Arizona Department of Transportation

Improving Data 

Quality on the 

System of Record 

Data Management on Arizona’s Linear 

Referencing System

SAFETY DATA CASE STUDY 

FHWA-SA-22-006 

Federal Highway Administration Office of Safety 

Roadway Safety Data Program 

http://safety.fhwa.dot.gov/rsdp
i SAFETY DATA CASE STUDY FHWA-SA-22-006

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.
ii SAFETY DATA CASE STUDY FHWA-SA-22-006

1. Report No.

FHWA-SA-22-006

2. Government Accession 

No.

3. Recipient's Catalog No.

 

4. Title and Subtitle

Improving Data Quality on the System of Record: Data Management on 

Arizona’s Linear Referencing System

5. Report Date

August 2022

6. Performing Organization Code

7.Author(s) 

Ian Hamilton and Lauren Coffey

8. Performing Organization Report No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc (VHB)

940 Main Campus Drive 

Raleigh, NC 27606 

10. Work Unit No.

11. Contract or Grant No.

DTFH61-16-D-00052

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety 

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

Case Study, January 2020-June 2022 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Jerry Roche. Sarah Weissman Pascual provided editorial review and support for 

technical content.

16. Abstract

This case study highlights a noteworthy example by the Arizona Department of Transportation (ADOT) as the State 

focuses on the quality of its linear referencing system (LRS). ADOT uses the LRS as the system of record to help manage 

and integrate datasets between several units and data owners in the agency. ADOT developed a framework for identifying 

critical issues with its LRS, such as gaps and overlaps in feature records. This process allows ADOT to trace systemic 

issues and correct them programmatically. This case study also explores how ADOT is advancing the State’s LRS to 

improve safety data integration and support advanced use cases in data management and analysis. Communication with the 

State’s traffic safety team and information technology staff has been critical to collecting and maintaining essential safety 

data on State highways and local roads. This has helped ADOT build institutional knowledge with respect to key concepts 

and pursue more advanced safety data applications.

17. Key Words: 

Linear referencing system, safety, data, integration

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

 17 

22. Price

Technical Documentation Page
iii SAFETY DATA CASE STUDY FHWA-SA-22-006

Acronyms 

Acronym Description

ADOT Arizona Department of Transportation 

AEGIST Applications of Enterprise GIS for Transportation

DOT department of transportation

DQI Data Quality Index

ETL extract, transform, and load

FDE Fundamental Data Elements

FHWA Federal Highway Administration 

FME Feature Manipulation Engine 

GIS geographic information system

HPMS Highway Performance Monitoring System

IT Information Technology 

LRS linear referencing system

MIRE Model Inventory of Roadway Elements

MPD Multimodal Planning Division

R&H Roads and Highways

RCI Roadway Characteristics Inventory
iv SAFETY DATA CASE STUDY FHWA-SA-22-006

Table of Contents 

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

Managing Data and the LRS ............................................................................................. 2

Improving Safety Data ...................................................................................................... 5

Next Steps: Space, Time, and the LRS ........................................................................... 6

Challenges .......................................................................................................................... 9

Conclusions......................................................................................................................... 9

Acknowledgements .........................................................................................................10

References ........................................................................................................................11

List of Figures

Figure 1. Graphic. ADOT’s LRS is the common link for many data systems............. 2

Figure 2. Equation. ADOT’s section scoring methodology. ......................................... 3

Figure 3. Graphic. Example of ADOT’s LRS data quality dashboard.......................... 5

Figure 4. Graphic. Example LRS spatial topology challenges....................................... 7

Figure 5. Graphic. Developing a spatiotemporal topology. .......................................... 8
v SAFETY DATA CASE STUDY FHWA-SA-22-006

Executive Summary

This case study highlights a noteworthy example by the Arizona Department of Transportation 

