporting traffic. All bridges that carry either 1-way traffic or 2-way traffic separated only by a centerline have no median] 

9 0 0 2 1 3 3 

1. Line scanning laser4. Optical or LIDAR. 

56 Bridge transitions - [The transition from 

approach guardrail to bridge railing requires that the approach guardrail be firmly attached to the bridge railing. It also requires that the approach guardrail be gradually stiffened as it comes closer to the bridge railing. The ends of curbs and safety walks need to be gradually tapered out or shielded] 

7 2 0 2 0 1 6 

57 Type of Service on Bridge - [Classification: 

Highway, Railroad, Pedestrian-bicycle, Highway-railroad, Highway-pedestrian, Overpass structure at an interchange or second level of a multilevel interchange, Third level (Interchange), Fourth level (Interchange), Building or plaza, Other] 

7 1 0 3 0 2 5 

1. E1isting inventory 

58 Left Curb/Sidewalk Width - [Widths of the left 

and right curbs or sidewalks to nearest tenth of a meter. "Left" and "Right" should be determined on the basis of direction of the inventory] 

8 1 0 2 1 2 3 

1. Line scanning laser4. LIDAR 

V. Railroad Crossing Descriptors 

59 Number of Tracks - [Number of railroad tracks] 

8 2 0 2 1 2 5 

1. LIDAR and Google 4. Optical or LIDAR. 

60 Railroad Crossing Control Type - [Crossbucks, 

gates, flashing lights, signal] 10 1 0 1 1 1 6 

1. Video; 4. Optical. LIDAR wfeatures. 

61 Railroad crossing number - [U.S. DOT inventory 

crossing number] 6 3 0 3 0 1 6 1. E1isting inventory 
74

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

62 Railroad crossing Four-quadrant (or Full Barrier) 

Gates - [Whether or not four-quadrant (or full barrier) gates are present at the crossing. Full barrier gates apply in the case of 1-way streets or where the gate arms reach across the entire roadway] 

9 0 0 3 0 2 6 

1. Video 

63 Railroad crossing Cantilevered (or Bridged) 

Flashing Lights - [The number of cantilevered (or bridged) flashing lights in the appropriate block. Separate cantilevered flashers from those over traffic lanes and those not reaching the roadway (over only parking lanes, turnout lanes, or shoulders). Count individual cantilever units; do not count the flasher head pairs mounted on the units] 

7 1 0 4 1 1 5 

1. Video; 4. Optical, or LIDAR 

64 Railroad crossing Wigwags - [The number of 

wigwag signals] 7 1 0 4 1 1 6 

1. Video?; 4. Optical, or LIDAR 

65 Railroad crossing Bells - [The number of all 

bells, if present, that are either alone or in conjunction with other train activated warning devices] 

6 1 0 4 0 1 7 

5. Bells are difficult to 

66 Crossing Surface (on Main Line) - [Type of 

crossing surface (if there are multiple tracks which have different types of surfaces, indicate the lower grade surface material): Timber, Sectional Treated Timber, Full Wood Plank, Asphalt, Asphalt and Flange, Concrete, Concrete Slab, Concrete and Rubber, Rubber, Metal, Metal Sections, Other Metal, Unconsolidated, Other] 

6 1 0 4 0 2 5 

4. Optical only. 

VI. Traffic Operations/Control Descriptors 

67 One/Two-Way Operations - [Whether the 

segment operates as a one- or two-way roadway] 10 1 0 0 0 2 6 7. Highway Driving S

Research Vehicle 

68 On-Street Parking Type - [On-street parking 

type] 8 2 0 1 1 2 5 

1. Google Earth?; 4. Optical 
75

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

69 Beginning of on-street parking - [GPS or 

Reference of start] 9 1 0 2 1 1 6 4. Optical or LIDAR. 

