i 

Finding a Win-Win: Planning and DataSharing Partnerships between 

Governments and Public Land 

Management Agencies

Final Report

June 30, 2023 
Prepared by the U.S. DOT Volpe Center ii

Executive Summary/Preface

Transportation data sharing partnerships are becoming increasingly important as transportation systems 

continue to evolve and become more complex. As the demand for transportation services grows, the 

need for accurate and timely data becomes paramount for staff and the public to make informed 

decisions. This report is a culmination of research and analysis conducted by the U.S. Department of 

Transportation (USDOT) Volpe Center on the topic of transportation data sharing partnerships between 

transportation and public land agencies. 

Funded by the Federal Lands Highway Division (FLH) of the Federal Highway Administration (FHWA), the 

purpose of this research project is to document and evaluate the integration of project planning and 

data sharing into transportation planning practices and processes within public land settings. The report 

highlights the importance of collaboration between transportation system owners and the need to 

establish clear roles, responsibilities, and feedback mechanisms to ensure effective implementation of 

these partnerships. 

While the benefits of data sharing are clear and numerous, there are several barriers to coordination

and little information or guidance on how to partner across public lands and transportation agencies. 

The deliverables from this research have begun to address the lack of guidance and provide information 

on how to overcome the barriers. The case studies in Appendix 1 provide examples of successful 

planning and data sharing partnerships for others to learn from. The case studies and Technical 

Research Panel helped the project team identify information gaps that were developed into functional 

tools, listed in the table below. The final toolkit contains these 11 tools that cover data governance, data 

types, and implementation work plans to support long-lasting partnerships. 

Governance Tools

Tracking of Best Management Practices

Templates for Developing Data Sharing Agreements

Synopsis of Laws and Regulations

Challenges Regarding Personally Identifiable Information

Data Tools

Existing Data Resources

User-Friendly Data Visualization Tools

Traveler Information Systems to Distribute Data

Big Data and Crowdsourced Data

Work Plans for Future Activities

Supporting Data User Groups

Communicating Funding Opportunities

Ongoing Technical Support

The critical final step in the research project is to publish and market the Planning and Data Sharing 

Partnership Toolkit widely. The most likely stakeholders for distributing the Planning and Data Sharing 

Partnerships toolkit are federal offices, state DOTs, academic institutions, non-profits, and public lands 

advocacy groups. However, other groups may become apparent as the toolkit becomes more widely 

known. 
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As transportation systems continue to evolve, it is critical to stay up to date on the latest developments 

in transportation data sharing partnerships. This report serves as a resource for transportation 

professionals and stakeholders looking to improve their understanding of the benefits and challenges of 

these partnerships, and to develop effective strategies for successful implementation.
Prepared by the U.S. DOT Volpe Center iv

Table of Contents

Executive Summary/Preface......................................................................................................................... ii

Abbreviations................................................................................................................................................ v

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

Study Approach............................................................................................................................................. 1

Process...................................................................................................................................................... 1

Technical Research Panel.......................................................................................................................... 2

Findings......................................................................................................................................................... 4

Planning and Data Sharing Partnerships Implementation Work Plan.......................................................... 7

Conclusion..................................................................................................................................................... 9

Appendix 1: Case Studies 

Appendix 2: Memo 1 - Background and Literature Review 

Appendix 3: Memo 2 - Methodology 

Appendix 4: Memo 3 - Data Collection 

Appendix 5: Memo 4 - Data Coordination and Sharing Toolbox 
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Abbreviations

The abbreviations for the following terms appear in this report:

AASHTO American Association of State Highway Transportation Officials

DOT Department of Transportation

FHWA Federal Highway Administration

FLH Federal Lands Highway Division

IT Information Technology

MOU Memorandum of Understanding

MPO Municipal Planning Organization

PLMA Public Land Management Agencies

TRP Technical Research Panel

USDOT United States Department of Transportation
1

Introduction 

Recreational travel to public lands is becoming increasingly popular and is expected to continue to rise in 

the coming decades. This trend has caused, and will continue to cause, a surge in the use of 

transportation infrastructure accessing and navigating these lands, which raises concerns about 

congestion, crowding, and the impact on natural resources and safe travel. It is essential that agencies 

such as the Federal Highway Administration’s (FHWA) Federal Lands Highway (FLH) Office, state 

Departments of Transportation (DOTs), local governments, and Public Land Management Agencies 

(PLMAs), work together to ensure the public has safe and efficient access to public lands, which 

generate significant economic benefits at the local, regional, and state levels. It is critical for these 

stakeholders to establish effective data sharing partnerships to avoid duplication of collection efforts 

and find opportunities to align schedules when appropriate. Partnerships that establish successful 

exchanges of data provide valuable insight into addressing the challenges of increased visitation to 

public lands. Agencies can use robust data to make informed decisions about infrastructure 

improvements, traffic management strategies, and resource protection measures. The cultivation of 

these processes ensure that public lands remain accessible and enjoyable for generations to come.

