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NCHRP Project 20–07/Task 357

A GUIDE TO COLLECTING, PROCESSING, AND MANAGING ROADWAY ASSET INVENTORY DATA

FINAL REPORT

Requested by:

American Association of State Highway and Transportation Officials (AASHTO)

Standing Committee on Highways Subcommittee on Maintenance

Prepared by:

Kathryn A. Zimmerman, P.E. Kartik Manda

Applied Pavement Technology, Inc. 115 West Main Street, Suite 400

Urbana, Illinois 61801

June 2015

The information contained in this report was prepared as part of NCHRP Project 20-07, Task 357, National Cooperative Highway Research Program.

SPECIAL NOTE: This report IS NOT an official publication of the National Cooperative

Highway Research Program, Transportation Research Board, National Research Council, or The National Academies.

ACKNOWLEDGMENTS

This study was requested by the American Association of State Highway and Transportation Officials (AASHTO), and conducted as part of the National Cooperative Highway Research Program (NCHRP) Project 20-07. The NCHRP is supported by annual voluntary contributions from the state Departments of Transportation. Project 20-07 provides funding for quick response studies on behalf of the AASHTO Standing Committee on Highways. The report was prepared by Applied Pavement Technology, Inc. The work was guided by a task group which included Tanveer Chowdhury, Virginia DOT; William D. “Bill” Drake, Jr, Louisiana DOTD; Christopher C. Harris, Tennessee DOT; Thomas J. Kazmierowski, Golder Associates; Mary A. Martini, Nevada DOT; Roger E. Smith, Texas A&M University (retired); Lonnie R. Watkins, North Carolina DOT; and Nastaran Saadatmand, FHWA. The project manager was Amir N. Hanna, NCHRP Senior Program Officer.

DISCLAIMER The opinions and conclusions expressed or implied are those of the research agency that performed the research and are not necessarily those of the Transportation Research Board or its sponsoring agencies. This report has not been reviewed or accepted by the Transportation Research Board Executive Committee or the Governing Board of the National Research Council.

NCHRP Project 20-07/Task 357


FINAL REPORT

Requested by:

American Association of State Highway and Transportation Officials (AASHTO)

Standing Committee on Highways Subcommittee on Maintenance

Prepared by:

Kathryn A. Zimmerman, P.E. Kartik Manda

Applied Pavement Technology, Inc. 115 West Main Street, Suite 400

Urbana, Illinois 61801

June 2015




A Guide to Collecting, Processing, and Managing Roadway Asset Inventory Data Final Report

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TABLE OF CONTENTS

CHAPTER 1 – INTRODUCTION .................................................................................... 1

PROJECT OVERVIEW ................................................................................................ 1 RESEARCH SCOPE AND TASKS .............................................................................. 1

Task 1: Summarize the State of the Practice ........................................................... 1 Task 2: Identify Trends ............................................................................................. 2 Task 3: Develop Guidance ....................................................................................... 2

Task 4: Prepare Documentation ............................................................................... 2 DISTRIBUTION OF RESEARCH PRODUCTS ........................................................... 3

CHAPTER 2 – SUMMARY OF PRACTICE .................................................................... 4 INTRODUCTION ......................................................................................................... 4 TECHNOLOGY BEING USED TO ESTABLISH ASSET INVENTORIES .................... 4

AVAILABLE REFERENCES ON BUILDING AN ASSET INVENTORY ....................... 5 Data Collection Techniques ..................................................................................... 5

LiDAR ....................................................................................................................... 8 Data Quality ............................................................................................................. 9

STATUS OF ASSET INVENTORIES IN STATE DOTS ............................................. 10 Drainage Assets ..................................................................................................... 11

Roadside Assets .................................................................................................... 11 Pavement and Bridge Assets ................................................................................. 12 Traffic Assets ......................................................................................................... 12

Special Facilities .................................................................................................... 13 EMERGING TRENDS ............................................................................................... 14

EMERGING TECHNOLOGY ..................................................................................... 15 360-Degree Camera .............................................................................................. 15

Flash LiDAR ........................................................................................................... 15 Airborne LiDAR ...................................................................................................... 15

Driverless Cars ....................................................................................................... 16

CHAPTER 3 – CONCLUSIONS ................................................................................... 17 FUTURE RESEARCH NEEDS .................................................................................. 17

REFERENCES .............................................................................................................. 19


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LIST OF FIGURES

Figure 1. Inventory status of drainage assets (NCHRP 2015) ..................................................... 11 Figure 2. Inventory status of roadside assets (NCHRP 2015) ..................................................... 12 Figure 3. Inventory status of pavement assets (NCHRP 2015) ................................................... 12

Figure 4. Inventory status of traffic assets (NCHRP 2015) ......................................................... 13 Figure 5. Inventory status of special facilities (NCHRP 2015) ................................................... 13

LIST OF TABLES

Table 1. Suitability of different methods of data collection (Zimmerman and Stivers 2007) ....... 6


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AUTHOR ACKNOWLEDGMENTS

The research report herein was performed under NCHRP Project 20-07, Task 357 by Applied

Pavement Technology, Inc. (APTech). Ms. Kathryn A. Zimmerman, P.E., served as the

Principal Investigator for this study. She was assisted by Mr. Kartik Manda, an Engineering

Associate at APTech.

ABSTRACT

This project was initiated by the National Cooperative Highway Research Program to develop

guidance for establishing and managing roadway asset inventories. The resulting Guide, which

was written as a standalone document, can be used by transportation agencies to help make

informed decisions on the type of technology most appropriate for collecting asset inventory

information and the considerations that must be taken into account for processing and managing

the data. The study concentrated on both manual and automated data collection approaches,

including manual surveys, photogrammetric methods, and remote sensing technology (e.g.,

mobile LiDAR).

The Guide includes considerations that should be evaluated during all phases of establishing or

updating an asset inventory. First, the Guide addresses technical considerations that should be

taken into account regardless of the data collection selected, such as developing criteria for

classifying assets and developing data collection standards. Secondly, the Guide presents factors

to consider in determining the appropriateness of each of the three technologies used in

collecting inventory data. This section includes factors such as the level of accuracy required

and the visibility of the asset from the road. Next, the Guide includes considerations for

collecting the data, including differences depending on whether the data will be collected using

in-house personnel or an outside contractor. Finally, the Guide suggests considerations for

managing the data effectively, including topics such as storage requirements and update

schedules.


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CHAPTER 1 – INTRODUCTION

PROJECT OVERVIEW

Over the past decade and since the passage of recent legislation (commonly known as Moving

Ahead for Progress in the 21st Century Act or MAP-21), there has been an increasing emphasis

on the use of performance data to drive agency investment decisions as part of a comprehensive

asset management program. While inventory and performance data has been collected on

pavement and bridge assets for many years, there is less consistency in the status of roadway

asset inventories for other assets such as guardrails, culverts, and signs.

National Cooperative Highway Research Program (NCHRP) Project 20-07, Task 357 was

initiated in 2014 to develop guidance for establishing and managing roadway asset inventories

among state Departments of Transportation using current technology. This document represents

the Final Report for the project. It summarizes the project activities and documents the

assessment of current practice that was conducted during the early stages of the project. The

information gathered from this activity served as the basis for developing the Guide, which is

presented as a standalone document as an attachment to this Final Report. The Guide considers

both manual and automated technologies and includes factors that highway agencies should

consider when deciding which approach to use for building its asset inventory. Once the

decision is made, the Guide includes recommendations for making the best use of the technology

for collecting, processing, and managing roadway asset inventory data, such as guardrails, tower

lighting, signs, and drainage features. It is important to note that the Guide does not address the

performance criteria that are often used to monitor the level of service being provided to the

traveling public or to prepare maintenance budgets. Although the Guide focuses primarily on

building, maintaining, and managing asset inventories, the same technology can often be used to

evaluate asset performance. As a result, many of the same considerations identified for

establishing an asset inventory are relevant to the process of assessing the condition of these

assets.

RESEARCH SCOPE AND TASKS

The project objective was to develop practical guidance that could be used by highway agency

practitioners for collecting, processing, and managing roadway asset inventory data. This

objective was accomplished through the completion of the four tasks described below.

Task 1: Summarize the State of the Practice

The project began with a series of activities designed to provide a good understanding of the

state of the practice. One of the activities included a literature search of the readily available

documentation on collecting, processing, and managing roadway asset inventories. One of the

major sources of information included the results from NCHRP Synthesis 470, titled

Maintenance Quality Assurance Field Inspection Practices (NCHRP 2015). The preparation of

the synthesis included the conduct of a survey into the practices in state highway agencies for

collecting inventory information on a variety of different types of assets (e.g., culverts,

sidewalks, fences, pavement shoulders, and signs). This information proved to be very useful in

determining the status of asset inventories and the methods used to collect the information.

Information from the synthesis is included in Chapter 2 of this report.

In addition to reviewing reports and other forms of documentation, the project team conducted

interviews with both data collection vendors working in the state data collection market and state


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DOT practitioners who have used different forms of technology to establish asset inventories.

The information from the interviews influenced the development of the Guide and some of the

information is used to illustrate the considerations identified.

At the conclusion of Task 1, a summary report was prepared and distributed to the project’s

Technical Panel for review and comment. The feedback provided by the Technical Panel was

also instrumental in shaping the Guide’s content.

Task 2: Identify Trends

The information obtained during Task 1 served as the basis for identifying trends in the asset

inventory information being collected by state agencies and the methodologies being used.

Additionally, the interviews with state practitioners provided a good understanding of the factors

that influenced the selection of a technology for building their asset inventories. The states

selected to be interviewed represented a range of data collection methodologies, including both

manual and automated approaches. The trends that were observed are incorporated into the

Guide.

Task 3: Develop Guidance

During Task 3, the research team used the information obtained during Tasks 1 and 2 to develop

the framework for creating the Guide. The Guide, which is presented as an attachment to this

Final Report, addresses the following four steps associated with collecting, processing, and

managing asset inventory data:

Step 1: Getting ready to select a methodology – This step includes the organizational

issues that need to be addressed to assess an agency’s needs. This step involves deciding

what assets to include in the inventory, identifying the users of the data, determining the

level of detail needed, and establishing the characteristics that will be used to describe

each asset.

Step 2: Selecting a methodology – Using the information obtained during step 1, the

second step involves selecting the most appropriate methodology to meet the agency’s

needs. The decision is based on a number of different factors, related to the visibility of

the asset from the road, the level of detail needed, safety considerations, the potential for

collaboration with other data collection activities, and available resources.

Step 3: Collecting the data – Immediately before and during the data collection processes,

steps need to be taken to ensure the quality of the data. This step includes the activities

involved in securing a data collection vendor (if appropriate), establishing test sites to

verify the technology provides the necessary data, and monitoring the quality of the data

throughout the data collection process.

Step 4: Processing and managing the data – The final step involves processing the data to

extract the necessary information and ensuring that the data is updated on a regular cycle.

This step contains the factors that must be taken into consideration to ensure the best

possible use of the information within the agency.

Task 4: Prepare Documentation

The last project task involved preparing this Final Report, which includes the guidance described

earlier.


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DISTRIBUTION OF RESEARCH PRODUCTS

This document was developed primarily for maintenance personnel in state DOTs responsible for

developing and maintaining a roadway asset inventory. It is designed to assist these individuals

in determining the type of technology most appropriate for building and maintaining the

inventory, the technical and organizational considerations that should be addressed prior to

building the inventory, and the data processing and management issues that should be addressed

with each of the different forms of technology. The considerations described in the Guide are

not unique to practices in state DOTs; therefore, the information provided in this document can

be equally useful to maintenance personnel in cities, counties, or other transportation agencies.

In addition to maintenance personnel, other practitioners may benefit from the information

provided in this Guide. For instance, the information may help an agency that is using

automated equipment for pavement management data collection find new uses for the digital

images that are being collected. Similarly, an agency that is using a vehicle equipped with

LiDAR for collecting inventory information may discover new applications for the technology to

support the agency’s design activities.

In addition to making this report available through the NCHRP website, the information

contained in this document will be distributed to practitioners through technical presentations at

meetings such as the Transportation Research Board (TRB) Annual Meeting and meetings of the

American Association of State Highway and Transportation Officials’ (AASHTO)

Subcommittee on Maintenance. Opportunities to present the information through webinars

and/or workshops will also be sought.


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CHAPTER 2 – SUMMARY OF PRACTICE

INTRODUCTION

An agency’s ability to make sound, defensible investment decisions relies in part on the

availability of a comprehensive asset inventory, a method of assessing current conditions and

performance, and tools for evaluating the impacts of different investment strategies on network

performance. Establishing an inventory is a fundamental step in establishing an asset

management program.

This chapter introduces the manual and automated technologies that are commonly used to

establish asset inventories and documents the use of the technology in practice. The chapter also

summarizes the status of asset inventories in state highway agencies, as documented in a

synthesis of practice and from phone calls with maintenance practitioners. It concludes with the

emerging practices identified from the literature and as part of the interviews with data collection

vendors.

As much as possible, the summary of practice focuses on the technology used for collecting,

processing, and managing roadway asset data. A great deal of information is also available on

assessing the condition and performance of roadway assets, but that information was considered

to be outside the scope. However, similarities in the technology used for establishing an

inventory and conducting a condition survey exist. For instance, cameras and other equipment

can be added to the vans used for conducting pavement management surveys to facilitate the

extraction of asset inventory data (AASHTO 2006).

The information obtained through the investigation into current practices, including the

interviews with state DOT practitioners, provided much of the basis for the information

contained in the Guide.

TECHNOLOGY BEING USED TO ESTABLISH ASSET INVENTORIES

There are several different methodologies being used to collect inventory information and to

assess the condition of roadway assets. These techniques range from manual surveys that use

“processes where people are directly involved in the observation or measurement of pavement

surface properties without the benefit of automated equipment (McGhee 2004)” to automated

surveys that involve “data collected by imaging or by the use of noncontact sensor equipment

(McGhee 2004).” Today’s manual surveys often take advantage of hand-held computers and

other forms of technology that have greatly improved the efficiency of data collection and

processing activities. Data collected using automated methods can be evaluated using software

tools that automate the extraction and interpretation of the data (commonly referred to as fully

automated) or through semi-automated methods that require some human interaction to extract or

interpret the data. Some agencies are also using mobile imaging with or without Light Detection

and Ranging (LiDAR), a three-dimensional (3-D) technology that can rapidly acquire a great

deal of highly-detailed geospatial information. Each approach has certain advantages and

disadvantages, which may include some of the following (McGhee 2004):

Manual data collection techniques are most appropriate for assets that are not readily

available from the travel lanes. Traditionally, the methodology is slow and safety of the

crews may be an issue, but the recent use of hand-held computers for recording survey

information has increased the efficiency of this process.


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Automated (or mobile) data collection techniques allow multiple assets to be assessed at

the same time while traveling at traffic speeds. However, the assets must be visible from

the travel lane and the equipment typically requires specialized equipment and operators.

Automated processing allows large amounts of data to be available quickly, but the

interpretation is constrained by the computer’s ability to recognize certain types of assets

and their characteristics.

Semi-automated processing is slower than automated processing, but it provides for

human interpretation of data from the field in a safe, workstation environment.

Mobile LiDAR data can be collected quickly and with high accuracy for 3-D mapping,

but the amount of data collected can require substantial resources to process.

AVAILABLE REFERENCES ON BUILDING AN ASSET INVENTORY

Data Collection Techniques

Recognizing that an asset inventory is a key component to a comprehensive asset management

program, AASHTO developed the Asset Management Data Collection Guide to address the data

collection needs associated with asset management (AASHTO 2006). This reference documents

the struggles transportation agencies have had to collect, store and analyze comprehensive

inventory data for non-pavement and non-bridge assets and the advances that have occurred with

handheld mobile computing devices in conjunction with Geographic Information Systems (GIS).

The report also provides guidance on prioritizing the assets to include in the inventory based on

asset category, rank, and relative importance to the agency (AASHTO 2006). Other factors, such

as asset value, the availability of data collection protocols, the ease of evaluation, the overall

value to users, and data collection frequency, are also factors to consider when prioritizing

assets. For those items included in the inventory, the report outlines the necessary decisions to

assess the condition of the asset, including the method of assessing performance, the level of

detail and accuracy needed, inspection frequency, and sampling strategy.