70 Roadway Lighting Presence - [Yes/No] 

12 0 0 0 1 3 4 

1. Vehicle mounted p4. Optical.; 7. Field Research Veh

71 Location of street lighting - [GPS or reference 

post of pole] 

11 1 0 0 2 2 4 

1. LIDAR and photom4. Optical or LIDAR.5. DHM with video cr7. Field Research Veh

72 Toll Facility? - [Toll facility indicator] 7 4 0 1 0 2 6 7. Highway Driving S

Research Vehicle 

73 Edgeline Marking Width - [To the nearest inch] 

10 1 0 1 1 3 4 

1. Mobile Retroreflec4. LIDAR; 7. Highway Driving SResearch Vehicle 

74 Edgeline Marking Color - 6 1 0 5 1 2 5 4. Optical. 75 Edgeline Location of marking - begin - [GPS or 

Reference of start] 

11 1 0 0 2 2 4 

1. GPS; 4. Optical or LIDAR.7. Highway Driving SResearch Vehicle 

76 Edgeline Marking Material Type - [Paint, 

Thermoplastic, Polyurethane] 6 2 0 4 0 2 6 
76

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

77 Edgeline Marking offset - [Offset of each type of 

line (center, lane & edge) from right edge of pavement] 9 2 0 1 2 2 4 

1. Provide calculated pavement marking retdetermine line separat4. LIDAR. Optical cameasure, but toleranc

78 Edgeline marking type 3 2 0 6 1 1 5 

VII. Horizontal and Vertical Curve Descriptors 

79 Curve Feature Type - [Type of horizontal 

alignment feature being described in the data record] 

9 1 0 2 1 2 4 

1. Google Earth? 

80 Sight Distance (Stopping) - [Report sight 

distance at 0.01-mile intervals for Horizontal Curves from PC to PT] 10 1 0 1 2 2 4 

1. LIDAR; 4. Optical. (Potentiallbe processed to calcul7. Field Research Veh

81 Sight Distance (Stopping) - [Report sight 

distance at 0.01-mile intervals for Vertical Curves from PC to PT] 11 1 0 0 2 2 4 

1. LIDAR; 4. Optical. (Potentiallbe processed to calcul7. Field Research Veh

VIII. Intersection and Interchange Descriptors 

82 Type of Intersection/Junction - [Type of junction 

being described in the data record] 11 1 0 0 1 3 4 

4. Optical.; 7. Highway Driving SResearch Vehicle 

83 Location Identifier for Road 1 Crossing Point - 

[Location on the first intersecting route (e.g., route-milepost)] 

7 2 0 3 1 1 6 

7. Highway Driving SResearch Vehicle 

84 Intersection/Junction No. of Legs - 

[Intersection/junction no. of legs] 10 1 0 1 2 1 5 

1. Google Earth?; 4. Optical or LIDAR. 
77

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

85 Intersection/Junction Geometry - 

[Intersection/junction geometry] 

11 1 0 0 2 1 5 

1. Google Earth?; 4. LIDAR with appro7. Highway Driving SResearch Vehicle 

86 Intersection Skew Angle - [Angle from 

perpendicular of intersection of the roads] 

11 1 0 0 0 3 5 

1. Google Earth?; 4. LIDAR.; 7. Highway Driving SResearch Vehicle 

87 Intersection/Junction Offset - [Whether 

crossroad approach centerlines are directly opposed or offset by some distance] 

8 2 0 2 0 3 5 

1. Google Earth?; 4. LIDAR 

88 Intersection/Junction Offset Distance - [Distance 

that approach centerlines are offset] 6 2 0 4 0 3 5 1. Google Earth? 

89 Type of signalized intersection - [Standard, 

Protected Turn, Permitted Turn] 11 1 0 0 0 1 7 7. Highway Driving S

Research Vehicle 

90 Flashing beacon present [at stop-controlled 

intersection] - [Yes/No (Flashing yellow/red beacon)] 10 2 0 0 1 1 6 

4. Optical.; 7. Highway Driving SResearch Vehicle 

91 Number of Intersection/Junction Quadrants With 

Limited Sight Distance - [Number of intersection/junction quadrants with limited sight distance] 

11 1 0 0 0 2 5 

7. Highway Driving SResearch Vehicle 

92 Roundabout-Inscribed Diameter - [Distance 

between the outer edges of the circulatory roadway] 

9 1 0 2 1 3 4 

4. LIDAR; 7. Highway Driving S

93 Channelization exists on approach - [Yes/No] 

10 1 0 1 2 1 5 

4. Optical or LIDAR.7. Highway Driving SResearch Vehicle 

94 Pedestrian Signalization Type - [Type of 

pedestrian signalization on approach] 10 1 0 1 0 2 6 4. Optical 
78

Data Elements and Categories A. This element has a high safety priority. B. Technologies exist to measure this element. C. C