Unfortunately, several barriers to coordination—such as staff capacity, incongruent agency missions, 

and incompatible data platforms—prove difficult to overcome.

The specific goals of this research were to:

• Identify examples of information coordination between PLMAs and transportation partners that 

result in process efficiencies, cost savings, and more effective transportation system delivery 

and/or management.

• Develop a suite of tools, resources, and usage guidelines to aid PLMAs and their partners in 

improving data coordination for better transportation systems.

This final report documents the culmination of research conducted from September 2020 through June 

2023.

Study Approach

The primary objective of the research project was to identify examples of cross-agency coordination 

between PLMAs and transportation agencies that result in process efficiencies, cost savings, and better 

transportation system delivery and/or management. The project focused on partnerships that

collaborated at the corridor-scale. Out of these observations, the project aimed to develop a suite of 

tools, resources, and usage guidelines to aid PLMAs and transportation agencies in sharing data and 

improving coordination for better transportation systems.

Process

In order to focus this research on planning and data sharing partnerships, the project team developed 

high-level research questions:

• Why do PLMAs and transportation agencies share data?

• How do PLMAs and transportation agencies share and use data?

• What are the challenges to data sharing? 
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These high-level questions served as the basis for the literature review and other tasks in the project. To 

address these questions, the project team then developed more specific research questions: 

• What types of data do PLMAs and transportation agencies use to support planning on public 

lands?

• How do PLMAs and transportation agencies use data to inform decision-making?

• What are the characteristics of successful PLMA and transportation agency data sharing 

examples? 

• What are the barriers to effective data sharing between PLMAs and transportation agencies?

• What do PLMAs and transportation agencies need to share data more effectively? 

The study began answering these questions by conducting a literature review of state and federal 

publications, academic journals, and related documents; analyzing existing data and tools; as well as 

engaging in conversations with planning staff and technical experts. To conduct the literature review,

the USDOT’s Volpe National Transportation Systems Center (Volpe Center) reviewed federal laws, 

regulations, and guidance that pertain to planning and data-sharing partnerships between 

transportation agencies and PLMAs; research documents; and other federal, state, and local 

documentation (e.g., case studies, guides, reports). The team identified gaps in the existing literature 

that informed the content of the study methodology. The final literature review is included as an 

appendix in this final report.

In addition to the literature review, the Volpe Center also sought answers by engaging with a diverse 

selection of PLMAs and government agencies involved in some form of a data sharing partnership. The 

Volpe Center held discussions with these groups and developed a selection of case studies to document 

their experiences and lessons learned during their partnerships. The process of holding these discussions

and developing case studies was nationally focused and not limited to any single location. 

Technical Research Panel 

As part of the overall project plan, a Technical Research Panel (TRP) was established to provide guidance 

and input to the project team. The TRP was comprised of representatives from various stakeholder 

groups, including federal and state agencies, local planning departments, nonprofit organizations, and 

private practitioners. TRP membership included at least one representative from each of the following 

groups: Bureau of Land Management, Bureau of Reclamation, city planning and/or department of 

transportation, county planning, economic development agency/organization, Federal-aid Division 

Office representation, National Park Service, nonprofit parks advocacy or research representation, 

private planning or engineering practitioner, state DOT, regional planning, state or local parks and/or 

other land management departments, United States Forest Service, university research representation, 

and US Army Corps of Engineers (Table 1). The TRP also had the flexibility to invite additional technical 

staff from other federal agencies as needed to provide expertise and advice to the TRP and project 

management team. Table 2 summarizes the objectives and findings of the panel meetings over the 

duration of the study. 
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Table 1: Technical Research Panel by Agency