A separate study conducted for NCHRP investigated the use of asset management principles for

managing ancillary assets other than pavements and bridges. Included in the report is a hierarchy

intended to serve as the basis for classifying information on these assets, which includes asset

class, asset elements, and sub-elements as appropriate (Rose et.al. 2014). The report also

provides guidance for managing signs, traffic signals, markings, barrier systems, and lighting

with information for establishing the inventory, assessing conditions, and estimating service life.

The AASHTO Asset Management Data Collection Guide compares the advantages and

disadvantages associated with manual, mobile and satellite data collection techniques (AASHTO

2006). For instance, manual data collection methods are reported to be relatively accurate and

they allow access to assets that are not visible from the road; however, the process can be slow

and labor intensive (AASHTO 2006). It also exposes agency personnel to safety hazards caused

by interactions with traffic. The Guide identifies the collection of multiple data items at traffic

speeds as an advantage to mobile data collection processes (AASHTO 2006). However, it is

only suitable for assets that can be seen from the road and it requires special equipment that often

forces agencies to contract out the data collection services. The suitability of different data

collection methods for various types of assets was documented by Zimmerman and Stivers

(2007) and is presented as Table 1.


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Table 1. Suitability of different methods of data collection (Zimmerman and Stivers 2007).

The AASHTO Asset Management Data Collection Guide indicates that it might be cost-effective

for state highway agencies using automated technology to collect pavement condition

information to begin extracting asset inventory data for some assets from the images that are

collected. It also identifies the manual method as the most commonly used method for

establishing a roadway asset inventory and assessing asset condition (AASHTO 2006).

The feasibility of using automated equipment for building roadway inventories was documented

in an NCHRP report that describes the use of technology for georeferencing the data (NCHRP

2000). A 2004 NCHRP Synthesis describes the use of automated data collection devices in state

highway agencies (McGhee 2004). A survey conducted for the synthesis found that the most

commonly employed methods of automated data collection make use of acoustic or laser sensors,

and image-processing tools. At that time, digital imaging was reportedly preferred over analog

imaging techniques (McGhee 2004).

In 2005, the FHWA conducted a case study to document the techniques being used by eight state

highway agencies to manage roadway safety hardware, such as longitudinal barriers, crash

cushions, attenuators, end treatments, breakaway supports, and work zone hardware (FHWA

Asset

Categories Asset Types

Data

Collection

Method

Asset

Categories Asset Types

Data

Collection

Method

Drainage Culvert Manual Traffic Items Signal Manual

Curb and gutter Manual Sign Manual or

Mobile

Sidewalk Manual Pavement markings Manual or

Mobile

Ditch Manual Pavement marker Mobile

Drop inlet and storm

drain

Manual Overhead sign structure Manual or

Mobile

Erosion control Manual Traffic barrier/median

barriers

Manual

Under or edge drain Manual Highway lighting Manual or

Mobile

Roadside Fence Manual or

Mobile

Guardrail &

Attenuators

Guardrail Manual or

Mobile

Grass mowing As Needed Guardrail end treatments Manual or

Mobile

Brush As Needed Impact attenuator Manual or

Mobile

Landscaping Manual Other Facilities Tunnels Manual

Sound barrier Manual Rest areas Manual

Pavement Shoulder Manual or

Mobile

Weigh stations Manual

Lane, paved Manual or

Mobile

Roadside Graffiti Manual

Lane, unpaved Manual or

Mobile

Roadside Litter Manual or

Mobile


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2005). The study found that the New Mexico Department of Transportation (DOT) used an

automated vehicle to capture right-of-way images for its state-maintained roadway safety assets.

The inventory is updated, and conditions assessed, by field personnel equipped with hand-held

computers with Global Positioning Satellite (GPS) features. In the office, a “Virtual Drive” was

set up to enable agency personnel to view highway segments and to determine the adequacy of

signage, guardrail treatments, and other roadway hardware. The report indicates that the other

seven states also use right-of-way images to build their asset inventories and handheld devices to

collect condition information. The report concluded that the use of right-of-way imagery and

GPS coordinates at a workstation was a common approach to establishing an asset inventory

among state agencies and that manual data collection methods were more commonly used to

collect condition information on these assets (FHWA 2005).

The use of automated and manual data collection techniques for collecting information on

roadway safety hardware was discussed with participants as part of a peer exchange on Asset

Management and Safety. Meeting participants reported that most were using manual field

inspections in combination with one or more additional data collection methods (FHWA 2011).

For example, the report indicates that surveys for night retroreflectivity of signs could be done

manually in conjunction with automated surveys featuring GPS capabilities to improve location

accuracy. The feasibility of using LiDAR to inventory roadway assets was also discussed, but

participants indicated that the cost-effectiveness of the technology had not yet been

demonstrated.

The Asset Management and Safety Peer Exchange participants reported that lack of resources

had been an obstacle to having data available on all safety assets. They indicated that they often

had robust inventories for signals, signs, guardrails, and lighting, but little information on road

edge delineators, for example (FHWA 2011). They also discussed the level of detail required for

inventorying and assessing the condition of safety assets and reported that they often had trouble

effectively using all the data collected. The most pressing issues identified by the Peer Exchange

participants concerning their safety asset inventories included (FHWA 2011):

Location referencing accuracy and consistency.

Temporal referencing accuracy and consistency.

Availability of trained personnel.

Availability of tools and systems in order to integrate safety-related asset data with other

data.

A Peer Exchange conducted in 2009 with state maintenance personnel indicates that manual data

collection techniques for inventorying and assessing the condition of roadway assets are used

most often, even though their agencies were using automated techniques for pavement distress

surveys (Adams et al. 2009). The participants in the peer exchange expressed interest in some of

the new technological advancements (e.g., LiDAR), but questioned the cost-effectiveness of the

technology.

A domestic scan that was conducted in October 2011 investigated best practices for collecting

and reporting highway maintenance performance information (NCHRP 2012). The participants

confirmed that most participants were using some type of manual survey to collect maintenance

inventory and condition information. However, some of the participants reported that

technology had advanced to the point that it could improve the efficiency of data collection


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activities. For example, the Utah DOT reported on a pilot study they had conducted that showed

that data collected using handheld devices as part of their manual surveys could be collected as

quickly and as accurately as data collected with automated data collection vans (NCHRP 2012).

In a study conducted for the North Carolina DOT, the Institute for Transportation Research

Education at North Carolina State University compared the results of both manual and mobile

data collection techniques to establish a roadway asset inventory (Cunningham et al. 2013). The

results indicate the mobile data collection vehicles located roadway assets accurately, as long as

there were no obstructions from landscaping or other vehicles (Cunningham et al. 2013). Mobile

data collection methods were reported to show promise for accurately identifying feature

characteristics, such as asset type, and measurements of asset height and road grade were

measured within allowable tolerances. These devices were found to be less accurate with

measurements parallel to the direction of traffic, such as offset distance or width. There were

also several point features, such as drop inlets or attenuators, which proved to be difficult to

georeference (Cunningham et al. 2013).

An ongoing Strategic Highway Research Program 2 (SHRP2) study compared the accuracy of

data produced by mobile imaging techniques with data collected from a manual inventory of

eleven different roadway attributes. The initial findings indicate that there is a high degree of

agreement between the two approaches in terms of the total number of items counted, but less

accuracy in identifying the geospatial location of each item (Smadi 2014).

LiDAR

The use of LiDAR in transportation agencies was explored in a report prepared by the Wisconsin

DOT (WI DOT 2010). This study documents applications for three types of LiDAR, including

airborne, mobile, and terrestrial LiDAR. It explores applications in surveying, highway design,

corridor development, critical infrastructure protection, traffic flow, highway safety, rock cuts,

and geology. The study found that there are three technical aspects to LiDAR’s use in these

applications that are being refined: 1) data collection and analysis techniques, 2) error and

accuracy measures, and 3) the integration of LiDAR and photogrammetry (WI DOT 2010). The

report also includes a list of useful references on the use of LiDAR.

The Michigan DOT also explored the use of remote technology to inventory highway roadside

assets, comparing the use of aerial and mobile imaging with LiDAR, mobile imaging with photo

logging, and manual data collection (MDOT 2014). The report claims that the use of aerial

LiDAR eliminates worker exposure to traffic and represents the fastest mode of data collection.

The output from the process is a point cloud, with millions of data points spatially located within

a 3-D file. The aerial LiDAR equipment can be attached to a vehicle driven at traffic speeds or it

can be carried on an aircraft flown at approximately 1,600 feet. The data collected using this

technology can reportedly be collected once and used for a variety of applications where the

height, width, and depth of an asset is needed. The disadvantages reported with aerial LiDAR

indicate that the 900-foot perspective on the asset is better for statewide issues rather than

project-specific issues, it is expensive to collect and process, and it is difficult to capture data in

mowable areas.

The Michigan DOT report describes mobile imaging as cameras set up on a vehicle to capture a

variety of images at a regular interval, such as fifty feet, using panoramic and side-mounted

cameras (MDOT 2014). The combination of images captures assets that can be seen from the

roadway, such as signs, signals, and other roadside hardware. The images can be viewed at a


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workstation, so a user can virtually “drive” any route captured by the cameras, without the safety

hazard of being in the field or the time required to visit a remote site. The report indicates that

some vendors have automated the processes to identify certain assets within the image set.

Manual data collection is described in the report as an inspection that requires personnel in the

field to record asset inventory information manually. The study found that manual methods are

most appropriate for assets such as culverts, which are difficult to see from the travel lane

(MDOT 2014).

The Michigan DOT conducted a pilot project to collect data on twenty-seven predetermined

assets. The results were analyzed in terms of cost-effectiveness and the amount of time required

to collect and process the data. The report indicates that aerial LiDAR was twice as expensive as

collecting the data manually, without much time savings (MDOT 2014). However, the

researchers suggested that the high costs were due, in part, to the use of short segments in the

pilot study. They hypothesize that the costs would have been reduced if the sections had been

longer.

Other findings from the study included the following (MDOT 2014):

Remote technologies were able to collect data on most assets studied with the exception

of those assets not readily visible form the roadway, such as culverts.

LiDAR is appropriate for some applications, but was found to produce a level of detail

that was not needed for the assets included in the study.

Mobile imaging technology is an effective way to collect highway asset data at a reduced

overall cost. It reportedly decreases worker exposure to traffic, speeds up data collection,

and improves the accuracy of the data.

NCHRP Report 748, Guidelines for the Use of Mobile LiDAR in Transportation Applications,

provides a comprehensive summary on procurement considerations, data mining techniques, and

quality control associated with the use of this technology. The report summarizes several

advantages associated with the use of mobile LiDAR. For instance, inventory data can be

collected for several assets in one pass and the data can be collected at highway speed without

placing workers in traffic. This makes LiDAR very cost-effective in most situations. In

addition, the results can be shared among different departments, so there is often wider use of the

data collected. However, the report recognizes that while LiDAR is one of the “tools in the

toolbox” that transportation agencies should consider, a benefit/cost analysis should be

conducted to determine the cost-effectiveness of mobile LiDAR for specific applications

(NCHRP 2007).

Data Quality

The importance of quality data is also addressed in the literature. For example, in 2013 the

FHWA published its Practical Guide for Quality Management of Pavement Condition Data

Collection. Although the focus of the Guide is on the collection of pavement condition data, it

provides a useful framework for implementing quality management practices that can be used for

a variety of data collection efforts, including measures related to resolution, accuracy, and

repeatability; responsibilities for managing the quality of the data before, during, and after data

collection; quality control processes; and quality acceptance processes (FHWA 2013).


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The Utah DOT proactively addressed some of the data quality challenges it faced as it adapted its

approach to managing high-priority roadway assets (such as guardrails, traffic signals, signs,

drainage, and pavement markings) using new technology. For example, the agency initially

focused its attention on the high-priority assets and developed guidelines to ensure consistency in

inspection and data collection procedures. To aid in developing its comprehensive roadway asset

inventory, and to train personnel in the use of the new system, peer forums were conducted to

disseminate best practices (FHWA 2012). The Utah DOT has set a target of two years before it

has complete asset inventories for all its maintenance assets. The agency recognizes that

establishing an asset inventory takes time, but anticipates benefits in being able to better allocate

budgets to address needs based on performance in the future (FHWA 2012).

STATUS OF ASSET INVENTORIES IN STATE DOTS

NCHRP recently published a synthesis of field inspection practices associated with Maintenance

Quality Assurance (MQA) programs. The results of this synthesis provide a current snapshot of

the status of asset inventories in the twenty-eight state DOTs with MQA programs in place that

responded to the survey (NCHRP 2015).

General observations from the synthesis include the following (NCHRP 2015):

With the exclusion of pavements and bridges, the establishment of inventories for

culverts, overhead sign structures, signs, signals, variable message boards, impact

attenuators, pavement markings, guardrail end treatments, and rest areas has been

completed or is in the process of being established in more than twenty of the twenty-

eight agencies that have formal Maintenance Quality Assurance (MQA) programs in

place.

By far, manual methods of data collection are most common for these assets. However,

there are indications that the use of handheld computers and GPS units are being

increasingly incorporated into the survey process. Additionally, some states are using

automated mobile approaches to establish the inventory of some assets that are visible

from the roadway. The use of automated equipment to build an asset inventory appears

to be of interest for a number of states.

The NCHRP Synthesis, Maintenance Quality Assurance Field Inspection Practices, asked

respondents with MQA programs in place to complete a survey describing the status of their

asset inventory and the methods used to assess the condition of the assets. For purposes of this

report, information on the status of asset inventories and the methods of building the inventory

are highlighted for the following asset categories and features:

Drainage assets, including culverts, flumes, curbs and gutters, sidewalks, ditches or

slopes, drop inlets, and underdrains/edgedrains.

Roadside, including fence, landscaping, plant beds, and sound barriers.

Pavement, including paved shoulders, unpaved shoulders, and paved roadways.

Bridge, including all bridge structures greater than 20 feet in length.

Traffic items, including signals, signs, pavement markings, pavement markers, guardrail

end treatments, overhead sign structures, impact attenuators, and protective barriers.


11

Special facilities, including rest areas, tunnels, weigh stations, and traffic monitoring

systems.

The inventory status of each asset category is presented separately.

Drainage Assets

According to the information presented in Figure 1, few states have developed complete

inventories for all of the elements included in this category. Of the seven elements included in

the survey, the largest number of states have either established or are in the process of

establishing an inventory of their culverts. More than half of the agencies responding to the

survey have established, or are in the process of establishing, inventories for curb and gutter,

drop inlets, ditches or slopes, and sidewalks. A smaller number of states indicate that they have

established inventories for flumes or underdrains and edgedrains.

Figure 1. Inventory status of drainage assets (NCHRP 2015).

Roadside Assets

Sound barriers, fences, landscaping, plant beds are all examples of roadside assets. As shown in

Figure 2, half of the state agencies that responded to the survey have established, or are in the

process of establishing, asset inventories for sound barriers, but the status of inventories for other

roadside assets are not as far along.

Number of Responses


12

Figure 2. Inventory status of roadside assets (NCHRP 2015).

Pavement and Bridge Assets

The asset inventories for pavements and bridges in state DOTs are essentially complete. The

inventories of other assets in this category, including paved and unpaved shoulders are shown in

Figure 3.

Figure 3. Inventory status of pavement assets (NCHRP 2015).

Traffic Assets

The traffic asset category includes a variety of safety-related assets, such as signs, signals,

pavement markings and markers, guardrail end treatments, overhead sign structures, and variable

message boards. The status of asset inventories in the twenty-eight state DOTs with MQA

programs in place is shown in Figure 4. As shown, there are at least three assets in this category

Number of Responses

Number of Responses


13

in which more than half of the states have established complete inventories (i.e., overhead sign

structures, signals, and variable message boards). At least half of the agencies responding to the

survey have begun creating the asset inventory for each of the assets listed.

Figure 4. Inventory status of traffic assets (NCHRP 2015).