95 Roundabout-Entry Radius - [Minimum radius of 

curvature of the curb on the right side of the entry] 9 1 0 2 2 2 4 

4. LIDAR; 7. Highway Driving SResearch Vehicle 

96 Roundabout-Pedestrian Facility - [Type of 

pedestrian crossing facility on this approach to roundabout] 9 1 0 1 1 2 5 

1. Video?; 4. Optical.; 7. Highway Driving SResearch Vehicle 

97 Roundabout-Splitter Island Width - [Width of 

the splitter island separating entry and exit legs (measured at the inscribed circle)] 8 1 0 3 2 1 5 

4. LIDAR; 7. Highway Driving SResearch Vehicle 

98 Ramp Location - [GPS Coordinates or Reference 

Post of gore area] 8 2 0 2 1 4 3 1. GPS; 4. Optical or 

99 Ramp Length - [Length of ramp] 

10 1 0 1 1 4 3 

1. Local DMI or GPS4. Optical or LIDAR.5. Depends on speed.7. Highway Driving SResearch Vehicle 

100 Location Identifier For Roadway at Beginning 

Ramp Terminal - [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] 

6 3 0 3 1 2 5 

4. Optical or LIDAR. 
79

Appendix D: FHWA List of Element Priorities 

The element priorities provided by FHWA as well as the background and criteria for their 

classification12 are presented in the following sections. 

D.1 Background 

Most agencies with responsibilities for the safety of road and highway systems clearly 

recognize the need for comprehensive, high-quality data about their roads and roadside 

environments. Nationally, this is reflected in FHWA’s strategic commitment to data-driven 

decisions.13

A number of projects and programs are currently underway to identify the information 

needed to understand what is happening on U.S. roads and highways, and to provide analysts and 

designers with the data which will allow them to identify, characterize and prioritize safety 

improvements to best use the limited funds available for making our roads safer. These projects 

include: 

• Safety analysis tools: The Interactive Highway Safety Design Model (IHSDM) and 

SafetyAnalyst are two recently-developed analysis tools which use data about a road 

system and its operational details to help practitioners make good safety decisions. 

• Minimum Inventory of Roadway Elements (MIRE – formerly MMIRE): MIRE is a list 

of standard definitions of important data elements to collect about a road system, along 

with standard forms for coding that data for inclusion in databases. 

 

12 The information was provided by Mr. Lincoln Cobb, FHWA Office of Safety R&D, HRDS-2. 

13 FHWA Strategic Objectives – System Performance Goal 2.1: “Implement comprehensive, integrated, and datadriven safety programs and countermeasures at the Federal, State, and local level.”
80

• Strategic Highway Research Program 2 (SHRP2): The Safety Focus area of SHRP2 will 

collect enormous data sets regarding the roads, and their immediate environments, 

travelled by volunteer subjects in the Naturalistic Driving Study. 

• Intellidrive Applications: A current trade study is considering the roadway data needs for 

Intellidrive applications. Intellidrive data needs are not precisely those of a highway 

safety analyst, but there are considerable overlaps. Intellidrive systems will need to 

distinguish between more and less safe conditions through which the vehicle is passing. 

The roadway safety community has conducted little research which would allow 

objective ranking of the potential impact of safety data elements. When asked which roadway 

data elements are most important, experts often respond by gut feel. What has been and remains 

the case is that everyone involved in roadway safety has an opinion about what information is 

important, and each may even have devolved a personal ranking of the elements. Therefore, the 

use of road data elements in contemporary safety analysis software tools (whose data elements 

have a practical impact on safety) was chosen as a surrogate for the direct significance of 

individual elements. 

D.2 Criteria for Defining High Priority Data Elements 

Among the data elements which would support the best safety decisions, the most 

common denominator is geometric alignment information, particularly basic information about 

horizontal and vertical curvature. All of the above-mentioned data sets or sources include such 

basic geometric elements. In each instance this basic geometric information is considered 

critical though not typically ranked higher or lower than other “important” data elements. 
81

In selecting the specific elements to include in the High Priority list, the alignment 

elements from several sources were reviewed: 

• MIRE 

• SHRP2 S04 project (in progress). 

• Interim data element set proposed within the ongoing "Roadway Geometry and Inventory 

Trade Study for IntelliDriveSM Applications." 