Name Agency Role

1. Cassie Bhat ICF International Senior Management Consultant

2. Meredith Bridgers US Army Corps of Engineers Outdoor Recreation Planner

3. Kayla Carter Northeast Tennessee Regional 

Economic Partnership

Manager, Outdoor Development

4. Randy Chatterton Bureau of Land Management Colorado Office Staff

5. Hanna Cockburn City of Greensboro Transportation Planner

6. Erica Cole National Park Service Transportation Planner

7. Stacy Corless Mono County, California Board of Supervisors

8. Emily Douce National Parks Conservation 

Association

Director of Operations and Park 

Funding

9. Rob Gillespie United States Forest Service National Program Manager

10. Michael Gray Virginia Department of Transportation Salem District Planning Manager

11. Maureen Gresham FHWA Pennsylvania Division Office Community Planner 

12. Matt Johns Rapides Area Planning Commission Executive Director

13. Marlene Kelley Bureau of Indian Affairs Supervisory Highway Engineer

14. Tom Lamar Latah County Idaho Commissioner

15. Daniel Pietkowski University of Nebraska Assistant Professor

16. Dan Staton Bureau of Reclamation Operations and Maintenance

Structures Program Manager

17. Jaime Sullivan Western Transportation Institute Senior Research Engineer

18. Peter Tomczik US Fish and Wildlife Service Transportation Asset 

Management Coordinator

19. Rick Williams Oregon Department of Transportation Principal Planner
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Table 2: Summary of Research Panel Meetings

Meeting Date Meeting Objective Meeting Outcome

January 11, 2021 Identify examples of cross-agency information 

coordination between PLMAs and 

transportation partners that result in process 

efficiencies, cost savings, and a development

of tools and resources.

Initial establishing of criterion for case 

studies and context for research. 

March 17, 2021 Introduce two-week window for methodology 

review.

Further established a criterion for 

selecting case studies and the contexts 

that should be included in the research.

March 4, 2022 Project team presents data collection process 

including evaluation of 24 case study 

candidates, discussions with 14 project groups, 

and documentation of case study interviews. 

Four common themes for project motivations 

include: analyzing travel, understanding 

partner priorities, overcoming institutional 

barriers, and collaboration on specific tasks. 

The results of data sharing partnerships 

included coordinated plans and projects, 

enhanced awareness of partner goals and 

projects, identifying common priorities 

and objectives, and coordinated project 

delivery. 

June 21, 2022 Project team requests TRP participants to 

review and comment on 12 drafted case 

studies. As a part of the toolkit development 

process, the group reviews what tools already 

exist and where gaps exist to inform what tools 

are necessary. 

The session helped establish a 

comprehensive understanding of existing 

resources and where gaps exist in data 

sharing tools. The creation of new tools

was determined to include governance 

tools, data tools, and work plans to aid 

with project implementation. 

September 7, 2022 Identify several actions, including creating a 

summary of current data sharing processes 

and obstacles, creating a format for sharing 

best practices, including laws and regulations 

related to data sharing, providing resources 

outside of the federal realm, and identifying an 

IT-focused user group.

Initiated training sessions and working 

groups, reached out to Municipal 

Planning Organizations (MPOs) and 

attending conferences, and created a 

monitoring phase to determine the 

effectiveness of the guide. 

January 11, 2023 A presentation of the toolkit, designed to 

provide governance and data tools for 

transportation professionals. The governance 

tools include best management practices, data 

sharing agreement templates, synopsis of laws 

and regulations, and challenges stemming 

from personally identifiable information (PII). 

Discussed work plans for future activities, 

including supporting data user groups, 

communicating funding opportunities, 

and ongoing technical support.

May 26, 2023 A presentation of the final toolkit formatting 

and overview of remaining research project 

deliverables. This was the final meeting for the 

research panel.

Limited comments and discussion of final 

toolkit in advance of panel review. The 

participants noted that they have been 

engaged along the process and are 

familiar with the outcomes.

Findings

The project team answered the overarching and specific research questions as the study progressed 

from literature review to case studies and eventually toolkit development with consistent input from the 

TRP along the way. The findings are intended to justify and promote planning and data sharing 
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partnerships, define a path to form those partnerships and initiate data sharing, and help stakeholders 

overcome challenges that others have faced when establishing lasting data sharing partnerships.

Why do PLMAs and transportation agencies share data?

Part of the answer to why data is shared relates to legislative requirements in the transportation 

planning process that must be followed by state DOTs. The literature review highlighted important 

legislative context for the transportation planning process. Title 23 of the U.S. Code (USC) is the primary 

legislation governing transportation decision-making for PLMAs and transportation agencies.