Special Facilities

Some state DOTs are responsible for the maintenance and management of special features, such

as rest areas, tunnels, weigh stations, and traffic monitoring systems. As shown in Figure 5,

these inventories are fairly well established in agencies that manage these types of assets.

Figure 5. Inventory status of special facilities (NCHRP 2015).

Number of Responses

Number of Responses


14

EMERGING TRENDS

As technology improves and becomes more commonly available to practitioners, it is likely that

more of the data collection, processing, and managing functions will become more automated

than in the past. Additionally, as practitioners become more familiar with the technology, they

will find new and innovative applications that expand its use within the agency and improve its

cost-effectiveness.

In addition to changes in technology, transportation agencies are continually responding to

legislative, funding, and system demand changes that impact the way they do business. The

increased use of public-private partnerships and performance-based warranty contracts in

transportation agencies are examples of agency responses to the changing operational

environment.

As a result, it is important that transportation agencies develop business processes that provide

enough flexibility to be able to respond to the changes they face. It is also important that

transportation agencies realize that it takes time for changes, such as new technology, to be

incorporated into the on-going business activities. The Utah DOT, for example, indicates that it

generally takes between two and four years for applications of new technology to mature and

become integrated into routine work activities (FHWA 2012).

Based on the experience of the research team, and the knowledge gained during this project, the

following general trends are observed in how state DOTs are building or updating their roadway

asset inventories.

As agencies realize the benefits associated with performance-based decision making,

there will be increasing interest in using performance data on assets other than pavements

and bridges for establishing and defending budget needs, allocating funds, and

documenting the effectiveness of investments. Additionally, as agencies evaluate and

manage risks from an enterprise level, they will recognize that the availability of reliable

data on key assets can help mitigate some of the agency’s risks. As a result,

transportation agencies are expanding the scope of their data collection projects and will

continue adding assets to their roadway inventories.

Limited resources will continue to force transportation agencies to be more effective with

their data collection efforts by finding additional uses for the data and/or collecting more

data with each pass. The development and use of data governance standards and the

availability of tools that allow the integration of data sets, will become increasingly

important to realize these efficiencies.

Transportation agencies are recognizing that their workforces need to develop new skills

to fully utilize the new technology or that non-traditional hires are needed to provide the

necessary capabilities. Individuals with GIS training, database skills, communication

experience, and working knowledge of computers and technology are helpful in turning

data into useful knowledge.

As traditional DOT functions are privatized, transportation agencies will need to develop

strategies for using data to monitor the contractor’s activities. For instance, pavement

condition data used to monitor a treatment warranty may require a higher level of data

reliability than the data used to report network conditions. How to meet these demands

using technology will become an important priority in the future.


15

EMERGING TECHNOLOGY

As technology continues to advance, a number of technologies that are currently either in the

development phase or undergoing feasibility testing may become viable additions to the

methodologies considered in the Guide. Some of the more promising technologies available are

presented in the remainder of this section.

360-Degree Camera

One emerging technology is the development of a camera that consists of six lenses, positioned

complementary to one another, providing a 360-degree horizontal perspective of an area. The

camera uses mathematical algorithms to stitch together the various images to create the 360-

degree view of the roadway. One of the six cameras is positioned vertically rather than

horizontally to create a spherical view. These images, as in photogrammetry, are linked with

GPS coordinates to identify field locations of the extracted data. This technology may provide

additional benefits to traditional photogrammetry techniques in situations that would benefit

from a 360-degree perspective, such as intersections and interchanges.

Flash LiDAR

In contrast to mobile LiDAR in which every single point is illuminated individually with a laser,

flash LiDAR illuminates a whole scene at once. As a result, each pixel provides an indication of

the amount of time that passed for the camera’s laser flash pulse to hit the targeted asset and

bounce back to the camera’s focal plane. The time measurements are resolved using the speed of

light, resulting in a 3-D image from the depth measurements for each point. Flash LiDAR is

currently being tested for applications in the military and automobile industry due to its ability to

provide real-time information. Flash LiDAR is also referred to as time-of-flight (TOF) cameras.

Airborne LiDAR

Aerial or airborne LiDAR has been around for several years but its use has been limited due to

Federal Aviation Agency (FAA) flight restrictions that were imposed to avoid any conflicts with

air traffic. Airborne LiDAR captures data on a scale that lends itself more to design and

planning activities rather than building roadway asset inventories. For example, scans from

airborne LiDAR have been used to create 3-D models of complex objects, such as piping

networks, roadways, archeological sites, buildings, and bridges. Airborne LiDAR has been used

on large, civil engineering projects to assist with grading, utilities, drainage analysis, erosion

control, and roadway design. It has also been used by the military and in the archaeological and

agricultural fields.

There are several advantages to airborne LiDAR that make the technology appealing to the

transportation community. For example, objects can be measured remotely without interfering

with traffic. Additionally, the equipment can be operated under a variety of weather conditions

and its sensors are not affected by low sun angles. Airborne LiDAR can even be used at night.

In the past year, the FAA has granted approval to four companies to fly commercial drones to

conduct aerial surveys, monitor construction sites, and inspect oil flare stacks (USA Today

2014). The results of the trials are expected to influence the future use of this technology.


16

Driverless Cars

Automobile manufacturers have increasingly shown interest in the concept of driverless cars and

the everyday use of this technology could soon become a reality. If that were to be the case,

transportation agencies may have to shift their data collection priorities since driverless cars

could require different roadway features to operate effectively. For example, striping could

become increasingly important to keep the cars in the driving lane. Transportation agencies will

face a major paradigm shift in terms of data collection and asset performance as this new

technology becomes more common.


17

CHAPTER 3 – CONCLUSIONS

The availability of roadway asset inventory information is a fundamental component of a

comprehensive asset management program that uses performance data to drive investment

decisions. Although many transportation agencies have complete inventories of their pavements

and bridges, fewer agencies have complete inventories for the other roadway assets that they

manage and maintain. As a result, transportation agencies have been limited in their ability to

determine maintenance needs and to convey these needs to their stakeholders.

Today, data collection and processing technology has advanced to the point that it can improve

the cost-effectiveness of establishing an asset inventory. Some agencies have found that by

consolidating disparate data collection efforts, several data needs can be satisfied quickly without

impacting traffic. However, since no single technology is appropriate for all applications,

guidance was needed to help agencies evaluate the appropriateness of the different options for

different situations. This report documents the state of practice and the information obtained

through the project tasks led to the development of the Guide, which is included as an

Attachment to this report.

The investigation into the approaches used to establish roadway asset inventories indicate that

the manual methods of collecting the data, often supplemented with handheld technology, remain

the most common approaches in state DOTs. Some agencies that are using automated

technology for conducting pavement management surveys have used the same tools to extract

some asset information. At least one state is using LiDAR in an attempt to inventory all of its

roadway assets within the next several years.

The information provided demonstrates the feasibility of using automated technology for

establishing or updating an asset inventory. The differentiation between using photogrammetric

methods versus remote sensing technology (such as LiDAR) depends on the specific needs of the

agency. In general, LiDAR is advantageous in situations where vertical clearances or highly

accurate offset distances are needed, or if the data can be used to support other agency functions,

such as planning and/or design.

Even though a technology is feasible, there are many additional considerations that have to be

taken into account when selecting a technology for establishing a roadway asset inventory.

These considerations include resources available, asset location (i.e., visibility from the road),

number of assets to be included, level of detail required, and safety requirements. Additionally,

each methodology has different requirements in terms of equipment needed, level of technical

expertise required to operate the equipment, and data storage requirements that should be taken

into account. These considerations are all addressed further in the Guide that was developed as a

result of this project to serve as a useful resource to transportation agencies interested in

establishing or expanding their roadway asset inventory.

FUTURE RESEARCH NEEDS

The results of this research identified several gaps in current knowledge that could benefit from

additional research. Recommendations for further research include the following:

Strategies for cost-effectively expanding the application and use of remote sensing

technology within a transportation agency.


18

Guidance with establishing quality measures for roadway asset inventory items using

automated data collection processes.

Methods of processing automated data to reduce the time between data collection and

data delivery.

Identification of successful strategies used by transportation organizations to facilitate the

timely implementation of innovations into agency practices.


19

REFERENCES

Adams, T., S. Janiowiak, W. Sierzchula, and J. Bittner. 2009. Maintenance Quality Assurance

Peer Exchange 2. Project 08-15. Midwest Regional University Transportation Center,

University of Wisconsin, Madison, WI.

American Association of State Highway and Transportation Officials (AASHTO). 2006. Asset

Management Data Collection Guide. Task Force 45 Report. American Association of State

Highway and Transportation Officials, Washington, DC.

Cunningham, C. M., D. J. Findley, K. Hovey, P. B. Foley, J. Smith, T. Fowler, J. Chang, and J.

E. Hummer. 2013. Comparison of Mobile Asset Data Collection Vehicles to Manual Collection

Methods. North Carolina Department of Transportation, Raleigh, NC.

Federal Highway Administration (FHWA). 2005. Roadway Safety Hardware Asset

Management Systems Case Study. FHWA-HRT-05-073. Federal Highway Administration,

Washington, DC.

Federal Highway Administration (FHWA). 2011. Asset Management and Safety Peer Exchange

Report. FHWA-HRT-12-005. Federal Highway Administration, Washington, DC.

Federal Highway Administration (FHWA). 2012. Managing and Maintaining Roadway Assets:

The Utah Journey. A Transportation Asset Management Case Study. Federal Highway

Administration, Washington, DC.

Federal Highway Administration (FHWA). 2013. Practical Guide for Quality Management of

Pavement Condition Data Collection. Federal Highway Administration, Washington, DC.

McGhee, K. H. 2004. Automated Pavement Distress Collection Techniques. NCHRP Synthesis

334. National Cooperative Highway Research Program, Transportation Research Board,

Washington, DC.

Michigan Department of Transportation. 2014. Monitoring Highway Assets with Remote

Technology. RC -1607. Michigan Department of Transportation, Research Administration.

Lansing, MI.

National Cooperative Highway Research Program (NCHRP). 2000. Collection and

Preservation of Roadway Inventory Data. NCHRP Report 437. Transportation Research Board,

National Research Council, Washington, DC.

National Cooperative Highway Research Program (NCHRP). 2007. Use of Mobile LiDAR in

Transportation Applications. NCHRP Report 748. Transportation Research Board, National

Research Council, Washington, DC.

National Cooperative Highway Research Program (NCHRP). 2012. Best Practices in

Performance Measurement for Highway Maintenance and Preservation. Scan Team Report,

NCHRP Project 20-68A, Scan 10-03. Transportation Research Board, National Research

Council, Washington, DC.


20

National Cooperative Highway Research Program (NCHRP). 2015. Maintenance Quality

Assurance Field Inspection Practices. NCHRP Synthesis 470. National Cooperative Highway

Research Program, Transportation Research Board, Washington, DC.

Rose, D., K. Shah, J. P. O’Har, and W. Grenke. 2014. Transportation Asset Management for

Ancillary Assets. NCHRP 08-36, Task 114 Final Report. National Cooperative Highway

Research Program, Transportation Research Board, National Research Council, Washington,

DC.

Smadi, O. 2014. Personal notes transcribed from an interview.

USA Today. 2014. “FAA Lets Four Companies Fly Commercial Drones.” USA Today.

Accessed December 10, 2014: http://www.usatoday.com/story/money/business/2014/12/10/faa-

drones-trimble-vdos-clayco-woolpert-amazon/20187761/

Wisconsin Department of Transportation (WI DOT). 2010. LiDAR Applications for

Transportation Agencies. Transportation Synthesis Report. Wisconsin Department of

Transportation, Madison, WI.

Zimmerman, K. A. and M. L. Stivers. 2007. A Guide to Maintenance Condition Assessment

Systems. NCHRP Project No. 20-07, Task 206. National Cooperative Highway Research

Program, Transportation Research Board, Washington, DC.

http://www.usatoday.com/story/money/business/2014/12/10/faa-drones-trimble-vdos-clayco-woolpert-amazon/20187761/


NCHRP Project 20–07/Task 357


FINAL VERSION

June 2015




A Guide to Collecting, Processing, and Managing Roadway Asset Inventory Data

i

TABLE OF CONTENTS

CHAPTER 1 – INTRODUCTION .................................................................................... 1

PURPOSE OF THIS GUIDE ........................................................................................ 1 GUIDE ORGANIZATION ............................................................................................. 1 GUIDE FOCUS ............................................................................................................ 2 USING THE GUIDE ..................................................................................................... 2

CHAPTER 2 – DATA COLLECTION METHODS ........................................................... 3

MANUAL TECHNIQUES ............................................................................................. 3 AUTOMATED TECHNIQUES ...................................................................................... 7

Photogrammetry ....................................................................................................... 7 Mobile LiDAR ......................................................................................................... 10

SUMMARY ................................................................................................................ 12

ADDITIONAL READING MATERIAL ......................................................................... 12

CHAPTER 3 – GUIDELINES ........................................................................................ 14 STEP 1: GETTING READY TO SELECT A METHODOLOGY .................................. 15

Select Assets to Include in the Inventory ............................................................... 15

Determine Resource and Other Constraints .......................................................... 17 Identify Users ......................................................................................................... 17

Establish a Data Dictionary .................................................................................... 18 STEP 2: SELECTING A METHODOLOGY ............................................................... 19

Evaluate Asset Visibility from the Road .................................................................. 19

Consider Accuracy Requirements .......................................................................... 19 Assess Agency Maturity ......................................................................................... 20

Consider Safety Requirements .............................................................................. 20

Evaluate Resources ............................................................................................... 20

Identify Other Data Collection Efforts ..................................................................... 21 Summary ................................................................................................................ 22

STEP 3: COLLECTING THE DATA ........................................................................... 22 Secure Data Collection Equipment and/or Vendor ................................................. 22 Develop Data Collection Protocol ........................................................................... 23

Conduct Personnel Training and Equipment Calibration ........................................ 24 Conduct Quality Control and Acceptance Testing .................................................. 25

STEP 4: PROCESSING AND MANAGING THE DATA ............................................. 25 Develop In-House Technical Expertise .................................................................. 25 Formulate Data Processing Procedures ................................................................ 25 Provide Access to Data .......................................................................................... 26 Address Organizational Issues ............................................................................... 27

Implement Data Governance Standards ................................................................ 27 Develop Plans for Inventory Updates ..................................................................... 27

Other Considerations ............................................................................................. 28 EXAMPLE .................................................................................................................. 29

The Scenario .......................................................................................................... 29 The Process ........................................................................................................... 29

ACCELERATING THE LEARNING CURVE .............................................................. 30 Challenges and Possible Remedies ....................................................................... 31 Benefits Realized ................................................................................................... 31

ADDITIONAL READING MATERIAL ......................................................................... 32


ii

CHAPTER 4 – FUTURE DIRECTIONS ........................................................................ 33 FUTURE MODIFICATIONS TO THE DATA COLLECTION PROCESS .................... 33

EMERGING TECHNOLOGIES .................................................................................. 34 360 Degree Camera ............................................................................................... 34 Flash LiDAR ........................................................................................................... 34 Airborne LiDAR ...................................................................................................... 34

ADVANCEMENTS IN DATA PROCESSING TECHNIQUES .................................... 35 SUMMARY ................................................................................................................ 35 ADDITONAL READING MATERIAL .......................................................................... 36

REFERENCES .............................................................................................................. 37

APPENDIX A – SAMPLE DATA DICTIONARY ..........................................................A-1

APPENDIX B – TYPICAL CONTENT IN A DATA COLLECTION RFP ......................B-1


iii

LIST OF FIGURES

Figure 1. Characteristics associated with manual data collection techniques. .............................4 Figure 2. Extract from a manual data collection form used by the Alabama DOT

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

ata%20Collection%20Form.pdf). .................................................................................5 Figure 3. Characteristics associated with photogrammetry. ........................................................9 Figure 4. Characteristics associated with mobile LiDAR. .........................................................11 Figure 5. Guidelines for developing or updating a roadway asset inventory. ............................16

Figure 6: Relation between decision making levels and detail and amount of data required

(Flintsch 2006). ...........................................................................................................18 Figure 7: Relative comparison of resource requirements, data utility, and costs (not to scale). .....21 Figure 8. Factors in selecting a methodology for building a roadway asset inventory. .............22 Figure 9. Screenshot of New Mexico RFI spatial map with assets identified (Hensing and

Rowshan 2005). ..........................................................................................................27

LIST OF TABLES

Table 1. Applicability of each data collection methodology to inventory roadway assets. .....13


1

CHAPTER 1 – INTRODUCTION

PURPOSE OF THIS GUIDE

The management, preservation, and improvement of the highway system is a critical component

to the nation’s economy. Transportation agencies are responsible for the maintenance and

management of the highways, roads, bridges, and other physical assets that keep the public

moving safely and reliably. To help make investment decisions for preserving these valuable

assets, many transportation agencies collect inventory and performance data. While this

information has been collected on roads and bridges for many years, constrained resources have

kept many transportation agencies from collecting data on all of the other assets they maintain,

including guardrails, culverts, and signs. However, with improvements in technology and in data

management over the last several years, new methods of collecting and processing inventory data

are being used by some agencies.