This review found no new underlying alignment characteristics, though different data sets 

reflected different notions about the minimum information needed regarding vertical and 

horizontal curves. Accordingly, the existing elements in the Roadway Alignment Descriptors 

section of the MET were defined as the High Priority elements (see Table D.1). 

Table D.1. List of FHWA High Priority Elements 

MET Element Index Element Description 

297 Curve Identifiers and Linkage Variables 

298 Curve Feature Type 

299 Horizontal Curve Degree or Radius 

300 Horizontal Curve Length (Including Spiral) 

301 Curve Superelevation or Superelevation Adequacy 

302 Horizontal Curve PC (Point of Curvature) 

303 Horizontal Curve PT (Point of Tangency) 

306 Horizontal Curve Intersection/Deflection Angle 

307 Horizontal Curve Direction 

308 (Horizontal) Sight Distance (Stopping) 

309 Grade Identifiers and Linkage Variables 

310 Vertical Alignment Feature Type 

311 Percent of Grade 

312 Location of measurement [of grade] 

313 Grade Length 

314 Vertical Curve Length 

315 Vertical Curve Radius 

317 Vertical Curve PC (Point of Curvature) 

318 Vertical Curve PT (Point of Tangency) 

319 (Vertical) Sight Distance (Stopping) 
82

D.3 Criteria for Defining Medium Priority Data Elements 

There are two safety analysis tools in particular – IHSDM and SafetyAnalyst – which 

have taken prominent positions in the road safety field. These are fundamental parts of the first 

release of the Highway Safety Manual (HSM), which will be published in the late Spring of 

2010. Therefore, the road data elements which are inputs for these two tools will have 

considerable impact on safety via the analyses which will be conducted using them. On this 

basis, the inputs into IHSDM and SafetyAnalyst are collectively considered the Medium Priority 

Data Elements for the purposes of this study and are presented in Table D.2. 