Additionally, the Moving Ahead for Progress in the 21st Century (MAP-21) Act of 2012 and the Fixing 

America’s Surface Transportation (FAST) Act of 2015 authorized several programs and requirements 

regarding transportation planning, programming, performance management, and other decisionmaking. There are no nationwide requirements for state and local PLMAs, but many states have laws 

and regulations pertaining to non-federal PLMAs. With these requirements in mind, transportation 

agencies and PLMAs can identify opportunities for collaboration, such as a long-range transportation 

plan.

The case studies revealed motivations beyond the legislative requirements for sharing data. Among 

them are:

• Identifying shared priorities, missions, goals, and objectives

• Developing a common understanding of how projects from each agency interact with other 

agencies’ existing or planned efforts

• Leveraging additional data to make project planning and analysis more robust

• Analyzing travel patterns, transportation networks, and gaps at a regional scale 

• Addressing challenges that cross jurisdictional boundaries, such as congestion and 

environmental protection

• Overcoming institutional barriers to data sharing, such as firewalls, incompatible technologies, 

and inefficient one-off data transfers 

• Working together on a particular task, such as a project of mutual benefit

How do PLMAs and transportation agencies share and use data?

There are seemingly endless ways to share and use data between PLMAs and state transportation 

agencies; however, there are critical activities that should happen before any data files are transferred. 

The study's findings show that building relationships and trust between the involved parties was critical 

and having a designated champion or representative in a leadership role was important. Related to the 

role of a champion, the study found communication of the importance of data sharing partnerships to 

agency leadership and the public led to greater support. 

The mechanisms used for data sharing varied depending on the situation, including formal and informal 

methods. Formal data sharing partnerships use agreements, such as a Memorandum of Understanding 

(MOU), to establish roles and responsibilities as well as what data partners share, data management 

protocols, privacy and access limitations, and other considerations. An informal data sharing partnership 

may become formalized over time.

The research team did not define a specific set of data that should be shared across different 

partnerships, as the context of transportation planning varies by location and other circumstances. 

Rather, the team found it is important for all partners in a data sharing partnership to have access to the 
Prepared by the U.S. DOT Volpe Center 6

same technologies and platforms. In some cases, partners achieved this with commonly available 

platforms, such as online mapping software. In other cases, partners use more complex platforms 

developed and maintained by third-party vendors. In either case, it is important for partners to have 

access to the data platforms and the training and resources needed to analyze the data. 

The results of data sharing partnerships included coordinated plans and projects, enhanced awareness 

of partner goals and projects, identifying common priorities and objectives, and coordinated project 

delivery. These results, in turn, helped the PLMAs and transportation agencies with transportation 

decision making. Additionally, while the study focused on transportation access to public lands, the highlevel findings are universal across sectors and stakeholders.

What are the challenges to data sharing?

Challenges to planning and data sharing include:

• Limited leadership support – Leadership in an organization directs priorities and where to invest 

limited funding and staff resources. 

• Staff turnover across agencies – People change jobs and often do not overlap with their 

successor. The result can be that cross-agency relationships do not last when they are tied to an 

individual instead of a designated staff role or position.

• Unfamiliar organizational structures – Other agencies can seem opaque to outsiders who do 

not know the right person to contact for data sharing. 

• Limitations due to agency policies – Agencies may have specific rules about what data can be 

shared externally and how new data can be brought into their systems. While these limitations 

are intended to protect the organization from sharing sensitive data or cyberattacks, there may 

be ways to work with information technology (IT) staff to allow data sharing under certain 

conditions.

• Other work taking priority – This challenge relates to leadership support but also points to 

issues that stem from many demands on individual staff. 

Planning and Data Sharing Partnership Toolkit

There is no single solution to address all these challenges; however, there are actions that can reduce 

these barriers. The TRP and case studies helped identify a set of 11 tools (Table 3) to help reduce 

barriers to planning and data sharing partnerships that the project team organized into three categories.

These tools together form the Planning and Data Sharing Partnership Toolkit.

• Governance Tools – These are tools that provide support and resources related to different 

agencies working together successfully to achieve their common goals. They touch on formal 

agreements, legal requirements, and best practices.