This Guide, which was developed under National Cooperative Highway Research Program

(NCHRP) Project 20-07, Task 257, serves as a resource to help transportation agencies make

informed decisions on the type of methodology most appropriate for collecting asset inventory

information and the considerations that must be taken into account for processing and managing

the data.

Although the study considered a variety of different types of methodology for building

inventories, the Guide focuses on the three most commonly used approaches in today’s

transportation agencies: manual surveys and two forms of automated surveys (photogrammetric

methods, which is also known as mobile imagery, and mobile LiDAR, which stands for Light

Detection and Ranging).

GUIDE ORGANIZATION

The Guide is organized into three sections. The first section, Chapter 2, introduces the three

methodologies that are commonly being used in transportation agencies for collecting asset

information. The guidance is presented in Chapter 3, which outlines a 4-step process that

involves the following activities:

Step 1: Getting ready to select a methodology.

Step 2: Selecting a methodology.

Step 3: Collecting the data.

Step 4: Processing and managing the data.

Chapter 4 includes a summary of how the technology is expected to evolve in the next several

years and how that will impact the decisions transportation agencies make today.

To assist the reader in various aspects of collecting, processing, and managing the asset

inventory data, additional information is provided as appendices to the guide. Included in the

appendices is an excerpt from a data dictionary used to describe the characteristics of interest for

the inventory (see Appendix A), and a summary of the content typically included in Requests for

Proposals (RFP) if an automated data collection vendor is to be used (see Appendix B).


2

GUIDE FOCUS

The Guide focuses on the collection, processing, and management of data used to develop and

maintain a roadway asset inventory. Since most transportation agencies have complete

inventories of their pavements and bridges in place, the Guide concentrates primarily on the

other roadway assets maintained by state DOTs, such as guardrails, tower lighting, signs, and

drainage features. The Guide covers only topics related to establishing and maintaining a

roadway asset inventory. As a result, it does not address the development and use of

performance criteria to monitor the level of service being provided to the traveling public or the

funding needed to address maintenance needs.

USING THE GUIDE

This Guide was developed primarily for the maintenance personnel in state DOTs who are

responsible for developing and maintaining a roadway asset inventory. It is designed to assist

these individuals in determining the type of methodology most appropriate for building and

maintaining the inventory, the technical and organizational considerations that should be

addressed prior to building the inventory, and the data collecting and management issues that

should be addressed with each of the three different approaches. The considerations described in

the Guide are not unique to practices in state DOTs. The guidance provided in this document

can be equally useful to asset and maintenance management personnel in cities, counties, or

other transportation agencies.

In addition to maintenance personnel, other practitioners may benefit from the information

provided in this Guide. For instance, the information may help an agency that is using

automated equipment for pavement management data collection find new uses for the digital

images that are being collected. Similarly, an agency that is using a vehicle equipped with

LiDAR for collecting inventory information may discover new applications for the technology to

support the agency’s design activities.


3

CHAPTER 2 – DATA COLLECTION METHODS

Data is a key element to making decisions for the maintenance and operation of roadway assets.

Methods of collecting, analyzing, and reporting data have changed over the years as technology

has advanced, especially for assessing highway pavement conditions using automated

technology. The same equipment that is being used to collect pavement condition data can also

be used, with just minor adjustments, to support other data collection activities within a

transportation agency, including the establishment or update of roadway asset inventories.

An agency’s ability to make sound, defensible investment decisions relies in part on the

availability of a comprehensive asset inventory, a method of assessing current conditions and

performance, and tools for evaluating the impacts of different investment strategies on network

performance. Establishing an inventory is a fundamental step in establishing a strong asset

management program.

The guidance provided in the next chapter concentrates on the use of three different types of

technology, including manual techniques and two different types of automated technology.

These techniques include the following:

Manual techniques, which involve recording inventory information while walking or

viewing assets from the windshield of a vehicle. Manual techniques may involve nothing

more sophisticated than recording information with pencil and paper, or they may utilize

Global Positioning Satellite (GPS) technology to locate assets in the field and/or

handheld computers to record the field data.

Automated techniques, which usually involve driving a specially-equipped vehicle at

near-traffic speeds over the highway. The technology used by these vehicles, which is

generally characterized as photogrammetry, typically includes laser sensors to monitor

pavement-related characteristics (such as rutting and roughness) and digital cameras

strategically placed to capture different roadway features. Some of the vans are also

equipped with remote-sensing technology that measures distances by analyzing the

reflected light from an asset after being lit by a laser. This additional technology is

commonly referred to as mobile LiDAR.

Each of these techniques is explored further in the remainder of this chapter, including a

summary of the types of data that can be collected, the special characteristics that make the

technique most appealing, and practical limitations that should be considered.

MANUAL TECHNIQUES

Manual techniques for building a roadway asset inventory typically involve surveys conducted

by field personnel while either inside or outside of a vehicle. If the surveys are being conducted

from the windshield of a vehicle, the surveys are typically done at a slow speed or from the

shoulder of the road. Manual surveys can be very low-tech, meaning that little technology is

used beyond pen and paper to record information, or they may make use of GPS technology

and/or handheld computers to help automate parts of the process. Figure 1 shows manual data

collection characteristics. According to a recent synthesis of practice, manual techniques are

currently the most common methodology being used to build asset inventories for assets other

than pavements and bridges (NCHRP 2015). When manual processes are used for building an

inventory, the field crews often assess the condition of the assets at the same time so another trip


4

Data Collection and Processing

Data are collected by personnel walking in the field or

individuals recording information while looking out the

windshield of a vehicle. Data is recorded either on

paper or with hand-held computers.

The information collected during a manual survey can

be very detailed if necessary.

Data has to be processed manually if collected on paper.

If data are collected using hand-held computers, the

information is uploaded into a software program prior to

its use.

Little to no prior technical expertise is required for data

collection and processing activities.

Provides a way to survey assets that are not visible from

the roadway, such as drainage assets.

This technique is the most common approach used by

state highway agencies for developing an inventory and

it is the only method being used to inventory drainage

assets (NCHRP 2015).

Application

Can be used to inventory all

roadway assets including ones

that are not visible from the

roadway.

Many agencies use hand-held

computers to enhance their

data collection and processing

efficiency.

Value Added

Does not require specialized

equipment so it can be

implemented easily at a

relatively low cost.

Assets that are not easily

visible from the road can be

surveyed.

The physical presence of an

inspector in the field usually

leads to good quality data.

Limitations

The accuracy of distance

measuring devices is limited

to a few feet.

Quality assurance and lack of

consistency can be an issue if

raters are not trained.

Data collection is slower than

other available methods and

may expose raters to traffic.

Figure 1. Characteristics associated with manual data collection techniques.


5

to the field is not required. An example of the type of data collection form often used for manual

surveys is provided in Figure 2. The example was extracted from the full data collection form

available from the Alabama Department of Transportation website

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

ection%20Form.pdf).

Figure 2. Extract from a manual data collection form used by the Alabama DOT

While the use of manual data collection forms is common, there has been a rise in the use of

hand-held computers for entering data in the field. The development of software programs and

mobile applications for the use in tablets and smart phones has made the data collection process

more efficient and reliable than traditional methods. For instance, the use of tablets with cameras

and GPS capabilities has not only allowed agencies to create more extensive inventories with

GIS referencing, but has also reduced the amount of equipment needed in the field. These

applications can be used off-line but also offer the option to instantly update a database located

on a server when online. The Iowa DOT used this technology for creating a culvert and guardrail

inventory and the Massachusetts DOT use it for improving and updating its ADA ramp

inventory. Other agencies have used this technology for creating sign inventories.

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

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


6

Scope: Since manual techniques provide an opportunity for an inspector to walk to the location

of most assets, an asset inventory can be established for virtually any roadway asset using this

technique. For some assets, such as those that are not directly visible from the roadway surface

(e.g., drainage assets), this is essentially the only viable data collection method available. Other

techniques are limited in their ability to assess partially submerged assets or those located outside

a driver’s line-of-sight. With the increase use of hand-held computers and the development of

software programs and mobile applications for collecting and reporting inventory information,

the efficiency of manual processes has improved over traditional methods featuring paper forms.

Data Collection: The inventory of roadway assets is established by driving along the roadway

and stopping at the location of each asset to collect the required data. The data collected from

this activity can be very detailed if necessary, but it does not have to be. Manual surveys do not

require much technical expertise beyond training in what features and characteristics are being

collected. Information collected in the field is usually recorded with paper and pencil, although

the use of hand-held computers is becoming more popular as a way to improve efficiency and

reduce the inconveniences associated with the use of paper, such as loss of data or delays in

entering data. The use of handheld or tripod-mounted mobile laser distance measuring

instruments has become a practical way for survey personnel to measure vertical clearances in

the field, which has augmented the type of data that can be collected manually. The degree of

reproducibility of the data with the manual method can be relatively low compared to other

techniques if there is a lot of subjectivity to the process of collecting the data. This subjectivity

can be reduced by regular training and certification programs for surveyors.

Data Processing: If the data is collected using paper and pencil, the data processing will have to

be done manually, which can be a very time-consuming process. It also introduces the

possibility of errors in data entry. On the other hand, if hand-held computers are used to collect

data, the data can be processed and summarized efficiently using the software provided with the

data collection tool. Many applications now allow real-time entry of field data into a database.

Applications: This technique provides the best option for collecting data for assets not directly

visible from the roadway surface. Drainage assets, rest areas, and weigh stations are examples of

the types of assets that are best surveyed using this methodology. The use of hand-held

computers and mobile lasers has expanded the scope of this methodology and improved its

efficiency.

Advantages: The data collected using this method can range from being very detailed to very

general. The physical presence of personnel inspecting each asset positively influences the type

and extent of information that can be collected, which may lead to high-quality information.

Since this method of data collection does not require specialized equipment, agency personnel

can be trained to conduct the inspections and costs are limited to labor expenses. Overall, its

biggest advantage is that it enables data to be collected on assets that are not easily viewed from

the road.

Limitations: The pace at which data is collected is slower than the other methods available. The

significant involvement of personnel in the field could lead to safety issues since personnel must

interact with traffic. It also introduces the possibility of human error or subjectivity in the data

collection process. In addition, location measurements are made using distance measuring

instruments (DMIs) and GPS units for georeferencing, which are only accurate within a few feet.


7

Also, in order to perform quality checks on the data collected, additional personnel must go out

in the field, which adds to the cost.

In summary, while manual surveys are limited by a comparatively slower rate of data collection

than the other available options, this technique offers access to assets not directly visible from

the roadway and requires little to no specialized equipment. This technique is very commonly

used by maintenance personnel since surveys can be done by field personnel as they have time

available. The work can also be contracted out, depending on the resources available to the

agency.

AUTOMATED TECHNIQUES

There are two automated data collection techniques that are used for establishing roadway asset

inventories: photogrammetry and mobile LiDAR. Both of these techniques are similar in that they

use specially-equipped vehicles to collect the information at near traffic speeds. The processing of

the data collected can be done using either automated or semi-automated techniques. In general,

information collected using lasers is processed through automated techniques. Information to be

extracted from the digital images may be processed using automated techniques, or the process

could be semi-automated, meaning that the data are interpreted at workstations by individuals

viewing the images.

Photogrammetry

Photogrammetry refers to the process of determining measurements from photographs or digital

images, such as locating the position of a sign in the field (see Figure 3). The technique

originally dates back to the mid-nineteenth century and it is still being used to create maps,

drawings, and 3-D models. Many maps used today are created with photogrammetry, using

photographs taken from aircraft.

Photogrammetry has been adapted for use in conducting pavement condition surveys for more

than 20 years. These surveys are conducted while a van outfitted with multiple cameras and

other devices drives down a road at traffic speeds. The cameras are normally oriented so one of

them is positioned to capture the road right-of-way (ROW) and several others are positioned in a

downward-facing position to capture pavement surface details. Additional cameras can be added

to the van and positioned at various angles to capture roadway features, such as signs, guardrails,

and lighting structures.

Scope: Photogrammetry is used by a number of transportation agencies across the United States

to assess pavement conditions. With the addition of strategically-placed cameras on the van, this

equipment can also be used to establish an inventory of roadside assets that are visible from the

road.

Data Collection: The roadway asset inventory data is obtained by driving along the roadway at

highway speeds, with cameras mounted on a van. The camera placement influences the

maximum field of view that can be captured in the image and any assets outside of the cameras’

view cannot be surveyed using this approach. GPS instruments added to the survey vehicle

provide the geo-referenced coordinates that are needed to synchronize the data from the various

cameras. The operation of the equipment requires a certain amount of technical expertise and

training, but the equipment provides a relatively high degree of reproducibility. The quality of


8

the images are influenced by the quality of the cameras, the survey conditions, lighting

conditions, and other factors.

Data Processing: The asset characteristics are extracted from the digital images from the ROW

or other cameras. Data extraction is most often conducted by personnel at a computer

workstation, where the images can be viewed and specialized software provided by the data

collection vendors can be used to calculate distances and track assets. Some vendors have

developed tools that automate the data extraction process. The data provided by the automated

processes can be viewed and verified at a workstation as part of a quality control process.

Applications: This technique is a practical method for building a roadway asset inventory for

assets that can be seen from the roadway. Supplemented with a manual survey for assets that are

not visible from the road, photogrammetry provides a reliable method of collecting data on

guardrails, sound barriers, retaining structures, fences, and other assets where a high degree of

accuracy is not needed. The data extraction can be conducted at any time, even years after the

surveys were conducted.

Advantages: This method requires little human intervention during the data collection process, so

consistency in the data is typically high. Data can be collected at highway speeds without

requiring personnel or slow-moving vehicles to interact with traffic. Quality control checks of

the data can be conducted at a workstation, which eliminates the need for follow-up surveys in

the field. The technique is very cost-effective, especially if used for multiple applications, such

as conducting pavement management surveys and building asset inventories.

Limitations: Only assets visible from the roadway surface can be inventoried using this method.

The amount of detail that can be collected is limited to what can be viewed on the camera image.

Measurement accuracy typically falls within a few feet. It is limited by the capabilities of the

DMIs and GPS units used for georeferencing. Photogrammetry has traditionally not been used

for certain data elements, such as vertical clearances, but mobile laser distance measuring

devices can be added to overcome this limitation.

In summary, photogrammetry is a viable option for building an asset inventory for many roadway

assets. It is most economical when ROW cameras are combined with lasers and downward-facing

cameras used for pavement management surveys. Data can be collected at highway speeds at

accuracies within a few feet without requiring personnel to interact with traffic. Additionally, this

methodology makes quality checks easy to complete because of the ability to quickly review

images at a workstation. While the use of this method is limited to assets visible from the

roadway, it can be combined with manual surveys to take advantage of the benefits associated with

each methodology.


9

Figure 3. Characteristics associated with photogrammetry.


The data can be collected at the same time as data for other

applications by adding a ROW camera and other specially-

positioned cameras. The surveys are conducted at traffic

speeds.