Table D.2. List of FHWA Medium Priority Elements 

MET Element Index Element Description 

1 County 

2 City/Local Jurisdiction 

3 Route Number 

5 Section End-Points Descriptors 

6 Section Identifier 

7 Section Length 

8 Highway District 

11 Route Signing 

16 Functional Class 

17 Rural/Urban Designation 

19 Access Control 

21 Surface Type 

63 No. of Thru Lanes 

65 Lane Widths 

69 Average Thru Lane Width 

76 HOV Lanes 

79 Presence/Type of Bicycle Facility 

83 Right Shoulder Type 

84 Right Shoulder Total Width 

85 Right Paved Shoulder Width 

90 Left Shoulder Type 
83

MET Element Index Element Description 

91 Left Shoulder Total Width 

92 Left Paved Shoulder Width 

97 Shoulder Rumble Strip Presence 

112 Median Type 

113 Median Width 

116 Median Barrier Type 

136 Median Left Turn Lane Type 

138 Roadway Cross Slope 

139 Location of measurement [of Cross Slope] 

146 Sideslope 

148 Roadside Rating 

150 Driveway Information 

150 Driveway Type (MMIRE = Driveway Information) 

174 Type of roadside obstacles 

175 Offset of roadside obstacle 

176 Location of roadside obstacle 

177 Terrain Type (e.g., Mountainous, Level) 

242 Average Annual Daily Traffic Volume 

243 AADT Year 

244 AADT Annual Escalation Percentage 

245 Percentage Truck or Truck AADT 

247 Bicycle Count/Exposure 

249 Hourly Traffic Volumes (or Peak and Off-Peak 

AA ) 251 Future AADT 

252 Future AADT Year 

253 Directional Factor 

256 One/Two-Way Operations 

257 Speed Limit 

259 On-Street Parking Presence 

260 On-Street Parking Type 

261 Beginning of on-street parking 

262 Ending of on-street parking 

263 Side of street with on-street parking 

264 Roadway Lighting 

265 Roadway Lighting Presence 

296 85th % Speed 

297 Curve Identifiers and Linkage Variables 

299 Horizontal Curve Degree or Radius 

300 Horizontal Curve Length (Including Spiral) 

301 Curve Superelevation or Superelevation Adequacy 

304 Horizontal Transition/Spiral Curve Presence 

307 Horizontal Curve Direction 
84

MET Element Index Element Description 

311 Percent of Gradient 

313 Grade Length 

320 Unique Intersection Identifier 

322 Location Identifier for Road 1 Crossing Point 

324 Location Identifier for Road 3, 4, etc., Crossing 

i ( il ) 325 Intersection/Junction No. of Legs 

326 Intersection/Junction Geometry 

329 Intersection Skew Angle 

330 Intersection/Junction Offset 

331 Intersection/Junction Offset Distance 

332 Intersection/Junction Traffic Control 

333 Signalization Type (e.g. Actuated, Fixed, System) 

337 Number of Intersection/Junction Quadrants With 

i i d Si h i 338 Intersection/Junction Lighting 

344 Approach AADT 

346 Approach is Two-Way, One-Way 

349 No. of Exclusive Left Turn Lanes 

351 No. of Exclusive Right Turn Lanes 

354 Median Type at Intersection 

355 Approach Traffic Control 

357 Left Turn Protection 

358 Signal Progression 

362 Crossing Pedestrian Count/Exposure 

363 Left/Right Turn Prohibitions 

364 Left Turn Counts/Percent 

365 Right Turn Counts/Percent 

380 Interchange Type 

383 Ramp Location 

384 Type of Ramp Terminal [entry or exit] 

385 Ramp Length 

386 Ramp No. of Lanes 

387 Ramp AADT 

390 Ramp Descriptor at Beginning Ramp Terminal 

394 Ramp Descriptor at Ending Ramp Terminal 

D.4 Criteria for Defining Low Priority Data Elements 

The data elements in the MET that are not classified as High or Medium are considered 

Low Priority Data Elements. 
85

Appendix E: Combined List of Element Priorities 

Tables E.1, E.2 and E.3 present the high, medium and low, respectively, prioritized list of 

data elements that were merged from the FHWA survey (Appendix C) and the FHWA lists given 

in Appendix D. 