• Data Tools – These are tools that focus on the types of data that agencies typically share for 

transportation planning across jurisdiction boundaries. The tools include existing data 

clearinghouses and the application of different types of data. 

• Work Plans for Future Activities – The work plans are intended to be action items for different 

stakeholders to expand and support planning and data sharing partnerships. They relate to 

funding and ongoing support for stakeholders to pursue partnerships for coordinated 

transportation planning, including regular updates of the toolkit itself.
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Table 3: List of Planning and Data Sharing Tools by Category

Governance Tools

Tracking of Best Management Practices

Templates for Developing Data Sharing Agreements

Synopsis of Laws and Regulations

Challenges Regarding Personally Identifiable Information

Data Tools

Existing Data Resources

User-Friendly Data Visualization Tools

Traveler Information Systems to Distribute Data

Big Data and Crowdsourced Data

Work Plans for Future Activities

Supporting Data User Groups

Communicating Funding Opportunities

Ongoing Technical Support

Overall, the research project emphasized the importance of data sharing partnerships between PLMAs 

and transportation agencies for better transportation systems. The project's findings and toolkit will 

serve as a valuable resource to aid PLMAs and transportation agencies in sharing data and improving 

coordination for better transportation systems.

Planning and Data Sharing Partnerships Implementation Work Plan 

The research findings justify and encourage planning and data sharing partnerships, which is a long-term

effort. The scope of this research project does not include ongoing support for disseminating the 

Planning and Data Sharing Partnership Toolkit or technical assistance to help stakeholders establish 

planning and data sharing partnerships. Rather, it provides a high-level implementation work plan for

FHWA and other stakeholders to share the toolkit and establish a process for ongoing support for such

partnerships. 

A comprehensive work plan is crucial for successfully implementing any project or initiative. It is an 

active guide that requires regular updating to address changes in resources, new challenges, and 

priorities. A work plan provides a clear roadmap of what needs to be done, who is responsible for each 

task, and when each task should be completed. It helps to identify the next steps and limitations that 

must be addressed to move a project forward. FHWA and transportation planning stakeholders must 

justify agency spending and a well-developed work plan can establish a strong business case for 

investing in project implementation. By outlining the specific benefits of planning and data sharing 

partnerships and the resources required to support them, a work plan can demonstrate the need for 

investment, as well as a clear understanding of the FHWA resources required to achieve project 

objectives.

Work Plan Audience

When developing an implementation work plan, it is important to consider what is being implemented 

and the target audiences for each potential task. This project can be viewed as having two 

implementation work plan audiences: the stakeholders that engaged in the research and have interest 
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in seeing it progress beyond the toolkit being created and the stakeholders wanting to create 

partnerships (toolkit users).

The target audience for the Planning and Data Sharing Partnerships work plan to implement the findings 

of this research project and their likely roles are in Table 4, which is also part of the Ongoing Technical 

Support tool in the toolkit. The federal agencies in the table include FHWA who invested in the research, 

other federal agencies that participated in development of the research needs statement that led to the 

research being funded, and others that create or compile datasets for public use. It is also important

that the work plan conveys to groups that financially support the specific partnership being established

that their investment will result in tangible transportation improvements by highlighting the potential 

benefits of implementing the plan, such as increased efficiency, reduced costs, and improved safety.

Work plan task owners and tool developers are also champions of the Planning and Data Sharing 

research. They are involved in the development of the final, more detailed, work plan and committed to 

its success. Their involvement and commitment help ensure that the plan is implemented effectively 

and efficiently.

Table 4: Planning and Data Sharing Partnership Implementation Roles and Responsibilities

Entity

Contribute to 

keeping tools 

updated

Outreach and 

marketing to inform 

audience of TA*

Provide TA 

Services as 

needed

Funding support 

to provide TA 

services

Federal Agencies

State Agencies

Local Agencies + 

Technical Assistance 

Programs1

Academic Institutions, 

Non-Profits

Public Land Advocates

Software + Database 

Companies

*TA = technical assistance

Work Plan Components

A valuable work plan is a comprehensive document that outlines the objectives, activities, roles and 

responsibilities, time frame, and measurable outcomes of a project. The objectives should be clear, 

specific, and achievable within the defined time frame. The activities needed to achieve these objectives 

are included along with how the work plan will be shared with stakeholders and how feedback will be 

gathered. The Planning and Data Sharing work plan described in this document is a high-level version of 

an implementation work plan to move the research forward. FHWA and other stakeholders will need to 

create a more detailed work plan that also includes a time frame that clearly identifies when each task 

will be completed, measurable outcomes to evaluate the project's success, and feedback mechanisms to 

1 Technical assistance programs include Local Technical Assistance Programs (LTAPs, 

https://www.fhwa.dot.gov/clas/ltap/) and Tribal Technical Assistance Programs (TTAPs, 

https://www.fhwa.dot.gov/clas/ttap/).
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gather feedback on the project's progress. Finally, to help ensure the successful implementation of the 

work plan, potential Planning and Data Sharing pilots should be defined, along with identifying possible 

funding sources.

