5.2 Case Studies

5.2 Case Studies

Case Study Format and Content

The interpretation and application of the improvement recommendations will vary amongst DOTs based on size, organizational structure, leadership objectives, and other factors. 

However, by complementing improvement recommendations with real project examples aligned with various areas of the assessment, DOTs will be able to see how others have approached similar challenges, how those challenges were addressed and how desired outcomes were achieved.

Each case study is is provided in a consistent format.  This format provides the reader a concise and clear description of why the project was undertaken, the approach applied, the value delivered, and the key challenges faced.  Supporting graphics are included with each case study to provide visual context in the form of charts, workflows, screen captures or other artifacts.


The motivation section of the case study is designed to create a relatable position for why a DOT would undertake such a project.  The goal of the motivation description is to help the reader identify with the originating challenge or opportunity and related it to a similar challenge or opportunity within their own organization.


The approach is intended to provide a high-level walkthrough of the key steps the DOT took to execute the project or initiative.  Specific step, actions, tactics and engagement strategies employed by the DOT are detailed as applicable.

Value Delivered

In the value delivered section, the outcome of the project or initiative is described in qualitative or quantitative fashion.  By providing the outcome value information the reader can infer similar outcome value propositions for improvements that they are considering that might be associated with the particular case study.

Key Challenges Faced

Each case study highlights key organizational requirements and challenges faced during implementation.  Each case study categorizes these challenges by time, resources, expertise, coordination, and change.

Supporting Graphics and Context

In order to bring the project or initiative to life, select images are provided to support the textural portion of the case study.  These images may be photographs, screen shots or applications, charts, or other representative graphics to help illustrate motivation, approach, value or challenges.

Case Study Overview

Case study selection is guided by an understanding of some of the more challenging and progressive areas of the guidance. Each case study is aligned to an assessment area with a single case study potentially covering more than one area or element.

Below are listed the case studies organized by assessment area,  section and/or element references identified. A brief case study description outlines how the case study provides a useful example of practice and how it is linked to the assessment and improvement framework.

Detailed case study materials are found in Appendix G.

Establishing and Applying a Governance Framework

Area:  Specify and Standardize (A)

Section:  Governance (A.5)

Element: All (A.5a – A.5.d)

Organization: Ohio DOT

Project: Establishing and Applying a Data Governance Framework

Description: This project illustrates the criticality of stewardship and formal oversight for data standards within an organization.  The case study reveals the necessity to engage across all levels of the organization to ensure that there is investment to provide a comprehensive, sustainable governance structure established by policy.

This case study demonstrates how a specific DOT could advance governance elements from practice level 1 or 2 up to practice level 3, by implementing improvements for stewardship roles and governance structures, data management maturity self-assessments, and data and integration through process mapping.

Statewide Vehicle-Based Data Collection

Area:  Collect (B)

Section:  Inventory, Condition, and Performance Collection (B.1)


  • Coverage (B.1.a, B.2.a, B.3.a)
  • Automation (B.1.b, B.2.b, B.3.b)

Organization: Utah DOT

Project: Statewide Mobile LiDAR Data Collection

Description: This project demonstrates establishing enterprise standards and driving consistency so that statewide inventory can be collected uniformly.  This project also illustrates the careful analysis of determine data value to guide investment decisions on how much data to collect and when dealing with large data sets the importance of automating processing steps to create efficiencies.

This case study demonstrates how a specific DOT could advance these elements from practice level 2 to practice level 3, by implementing improvements for manual data collection automation and collection tools and methods consolidation.

Data Collection Quality Management Plan Implementation

Area:  Collect (B)

Section: Inventory condition and performance (B.1)

Elements:  Quality (B.1.c, B.2.c, B.3.c)

Organization: Colorado DOT

Project: Pavement Data Quality Management Plan

Description: This project showcases the ability to leverage Federal requirements as an impetus to addressing a larger and more complex issue.  Additionally, this project reveals the importance of change management and careful attention to understanding in entirety:

  • the business process,
  • what will change, and
  • how it will affect the stakeholders.

The training and certification aspects of the case study illustrate a method to support sustained change.

This case study demonstrates how a specific DOT could advance these elements from practice level 1 to practice level 2 or 3, by implementing improvements for data quality collection plan.

Mobile Field Data Collection Implementation

Area:  Collect (B)


  • Inventory, Condition, and Performance Collection (B.1),
  • Project Information (B.2),
  • Maintenance Information (B.3)

Element:  Quality (B.1.c, B.2.c, B.3.c)

Organization: Virginia DOT

Project: Mobile Field Data Collection of Maintenance Work Accomplishments

Description: This project showcases the value of defining data collection standards and data capture strategies that allow for consistency field data collection.  These are foundational to development of mobile field data collection tools and downstream analysis tasks.  Also highlighted in this project is the cost benefit analysis that must be made with respect to software customization decisions.

This case study demonstrates how a specific DOT could advance these elements from practice level 3 to practice level 4, by implementing improvements for automated data quality collection audits.

Mobile LiDAR and BIM/CADD Integration

Area:  Store, Integrate and Access (C)

Section:  Asset life cycle data integration workflows (C.2)


  • Project development to project delivery (C.2.b)

Organization: Utah DOT

Project: Integration of 3D Modeling Data to Support Asset Management

Description: This project exemplifies the value of leveraging asset inventory and condition data into the project delivery phase.  This project also shows that investments in one asset life-cycle stage can pay dividends in another, the additional returns on investment by looking at the larger life cycle viewpoint can be considered to aid justification of new data and digitalization projects.

This case study demonstrates how a specific DOT could advance this element from practice level 3 to practice level 4, by implementing improvements for asset life-cycle data transfers automation.

Multi-Objective Project Prioritization Program Implementation

Area:  Act (E)

Section:  Project Planning, Scoping, and Design (E.2)

Element: Data-Driven Project Planning and Scoping (E.2.a)

Organization: Ohio DOT

Project: Transportation Asset Management Decision Support Tool (TAMDST)

Description: The project illustrates the accumulated value and derived benefits from normalizing ratings and metrics to support cross asset planning.  This work further demonstrates the value of dashboards and visualization techniques to support decision making as well as making those decisions on prioritization defensible.

This case study demonstrates how a specific DOT could advance this element from practice level 2 to practice level 3, by implementing improvements for network-level performance monitoring programs.