Table E.1. High Priority Data Elements 

MET Element Index Element Description 

34 Polished Aggregate 

40 Bleeding 

46 Shoving 

54 Lane-to-Shoulder Separation 

55 Transverse Construction Joint Deterioration 

71 Exclusive Left Turn Lane Length 

74 Auxiliary Lane Presence/Type 

75 Auxiliary Lane Length 

78 Reversible Lanes 

79 Presence/Type of Bicycle Facility 

82 Number of Peak Hour Lanes 

83 Right Shoulder Type 

85 Right Paved Shoulder Width 

97 Shoulder Rumble Strip Presence 

98 Rumble Strip Type 

100 Location of Rumble Strip - Begin 

102 Rumble Strip Offset 

106 Sidewalk is separated from edge of road 

108 Curb Type 

115 Median Opening Location 

116 Median Barrier Type 

120 Barrier offset - beginning 

122 Barrier height 

123 Post type 

124 Offset bracket 

125 Rub rail 

133 Location of Rumble Strip - Begin 

135 Rumble Strip Offset From edge of lane 

136 Median Left Turn Lane Type 

137 Median Left Turn Lane Width 

143 Drop Inlet Location 

144 Drop Inlet Blockage 

145 Roadside Clearzone Width 

146 Roadside Clearzone Cross Slope (Assume Sideslope = Clearzone Cross Slope) 

150 Driveway Type (MMIRE = Driveway Information) 

153 Barrier Begin Location 
86

MET Element Index Element Description 

155 Barrier offset - beginning 

158 Post type 

159 Offset bracket 

160 Rub rail 

161 Barrier Condition 

162 Barrier end treatment (beginning) 

164 Attenuator present (barrier beginning) 

165 Attenuator type 

166 Attenuator Condition 

175 Offset of roadside obstacle 

176 Location of roadside obstacle 

180 Approach Slab Settlement Exists 

182 Offset of bridge rail 

186 Bridge Median 

188 Transitions 

191 Type of Service on Bridge 

194 Left Curb/Sidewalk Width 

202 Number of Tracks 

203 Railroad Crossing Control Type 

206 Railroad crossing number 

219 Four-quadrant (or Full Barrier) Gates 

220 Cantilevered (or Bridged) Flashing Lights 

225 Wigwags 

226 Bells 

234 Crossing Surface (on Main Line) 

256 One/Two-Way Operations 

260 On-Street Parking Type 

261 Beginning of on-street parking 

265 Roadway Lighting Presence 

266 Location of street lighting 

270 Edgeline Marking Width 

271 Edgeline Marking Color 

272 Edgeline Location of marking - begin 

274 Edgeline Marking Material Type 

275 Edgeline Marking offset 

297 Curve Identifiers and Linkage Variables 

298 Curve Feature Type 

299 Horizontal Curve Degree or Radius 

300 Horizontal Curve Length (Including Spiral) 

301 Curve Superelevation or Superelevation Adequacy 

302 Horizontal Curve PC (Point of Curvature) 

303 Horizontal Curve PT (Point of Tangency) 

306 Horizontal Curve Intersection/Deflection Angle 

307 Horizontal Curve Direction 

308 Sight Distance (Stopping) 

309 Grade Identifiers and Linkage Variables 

310 Vertical Alignment Feature Type 

311 Percent of Gradient 

312 Location of measurement [of grade] 

313 Grade Length 
87

MET Element Index Element Description 

314 Vertical Curve Length 

315 Vertical Curve Radius 

317 Vertical Curve PC (Point of Curvature) 

318 Vertical Curve PT (Point of Tangency) 

319 Sight Distance (Stopping) 

321 Type of Intersection/Junction 

322 Location Identifier for Road 1 Crossing Point 

325 Intersection/Junction No. of Legs 

326 Intersection/Junction Geometry 

329 Intersection Skew Angle 

330 Intersection/Junction Offset 

331 Intersection/Junction Offset Distance 

334 Type of signalized intersection 

336 Flashing beacon present [at stop-controlled intersection] 

337 Number of Intersection/Junction Quadrants With Limited Sight Distance 

341 Roundabout-Inscribed Diameter 

343 Channelization exists on approach 

360 Pedestrian Signalization Type 

369 Roundabout-Entry Radius 

373 Roundabout-Pedestrian Facility 

375 Roundabout-Splitter Island Width 

383 Ramp Location 

385 Ramp Length 

391 Location Identifier For Roadway at Beginning Ramp Terminal 

Table E.2. Medium Priority Data Elements 

MET Element Index Element Description 

1 County 

2 City/Local Jurisdiction 

3 Route Number 

5 Section End-Points Descriptors 

6 Section Identifier 

7 Section Length 

8 Highway District 

11 Route Signing 

16 Functional Class 

17 Rural/Urban Designation 

19 Access Control 

21 Surface Type 

41 Pumping 

52 Scaling 

63 No. of Thru Lanes 

65 Lane Widths 

67 Lane Add Point 

69 Average Thru Lane Width 
88

MET Element Index Element Description 

76 HOV Lanes 

77 HOV Lane Types 

84 Right Shoulder Total Width 

90 Left Shoulder Type 

91 Left Shoulder Total Width 

92 Left Paved Shoulder Width 

109 Curb Location 

110 Curb Blockage 

111 Curb Damage 

112 Median Type 

113 Median Width 

138 Roadway Cross Slope 

139 Location of measurement [of Cross Slope] 

148 Roadside Rating 

174 Type of roadside obstacles 

177 Terrain Type (e.g., Mountainous, Level) 

178 Bridge Begin Location (MMIRE: Bridge Descriptors for Bridges in Segment) 

242 Average Daily Traffic Volume 

243 AADT Year 

244 AADT Annual Escalation Percentage 