Conclusion 

Transportation planning in and around public lands is important, especially given the rising amount of 

recreational travel. The congestion from this visitation crosses boundaries between public lands and 

multiple transportation agencies. It is essential that agencies work together to ensure the public has safe 

and efficient access to public lands. These agencies can benefit from sharing data and coordinating 

transportation planning by avoiding duplication of data collection efforts and identifying opportunities 

to align capital construction projects or operational changes. The agencies involved can leverage robust

data collection to make informed decisions about infrastructure improvements, traffic management 

strategies, and resource protection measures. 

While the benefits of data sharing are clear and numerous, there are several barriers to coordination

and little information or guidance on how to partner across public lands and transportation agencies.

The deliverables from this research have begun to address the lack of guidance and provide information 

on how to overcome the barriers. The case studies in Appendix 1 provide examples of successful 

planning and data sharing partnerships for others to learn from. The tools in the Planning and Data 

Sharing Partnership Toolkit were identified through a combination of gap analysis from a literature 

review and case studies, and input from the Technical Research Panel. The final toolkit contains 11 tools

that cover data governance, data types, and implementation work plans to support long-lasting 

partnerships. The next steps in supporting Planning and Data Sharing Partnerships are to make the 

toolkit available to a wide audience of stakeholders and provide support to transportation agencies and 

public lands management agencies in their efforts to coordinate data and planning.
Appendix 1: Case Studies
FHWA Planning & Data Sharing 

Partnerships Case Study Series

1

Alaska Transportation Working Group 

Interviewed Organization(s): Federal Highway Administration, Alaska Department of Transportation 

& Public Facilities, U.S. Forest Service, National Park Service)

Forming a Data Sharing Partnership 

The Alaska Transportation Working Group (TWG) formed 

in 2008 and consists of the following members:

• Federal Highway Administration (FHWA), Western

Federal Lands (WFL) Division and Alaska Division

• Bureau of Land Management (BLM)

• National Park Service (NPS)

• U.S. Fish and Wildlife Service (FWS)

• U.S. Forest Service (FS)

• Alaska Department of Transportation & Public

Facilities (DOT&PF)

• Alaska Municipal League (AML)

• Denali Commission

• Fairbanks Area Surface Transportation (FAST)

MPO (joined in 2022)

• Anchorage Metropolitan Area Transportation

Solutions (AMATS) MPO (joined in 2022)

The TWG initially formed to develop a Collaborative LongRange Transportation Plan (LRTP) for federal lands in 

Alaska. At that time, the national surface transportation 

authorization required that federal land management

agencies develop LRTPs, and the partners in Alaska chose 

to work together to develop their first LRTPs in the state 

together. Through this process, they developed a forum for

sharing data, collecting new data to support research, and 

planning efforts, and coordinating project programming and 

delivery. Since formation, the TWG completed its initial 

LRTP in 2012 and an update in 2020, conducted joint

research on a range of transportation management topics,

and has been a forum for members to identify and 

complete projects of mutual interest. The TWG continues to 

meet monthly via teleconference and holds annual project

coordination meetings.

Data Fields & Tools 

Because the TWG was originally formed to develop the first 

Collaborative LRTP in the state, it was not initially obvious 

what data the group needed. The group worked together to 

identify the data needed to establish baselines, analyze 

trends, and develop performance measures for the LRTP’s 

six goal areas: system management, user experience, 

safety and mobility, environment, climate change, and 

partnerships. Through this process, the TWG members 

shared existing data on their respective transportation 

systems. The TWG also identified data gaps and 

developed joint research projects to fill those gaps. These 

projects included the Collaborative Visitor Transportation 

Lessons Learned

Lesson #1 – The project champion

One lesson of the Alaska TWG is the importance of a 

champion who can convene, organize, and support the 

group’s activities. This champion is responsible for 

keeping the group moving forward and providing vision 

and institutional memory. WFL has assumed the 

facilitation lead for the Alaska TWG, and individual 

agency staff play the champion role for different TWG 

focus areas.