The asset inventory is created by extracting data

using a data processing interface that is typically licensed

from the equipment manufacturer.

There is limited specialized knowledge required to build

an inventory using this methodology.

Data can be extracted from images at a time that is

convenient to the agency. For instance, the data can be

extracted a year after the surveys were conducted if

resources are not available prior to that.

Quality checks on the data can be conducted at the

workstation without requiring personnel to go out to the

field.

Application

Can be used to inventory all

assets visible from the

roadway.

\ Data can be collected for

multiple assets simultaneously

at highway speeds.

Value Added

Increases consistency by

limiting the amount of human

intervention.

Images can be reused for

multiple purposes without

another survey.

Improves safety by reducing

the number of personnel in the

field.

Limitations

The accuracy of the roadway

asset location may be limited to

a few feet.

Dimensions of assets cannot be

extracted accurately.

Assets that are not visible from

the roadway surface cannot be

inventoried using this method.


10

Mobile LiDAR

Mobile LiDAR is a remote sensing technology that measures distance by illuminating a target

with a laser and analyzing the reflected light (see Figure 4). Mobile LiDAR is most commonly

used to make high-resolution maps, with applications in areas such as archaeology, geography,

and geology. Its application and use for asset management applications has been growing in

recent years. Since many agencies do not have experience using mobile LiDAR, NCHRP

produced a report outlining guidelines for using mobile LiDAR in transportation applications

(NCHRP 2007).

Scope: Mobile LiDAR can locate objects in the field to a high level of precision, within 3 in. up

to a range of approximately 250 feet. LiDAR produces a 3-D point cloud that can be used to

develop offsets or to measure vertical clearances. Similar to photogrammetry, this method is

limited to assets directly visible from the roadway within the range of the camera.

Data Collection: Mobile LiDAR is a 3-D measurement technology that can rapidly acquire a

substantial amount of highly-detailed geospatial information. Additional sensors, such as

cameras, reflectometers, laser crack measurement systems, or inertial profilers can be mounted

on the same vehicle to collect additional information at the same time as the mobile LIDAR data

acquisition. The data are collected while traveling at highway speeds but are limited to assets

visible from the roadway. The use of mobile LiDAR requires a significant amount of technical

expertise associated with the processing and use of the data. The point clouds generated by

mobile LiDAR result in large files so data storage can be an issue. The technique has a high

degree of reproducibility that is most influenced by the distance from the source and line of

sight.

Data Processing: Once the data is collected, some amount of processing is necessary for the data

to be georeferenced. Roadway inventory assets are extracted automatically from a point cloud

using proprietary software, usually developed by the data collection equipment manufacturer.

Data extraction can be conducted at any time, even years after the surveys were conducted.

Applications: This method can be used in conjunction with a manual survey to capture assets that

are not visible from the road. The equipment is used effectively to measure asset features, such

as pavement widths, that require a relatively high degree of accuracy. The equipment can be used

to determine vertical clearances and to capture information about tunnels.

Advantages: This technique requires little human intervention during the data collection process

so it provides a consistent approach to collecting roadway inventory data. The use of mobile

LiDAR improves safety by eliminating the need for personnel and slow-moving vehicles to

interact with traffic. Data quality control checks can be conducted by reviewing the data at a

workstation without requiring a new survey or forcing field personnel to drive back to the site of

the asset for confirmation. Mobile LiDAR vehicles collect data at traffic speeds and provide a

high degree of accuracy (± 3 in.). This method is also effective for estimating vertical

measurements, such as vertical clearances.

http://en.wikipedia.org/wiki/Laser

http://en.wikipedia.org/wiki/Archaeology

http://en.wikipedia.org/wiki/Geography

http://en.wikipedia.org/wiki/Geology


11


LIDAR is a remote sensing technology that collects data

in 3-D point clouds. This technology is being used for a

wide range of applications outside of asset management.

Data is captured by a van driven at traffic speeds with a

LiDAR sensor mounted on top and paired with a scanner,

photo detector, and GPS. The equipment can be coupled

with lasers and cameras for other applications, such as

pavement condition surveys.

Data are extracted using a data processing interface

typically provided by the equipment manufacturer.

Specialized expertise is beneficial in order to process and

manage the LiDAR data effectively.

Survey images can be used later to inventory a new asset

(not identified prior to the survey) without resurveying.

The process becomes increasingly cost-effectiveness as

the number of applications for the data increases.

Special Considerations

Mobile LiDAR produces a significant amount of data so

an agency might have to make special provisions to store

the data. If used only for building a roadway asset

inventory, many of the benefits to using this technology

are not realized.

Application

Can be used to inventory all assets

visible from the roadway.

Data can be collected for multiple

assets simultaneously at highway

speeds.

Value Added

Assets can be located accurately to

within a few inches.

Vertical clearances and dimensions

can be estimated within a few

inches.

Increases consistency by reducing

human intervention.

Point cloud can be reused for

multiple purposes without another

survey.

Improves safety by reducing

personnel in the field.

Limitations

LiDAR by itself does not capture

objects in color, which may be used

to classify some assets (such as

signs).

Assets that are not visible from the

roadway surface cannot be

inventoried using this method.

Figure 4. Characteristics associated with mobile LiDAR.


12

Limitations: Only assets visible from the roadway surface can be inventoried using this method.

The file size generated by the point clouds is large and may require agencies to make special

provisions for data storage. Unless the technology is used for multiple applications that require

the high degree of precision possible with mobile LiDAR, the full benefits of the technology are

likely unrealized. Data generated from mobile LiDAR is gray scale, so additional capabilities

must be added if color is used to differentiate some assets (such as signs).

In summary, mobile LiDAR is a viable option for developing an asset inventory at traffic speeds

to a high-degree of accuracy. Vertical elements and dimensions can be recorded without

additional effort and the methodology can be used for applications beyond asset management.

While some state DOTs have used mobile LiDAR successfully, its full benefits are realized

when the data are used for a wide range of applications, including design applications. This

method is limited to assets visible from the roadway, but can be combined with manual surveys

to minimize this deficiency.

SUMMARY

Based on the information provided in this chapter, Table 1 summarizes the applicability of each

of the three data collection methodologies for various roadway assets. Transportation agencies

rarely collect data on a single asset at one time, so the most feasible methodology should

consider all assets included in the inventory.

ADDITIONAL READING MATERIAL

Michigan Department of Transportation (MDOT). 2014. Monitoring Highway Assets with

Remote Technology. Michigan Report Number RC – 1607. Michigan Department of

Transportation, Lansing, MI.

National Cooperative Highway Research Program (NCHRP). 2013. Guidelines for the Use of

Mobile LiDAR in Transportation Applications. NCHRP Report 748. National Cooperative

Highway Research Program, Transportation Research Board, Washington, DC.

Yen, K. S., B. Ravani, T. A. Lasky. 2011. LiDAR for Data Efficiency. WA-RD 778.1.

Washington State Department of Transportation, Olympia, WA.

FHWA. 2013. Iowa Department of Transportation's Tablet Asset Data Collection. FHWA.

September, 2013: http://www.gis.fhwa.dot.gov/documents/Newsletter_Summer2013.asp

Massachusetts Department of Transportation. 2013. Curb Ramp Inventory System. MassDOT.

http://www.massdotinnovation.com/Pdfs/Session4MB-ADABurbRamp.pdf


13

Table 1. Applicability of each data collection methodology to inventory roadway assets.

Roadway Assets to be

Inventoried

(F)easible/(P)referred Methodology

Comments Manual Photogrammetry LiDAR

Signs Boards F P F

Photogrammetry captures color

if needed to differentiate sign

type

Noise Barriers F F P

Required dimensions lend

themselves to use of mobile

LiDAR

Culverts P Not visible from the roadway

Fences F P F

Accuracy offered by

photogrammetric methods is

sufficient

Earth Retaining Structures F F P



LiDAR

Other Drainage Structures P Not visible from the roadway

Guardrail F P F

Accuracy offered by


sufficient

Concrete Barrier F P F

Accuracy offered by


sufficient

Overhead Sign Structures F F P Vertical clearances best

measured with LiDAR

Pavement Markings F P F Photogrammetry captures color

needed to differentiate markings

ITS F P P Automated techniques save time

Lighting F P F

Accuracy offered by


sufficient

Rest Areas P Not visible from the roadway

Sidewalks F F P



LiDAR

Tunnels F F P



LiDAR


14

CHAPTER 3 – GUIDELINES

There are many considerations that must be taken into account when selecting an approach for

establishing a roadway asset inventory. The selection of the appropriate technology is only one

of the considerations that has to be made. Prior to that decision, there are a number of important

choices that have to be made regarding the assets that will be included in the inventory, the level

of accuracy required, and the resources available to collect, process, and maintain the data.

These decisions are influenced by many different factors, including the following:

The importance or visibility of the asset – For instance, it is more important to complete

an inventory on a small number of highly-visible assets (such as tunnels) than on a small

number of assets primarily installed for the convenience of the traveling public (such as

recreational signs).

The asset’s role in reducing agency and user risk – Transportation agencies face many

risks in managing their assets from events such as natural disasters, financial

uncertainties, and legislative changes. Building an inventory of high-risk assets may be

an important strategy for helping to mitigate these risks. For example, some agencies

have established inventories of areas susceptible to rock slides as a risk-mitigation

strategy. An agency should also consider the potential risks to the users of the roadway if

the assets are not maintained adequately. Certain types of assets, especially those in

place to address safety concerns, are often the highest priority assets when establishing an

asset inventory.

The amount spent on maintaining the asset – When prioritizing asset inventories, it may

benefit the agency to evaluate the relative amount of money spent on maintaining an

asset, or the total number of assets being maintained, as compared to the entire asset

population. Assets that consume a large portion of the maintenance budget may be a

higher priority for establishing an asset inventory than other assets.

The relevance to the agency’s strategic goals and objectives – As agencies mature in their

use of performance-based data for making investment decisions, it will become

increasingly important that decision-makers have the information needed to support their

strategic goals and objectives. Therefore, a higher priority may be established for

building an inventory of assets that enable the agency to meet its performance targets.

The availability of consistent protocols for collecting and storing data – To help ensure

that data are developed consistently throughout the agency, it is important that data

collection protocols or guidelines are established for each asset that will be added to the

agency’s inventory. This step should be completed before steps are taken to build the

asset inventory.

The existence of regulations or other mandated requirements – Transportation agencies

have to adhere to certain regulations (e.g. retro-reflectivity standards, ADA compliance)

that have been established by Federal, State, or other regulatory agencies. It is important

to build and maintain the inventory of these assets to meet requirements, address safety

concerns, and avoid negative consequences.

The existence of legacy systems and processes – Over the years, transportation agencies

have implemented many software programs that are used to manage highway assets. The

existence of these systems may influence the type of data to be collected, and the format

that is used for storing the information. In today’s organizations, there is an emphasis on


15

data integration of multiple data sources so the availability of geospatial references to

link features is becoming increasingly important.

Even after selecting a technology, the agency must decide how the data will be collected and

whether an outside source will be used. Additionally, decisions regarding data processing and

management will also need to be made, to help ensure that the data is used effectively by as

many users as possible.

The guidance provided in this chapter introduces the considerations that should be made at each

step in the process. Examples of practice are provided throughout the chapter to help illustrate

the points being made and supporting information is provided in the Appendices to help agencies

better understand the type of information that is needed. The information is organized into the

following steps, each of which is an important part of the decision process:

Step 1: Getting Ready to Select a Methodology.

Step 2: Selecting a Methodology.

Step 3: Collecting the Data.

Step 4: Processing and Managing the Data.

The considerations that should be taken into account at each step of the process are illustrated

graphically in Figure 5 and described throughout this chapter. Collectively, the information

provided in this chapter guides the reader through the process of navigating these issues

successfully while helping to establish a good understanding of the influence that each factor has

on the usefulness of the data and the cost-effectiveness of the technology.

STEP 1: GETTING READY TO SELECT A METHODOLOGY

Even before discussing the options for selecting a methodology to be used for developing the

inventory, an agency needs to identify its data needs, determine the characteristics of each asset

that will be collected, and identify any financial constraints that may impact the methodology

selected. These activities should be completed before any data collection efforts are initiated,

regardless of the methodology that will be used.

Select Assets to Include in the Inventory

One of the first steps for an agency is to determine which roadway assets will be included in the

inventory. This can be a challenging activity because there is often pressure from field personnel

to establish inventories for all roadway assets. However, agencies just beginning to track asset

inventory information may find it beneficial to start by adding a few high-profile assets and add

to the inventory over time.

There are a number of different approaches to take in selecting the assets to include in the

inventory. According to a synthesis of practice, the most complete inventories in state DOTs

(excluding pavements and bridges) include culverts, overhead sign structures, signs, signals,

variable message boards, impact attenuators, pavement markings, guardrail end treatments, and

rest areas (NCHRP 2015). Some states, such as Ohio and Nevada DOT, have conducted studies

to assess the completeness of asset information, its contribution to agency decisions, and the risk

associated with missing or incomplete data. The results of these studies have served as the basis

for assigning a priority ranking to each asset. The asset inventories are then established for the


16

highest priority assets first. Assets related to an agency’s safety goals can often be found in the

top priority category.

Figure 5. Guidelines for developing or updating a roadway asset inventory.


17

In some cases, the assets that are included will influence the manner in which the data is

collected, as in the case of culverts that are not visible from the driving lanes. For the majority

of the remaining assets, though, any of the three data collection methodologies is viable. While

it is tempting to collect information on as many assets as possible, an agency should carefully

consider its decision since the data must be maintained over time. If data collection and

governance standards have not been developed for a particular asset, it is generally better to

delay the data collection process until these steps have been completed.

The number of assets included in the inventory will influence the amount of time required to

collect the data if manual techniques are used. Since photogrammetry and mobile LiDAR both

collect data at traffic speeds, data collection efforts are not influenced by the number of assets

being collected. However, the data processing activities will likely be influenced by the number

and type of assets included in the inventory. In general, the automated data collection methods

can process data on a large number of assets efficiently.

Determine Resource and Other Constraints

Another set of factors that must be determined prior to data collection are the resource and/or

contracting constraints that may influence the methodology selected. One example of a resource

constraint includes the funding available for the data collection activity, both for the initial

efforts and for future efforts to maintain the data. Another consideration is the availability of in-

house personnel to collect, process, and manage the data that is collected. Manual data

collection techniques conducted by in-house staff represent a significant investment of

manpower, so an agency considering this methodology needs to ensure that the data collection

efforts will become a regular, on-going part of the workload for maintenance personnel.

Photogrammetry and mobile LiDAR lessen the workload for in-house personnel, but introduce

contracting requirements for obtaining a contractor or buying the equipment. Since both of these

technologies are considered specialized services, agencies will have to verify that there are no

contracting requirements to “purchase locally” for these services.

The use of a contractor also introduces other issues that the agency will have to consider. For

example, consistency in the data from year to year is extremely important since the results are

used to influence investment decisions. This consistency can be impacted by changes in

equipment, technology, and/or contractors. To minimize these impacts, some state DOTs have

established multi-year contracts that include options for additional surveys in future years so that

the same contractor, equipment, and technology can be used for several consecutive surveys.

Identify Users

The data collection activities associated with building a roadway asset inventory can often be

conducted in conjunction with other existing activities, such as Maintenance Quality Assurance

(MQA) inspections or pavement management surveys. Additionally, the results are often used

by multiple divisions within an agency, including Maintenance, Operations, Safety, Traffic,

Asset Management, Design, and Planning. Prior to initiating data collection efforts, it is

important to identify the potential users of the data to a) identify their specific information needs,

b) determine the data format that is needed to integrate into legacy software programs, c)

establish the frequency for updating the information, and d) identify the resources available to

support these efforts. The information obtained through this process will help to determine

whether one methodology is more viable than another. In general, the more users available to


18

share the data and the costs associated with data collection and processing, the more cost-

effective automated techniques become.