245 Percentage Truck or Truck AADT 

247 Bicycle Count/Exposure 

249 Hourly Traffic Volumes (or Peak and Off-Peak AADT) 

251 Future AADT 

252 Future AADT Year 

253 Directional Factor 

257 Speed Limit 

259 On-Street Parking Presence 

262 Ending of on-street parking 

263 Side of street with on-street parking 

264 Roadway Lighting 

268 Toll Facility? 

277 Edgeline marking type 

296 85th % Speed 

304 Horizontal Transition/Spiral Curve Presence 

320 Unique Intersection Identifier 

324 Location Identifier for Road 3, 4, etc., Crossing Point (e.g., Route-Milepost), etc. 

332 Intersection/Junction Traffic Control 

333 Signalization Type (e.g., Actuated, Fixed, System) 

338 Intersection/Junction Lighting 

344 Approach AADT 

346 Approach Is Two-Way, One- Way 

349 No. of Exclusive Left Turn Lanes 

351 No. of Exclusive Right Turn Lanes 

354 Median Type at Intersection 

355 Approach Traffic Control 

357 Left Turn Protection 

358 Signal Progression 

362 Crossing Pedestrian Count/Exposure 

363 Left/Right Turn Prohibitions 
89

MET Element Index Element Description 

364 Left Turn Counts/Percent 

365 Right Turn Counts/Percent 

380 Interchange Type 

384 Type of Ramp Terminal [entry or exit] 

386 Ramp No. of Lanes 

387 Ramp AADT 

390 Ramp Descriptor at Beginning Ramp Terminal 

394 Ramp Descriptor at Ending Ramp Terminal 

Table E.3. Low Priority Data Elements 

MET Element Index Element Description 

4 Street Name 

9 Governmental Ownership 

10 Type of Governmental Ownership 

12 Route Signing Qualifier 

13 Coinciding Route Indicator 

14 Coinciding-Route Primary Route Number 

15 Direction of Inventory 

18 Federal Aid/ Route Type 

20 Operational Class 

22 Surface Friction 

23 Surface Texture 

24 Surface Friction Date 

25 Total Surface Width 

26 Roughness 

27 Roughness Date 

28 Condition 

29 Condition Date 

30 Critical Failure 

31 Patches (type & no. & area) 

32 Longitudinal Cracking 

33 Transverse Cracking 

35 Lane-to-Shoulder Drop-off 

36 Water Bleeding 

37 Alligator Cracking (Fatigue Cracking) [area] 

38 Raveling 

39 Oxidation 

42 Block Cracking 

43 Edge Cracking 

44 Reflection Cracking at Joint 

45 Potholes 

47 Rutting 

48 Durability Cracking (D-Cracking) 

49 Map Cracking 

50 Spalling of longitudinal joint 
90

MET Element Index Element Description 

51 Lane-Joint Seal Damage 

53 Blow up 

56 Corner Cracking (Corner Break) 

57 Spalling of transverse joint 

58 Joint Seal Damage 

59 Faulting of Transverse Joints 

60 Punchouts 

61 Narrow Cracks 

62 'Y' Cracks 

64 Number of Lanes 

66 Location of Measurement [of lane widths] 

68 Lane Drop Point 

70 Exclusive Left Turn Lane Presence 

72 Exclusive Right Turn Lane Presence 

73 Exclusive Right Turn Lane Length 

80 Width of Marked Bicycle Lane or Bike Path 

81 Width of Wide Curb Lane 

86 Right Location of Measurement [of width] 

87 Right Low Shoulder 

88 Right High Shoulder 

89 Right Lane-joint seal type 

93 Left Location of Measurement [of width] 

94 Left Low Shoulder 

95 Left High Shoulder 

96 Left Lane-joint seal type 

99 Rumble Strip Lateral Location 

101 Location of Rumble Strip - End 

103 Sidewalk Presence 

104 Location of Sidewalk - Begin 

105 Location of Sidewalk - End 

107 Curb Presence 

114 Location of Measurement [of width] 

117 Location 

118 Beginning point location 

119 Ending point location 

121 Barrier offset - ending 

126 End treatment type - beginning 

127 End treatment type - end 

128 Median (Inner) Paved Shoulder Width 

129 Median Shoulder Rumble Strip Presence 

130 Median Rumble Strip Type 

132 Rumble Strip Lateral Location 

134 Location of Rumble Strip - End 

141 Distance of Pavement Edge Drop-off 

142 Location of Measurement [of drop-off] 

147 Location of measurement [of Cross Slope] 

149 Driveway Location (MMIRE = Driveway Information) 

151 

Barrier Type (MMIRE Roadside Hardware Descriptors breaks up into all elements left 

and below) 

152 Barrier Location 
91

MET Element Index Element Description 

154 Barrier End Location 

156 Barrier offset - ending 

157 Barrier height 

163 Barrier end treatment (end) 

167 Sign Location 

168 [Sign] Support Type 

169 [Sign] Support Location 

170 [Sign] Multi-sign 

171 Sign Type(s) 

172 Sign Size 

173 Sign Retroreflectivity 

179 Bridge End Location 

181 Bridge Rail Exists 

183 Number of lanes on structure 

184 Number of lanes under structure 

185 Approach Roadway Width 

187 Bridge Railings 

189 Approach Guardrail 

190 Approach Guardrail Ends 

192 Type of Service Under Bridge 

193 Inventory Rte Total Horz Clearance 

195 Right Curb/Sidewalk Width 

196 Bridge Roadway Width Curb-to-Curb 

197 Min Vert Clear Over Bridge Roadway 

198 Minimum Vertical Underclearance 

199 Minimum Lateral Underclearance On Right 

200 Min Lateral Underclearance on Left 

201 

At-Grade Railroad Crossing Location (RR Grade Crossing Descriptors for Crossings in 