Lesson #2 – Set common goals

It is important to have established, common goals and 

objectives to drive a collaborative planning effort’s 

success. This can take the form of a Collaborative 

LRTP like the Alaska TWG, or it could be another type 

of organizing vision and purpose.
2 

Survey (CVTS), which conducted a survey of visitors to 

federal lands across Alaska; a safety performance baseline 

analysis that combined a wide range of multimodal 

transportation incident data; and case studies of climate 

change impacts on transportation infrastructure such as 

permafrost melting and coastal erosion.

Implementation Challenges

The TWG’s first challenges were organizational in nature. It 

took substantial work to identify the correct contacts at 

each agency and to establish the working group. The initial 

TWG members also had to communicate the value of the 

group to their agencies’ leadership to justify the time and 

resources required for participation. Maintaining agencies’ 

management support for the TWG has been an ongoing 

challenge, yet critical to the group’s success. To justify the 

TWG’s effort, the TWG communicates its achievements in 

planning, programming, and project coordination. The TWG 

has particularly benefitted from the Federal Lands Access 

Program (FLAP) because they have demonstrated how the 

TWG’s project coordination has led to projects of mutual 

benefit. As the TWG continues to collaborate, limited staff 

resources in member agencies presents an additional 

challenge to implementation, particularly for smaller 

agencies.

The TWG has also encountered data-related challenges in 

terms of both data sharing and compatibility. In terms of 

data sharing, TWG has struggled to overcome agency 

firewalls and access to data sharing platforms. Different 

agencies’ IT departments support or block different data 

sharing platforms, which makes it difficult to transfer files 

between agencies. In terms of data compatibility, different 

agencies often collect or maintain data in different formats 

or with different attributes, which can make it difficult to 

conduct analyses across agencies. For example, the TWG 

has put substantial effort into developing a common 

understanding of transportation asset condition, which has 

required cross-walking between different agencies’ asset 

management datasets.

Results/Main Takeaways

The Alaska TWG has achieved success in a wide range of 

joint projects, including the development of two 

Collaborative LRTPs, joint research projects, and ongoing 

project coordination. The TWG has provided a forum for 

identifying and developing FLAP projects and other 

projects of mutual benefit. Coordination on projects across 

jurisdictions has helped the TWG members achieve more 

efficient projects; for example, combining nearby projects 

can reduce contractor mobilization costs. TWG members 

also stress the value of relationship-building. 

Understanding the transportation system outside of agency 

borders and knowing the right staff at the TWG partner 

agencies to work with has been valuable for a range of 

planning and project needs. The TWG membership 

continues to grow as new agencies see the benefit of 

coordinating with the TWG.

Looking Forward

The Alaska TWG continues to work together after over 14 

years of collaboration. The TWG currently focuses on 

implementation of its 2020 Collaborative LRTP update. The 

TWG also continues to work on improving data collection 

and standardizing as much data as possible to encourage 

better coordination across jurisdictions. The Alaska TWG’s 

model of collaboration has been duplicated through a TWG 

in Oregon and Washington, and integrated planning efforts 

in Colorado and Nevada have also drawn on lessons from 

the Alaska TWG.

Quick-Reference Information

• Project Phase: System and Project Planning, Project Selection and Programming,

Project Design and Environmental Review

• Agencies/Partners Involved: Federal Highway Administration, Bureau of Land

Management, National Park Service, U.S. Fish and Wildlife Service, U.S. Forest

Service, Alaska Department of Transportation & Public Facilities, Alaska Municipal

League, Denali Commission, Bureau of Indian Affairs, Fairbanks Area Surface

Transportation MPO, Anchorage Metropolitan Area Transportation Solutions MPO

• Location: Alaska (statewide)

• Type(s) of Data Shared: Transportation systems geospatial data, asset condition,

safety, visitation, environment, climate, and resilience

• Method of Data Sharing: Working group charter, collaborative planning efforts,

annual project coordination meetings

Special Thanks 

• Kevin Doniere, Alternative Transportation

Program Manager, Alaska Region, NPS

• Amy Thomas, Deputy Director of Engineering,

U.S. Forest Service

• Paul Escamilla, Project Engineer, U.S. Forest

Service

• Eric Taylor, Transit Program Manager, Alaska

Department of Transportation & Public

Facilities

• Roxanne Bash, Transportation Planning Team

Lead, FHWA WFL
1

Collaborative Visitor Transportation Survey 

Interviewed Organization(s): USDOT Western Federal Lands Highway Division, US Forest 

