Fewer 

Fatalities 

& Serious 

Injuries 

Better 

Targeted 

Safety 

Investment 

More 

Informed 

Decision 

Making 

Improved 

Data 

Collection 

& Analysis 

Roadway Safety Data Program
 

COLLECTION AND USE OF ROADWAY ASSET DATA
 

IN UTAH
 

ROADWAY SAFETY DATA AND ANALYSIS
 

CASE STUDY
 

FHWA-SA-14-078
 

Prepared for
 

Federal Highway Administration
 

Office of Safety
 

Roadway Safety Data Program
 

http://safety.fhwa.dot.gov/rsdp/
 

August 4, 2014
 
 

 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

Notice 

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

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

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

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

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

objective of the document. 

Quality Assurance Statement 

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

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

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

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

and processes to ensure continuous quality improvement. 

i 
 

 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

ACKNOWLEDGEMENTS 

The Roadway Safety Data Program team would like to thank the following individuals who 

graciously provided information needed to develop this case study. 

Name Title Department Email/Phone# 

Stan Burns Director of Asset 

Management Utah DOT 

sburns@utah.gov 

801.965.4150 

Utah Department of Transportation 

Asset Management Division, Traffic & Safety Division 

4501 South 2700 West 

PO Box 143200 

Salt Lake City, UT 84114-3200 

ii 
 

 

 

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

BACKGROUND ........................................................................................................ 1
 

OBJECTIVE ............................................................................................................... 2
 

AUDIENCE................................................................................................................ 2
 

PROCUREMENT PROCESS .................................................................................... 2
 

ROADWAY ASSET DATA COLLECTED .............................................................. 3
 

BENEFITS .................................................................................................................. 5
 

DATA COLLECTION TECHNOLOGY .................................................................. 5
 

SUMMARY................................................................................................................. 6
 

Management:........................................................................................................... 6
 

Personnel/Level of Effort: ....................................................................................... 7
 

Funding: ................................................................................................................... 7
 

Planning: .................................................................................................................. 8
 

Challenges (prior to implementation):.................................................................. 8
 

Challenges (after implementation):....................................................................... 9
 

Added Value: ........................................................................................................... 9
 

Lessons Learned:................................................................................................... 10
 

REFERENCES .......................................................................................................... 11
 

INDEX...................................................................................................................... 12
 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

TABLE OF CONTENTS
 

iii 
 

List of Figures 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

Figure 1 The contractor’s vehicle with LiDAR senors and other equipment ............ 5
 

Figure 2 Photographs taken with the data maps ......................................................... 6
 

Figure 3 Distribution of funding contributions ............................................................ 8
 

iv 
 

AADT Annual average daily traffic 

AASHTO American Association of State Highway and Transportation Officials 

DOT Department of Transportation 

FDE Fundamental Data Elements 

FHWA Federal Highway Administration 

GIS Geographic information system 

GPS Global positioning system 

HPMS Highway Performance Monitoring System 

HSIP Highway Safety Improvement Program 

HSIS Highway Safety Information System 

HSM Highway Safety Manual 

KML Keyhole Markup Language 

LIDAR Light detection and ranging 

MIRE Model Inventory of Roadway Elements 

MIS Management Information System 

NCHRP National Cooperative Highway Research Program 

NHS National Highway System 

OIT Office of Information Technology 

TMS Traffic Monitoring System 

XML Extensive Markup Language 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

ACRONYMS 

v 
 

 

 

 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

EXECUTIVE SUMMARY
 

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

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

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

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

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

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

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

effectiveness of the those countermeasures. Comprehensive roadway safety data include 

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

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INTRODUCTION 

State DOTs manage multi-million dollar assets. Being able to quantify those assets helps not 

only to improve efficiency of funds allocated within the agency, but also to improve safety and 

design of the State maintained roadways. In Utah, the four largest assets by cost value are 

pavement, bridges, culverts, and signs. Gathering data on these assets has traditionally required 

an excessive amount of time for field site evaluations conducted by UDOT staff or contractor 

survey crews. Additionally, safety is a concern for those collecting asset data near moving 

traffic. 

The purpose of this case study is to describe the successes and ongoing ch allenges related to 

statewide data collection to support asset management, safety, and planning activities. This 

report has relevance for State DOTs as an example using new technologies and enhanced data 

management practices to create a multipurpose resource. The example may be extended to 

local agency participation as well. 