The uses for the data that will be collected has a significant impact on the level of detail that is

needed from the survey. For instance, information that is used to make strategic decisions (e.g.,

agency goals) is typically less detailed than the information needed to identify projects and

treatments. This concept is illustrated in Figure 6, which shows that as decisions move up in the

organization, the level of detail and the quantity of data tend to decrease (Flintsch and Bryant

2009). As a result, an agency that simply wants to have an estimate of the number of signs needs

much less detail than a maintenance supervisor who needs to know the type of sign and its

location for scheduling maintenance activities.

Figure 6: Relation between decision making levels and detail and amount of data required

(Flintsch and Bryant 2009).

Establish a Data Dictionary

To help ensure that the data collected contains each of

the asset attributes needed, it is recommended that

--------------------------------------------

Request for Proposal (RFP) Tip:

Include a data dictionary in your

RFP that defines the attributes you

want identified for each asset.

--------------------------------------------


19

each agency develop a “data dictionary” prior to selecting a methodology. This step is important

to help ensure consistency in the data collection process and to verify that the data collection

efforts will result in meaningful and complete information. A data dictionary describes, for

example, the attributes that are to be collected, the level of detail required, and the level of

accuracy that is expected. An excerpt from a data dictionary produced by the Tennessee

Department of Transportation (TDOT 2014) is included as Appendix A. It illustrates the level of

detail required to establish the inventory. If the work is being done by a contractor, the data

dictionary is used by the contractor to prepare the project bid.

STEP 2: SELECTING A METHODOLOGY

Once data collection needs are understood and any constraints have been identified, an agency

can begin the process of selecting the most appropriate methodology. In practice, there is no

single methodology appropriate for all agencies. Rather, the selection of an appropriate data

collection methodology is influenced by a number of different factors. These factors, and their

influence on each of the methodologies, are discussed under this step of the four-step process.

Evaluate Asset Visibility from the Road

Of the three data collection methodologies included in this Guide, only manual techniques can be

used to build an inventory for assets that are not visible from the traveling lanes of a road.

However, the use of manual techniques for certain assets, such as drainage structures, does not

prevent an agency from using another method of data collection for the other assets included in

the asset inventory. For most other roadway assets, any of

the three methods of data collection provide a viable option

for establishing an asset inventory. According to a recent

survey of practice, manual techniques are most commonly

being used to build asset inventories (NCHRP 2015).

However, as agencies streamline their data collection

processes, they are exploring the opportunity to consolidate

data collection efforts (as discussed later in this section).

In general, if agency personnel will be establishing and

maintaining the asset inventory and updating the inventory as

work activities are conducted, manual data collection techniques will likely continue to be used

heavily. However, the automated data collection techniques provide an opportunity for agency

personnel to establish an inventory using data extraction programs provided by the vendor while

sitting at a computer workstation. These options enable agency personnel to build the inventory,

without requiring them to go out in the field. Additionally, if automated technology is already

being used by the agency for other purposes (e.g., pavement management surveys), asset

information can be collected while the surveys are being conducted and processed at a later point

in time if that better serves the needs of the agency.

Consider Accuracy Requirements

The accuracy requirements for inventory data also have an influence on the methodology used

for data collection. Manual techniques have the lowest level of accuracy, with location

measurements considered to be accurate within a few feet. The accuracy of location

measurements for photogrammetry is approximately 1 foot, but mobile LiDAR can be accurate

to ±3 inches, if calibrated carefully. While the level of accuracy available with mobile LiDAR

might not be important for most assets, there may be certain assets for which a more precise level

-------------------------------------

While the Utah DOT uses

LiDAR for collecting most of

its roadway asset data, its

inventory of drainage assets

and underground utilities was

established by part-time

interns using manual data

collection techniques.

-------------------------------------


20

of accuracy is important. For instance, it may be important to know the width of paved

roadways to a high degree of accuracy if the information is used for developing project cost

estimates. Guidance on establishing the level of accuracy required is provided as part of Step 3.

Assess Agency Maturity

Another consideration in selecting the appropriate data

collection methodology is determining the maturity of the

agency in terms of being able to fully utilize the data

provided. Agency maturity takes into account several

aspects of the data collection process. First, it is important

to determine whether the data collected has applicability

across the agency. For instance, mobile LiDAR can provide

a large amount of detailed information; however, if that data

is not fully used within the agency, photogrammetry may be

a suitable alternative. A second aspect of maturity has to do

with the agency’s knowledge and understanding of each

methodology. The more complex the use of technology, the more important it is to involve

individuals with strong technical backgrounds in the selection process. For example, it may be

important to include Information Technology in the selection process to ensure that the large

files provided with the mobile LiDAR methodology can be managed by the agency. It may also

be important to work with individuals to ensure compatibility with existing legacy systems that

will use the data. For instance, involving individuals capable of working with the agency’s

Geographic Information System (GIS) is important for any methodology providing GPS

coordinates. Finally, there may be specialized training needed by agency personnel to be able to

work with the data obtained using one of the automated approaches since they involve the use of

technology that is not familiar to all maintenance personnel.

Consider Safety Requirements

As discussed in the previous chapter, the use of mobile LiDAR and photogrammetry reduces the

number of people who are collecting data in the field, which improves safety considerably.

Measures can be taken to make manual techniques as safe as possible, but some agencies

prohibit the use of manual survey techniques due to safety concerns. To significantly reduce the

number of agency personnel in the field, either of the two automated methodologies should be

used.

Evaluate Resources

Each of the three methods suggested for developing an asset inventory places different types of

demands on agency resources. Manual techniques typically place the burden for data collection

on individuals in the field, although contractors could be used. Photogrammetry reduces demand

on individuals in the field, but there are mobilization costs associated with the data collection

efforts, especially if data is collected by a vendor. Photogrammetry also requires resources to

extract the data from the images, but this activity could be conducted by either agency personnel

or a vendor. The resource requirements associated with data collection for mobile LiDAR are

similar to photogrammetry, except that additional features are required on the van. Since most

agencies do not own this equipment, mobile LiDAR is generally collected by a contractor.

Resources are required to extract information from mobile LiDAR, but most agencies rely on the

vendor to provide this service.

-------------------------------------

Digital images from

photogrammetry or mobile

LiDAR could be used by Safety

personnel to study the

relationships between crash

sites and the presence of crash

barriers.

-------------------------------------


21

The relative cost, resource requirements, and utility of the data from each of the three

methodologies is presented in Figure 7. As shown in the figure, mobile LiDAR has the highest

data utility, since the information can be used for so many applications within a DOT. Manual

techniques typically place the largest demand on agency resources, but this varies considerably

based on the number of assets included in the inventory, the size and location of the agency, and

the level of detail selected. The circles in Figure 7 represent the relative cost of each approach

when taking into account the equipment and services being provided. Again, the actual costs to

an agency should be carefully evaluated to determine a more realistic comparison.

Figure 7: Relative comparison of resource requirements, data utility, and costs (not to scale).

Identify Other Data Collection Efforts

Another consideration in selecting a data collection

methodology involves an assessment of other data collection

efforts that could be combined with the efforts to establish a

roadway asset inventory. Most commonly, transportation

agencies that are using photogrammetry for collecting

pavement management data are adding cameras or mobile

LiDAR to the equipment to broaden the applications

associated with the data collection process. Combining

-------------------------------------

Maryland DOT owns

automated equipment for

conducting pavement

condition surveys that is also

used for asset management

purposes.

-------------------------------------

--------------------------------------------------------------------------------------------------------------

Automated Data Collection Costs

The cost of collecting data using automated equipment varies considerably based on a

number of factors, including the project location (e.g., impacting mobility costs), the size of

the network, the number of different types of assets to collect, and the level of detail

required. Several examples of automated data collection costs are documented in the

literature, but the resulting costs are heavily influenced by the size and nature of the study

(MDOT 2014 and Jalayer et al. 2014). These resources indicate that photogrammetry costs

range from $72 to $88 per mile and mobile LiDAR costs range from $540 to $933 per mile.

However, data collection vendors indicate that the cost of mobile LiDAR is dropping as the

technology is developed and as it becomes more practical for transportation applications. In

conversations with several state agencies, their anecdotal experience has shown that mobile

LiDAR is about four times the cost of photogrammetry.

--------------------------------------------------------------------------------------------------------------


22

multiple survey approaches can be very economical because it eliminates duplicative efforts and

adds minimal additional cost to the original data collection efforts.

Summary

A summary of the key considerations in selecting a data collection methodology is provided in

Figure 8.

Figure 8. Factors in selecting a methodology for building a roadway asset inventory.

STEP 3: COLLECTING THE DATA

The next step in the process involves collecting the data using the methodology selected and

periodically evaluating the process to determine whether a different method of data collection is

warranted. This section of the Guide describes the activities typically conducted once the

methodology for data collection has been determined.

Secure Data Collection Equipment and/or Vendor

Mo

bil

e L

iDA

R

Ph

oto

gra

mm

etry

Ma

nu

al

Su

rvey

Fair degree of accuracy (± a few ft.)

Labor intensive

Safety issues with personnel in the field

Quality control activities require additional personnel in field

Best option for inventorying assets not visible from the road

Does not require specialized technical expertise or equipment

Most applicable when collecting a limited amount of data

Good accuracy (± 1ft.)

Not labor intensive

Requires specialized equipment

Operates at traffic speeds

Can only be used to inventory assets visible from the road

Easily used in conjunction with automated pavement condition surveys

Data can be used by multiple Divisions within an agency

Quality control activities can be done at a workstation

Requires some technical expertise

High degree of accuracy (± 3in.)

Not labor intensive

Requires specialized equipment

Operates at traffic speeds

Can only be used to inventory assets visible from the road

Provides features for estimating asset dimensions

Easily used in conjunction with automated pavement condition surveys

Data can be used by multiple Divisions within an agency

Quality control activities can be done at a workstation

Provides greatest benefit when data are used by multiple Departments

Requires specialized technical expertise

Generates large data files that must be managed


23

Once the data collection methodology has been selected, the agency must determine whether it

has the personnel, equipment, and expertise required to collect the information using in-house

resources. If an agency has elected to establish its inventory using an outside contractor, the next

step in the process is to acquire the services of a data collection vendor through normal

purchasing procedures. As a part of this process, agencies formulate and release Requests for

Bids or Requests for Proposals. Since the content of the proposal request serves as the basis for

the services that will be provided, an agency should spend considerable amounts of time

developing the technical specifications that will be

followed by the vendor. Several state transportation

agencies have issued RFPs for data collection services

that are available on their websites (UDOT 2011, TDOT

2014). A summary of the typical content included in a

data collection RFPs is included in Appendix B.

If the data will be collected using in-house personnel, the

agency will have to acquire any equipment needed to

build the asset inventory. If the agency has elected to

purchase an automated data collection vehicle from a

vendor, the agency may also need to obtain software

licenses from the vendor so that the data can be processed

by in-house staff or to allow viewing of the images at a

workstation.

Agencies that have used automated data collection for a

number of years have found it beneficial to set up their

data collection contracts for a multi-year period so that more than one data collection cycle is

included. This approach helps to ensure the consistency of the data between cycles since the

vendor will not have to repeat the learning curve that takes place each time a vendor works with

a new agency. Some agencies include later data collection cycles as options so the agency has

the opportunity to evaluate the vendor’s performance before deciding whether to extend the

contract. Agencies have also found it useful to reduce costs by limiting the number of software

licenses purchased from the vendor. This requires the agencies to process their data in a single,

central location and then distribute the data available to others in the agency in a format that is

not tied to proprietary software. Alternatively, statewide licenses can be obtained so that any

potential user of the data has the tools necessary to view the data at a workstation. However,

there are significant training requirements associated with this approach that have to be planned

for and that may be difficult to maintain over time.

Develop Data Collection Protocol

It is important for agencies to establish a well-defined data collection protocol to be used to help

ensure consistency in the results. The protocols can be documented in a data collection manual

that can be used by field personnel or data collection vendors to describe details about the data

collection process and to outline the steps that will be taken to ensure the quality of the data.

Guidance on developing a quality management plan for automated data collection activities

associated with pavement management are available in the literature (Pierce et al. 2010).

Although the documentation outlines considerations for monitoring pavement quality, many of

the same considerations should be incorporated into a data collection protocol for establishing a

roadway asset inventory. At a minimum, the quality management plan should include the

following (Pierce et al. 2010):

----------------------------------------

RFP Tip:

Agencies using automated data

collection vendors have found it

helpful to establish a contract

period that covers at least two

data collection cycles to help

ensure consistency. For

instance, an agency may

establish a contract for one data

collection cycle with an option to

renew the contract for another

cycle if the agency is satisfied

with the vendor’s performance.

----------------------------------------


24

The deliverables that will be provided, the protocols that will be used for collecting the

data, and the required resolution, accuracy, and repeatability to determine the quality of

the data.

The quality control activities that will be conducted and how frequently they will be

performed. In general, if data collection is being performed by an outside vendor, the

vendor is responsible for monitoring the quality of its processes, but the agency should

verify that the vendor has a plan in place and is following the plan.

The acceptance testing that will be performed to determine whether quality criteria have

been met and the corrective actions that will be taken if deliverables do not meet the

criteria. An agency is responsible for acceptance testing, regardless of whether the data

are collected by agency personnel or an outside vendor. Simple acceptance testing

should verify completeness of the data and the reasonableness of the values provided.

More complex acceptance testing includes tests to verify a portion of the data provided,

either in the field or at a workstation. Acceptance testing on approximately 5 percent of

the network is typical (Pierce et al. 2010).

Roles and responsibilities for each participant in the data collection process.

Plans for documenting the quality management activities.

A signature page verifying that each of the parties is familiar with the quality

management processes and understands his/her roles and responsibilities.

The data collection protocols provided to the field crews or to a vendor should provide enough

specificity to help ensure that the data collection process proceeds as planned. For roadway asset

inventory data, the level of detail provided in the data dictionary included as Appendix A is a

good start. Additional information to guide the data collection process, such as maps showing

the locations of all roads and ramps included in the survey and data formatting requirements,

may also prove to be important to the protocol. A trouble-shooting guide may also be a valuable

resource to personnel in the field.

Conduct Personnel Training and Equipment Calibration

Depending upon the choice of methodology and whether the agency decides to perform the data

collection in-house or by contract, the type and amount of training will vary. Training of data

collection personnel is important for quality control to help ensure consistency between raters

and between survey years. For manual surveys, training is a common activity used to ensure the

quality of data, but the availability of data collection manuals is also heavily relied on (NCHRP

2015). For agencies using automated equipment, it is important that the data collection crews

know how to calibrate, operate, and troubleshoot the equipment. Some agencies regularly certify

that data collection personnel have the necessary skills and knowledge to collect the data

accurately (NCHRP 2015).

If automated equipment is used to collect the data, it is important that the equipment is calibrated

prior to the start of the surveys and periodically during the data collection process. Calibration

sites may be established by the agency to verify that the data collection process is working as

planned immediately prior to the start of surveys. Agencies using calibration sites do not allow

the formal data collection processes to begin until the equipment has performed acceptably.

During the data collection process, blind sites may be established to further verify the data being


25

collected. A blind test site is a location that is known to the agency, but not to the individuals

responsible for collecting the data.

Conduct Quality Control and Acceptance Testing

During the data collection process, it is necessary to periodically monitor the data being collected

in accordance with the quality control processes established prior to the start of data collection.

This type of testing helps reduce the possibility of errors by identifying malfunctioning

equipment, anomalies in the data sets, or other types of faulty or missing data. The parties

responsible for collecting the data are responsible for performing quality control testing, so this

is a vendor’s responsibility if an outside contractor is used. If a manual process is used, the

agency is responsible for conducting any quality control checks that might be needed.

Acceptance testing is the responsibility of the data owner (the agency). It involves performing

checks on the data provided by the data collection team to verify that it meets the established

standards for quality. At its simplest level, acceptance testing is used to verify the completeness

of the data and the reasonableness of the values provided (e.g., they fit within established

ranges), but it can also involve manual checks of a small percentage of the data to verify the

accuracy of the data before accepting it into a database. Acceptance testing on 5 percent of the

network is used by some agencies (NCHRP 2015).

STEP 4: PROCESSING AND MANAGING THE DATA

The final step in the process involves the activities associated with extracting the information

from the data and presenting it in a format that can be used to support agency decisions.