(ADOT) as the State focuses on the quality of its linear referencing system (LRS). ADOT uses 

the LRS as the system of record to help manage and integrate datasets between several units 

and data owners in the agency. ADOT developed a framework for identifying critical issues 

with its LRS, such as gaps and overlaps in feature records. This process allows ADOT to trace 

systemic issues and correct them programmatically. This case study also explores how ADOT 

is advancing the State’s LRS to improve safety data integration and support advanced use cases 

in data management and analysis. Communication with the State’s traffic safety team and 

information technology staff has been critical to collecting and maintaining essential safety data 

on State highways and local roads. This has helped ADOT build institutional knowledge with 

respect to key concepts and pursue more advanced safety data applications.
1 SAFETY DATA CASE STUDY FHWA-SA-22-006

Introduction 

In 2019, the Federal Highway Administration (FHWA) completed the second U.S. Roadway 

Safety Data Capabilities Assessment (FHWA, 2019). This nationwide effort documented the 

safety data processes, policies, and procedures of all 50 States plus Washington D.C. and 

Puerto Rico. This effort highlighted the current state of practice with respect to all phases of 

safety data collection, management, integration, and analysis. It also revealed that State 

departments of transportation (DOTs) were eager to improve their capacity for data 

management and integration.

The purpose of this case study is to highlight a noteworthy example by the Arizona 

Department of Transportation (ADOT) focusing on the quality of its linear referencing system 

(LRS). This case study also explores how ADOT is advancing the State’s LRS to be a system of 

record for integrating data and managing change. An LRS that considers spatial, and potentially 

temporal, change will allow ADOT to analyze changes to the network over time and analyze 

safety performance using the roadway conditions at the time the crashes were recorded. This 

approach can support safety data integration and advanced use cases in data management and 

analysis. 

Purpose and Need

ADOT’s Multimodal Planning Division (MPD) has extensive experience with geospatial 

technology and analysis. The geographic information systems (GIS) team is tasked with 

managing the LRS, as well as the Roadway Characteristics Inventory (RCI) for federal reporting 

purposes. As a part of these management responsibilities, the GIS team conducts targeted 

outreach with business units that manage transportation data. These partnerships help ADOT

identify data overlaps and opportunities for integration within the overall GIS program. 

Safety data support is a practical example of how ADOT is able to use the LRS as a system of 

record to help achieve the DOT’s goals. At a broad level, the LRS supports transportation data

integration in Arizona. As a specific example within safety, ADOT’s traffic safety team collects 

and manages crash data in GIS format, and the MPD GIS team works closely with the traffic 

safety team to migrate workflows into a geospatial environment to improve data 

interoperability. This includes management of the federally-mandated Model Inventory of 

Roadway Elements (MIRE) Fundamental Data Elements (FDEs), as well as support for much of 

the safety analysis and roadway safety management process. In addition to capturing the spatial 

dimension of MIRE FDEs on Arizona’s roads, ADOT plans to explore the temporal component 

of these data and refine the State’s safety data capabilities. 
2 SAFETY DATA CASE STUDY FHWA-SA-22-006

Target Audience

The target audience for this case study is:

• State DOT GIS Staff

• Information Technology (IT) Staff

• Data Managers, Analysts, and Stewards

• Transportation Professionals in Planning, Engineering, and Safety

Managing Data and the LRS

ADOT began two projects to enhance data capabilities: 1) the RCI to collect MIRE FDEs on the 

Federal-Aid system, and 2) the Data Review and Visualization Project to support data quality 

and data management capabilities. These efforts reviewed major transportation-related data 

systems in the State, including the Highway Performance Monitoring System (HPMS), bridges, 

pavement, assets, and safety. ADOT recognized that data silos, poor data quality, and a lack of 

metadata were hindering data sharing and integration. ADOT’s approach to data integration 

involves translating these datasets through the agencywide LRS. This provides a common 

basemap for all data, improving interoperability between data systems (figure 1). 

Figure 1. Graphic. ADOT’s LRS is the common link for many data systems. 