BACKGROUND 

The Utah Department of Transportation (UDOT) maintains 5,869 centerline miles 1 of roadway 

and 310 miles of freeway ramps, which together comprise 15 percent of the total roadway 

centerline miles (41,508) open to the public in Utah. 19,056 of these miles consist of unpaved 

roads. The UDOT-maintained roads carry 67 percent of the vehicle miles traveled in the State.1 

The scope of the case study includes data collected on the State system only. Each road was 

driven in both directions during data collection, resulting in approximately 12,000 total miles 

driven. 

The data collection effort performed by UDOT was a first-of-its-kind, surveying the entire state 

roadway network and obtaining a wide set of data elements. LiDAR (a term that combines 

“light” and “radar”) was used by the selected vendor to collect a variety of asset data. The 

LiDAR technology creates a three dimensional model of the assets scanned along the roadway, 

and is performed in a single pass at highway sp eeds. 

This project has improved the way that UDOT manages roadway assets. It has also helped the 

agency be more transparent because the data are available to the public through several online 

programs. The main capabilities and benefits are: 

	 UDOT knows the quantities of the assets to a degree of accuracy never achieved before 

through previous methods. (For example, the maintenance division now knows how 

many signs they manage and the percentages of types of signs – guide, warning, 

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OBJECTIVE 

 

 

 

 

 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

regulatory, etc. Previously, estimates ranged between 100,000 and 140,000 signs. Data 

from this project showed that they have approximately 97,000.) 5 

	 The design group may be able to use the data to create topographic maps with greater 

detail than traditional surveying. If this effort proves successful, surveyors may 

experience safer working conditions by not needing to be near travel lanes. This could 

also reduce data collection time and lane closures.7 

	 Increased ability to share roadway attribute data across divisions. 5 

	 Data and maps are available online at http://uplan.maps.arcgis.com/. 

6 

The project had the following objectives: 

	 Obtain data for use in making safety, pavement, and asset management decisions. 1 

	 Deploy state-of-the-art collection methods to improve and develop rigorous safety, 

maintenance, and preservation programs. 8 

	 Gather the most data pertaining to roadway condition, location, and roadway assets in 

an economical way, while maintaining a high level of accuracy and quality. 8 

Specifically, the agency wanted to improve data on pavement distress and quantify square 

footage of pavement. The Maintenance Division also wanted to improve their sign data 

accuracy. UDOT added other roadway attributes to the collection effort as the project 

developed over time. 

AUDIENCE 

This case study applies to the following audiences: 

	 State Departments of Transportation: Safety Engineering, Design, Planning, Maintenance, 

GIS, and Asset Management Units. 

	 Local and Regional: City and County Public Works/Engineering/Transportation 

Departments, Metropolitan Planning Organizations, and Regional Planning Commissions. 

	 Local and Tribal Technical Assistance Programs. 

PROCUREMENT PROCESS 

The procurement process for this project was long (approximately 7 months), primarily due to 

the fact that the data collection methods being proposed at the time were a new concept that 

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COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

was not known to be attempted by previous State DOTs. Initially , the champions of the 

project learned of new technologies for gathering roadway asset data and decided to issue a 

Request for Proposal (RFP). To prepare for the RFP, UDOT welcomed presentations from 

eleven different companies, each highlighting different data collection methods. UDOT drafted 

the RFP based on the average level of technology so as not to exclude potential solutions 

providers from bidding. The RFP did not specify the LiDAR technology instead was neutral on 

the technology that was to be proposed by potential vendors. 

Vendors demonstrated their product, including a presentation, field test, and working in the 

computer lab to test data transfer compatibility with UDOT databases. UDOT ranked the 

shortlisted companies based on qualifications an d cost. Mandli Communications Inc. won the 

contract with a proposed data collection method that utilized 3D LiDAR. 

The UDOT Asset Management Division engaged other departments regarding what data would 

be beneficial to their asset management decisions. 8 UDOT developed the final dataset and 

requirements through an iterative process of internal UDOT discussions, testing by the 

contractor, and validation. The dataset was broken up into smaller pieces and each piece was 

given an internal owner (e.g., Maintenance, Traffic & Safety, etc.). Each owner chose how to 

validate the data. Some divisions had internal staff perform QC/QA and other divisions had 

help from consultants. 

ROADWAY ASSET DATA COLLECTED 

The UDOT Roadway Imaging and Inventory contract required collection of specific roadway 

assets, including: 8 

 Roadway condition data. 

 Roadway photolog. 

 Pavement photolog. 

 Number, length, and type of lanes. 

 Ramps and collectors. 

 Median and barrier presence (type and width). 

 Guardrails, shoulder barrier, and end treatments. 

 Striping and pavement messages. 

 Bike lanes. 

 Intersections (quantity, type, and signal equipment). 