Segment) 

204 Grade of Approach Side of Crossing 

205 Grade of Leave Side of Crossing 

207 Crossing Position 

208 Latitude (Decimal) 

209 Longitude (Decimal) 

210 No Signs or Signals 

211 Crossbucks 

212 Highway Stop Signs (R1-1) 

213 RR Advance Warning Signs (W10-1) 

214 Hump Crossing Sign (W10-5) 

215 Pavement Markings 

216 Other Signs 

217 Type of Warning Device at Crossing - Signs 

218 Gates 

221 Mast Mounted Flashing Lights 

222 Number of Flashing Light Pairs 

223 Other Flashing Lights 

224 Highway Traffic Signals 

227 Other Train Activated Warning Devices 

228 Specify Special Warning Device NOT Train Activated 

229 Channelization Devices With Gates 
92

MET Element Index Element Description 

230 Smallest Crossing Angle 

231 Number of Traffic Lanes Crossing Railroad 

232 Are Truck Pullout Lanes Present? 

233 Is Highway Paved? 

235 Does Track Run Down a Street? 

236 Nearby Intersecting Highway? 

237 Is It Signalized? 

238 Is Crossing Illuminated? 

239 Number of Signalized Intersections in Section 

240 Number of Stop-Controlled Intersections in Section 

241 Number of Uncontrolled/Other Intersections 

246 Total Daily Two-Way Pedestrian Count/Exposure 

248 Motorcycle Count or Percentage 

250 K-Factor 

254 Percent Combination Trucks - Daily Average 

255 Percent Single Unit Trucks - Daily Average 

258 School Zone Indicator 

267 Truck Route Designation 

269 Edgeline Presence 

273 Edgeline Location of marking - end 

276 Edgeline Marking retroreflectivity 

279 Centerline Presence 

280 Centerline Marking Width 

281 Centerline Marking Color 

282 Centerline Location of marking - begin 

283 Centerline Location of marking - end 

284 Centerline Bearing 

285 Centerline Marking Material Type 

286 Centerline Marking offset 

287 Centerline Marking retroreflectivity 

288 Centerline marking type 

289 Special pavement marking location 

290 Special pavement marking description 

291 Special Marking Material Type 

292 Raised pavement markers present 

293 Raised Pavement Marking Type 

294 Location of raised pavement markers 

295 No Passing Zone Code / Passing Permissibility 

305 Transition Curve Length 

323 Location Identifier for Road 2 Crossing Point 

327 School Zone Indicator 

328 Railroad Crossing Number if a RR Grade Crossing 

335 Type of stop-controlled intersection 

339 Roundabout - No. of Circulatory Lanes 

340 Roundabout - Circulatory Width 

342 Roundabout-Bicycle Facility 

345 Approach Use Type 

347 No. of Thru Lanes 

348 Exclusive left turn lanes exist 

350 Exclusive right turn lanes exist 
93

MET Element Index Element Description 

352 Length of Exclusive Left Turn Lanes 

353 Length of Exclusive Right Turn Lanes 

356 Location of traffic signal poles 

359 Crosswalk Presence/Type 

361 Pedestrian Signal Special Features 

366 Transverse Rumble Strip Presence 

367 Roundabout-Entry Width 

368 Roundabout-Number of Entry Lanes 

370 Roundabout-Exit Width 

371 Roundabout-Number of Exit Lanes 

372 Roundabout-Exit Radius 

374 Roundabout-Crosswalk Location (Distance From Yield Line) 

376 Unique Interchange Identifier 

377 Location Identifier for Road 1 Crossing Point 

378 Location Identifier for Road 2 Crossing Point 

379 Location Identifier for Road 3, 4, etc., Crossing Point, etc. 

381 Interchange Lighting 

382 Unique Ramp Identifier 

388 Ramp Posted Speed Limit 

389 Feature at Beginning Ramp Terminal 

392 Roadway Traffic Flow Direction at Beginning Ramp Terminal 

393 Feature at Ending Ramp Terminal 

395 Location Identifier for Roadway at Ending Ramp Terminal 

396 Roadway Traffic Flow Direction at Ending Ramp Terminal 
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