© ADOT
3 SAFETY DATA CASE STUDY FHWA-SA-22-006

This focus on the LRS as a system of record for interoperability across the agency led to an 

effort to standardize the LRS and provide consistent workflows and quality checks. ADOT 

migrated to Esri’s Roads & HighwaysTM (R&H) from its legacy systems in 2014, and the Data 

Review and Visualization initiative began in 2019. During that time, ADOT moved from an adhoc review of GIS data quality to one that uses a suite of Feature Manipulation Engine (FME) 

quality control checks. These checks form the foundation for data integration in Arizona using 

ADOT’s Data Quality Index (DQI).

The DQI is a framework for evaluating ADOT’s LRS and targeting specific issues in the data. 

The DQI consists of three sections:

• Source: This considers the authoritativeness and timeliness of the data.

• Process: This measures the sustainability of a process (i.e., replicability or automation).

• Quality: This considers confidence that ADOT has in a particular feature. 

ADOT adopted a series of checks within each section to compute a total DQI for each data 

element. These consist of both qualitative and quantitative evaluation criteria. ADOT staff 

assign qualitative scores manually; the data are either good quality (assigned a value of 1) or 

poor quality (assigned a value of 0). ADOT automates quantitative scores, and these reflect a 

measurable component of the data. Qualitative scores are an important component of the 

overall section score. Figure 2 shows how qualitative scores are multiplied, while quantitative 

scores are added. Therefore, if a qualitative check fails in the Quality section (i.e., assigned a 

value of zero) the data element being reviewed will receive a zero for the entire Quality 

section. 

The total score is computed by adding the three section scores (i.e., Source, Process, and 

Quality), and the DQI is the total score divided by the total number of individual checks across 

all three sections.

ADOT also implemented a series of logic checks that help diagnose individual or systemic 

issues (i.e., “pathologies”) in the agency’s data, such as: 

1. An event’s “From” measure is larger than the “To” measure – The record’s 

starting position is recorded to a location after its endpoint. 

2. An event’s geometric length is different than its spatial length – The record is 

longer or shorter than the attributes would indicate.

3. Multi-part event – The record is not one single feature, and it is spatially split into 

multiple locations.

Figure 2. Equation. ADOT’s section scoring methodology.
4 SAFETY DATA CASE STUDY FHWA-SA-22-006

4. Domain violations – A record contains a value other than a prescribed set of 

potential values.

5. Phantom events – Records that are not locatable on the network.

6. Overlapping events – More than one record overlaps on a particular location on the 

network.

Using these checks, ADOT reviewed its LRS according to a multi-step process:

1. Evaluate the impact and extent of each issue in Arizona’s data.

2. Trace the transactional data recorded in R&H. Tracing helps determine if the issue is an 

isolated event or a systemic issue and includes:

a. Time of the transaction.

b. Version of the software.

c. Frequency of errors.

d. Summary of errors by staff member.

3. Replicate the issue to diagnose how the issue occurs.

4. Prevent future possibilities for the issue to occur based on the issue tracing and 

replication.

This review of institutional processes helped ADOT understand its LRS data as it migrated to a 

new enterprise system. It also provides a method for evaluating data and promoting quality 

during edit sessions in real-time. ADOT refers to this as “defensive editing.” ADOT adopted a 

visual dashboard for applying defensive editing to its LRS (figure 3). This interactive diagnosis 

and analysis application allows ADOT to promote data quality as the LRS and the interoperable 

data evolve over time.
5 SAFETY DATA CASE STUDY FHWA-SA-22-006

Improving Safety Data

Through years of cooperative development, the LRS has become the system of record for 

ADOT to manage the State’s transportation data. This approach has greatly supported the 

State’s MIRE FDE inventory. ADOT developed a crosswalk table of existing data inventories 

and the MIRE FDEs as part of a comprehensive gap analysis. Collaboration with both the traffic 

safety team and ADOT’s IT unit was a critical part of identifying data gaps and refining data 

schemas to meet the MIRE FDE requirements. These discussions helped establish plans for 

collecting additional data without causing negative impacts on other business unit reporting. 