Develop In-House Technical Expertise

When adopting any type of new technology, it is important that the users of the data are trained

sufficiently so they understand its capabilities and limitations. With manual data collection

processes, the technical expertise likely resides in the agency already. However, the automated

technologies described in this Guide likely involve new equipment and data processing

techniques that may not be familiar to agency personnel. Even if a contractor is used to process

the data, it is important for agency personnel to be trained sufficiently to be able to conduct

quality acceptance testing and to use the data fully. If the agency is processing the data in-house,

additional training may be needed to master the skills involved with operating the data extraction

software.

Formulate Data Processing Procedures

The data collection process for a large transportation network generates a large amount of data.

To assist with the processing of the data, data collection vendors have developed software that

allows them to process the information efficiently. The software is frequently licensed to

transportation agencies to facilitate their use of the data at a computerized workstation. In some

cases, the transportation agency may elect to process the data collected using photogrammetry

with in-house personnel viewing the images on a workstation within the software obtained from

the vendor. Simple point-and-click routines perform the necessary identification and storage of

the data, so the process can be done relatively efficiently. However, the more assets that are

included in the inventory, the longer the data processing can take. Other agencies contract with

the data collection vendor to perform the data extraction and the vendor may elect to use either

automated or manual processes, depending on the method used to collect the data. If the vendor


26

processes the data, it is good practice for the agency to perform acceptance testing on a small

sample of the data submitted to ensure that the data requirements have been met.

If processing is to be done manually at a workstation with in-house personnel, it may be a good

idea to incorporate the following suggestions into the data processing procedures:

To help ensure that quality doesn’t suffer, limit the amount of time in front of the

workstation to 4 hours a day, with no consecutive block more than 2 hours in length.

Consider establishing processes that prevent in-house personnel from processing data in

an area where they are responsible for the maintenance of those assets. This helps to

ensure that the surveys are conducted by an independent party.

Identify an independent rater to check the accuracy of the ratings on randomly-selected

samples of the network.

Provide Access to Data

Once the data is collected and all necessary information has been extracted, it is important that

the information is made available to other potential users in an easily accessible format. The use

of Excel and Access datasheets is common for data collected manually. The data sets associated

with automated data collection are often very large and may need to be condensed to a more

manageable size for others to use the data. If the agency acquires a statewide license for the

workstation viewing software, most users could access the images at a workstation. However,

this may require training on an on-going basis, so the agency should make provisions for this. A

statewide viewing license is not the only way to make data available to users since images can be

linked to other programs that are more familiar to agency personnel.

In some cases, agencies have required a data collection vendor to deliver the processed data with

a graphical interface that will enable personnel from throughout the agency to view the

information. An example of the type of interface provided to the New Mexico DOT is provided

in Figure 9. The advantage to this type of graphical interface is that it makes it easy for a user to

find the particular information of interest through a map or list interface. Studies have shown

that minimizing the number of hurdles that have to be overcome to access the data leads to

increased use of the data and greater value to the organization (Hensing and Rowshan 2005).


27

Figure 9. Screenshot of New Mexico RFI spatial map with assets identified (Hensing and

Rowshan 2005).

Address Organizational Issues

For most agencies first establishing their roadway asset inventory, there are important

organizational issues that need to be addressed in order to utilize the information fully and to

help ensure that the information remains current and relevant to the agency’s business processes.

For agencies electing to use manual techniques for collecting data, there are likely to be very few

organizational issues that will need to be addressed for the activity to be successful. On the other

extreme, agencies electing to use mobile LiDAR should develop business processes that promote

the use of the data outside of Maintenance or Asset Management to fully realize the potential

benefits. This often means reaching across traditional organizational stovepipes to ensure that

the information collected through this process can be used fully. This may require the

development of processes to ensure that data conforms to the needs of existing legacy systems,

including maintenance management systems, construction management systems, and safety

management systems.

Implement Data Governance Standards

As the agency’s data collection activities advance, and especially as re-inspections are

conducted, it is important for the agency to monitor its efforts in accordance with its data

governance standards. These standards, which identify the attributes to be collected for each

asset type, also specify the data owner as well as all users of the data. This information is very

important in successfully managing the inventory data. It helps to ensure that changes to data

attributes and formats that could impact legacy software programs are not made inadvertently.

Develop Plans for Inventory Updates

Ideally, the content of the roadway asset

inventory is updated regularly as maintenance

personnel, contractors, and other field crews

perform work in the field. Periodically, it may

be beneficial to conduct an update to the asset

----------------------------------------------------

Sample state data collection approaches

and inventory cycles

* North Carolina DOT – Photogrammetry

– Updated every 3 to5 years

* Maryland SHA – Photogrammetry

– Updated every 5 years

* Tennessee and Utah DOTs – LiDAR

– Updated every 2 years

----------------------------------------------------


28

inventory to replace outdated information and to verify the accuracy of the inventory data. In

some instances, rather than establish business processes to update the inventory data, old

inventories are purged and replaced by newer data each time a survey is completed. This

approach is only feasible if the inventory data is not stored by asset in a maintenance

management database. Instead, agencies using this approach generally work from a count of

assets rather than manage each asset individually.

Agencies should take steps to ensure the quality and consistency of data from one inventory

cycle to another. Some agencies have used multi-year contracts that cover multiple data

collection cycles as a strategy to improve data consistency.

Other Considerations

In addition to the considerations already mentioned, there are several additional issues that

should be noted, as discussed below.

Before a second cycle of data is collected, the agency must determine whether the

previous inventory will be replaced or whether the new information will be used to

update the previous data. The latter approach is used when an agency maintains a

database for each asset in the asset inventory. A technique referred to as “ghosting” has

been used by some agencies to compare two data sets from different inspection cycles as

a process for updating the inventory. Ghosting allows agencies to see changes in the data

files from one data collection cycle to another. If the data files are merely replaced,

individual changes in the inventory cannot be identified, although total changes in the

number of assets can be determined.

When data is collected using either photogrammetry or mobile LiDAR, the images or the

point-cloud can be used at any point in the future to extract inventory information, or to

add an asset not extracted initially. This feature is very beneficial because it allows the

agency to expand its inventory without requiring additional field work or equipment

mobilization.

Pavement condition surveys continue to rely primarily on digital images from downward-

facing cameras associated with photogrammetry equipment. This same equipment can be

used to develop an asset inventory with only minimal adjustments to include additional

cameras. Mobile LiDAR can also be added to a van equipped with photogrammetry, if

desired. Mobile LiDAR on its own has not been

used for pavement condition surveys due to

limitations in the resolution of its point-cloud.

The point-cloud data generated by mobile

LiDAR is so significant in size that agencies

must plan how to manage the data sets. One

state reported that its statewide inventory files

reached seventeen terabytes of storage and cost

approximately $90,000 a month for storage on state servers. The contract covered

approximately 28,000 lane miles.

The mobile LiDAR point-cloud is a digital elevation model (DEM) that is very beneficial

for transportation design and planning applications. To fully realize the benefits

associated with mobile LiDAR, these types of applications should be explored.

-----------------------------------------

It cost one state transportation

agency $90k/month to store the

point-cloud data from one cycle

(17 TB) for a network of about

28,000 lane miles.

-----------------------------------------


29

Otherwise, unless a high-degree of accuracy or dimensions are needed, photogrammetry

may be a suitable substitution.

EXAMPLE

The decision regarding the use of manual or automated approaches to establish a roadway asset

inventory requires the consideration of many factors. These factors have been organized into a

series of four steps that include decisions that have to be made regardless of the technology,

those that will influence the selection of the most appropriate technique, and the remaining

considerations that have to be accounted for while collecting, processing, and managing the data.

To illustrate how these factors contribute to the selection of a technology, an example is

provided. The example is completely hypothetical and is intended only to illustrate the use of

the four steps to address all of the considerations involved in establishing and maintaining a

roadway asset inventory.

The Scenario

A fictional Department of Transportation, known as XDOT, has seen the number of fatalities due

to crashes rise in the past 5 years. The XDOT Safety Division is concerned about the increase in

fatalities and the Legal Department has also warned executive leadership about possible lawsuits

if the trend continues. The agency has determined that having information about its safety assets

would help XDOT address safety-related deficiencies. In particular, XDOT is interested in

correlating crash locations with the location of guardrails and message boards. Since there is no

inventory information on these assets, the Safety Division has identified the establishment of a

guardrail and message board inventory as a high priority.

The Process

Step 1: Getting Ready to Select a Methodology – In addition to the needs of the Safety Division

to georeference the location of guardrails and message boards, the Maintenance Division

expressed interest in collecting information about the type of guardrails and message boards in

place so they can establish a maintenance schedule for these assets. The Department also

discovered that the Pavement Management Division has been using equipment outfitted with

photogrammetry to collect information on pavement conditions each year.

Once the users had been identified, representatives from Safety, Maintenance, and Pavement

Management met to establish the inventory characteristics that would be collected on the

guardrails and message boards. The data dictionary that was referenced in Appendix A of the

Guide was very useful in establishing the level of detail that would be needed. The information

was incorporated into a data dictionary and Maintenance was assigned responsibility for storing

the data since a Maintenance Management System had recently been implemented.

Step 2: Selecting a Methodology – Each of the three methodologies presented in the Guide was

considered to be a viable option for collecting the inventory information on guardrail and

message boards since both assets are viewable from the travel lanes. The users identified the

location of the assets, the length of the guardrail, and the type of guardrail or message board as

the most important information to be obtained from the process. After much discussion, the

group decided that it was not necessary to determine the height of the guardrail as part of this

process. Therefore, the group decided that a location accuracy of 1 to 2 feet was acceptable.


30

Since the inventory was being established to reduce safety hazards, XDOT was hesitant to

require agency personnel to collect the inventory data in the field. The fact that a viable data

collection method was being used for pavement management purposes convinced the agency

representatives to select photogrammetry as the preferred methodology. To help ensure that the

data was processed as quickly as possible, XDOT elected to use the contractor to extract the

inventory information from the first run; however, the Department asked the vendor to price data

extraction as an optional cost in case agency personnel were interested in extracting data in a

future survey.

Step 3: Collecting the Data – Since photogrammetry was already being used for pavement

management purposes, the agency elected to advertise for a new data collection contract that

included ROW and side-oriented cameras to collect the guardrail and message board

information. Individuals from Maintenance and Safety worked with the pavement management

team to learn how to use the workstation for viewing asset data. In addition, a quality plan was

developed to help ensure that the data was collected in accordance with the standards outlined in

the data dictionary. The Request for Proposals was advertised and a vendor was selected and a

contract signed. The cost of the additional data collection and processing was nominal and

XDOT was pleased that the data could be obtained so cost-effectively. The data collection

process began with equipment certification and, upon approval, the vendor was authorized to

begin the data collection process. Data would be submitted in batches, closely following the

boundaries of each District within the State.

Step 4: Processing and Managing the Data – To minimize the amount of time required for

processing the inventory data, the guardrail and message board information was extracted by the

vendor using proprietary software they had developed. The contract with the vendor provided

two licenses for workstations, with one being placed in the Safety Division and the other placed

in Maintenance. Maintenance personnel were trained in data extraction by the vendor and two

central office maintenance personnel were assigned responsibility for using the workstations to

check the results of at least 5 percent of the data provided by the vendor. The vendor also

provided the data in a format that could be made easily accessible to other personnel within the

Department without using a workstation. The georeferenced data were linked to the agency’s

GIS map so it could be compared with crash locations.

Since a multi-year contract was established with the vendor, XDOT is assured that the inventory

will be updated on a 2-year cycle. The Maintenance Division expects to investigate the

feasibility of extracting additional information from the images as they become more familiar

with the technology. They have decided that signs and light structures will be their next highest

priority. Since the images and workstations are available to XDOT, the identification of signs

and light structures can be done at any time following the development of the data dictionary.

ACCELERATING THE LEARNING CURVE

During the process of developing the Guide, a number of state highway agencies shared their

experiences with building a roadway asset inventory using either manual or automated processes.

The information has been compiled into two categories: a) challenges and possible remedies, and

b) benefits realized.


31

Challenges and Possible Remedies

For some agencies, drafting the RFP was a challenge because of the lack of technical

expertise or prior experience with the technology. Opportunities for sharing RFPs and

discussing both positive and negative experiences with peers has helped overcome this

hurdle. Copies of the Tennessee and Utah DOT RFPs can be accessed online using the

following links: http://tn.gov/generalserv/cpo/sourcing_sub/documents/40100-40914.pdf

https://www.udot.utah.gov/public/ucon/uconowner.gf?n=11823602292354098

Acquiring, setting up, and learning new software has been a challenge for some agencies,

but including training for in-house personnel as part of the deliverables has helped

overcome this issue.

There is little guidance available regarding acceptable levels of accuracy and precision

for automated data collection efforts. The development of guidance in this area, as well

as training on quality management activities would be helpful.

In cases where agencies have transitioned from one data collection methodology to

another, they have had problems with populating their legacy software programs with the

new data unless specific steps are taken to address these issues. One of the challenges

concerns differences in the level of accuracy related to size and geospatial relationships

so that legacy systems can use the new data.

In some agencies, it has been a challenge to keep the inventory up to date because of

constrained resources. In some agencies that are building and maintaining inventories

manually, the work may not get done because of other demands on time. Some agencies

have elected to update their asset inventory on a 2- or 3-year cycle and the old data is

purged when the new data is received. This approach works for agencies that do not

track individual assets, but it would not work if an agency has a database that tracks

historical data for each asset individually.

According to information provided in the literature, the cost of LiDAR is significantly

higher than photogrammetry options. However, as the technology develops and becomes

more common in transportation agencies, the costs are expected to drop.

In some cases, the full advantages associated with the use of automated technology have

not been realized because the data has not be leveraged across the agency. In the future,

it may be hard to justify data collection costs unless the data can be shown to address

multiple needs within the agency. The more exposure the data has within the agency, the

more valuable it becomes.

The data sets produced by automated data collection techniques, especially LiDAR, are

extremely large. In some transportation agencies, data storage is a centralized

government function and DOTs are charged for the amount of storage used. To avoid

excessive data storage costs, some data collection vendors offer to store data on their

servers and provide client access through the cloud or through network/VPN links.

Benefits Realized

The use of automated technology features in this Guide has resulted in several benefits to

transportation agencies, including the following.

Improved safety by removing personnel from the field.

http://tn.gov/generalserv/cpo/sourcing_sub/documents/40100-40914.pdf



32

Improved organizational efficiency since multiple data needs can be addressed in one

data collection effort and data can be checked at a workstation rather than sending

individuals to the field.

Coordinated data collection efforts reduce duplication of efforts.

Leveraged data helps reduce the cost of data collection.

Reduced agency risks due to improved access to asset information.

Improved network conditions since agency priorities can be better addressed.

Enhanced communication to better convey funding needs and/or enhance accountability.

ADDITIONAL READING MATERIAL

National Cooperative Highway Research Program (NCHRP). 2003. Quality and Accuracy of

Positional Data in Transportation. NCHRP Report 748. National Cooperative Highway

Research Program. Transportation Research Board, Washington, DC.

National Cooperative Highway Research Program (NCHRP). 2007. Managing Selected

Transportation Assets Signals, Lighting, Signs, Pavement Markings, Culverts, and Sidewalks.

NCHRP Synthesis 371. National Cooperative Highway Research Program. Transportation

Research Board, Washington, DC.

Pierce, L. M., G. McGovern., and K. A. Zimmerman. 2010. Practical Guide for Quality

Management of Pavement Condition Data Collection. U.S. Department of Transportation.

Federal Highway Administration, Washington, DC.


33

CHAPTER 4 – FUTURE DIRECTIONS

Over the past 10 years, there have been tremendous advancements in the technology available to

support efforts to establish and update roadway asset inventories. As a result of these

advancements, the automated data collection techniques described earlier in this Guide are being

used more frequently to support infrastructure asset management and maintenance management

activities. Technology is expected to continue to advance, which will lead to further changes in

the methodologies used to update asset inventories in the future.