 Bridges, overhead obstructions, and other structures. 

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COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

 Surface areas and pavement width. 

 Lane miles. 

 Sign supports and faces. 

 Striping and pavement markings. 

 Shoulders. 

 Rumble Strips. 

 Curb and Gutter. 

 Drainage features (mainly drop inlets). 

To gather the above data, the contractor used a vehicle with the following equipment: 8 

 Velodyne LiDAR sensor. 

 Laser road imaging system. 

 Laser rut and crack measurement system. 

 Road surface profiler. 

 Position orientation system. 

The contractor obtained the roadway asset information through the equipment above, which 

also included photologs. The LiDAR data produced position and size of assets. The other 

sensors captured the pavement condition and the photos aided in post processing. 4

The 

contractor completed post processing before sending to UDOT. One post-processing example 

included scanning the photos to identify the correct barrier end treatment. 4 

UDOT received the data from the contractor and conducted a rigorous process of quality 

assurance, organizing the data and formatting to comply with their existing databases and 

software, which include: 

 Oracle and Oracle spatial. 

 Custom packages written in PL/SQL. 

 ESRI geodatabase. 

 ESRI ArcGIS server and an open source GIS. 

 Some special purpose programming to support the process and web applications. 

UDOT conducted QC/QA weekly meetings with the contractor and Stanley Consultants (who 

was enlisted as a partner to quality check some of the attributes). 8 

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COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

BENEFITS 

After this extensive data collection effort, UDOT improved its procedures for managing agency 

assets. Specifically, UDOT knows for each asset the location, certain attributes, and start/end 

points along the State-maintained roadway system. For example, UDOT knows the location and 

quantity of nearly all aboveground assets. Additionally, the project produced the following 

benefits: 

	 Knowledge of the quantity and quality of roadway assets improves budgeting for the 

department. 

	 Knowledge of the number of signs to manage. 

	 Enabled divisions to work more closely together by having shared access to roadway 

information. 

	 Enhanced the Traffic and Safety Division’s ability to perform systemic safety analyses 

based on roadway attributes and crash data. Specific methodologies being used include 

HSM Part C and Part D analyses, United State Road Assessment Program (usRAP), and 

a Bayesian model being developed by Brigham Young University. 

	 Enabled the Design Group to initiate a pilot project to create topogra phic survey maps 

for two locations. 

DATA COLLECTION TECHNOLOGY 

Figure 1 depicts the vehicle used by the contractor to collect the roadway asset data. 

Figure 1 The contractor’s vehicle with LiDAR senors and other equipment 

5
 
 

 

 

 

 

 

 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

After collecting the data, the contractor delivered to UDOT an accumulation of data from the 

various sensors as well as real time photographs. Figure 2 shows a small segment of the point 

cloud comprised of the individual data points detected using the vehicle -based sensors. 

Figure 2 Photographs taken with the data maps 

The contractor collected the point cloud with line-of-sight detection from the vehicle to the 

features on the roadway and stored it in a spatial database recording the 3D coordinates of 

each point. The resulting database consists of seven to ten terabytes for the point cloud alone. 6 

SUMMARY 

Management: 

The success of this project was in part due to the project champion. Each step of the process 

needed collaboration from different departments as well as approval from the agency 

executives. The champion of the project, Stan Burns, was able to promote the project and 

keep interest alive through completion of the initial data collection and beyond. Several 

challenges included the learning curve with new equipment, software, and technology as well as 

the size of the data collection effort. 5UDOT executives wanted to know future plans for 

keeping the information up-to-date. The project team agreed to gather the data every two 

years. 5

Furthermore, the team developed software to ease the process of updating data. 5As a 

result of the initial data collection round, UDOT learned the importance of setting clear 

contract requirements for data delivery timing and compatibility. They also learned the 

importance of using attribute nomenclature that would be consistent with the databases where 

the information would ultimately be stored. 

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COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

Personnel/Level of Effort: 

The Utah roadway imaging and inventory project was initiated by the UDOT Asset 

Management Division and championed by Stan Burns of that division. Stan Burns and Michael 

Butler provided the overall management and promotion of the project. During the projec t, Stan 

Burns spent an estimated 15-20 percent of his time on this effort, while Michael Butler spent 

approximately 35 percent. Additionally, the following UDOT division managers spent a 

percentage of their time to the effort over the course of a year: 2 

 Systems Planning & Programming – Gary Kuhl (10 percent), Lee Theobald (20 percent). 

 Central Maintenance – Lynn Bernhard (20 percent), Lloyd Neeley (5 percent). 