ADOT’s approach viewed roadway and safety data collection holistically. This placed an 

emphasis on capturing data for all applicable roads in the State to support comprehensive safety 

analysis and implementation of a new geospatially-enabled safety analysis software. A plan to 

collect MIRE FDEs for locally-maintained Federal-Aid routes was a key outcome of the gap 

analysis and the multi-disciplinary discussion. This collection focused on data elements that 

could be derived from aerial or street-level imagery (e.g., speed limit signs and traffic controls) 

rather than other types of data requiring resource-intensive efforts (e.g., traffic counts).

As ADOT collected these data, the agency also noted a reciprocal benefit for its LRS as a 

system of record—route dominance. Route dominance acknowledges that a single physical 

road may have more than one signed route associated with it (i.e., a U.S. route and a Statesigned route, etc.). By establishing route dominance early in the process, ADOT assigned data 

Figure 3. Graphic. Example of ADOT’s LRS data quality dashboard. 

© ADOT
6 SAFETY DATA CASE STUDY FHWA-SA-22-006

consistently to appropriate route levels and largely avoided issues of missing and inconsistent 

data. 

ADOT plans to use a suite of tools to help manage the State’s data supply chain into the future. 

This concept refers to the process ADOT employed for documenting the workflows, 

terminology, and metadata associated with key terms and schemas. The agency used Microsoft 

OneNoteTM and Esri Story MapsTM to engage staff (particularly non-safety or GIS staff) and 

record institutional knowledge. These systems can potentially help connect the State’s 

transportation data with financial management systems to better understand how the agency 

spends its resources. As with the LRS, authoritative data and a system of record are critical to 

effectively collecting data and maintaining it over time.

Next Steps: Space, Time, and the LRS

ADOT is working with FHWA through the Applications of Enterprise GIS for Transportation 

(AEGIST) pooled-fund program to explore innovative enhancements to how the State manages 

data on the LRS. ADOT is exploring new methods for managing the temporal dimension of its 

LRS and GIS data in addition to its traditional spatial component. The Managing Data and the 

LRS section provides some insight into how ADOT manages spatial rules, or topology1, in the 

State’s data. Gaps in the network, overlapping records, and events without a corresponding 

route location can be screened and addressed (figure 4).

1 Topology is a GIS-industry term that refers to the spatial relationships of individual data features (i.e., records) 

and the rules that govern them.
7 SAFETY DATA CASE STUDY FHWA-SA-22-006

Like spatial information, temporal information also has its own topology. With ADOT’s 

migration to R&H, the DOT can track the evolution of data over time according to two axes:

• Valid time: The lifespan of an event or geometric event (i.e., the start date of that 

feature to the date it is retired).

• Transaction time: The date database entries are posted (i.e., representing the date 

the facility is open to the public).

This offers new challenges to data managers, as well as opportunities. ADOT prefers to manage 

data visually. Examples like R&H provide a framework for visually correcting and managing 

spatial data, and ADOT can adapt this approach to also address changes in data over time. 

Figure 4. Graphic. Example LRS spatial topology challenges. 

© ADOT
8 SAFETY DATA CASE STUDY FHWA-SA-22-006

For instance, analysts can use these tools to address spatial issues, as well as temporal issues 

such as:

• Address new and historic issues in spatial topology.

• Archive historic conditions and parts of the network no longer active.

• Manage a composite model of the entire LRS network.

Figure 5 illustrates how LRS visualization and editing tools can help analysts address issues 

inherent in the data over time. In this example, a portion of Route 2 is retired in 2018, and 

Route 1 is extended (e.g., reflecting a road name, route designation, or ownership change). 

However, a gap in the spatial topology emerges as Route 1 is not extended far enough (see 

example in figure 4). As analysts adjust route extents and retire old records, ADOT could 

recognize issues with its data over time and proactively address them to match historic data 

from other business owners.