This chapter introduces some of the anticipated changes that may impact future efforts to build

asset inventories. In addition, it documents the importance of reviewing data collection efforts

regularly to ensure that the right information is being collected in the most efficient and effective

method possible.

FUTURE MODIFICATIONS TO THE DATA COLLECTION PROCESS

Because of resource limitations, most agencies prioritize their data collection efforts to help

ensure that the most important information is available to support existing business processes.

As a result, few agencies are able to collect data on all of the assets that they are responsible for

operating and maintaining. However, as transportation agencies become more comfortable with

their data collection efforts, or as the number of assets managed using performance-based

decision processes increase, it is likely that some agencies will elect to add to their roadway asset

inventory at various points in time. Other changes to the data collection processes may be

caused by further resource constraints that force an agency to revisit its existing methods of

collecting asset inventory data and evaluate whether alternate approaches would be beneficial.

Either of these situations illustrates the importance of periodically revisiting the steps outlined in

the Guide to evaluate whether a different methodology might be warranted.

For instance, an agency that is using photogrammetry to develop an inventory for guardrails,

signs, and highway lighting may elect to add additional assets to its roadway inventory at some

point in the future. The addition of some assets, such as pavement markings and protective

barriers, could easily be added to the list of assets inventoried using the existing survey

technique. And, depending on the amount of time that has passed since the data was last

collected, it is possible that previously-collected survey data could be used to establish the initial

asset inventory.

However, if the additional assets to be collected include assets with accurate dimensional

measurement that would benefit from the use of mobile LiDAR, the agency may want to change

its data collection methodology for these assets. The use of mobile LiDAR could also be

promoted by other business processes that want to build on the availability of the data collected

as part of the roadway inventory. For example, if an agency intends to collect data on bridge

clearances, mobile LiDAR provides an effective method of obtaining this information.

It is suggested that agencies establish regular intervals for evaluating their data collection

practices to determine whether alternate methods should be considered. This interval should be

established based on the frequency with which agency practices change, the rate at which

technology has evolved, and industry experience with each alternate approach.


34

EMERGING TECHNOLOGIES

As technology continues to advance, a number of technologies that are currently either in the

development phase or undergoing feasibility testing may become viable additions to the

methodologies considered in the guide. Some of the more promising technologies available are

presented in the remainder of this section.

360-Degree Camera

One emerging technology is the development of a camera that consists of six lenses, positioned

complementary to one another, providing a 360-degree horizontal perspective of an area. The

camera uses mathematical algorithms to stitch together the various images to create the 360-

degree view of the roadway. One of the six cameras is positioned vertically rather than

horizontally to create a spherical view. These images, as in photogrammetry, are linked with

GPS coordinates to identify field locations of the extracted data. This technology may provide

additional benefits to traditional photogrammetry techniques in situations that would benefit

from a 360-degree perspective, such as intersections and interchanges.

Flash LiDAR

In contrast to mobile LiDAR in which every single point is illuminated individually with a laser,

flash LiDAR illuminates a whole scene at once. As a result, each pixel provides an indication of

the amount of time that passed for the camera’s laser flash pulse to hit the targeted asset and

bounce back to the camera’s focal plane. The time measurements are resolved using the speed of

light, resulting in a 3-D image from the depth measurements for each point. Flash LiDAR is

currently being tested for applications in the military and automobile industry due to its ability to

provide real-time information. Flash LiDAR is also referred to as time-of-flight (TOF) cameras.

Airborne LiDAR

Aerial or airborne LiDAR has been around for several years but its use has been limited due to

Federal Aviation Agency (FAA) flight restrictions that were imposed to avoid any conflicts with

air traffic. Airborne LiDAR captures data on a scale that lends itself more to design and

planning activities rather than building roadway asset inventories. For example, scans from

airborne LiDAR have been used to create 3-D models of complex objects, such as piping

networks, roadways, archeological sites, buildings, and bridges. Airborne LiDAR has also been

used on large, civil engineering projects to assist with grading, utilities, drainage analysis,

erosion control, and roadway design. It has also been used by the military and in the

archaeological and agricultural fields.

There are several advantages to airborne LiDAR that make the technology appealing to the

transportation community. For example, objects can be measured remotely without interfering

with traffic. In addition, the equipment can be operated under a variety of weather conditions

and its sensors are not affected by low sun angles. Airborne LiDAR can even be used at night.

In the past year, the FAA has granted approval to four companies to fly commercial drones to

conduct aerial surveys, monitor construction sites, and inspect oil flare stacks (USA Today

2014). The results of the trials are expected to influence the future use of this technology.


35

Driverless Cars

Automobile manufacturers have increasingly shown interest in the concept of driverless cars and

the everyday use of this technology could soon become a reality. If that were to be the case,

transportation agencies may have to shift their data collection priorities since driverless cars

could require different roadway features to operate effectively. For example, striping could

become increasingly important to keep the cars in the driving lane. Transportation agencies will

face a major paradigm shift in terms of data collection and asset performance as this new

technology becomes more common.

ADVANCEMENTS IN DATA PROCESSING TECHNIQUES

In addition to advancements in the methodologies being used to obtain asset inventory

information, there are enhancements being developed for processing photogrammetry and

mobile LiDAR data that are expected to benefit the transportation industry. For instance, a

number of researchers are exploring the use of automated data extraction processes to obtain

information on signs and other features from the images collected in the field. One particular

application that shows promise is an automated matching and change detection technique that

compares different data sets to identify changes (Habib and Al-Ruzouq 2012). This technique

could be used to compare data sets from data collection efforts in different years so that changes

to the asset inventory can be tracked with time. Initial efforts to use this technique have had

limited success, but continued enhancements may make it viable in the future.

Additionally, some agencies are exploring techniques for extracting asset features from LiDAR

using ArcGIS software. The initial applications do not provide the same level of quality

provided by existing extraction software, but continued efforts may improve the viability of this

technology in the future.

SUMMARY

The establishment of an asset inventory is an important step in supporting an agency’s asset

management practices. Different methodologies are used to collect inventory information,

ranging from manual techniques that involve the use of personnel who are directly involved in

the measurement, to automated techniques that use noncontact sensor information and cameras

to collect the data. Each of the different methodologies has advantages and disadvantages

associated with it.

Because of to the increasing importance of establishing asset inventories and the changes in

technology that have been taking place over the last several years, there was a need for

developing this Guide to provide a practical basis evaluating the options associated with

collecting, processing, and managing roadway asset inventory data. As outlined in this Guide,

there are a large number of factors that must be considered when determining the most

appropriate methodology for establishing or updating an asset inventory. Further, the changing

needs of the agency as well as the continued advancement in technology support the necessity

for a regular assessment of the agency’s data collection needs and the most appropriate method

for obtaining that information.


36

ADDITONAL READING MATERIAL

Cheok, G. S., M. Franaszek, I. Katz, A. M. Lytle, K. S. Saidi, N. A. Scott. 2010. Assessing

Technology Gaps for the Federal Highway Administration Digital Highway Measurement Program.

Internal Report 7685. National Institute of Standards and Technology, Gaithersburg, MD.

Wang, K. C. P., Z. Hou, W. Gong. 2010. “Automated Road Sign Inventory System Based on

Stereo Vision and Tracking.” Journal of Computer-Aided Civil and Infrastructure Engineering.

Vol. 25, No. 6. John Wiley and Sons. pp. 468-477.

Habib, A. F. and R. I. Al-Ruzouq. 2012. “Linear Features for Automatic Registration and

Reliable Change Detection of Multi-Source Imagery.” Journal of Spatial Science. Vol. 57, No.

1. Taylor and Francis.


37

REFERENCES

Flintsch, G. W. and J. W. Bryant. 2009. Asset Management Data Collection for Supporting

Decision Processes. U.S. Department of Transportation, Federal Highway Administration,

Washington, DC.

Habib, A. F. and R. I. Al-Ruzouq. 2012. “Linear Features for Automatic Registration and

Reliable Change Detection of Multi-Source Imagery.” Journal of Spatial Science. Vol. 57, No.

1. Taylor and Francis.

Hensing, D. J. and S. Rowshan. 2005. Roadway Safety Hardware Asset Management Systems

Case Studies. U.S. Department of Transportation, Federal Highway Administration,

Washington, DC.

Jalayer, M., H. Zhou, J. Gong, S. F. Hu, and M. Grinter. 2014. “A Comprehensive Assessment

of Highway Inventory Data Collection Methods.” Journal of the Transportation Research

Forum. Vol. 53, No. 2. North Dakota State University, Fargo, ND. pp. 73-92.

Michigan Department of Transportation (MDOT). 2014. Monitoring Highway Assets with

Remote Technology. Michigan Report Number RC – 1607. Michigan Department of

Transportation, Lansing, MI.

National Cooperative Highway Research Program (NCHRP). 2007. Use of Mobile LiDAR in

Transportation Applications. NCHRP Report 748. National Cooperative Highway Research

Program, Transportation Research Board, National Research Council, Washington, DC.

National Cooperative Highway Research Program (NCHRP). 2015. Maintenance Quality

Assurance Field Inspection Practices. NCHRP Synthesis 470. National Cooperative Highway

Research Program, Transportation Research Board, Washington, DC.

Pierce, L. M., G. McGovern., and K. A. Zimmerman. 2010. Practical Guide for Quality

Management of Pavement Condition Data Collection. U.S. Department of Transportation,

Federal Highway Administration, Washington, DC.

Tennessee Department of Transportation (TDOT). 2014. Request for Proposals for Statewide

Roadway Asset Data Collection. Tennessee Department of Transportation, Nashville, TN.

Accessed online from: http://tn.gov/generalserv/cpo/sourcing_sub/documents/40100-40914.pdf

USA Today. 2014. “FAA Lets Four Companies Fly Commercial Drones.” USA Today.

Accessed December 10, 2014: http://www.usatoday.com/story/money/business/2014/12/10/faa-

drones-trimble-vdos-clayco-woolpert-amazon/20187761/

Utah Department of Transportation (UDOT). 2011. Roadway Imaging/Inventory Program Bid

Document. Utah Department of Transportation, Salt Lake City, UT. Accessed online from:


http://tn.gov/generalserv/cpo/sourcing_sub/documents/40100-40914.pdf





A-1

APPENDIX A – SAMPLE DATA DICTIONARY

Attenuators – Energy absorbing barriers which provide protection from vehicles striking rigid bodies such as bridge columns and barrier walls.

LRS to reference Log mile location of front nose of attenuator.

GPS to reference GPS location of front nose of attenuator.

Feature Type 04

Feature Char Choose from the following types

00758 - GREAT

00759 - TRACC

00760 - Quadguard

00761 - Hex-foam Sandwich

00762 - React

01330 - TAU-II

01331 - SCI

01332 - HEART

01333 – QUEST

Feature Location Choose from the following locations

1-Left

2-Right

4-Median Right

6-Median Left

7-Centerline

Height Report tallest height to nearest 0.1 feet. Measure vertically from the ground to the top of the attenuator

Width Report widest point to nearest 0.1 feet.

Length Report length to nearest 0.1 feet. Measure linearly along the centerline of the attenuator from the front nose to the point where the attenuator connects to


A-2

the rigid body that it is protecting.

Notes Only permanent installations shall be inventoried. Attenuators used for construction shall be excluded.

Attenuators located along the center of the roadway or at the end of median barrier walls shall be coded as Feature Location = 7-Centerline.

If an attenuator is found that does not match any of the examples provided, contact the State Project Manager.

Flat-Sheet Signs – A roadway sign which is fabricated using thin aluminum sheeting and a reflective sheeting to display directions and instructions to drivers. Flatsheet Signs are normally less than five feet in either width or height and do not contain reinforcing ribs on the back side.

LRS to reference Log mile location of the sign.

GPS to reference For ground-mounted signs, GPS will reference edge of sign closest to roadway (signs in right shoulder reference bottom left edge of sign, signs in left shoulder reference bottom right edge of sign). For overhead signs, GPS will reference center of bottom edge of sign.

Feature Type 06

Feature Char

The 5-digit code that corresponds to a specific MUTCD code will be entered (to be provided by the State)

Feature Location Choose from the following types

1-Left

2-Right

3-Overhead Right

4-Median Right

5-Overhead Left


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6-Median Left

7-Centerline

Feature Condition Choose from the following conditions

1-Poor: Sign may be damaged or non-reflective to the point that it cannot be clearly read by traffic. It may also be out of plumb enough that it is not legible.

2-Fair: Sign is clearly visible, mostly reflective, may have minor damage that does not interfere with the intended message of the sign, may be out of plumb, but still readable by traffic.

3-Good: Sign is clearly visible, reflective, free of damage, and plumb.

Sign Orientation Choose from the following

1-North

2-South

3-East

4-West

5-Northeast

6-Northwest

7-Southeast

8-Southwest

Sign Mount Type Choose from the following

01 Grnd Single Post-U shape

02-Grnd Single Post-square tube

03-Grnd Double Post-U-shape

04-Grnd Double Post-square tube

05-Grnd Double Post-W-beam

06-Grnd Triple Post-U-shape

07-Grnd Triple Post-square tube

08-Grnd Triple Post-W-beam

09-Bridge Mounted

10-Cantilever Overhead

11-Truss Bridge Overhead (This will include normal truss bridges intended solely for signs)

Comments Enter the sign legend (Anything not denoted by the MUTCD code such as speed limit value for speed limit signs or other text like town names).

Height Report height of sign to nearest 0.1 feet. Use the standard size that matches closest to the measurement; otherwise record to the closest inch. All measurements will be converted to feet before delivery.


A-4

Width Report width of sign to nearest 0.1 feet. Use the standard size that matches closest to the measurement; otherwise record to the closest inch. All measurements will be converted to feet before delivery.

Notes Legend that is written in the comments will follow rules put forth in Appendix A to ATTACHMENT E.

Digital signboards will not be extracted. Only permanent sign installations will be collected, construction signs will be excluded.


B-1

APPENDIX B – TYPICAL CONTENT IN A DATA COLLECTION RFP

The guide provides links to Requests for Proposals that were issued by the Tennessee and Utah

DOTs for building their roadway asset inventories using automated techniques. Since publishing

the RFPs in full is prohibitive, a summary of the key technical portions of a data collection RFP

is provided here.

In practice, the RFPs that were used in building this summary contained requirements for

collecting both roadway asset inventory information as well as pavement condition data for

pavement management surveys. Because of this, the RFPs may contain more detail than is

required if the contract had been issued only for the roadway asset inventory. However, agencies

realize the greatest benefits from the use of automated technology when multiple agency needs

are addressed, so this was not considered to be a major issue. Agencies are encouraged to

carefully consider their data collection goals and objectives, their data needs, and available

resources when using this information to develop an RFP.

The introductory sections of an RFP typically include the following.

Reason for issuing the RFP and the goals it is intended to accomplish.

Background information on the current state of the agency and its asset inventory.

Information about the procurement process and pre-bid meeting.

Intended length of the contract and the price guarantee period.

Regulations on partnering, joint ventures, use of sub-contractors and so on.

Terms and conditions relating to insurance, auditing, contract issuance and other agency

policies.

The technical specifications outlined in the body of the RFP typically includes the following

types of information.

General information on mileage, anticipated schedule, and other basic requirements.

Data collection specifics concerning data accuracy, routes to be included, route

numbering approach, and so on.

Division of responsibilities among the agency and the contractor, including a list of the

information and assistance that will be provided by the agency.

Specifics regarding the agency’s data collection requirements and guidelines.

Data processing specifics (if any) and data delivery format, including any requirements

for the vendor to provide data in a digital user format that can be accessed without

proprietary software.

Functionalities expected in the software and tools provided by the vendor.

Proposed training schedules for agency personnel to collect, process, and manage the data

using tools provided by the vendor.

Data storage and data hosting responsibilities.

Data ownership declarations.


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Detailed quality control/quality assurance strategies and timelines for remedies.

Historic data integration strategy.

Incentives and disincentives based on quality, timeline, and deliverables.

Some of the common appendices or addendums included with an RFP are listed below.

Selection criteria.

Network maps.

Data dictionaries for assets to be included in the inventory.

Condition assessment manuals (if in the same RFP).

A sample contract with standard provisions that will be included.

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