 Central Traffic & Safety – W. Scott Jones (10 percent), Travis Jensen (10 percent). 

Additionally, the following departments contributed the estimated levels of effort on the 

project over the course of a year (in addition to the individuals liste d above): 2 

 Traffic – 3 staff involved, FTE: 0.25. 

 Structures – 2 staff involved, FTE: 0.05. 

 Maintenance – several staff involved on the QA efforts, FTE: 0.25. 

 Asset Management – 5 staff involved, FTE: 0.5. 

GIS/Engineering Technology Service – 3 staff involved, FTE: 1.0. 

Funding: 

Funding was a main concern for a project of this magnitude. The total cost for the first round 

of data collection was $2.25 Million – a relatively large sum for a data project in UDOT. 1

The 

champion worked with each of the divisions involved to establish a method for funding the 

project prior to the RFP. The majority of the funds were from Systems, Planning, and 

Programming; Central Maintenance; and Central Traffic & Safety. By pooling funds together, 

each group gained a sense of ownership over the project. 3Two sources of federal funding 

were used in the data collection effort – $725,000 of HSIP and $825,000 of SPR. The rest of the 

money used was state funds. Figure 3 shows the distribution of funding. 

7
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

HSIP, 

$725,000.00 

SPR, 

$825,000.00 

State, 

$700,000.00 

Figure 3 Distribution of funding contributions 

Planning: 

The planning phase involved the champion, the agency executives, the three main divisions 

(Systems, Planning, & Programming; Central Maintenance; and Central Traffic & Safety), and the 

contractor. The contractor gathered the first round of roadway asset data in 2012 and will 

collect the first update in 2014. The agency plans to update the data every 2 years within a 6-

year contract to the vendor. Based on the experiences with the initial data collection, UDOT 

created software to help integrate data from future updates into UDOT’s databases and has 

streamlined a process of checking, organizing, and storing the data. Industry collaboration has 

included ESRI, Oracle, Bentley, Mandli, Deighton, AgileAssets, Virtual Geomatics, and the AAA 

Foundation for Traffic Safety. 1,10 

Challenges (prior to implementation): 

One challenge to overcome was creating definitions of roadway elements. Overall, eight to ten 

different groups within UDOT had requested roadway data and there were slight differences 

on how each group defined parts of the roadway. For example, the Maintenance Division 

defines the shoulder as the non-paved area beyond the edge of the pavement, where others 

define the shoulder as the paved area beyond the edge line. UDOT internally had to resolve 

these differences prior to data collection so that the vendor could collect each asset using a 

single, precise definition. 

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COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

Challenges (after implementation): 

Once the vendor supplied the data, UDOT performed quality checks and integrated the data 

existing databases and business processes. UDOT expected that a new data collection effort of 

this magnitude was likely to run into unanticipated delays and challenges. Accuracy and 

precision tolerances were set in the data delivery contract and these tolerances were generally 

met, although a few variables such as horizontal curvature and barrier end treatments did prove 

challenging to accurately assess. For example, several barrier end treatments needed to be 

identified and some were indecipherable by looking at a static photo. Another challenge was 

reconciling different versions of UDOT’s linear referencing system (LRS) because it changed 

between the time UDOT gave it to the contractor (before the data collection) and when data 

were being delivered. 

Another issue was with the vertical clearances of a subset of bridges. The contractor provided 

thousands of points beneath an overpass and from this produced a vertical clearance. The 

structures group field verified these clearances and found discrepancies up to 1.5 inches (for 

approximately 10 percent of the structures). There were two reasons given by UDOT for 

these differences: 

 The structures survey was not at the exact low point during field verification. 

 The LiDAR sensors may have picked up “noise” or points that were outside of the 

expected range. 

It is important to note that all of the contractors reported clearances were of a smaller value 

than the field-verified survey. In other words, the contractor’s values were always more 

conservative than the field-verified values. 

Added Value: 

The LiDAR point cloud is considered mapping grade with an absolute accuracy of plus or minus 

1 meter. While this accuracy is suitable for feature extraction it is not usable for design 

applications. UDOT worked with a local survey company (Meridian Engineering) and a software 

company (Virtual Geomatics) to transform the point cloud to design grade accuracy of plus or 

minus 3 cm on two pilot design projects. Cost and schedule savings of approximately 25 

percent were achieved over traditional surveys. 

UDOT has further added value to the design and construction process by prepopulating project 

quantities directly from the asset inventory. UDOT has developed software integration tools 

between its asset and construction databases. At the click of a mouse, a designer can extract 

any number of assets (e.g. signs, paint striping, pavement, etc.) and incorporate the quantities 

9
 
 

 

 

 

 

 

 

 

 

COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

with appropriate attributes into the construction bid set. The dataset does not include all 

desired data elements, but the location of each asset and some condition information is readily 

available. 