Addressing the temporal dimension of ADOT’s data should yield benefits for several other 

business processes that rely on the LRS, including safety. More accurate and reliable data over 

time can help ADOT link crashes with the accurate conditions of the road network at the time 

of the crash. Not only could this benefit network screening, but diagnosis and countermeasure 

selection could be improved with better understanding of the conditions during which crashes 

occurred. Furthermore, this approach has the potential to support more robust data analytics

Figure 5. Graphic. Developing a spatiotemporal topology. 
9 SAFETY DATA CASE STUDY FHWA-SA-22-006

and use cases, including machine learning for crash analysis and automated methods for feature 

data extraction (e.g., MIRE FDE data maintenance).

Challenges

ADOT’s methodical approach helped address many of the issues inherent with the agency’s 

existing data. However, ADOT noted many challenges as it improved its system of record. As 

ADOT reviewed many of its existing datasets (e.g., HPMS), it noted that these lacked the 

specificity in certain cases to meet MIRE requirements. This required ADOT to develop an 

extract, transform, and load (ETL) process to make these data readily available for safety 

analysis. Furthermore, datasets that were not readily available prior to extensive data 

collection, such as intersections and intersection approaches, could be collected according to 

specifications established during the multidisciplinary collaborative sessions. Finally, even as the 

agency collected and validated MIRE FDEs for many essential datasets in the State, obtaining 

timely updates to existing conditions, particularly on off-system roads, remains a persistent 

challenge. 

Conclusions

ADOT’s approach to managing data on the State’s LRS illustrates how State DOTs can 

proactively approach data concerns. Authoritative and accessible data are key components of 

data governance and data integration. By investing in the agency’s system of record, ADOT is 

making it easier to integrate safety data with other datasets. Furthermore, future MIRE datasets, 

such as the intersection and intersection approach inventories, are more readily integrated into 

the system due to fewer concerns with the integrity of the LRS and its fidelity to existing 

conditions. 

Collaboration between the MPD GIS team and other business units, such as safety and IT, helps 

identify data gaps, streamline data collection, and avoid potential conflicts with other business 

units. These connections will continue to benefit ADOT as the MPD GIS team explores 

innovative approaches to data management. Formalizing these relationships, roles, and 

responsibilities are important future considerations for ADOT’s data program. Clearly defined 

business cases for robust analytics and feature extraction methods (e.g., from computer-aided 

design as-builts and project plans), as well as embracing industry best practices will help mitigate 

future risk of data issues.
10 

Acknowledgements

FHWA would like to express appreciation to the Arizona Department of Transportation for providing 

information used in developing this case study. For more information or clarification on the concepts in 

this case study, practitioners should contact ADOT and carefully consider the applicability of these

approaches in their own agency.
11 

References

 Federal Highway Administration. (2019). Second U.S. Roadway Safety Data Capabilities 

Assessment, Report No. FHWA-SA-19-018, Washington, D.C. Available online: 

https://rosap.ntl.bts.gov/view/dot/55617. 

 James Meyer and Bo Guo. (2021, April). Discover, Assess, Expose, Fix, Immunize: ADOT’s 

Multi-prong Approach to Safe Guard LRS Data Quality [Presentation].

 Lefler, N., Zhou, Y., Carter, D., McGee, H., Harkey, D., and Council, F. (2017). Model 

Inventory of Roadway Elements – MIRE 2.0, Report No. FHWA-SA-17-048, Washington, 

D.C. Available online: https://safety.fhwa.dot.gov/rsdp/downloads/fhwasa17048.pdf. 

 Patrick Whiteford and Bo Guo. (2021, April). Anchoring Data in Time & Space: ADOT’s 

Journey in Harnessing LRS Spatiotemporality [Presentation].

 Patrick Whiteford and James Meyer. (2020, January). MIRE Data: A GIS Perspective 

[Presentation].

Contact

Arizona Department of Transportation

Patrick Whiteford, Geographic Information Systems Group Manager

mpwhiteford@azdot.gov