Lessons Learned: 

The project proved effective for managing roadway a ssets for UDOT in several ways. One 

outcome is that the data will be stored in one centralized data warehouse for all of the UDOT 

divisions to use. The cost savings this will create for each of the divisions is large, but not easily 

measured due to the potential for increased efficiency and time savings. 

The data also improved pavement management and sign management. The 3D technology 

accurately recorded cracks in the pavement, which in turn gave UDOT a more accurate basis 

for deterioration models and funding projections. Similarly, knowing the total number of signs 

and their locations has improved sign maintenance and spending forecasts. Besides cost savings, 

UDOT created time savings for staff and its contractors through the data and the software 

developed to manage the data. For example, the bicycle coordinator was able to find out how 

many bicycle lanes the State maintains within 30 minutes. Previously, that task would have been 

difficult to do and taken at least several days to complete. 

Specifically, the following lessons were learned from this Case Study: 1 

	 Prepare a plan to store, distribute, analyze, and utilize the data. 

	 Build support from senior leadership. This was accomplished by the champion meeting 

with senior leadership to make them aware of and up to date on the project. He also 

presented the concept to the Utah Transportation Commission and the Executive 

Director of UDOT. 

	 Provide adequate time for the procurement process. 

	 Work extensively with the vendor from selection to final data collection/delivery . 

	 Do not expect to fund your entire data wish list up front. 

10
 
COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH 
 

REFERENCES 

The following resources were consulted in development of this case study: 

1.	 Collection and Use of Roadway Asset Data - The Utah Experiment. Jones, W. Scott. 2013. 

Roadway Safety Data Partnership Peer Exchange. 

2.	 Butler, Michael. Email Correspondence. February 24, 2014. 

3.	 Traffic & Safety Divis ion - W. Scott Jones, Mike Butler, and Travis Jensen. Telephone 

Interview. February 18, 2014.
 

4.	 Maintenance Division - Lloyd Neely, Lynn Bernhard and Michael Butler. Telephone 

Interview. February 19, 2014.
 

5.	 Asset Management Division - Stan Burns and Michael Butler. Telephone Interview.
 

February 19, 2014.
 

6.	 Engineering Technology Systems Division - Steve Quinn, Frank Pisani and Michael Butler.
 

Telephone Interview. February 25, 2014.
 

7.	 Central Preconstruction - John Guymon, Paul Wheeler and Fred Doehring. Telephone 

Interview. February 25, 2014.
 

8.	 UDOT.UTAH.GOV. Utah DOT Leveraging LiDAR for Asset Management Leap. [Online] 

February 25, 2013. 

9.	 Better Reporting. Dingess, Robert. January, Washington, D.C.: Roads & Bridges, 2014. 

10. AAA Foundation Risk Maps Prioritize Scarce Resources for Road Safety Improvements.
 

AAA Foundation for Traffic Safety. [Online] [Cited: April 16, 2014.]
 

https://www.aaafoundation.org/sites/default/files/2010usRAPMapsUT.pdf.
 

11. UDOT. UDOT FX. UDOT Transportation Blog. [Online] January 26, 2012. [Cited: March 

5, 2014.] http://blog.udot.utah.gov/2012/01/lidar-measures-transportation-assets-udot/. 

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COLLECTION AND USE OF ROADWAY ASSET DATA IN UTAH
 

INDEX 

Maintenance Management Quality Assurance (MMQA) measures performance of 

assets, sets targets, and helps to establish budgets. 

Mobile LiDAR is a data collection technology that when mounted to a vehicle “creates a point 

cloud for a continuous accurate measurement of pavement and all surrounding roadway 

assets”. (11) 

Operations Management System (OMS) is the system housing much of the asset 

inventory data pertinent to maintenance operations, used by the Maintenance Division to plan 

and manage budgets, allocate resources including manpower, equipment, and materials, record 

work done, and analyze maintenance needs. The MMQA program is housed within OMS. 

UGate is the UDOT Oracle database for the point cloud data. 

http://www.udot.utah.gov/ugate/f?p=111:2:0::NO 

uPlan is a program created by the UDOT Systems Planning & Programming Division to share 

all the data needed to plan and program a project. 1 uPlan is distributed on an ArcGIS platform, 

in conjunction with UDOT’s existing Oracle – based systems.8

http://uplan.maps.arcgis.com/ 

12