Service, USDOT Volpe Center

Forming a Data Sharing Partnership 

The Alaska Transportation Working Group (TWG) – a 

group of federal land management and transportation 

agencies in Alaska – convened in 2008 to develop their 

first Collaborative Long-Range Transportation Plan (LRTP) 

and to coordinate on projects of mutual benefit. While 

developing their first LRTP, the TWG identified a need to

collect better data on user experience. As such, they 

conducted a statewide, multi-agency survey of users of 

federal lands to better understand their transportation 

experiences to and within federal lands sites. 

One barrier to collecting user survey data is the need to 

obtain Office of Management and Budget (OMB) clearance 

for survey questions in compliance with the Paperwork 

Reduction Act (PRA) of 1980. To address this challenge 

and to create a resource for other public lands and 

transportation agencies across the U.S., the TWG 

developed the Collaborative Visitor Transportation Survey 

(CVTS), which is a generic clearance with a range of public 

lands transportation-related questions. The Alaska TWG 

implemented the CVTS at several public lands sites across 

Alaska in 2016 and used the results of the survey in the 

2020 Collaborative LRTP update. 

The CVTS is a multi-agency collaboration with a range of 

specific roles, led by FHWA’s Western Federal Lands 

(WFL) Highway Division with technical support from the 

U.S. Department of Transportation’s Volpe National 

Transportation Systems Center. The U.S. Forest Service 

houses the CVTS Generic Clearance and submits use 

requests to OMB for final approval. Other CVTS users and 

contributors include the Bureau of Land Management, 

National Park Service, U.S. Fish and Wildlife Service, U.S. 

Army Corps of Engineers, and the Alaska Department of 

Transportation and Public Facilities. 

Data Fields & Tools 

The CVTS is a tool for collecting social science survey data 

to understand the transportation experiences and 

perceptions of public lands users. The CVTS generic 

clearance includes an OMB-approved compendium of 

Lessons Learned

Lesson #1 - Identify a project champion 

The CVTS demonstrates the need for a project 

champion to work with a range of project partners and 

move the tool forward. This project had a few key roles: 

project management (WFL), social science technical 

lead (USDOT Volpe Center), and tool host (U.S. Forest 

Service). The project champion is particularly important 

for maintaining institutional memory and bringing in 

new agency contacts as staff turnover.

Lesson #2 - Design for flexible application 

One aspect of the CVTS that has made it successful is 

that its creators designed it to be applicable to a wide 

range of agencies and contexts. By designing the 

survey compendium with a range of questions that are 

generally useful for agencies who want to collect visitor 

transportation survey data, they have helped make the 

CVTS sustainable beyond their initial survey effort and 

created a tool that other agencies have used 

throughout the U.S. 

FHWA Planning & Data Sharing 

Partnerships Case Study Series
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transportation survey questions and data collection 

methodologies. Public lands and transportation agencies 

can use the approved questions and data to measure user 

experience performance metrics related to transportation. 

They can also quantify users’ transportation experiences to 

help target transportation improvements. By providing an 

OMB-approved compendium of questions, the CVTS 

streamlines the OMB approval process, reducing the time 

and expense required to conduct individual surveys. The 

CVTS allows for more uniform survey data responses 

across users by asking standard questions. 

Implementation Challenges

The CVTS is a complex effort that requires staff input from 

several federal agencies. Identifying appropriate contacts 

from agencies and maintaining relationships despite staff 

turnover has been a challenge. 

Having a consistent champion to maintain and promote the 

CVTS has been important to the tool’s success. USDOT 

Federal Land Highways (FLH) has been the champion of 

the CVTS. Their role coordinating transportation 

investments across FLMAs gives them insight into the 

importance of user experience data when prioritizing 

projects. Additionally, FLH understands the OMB process 

as a federal agency that has engaged in the process and 

the value of having a shared streamlining tool.

The CVTS team emphasized communicating the value of 

the CVTS to potential users and to the OMB to ensure 

support for using the generic clearance approach for other 

topics of national and multi-agency interest.

Results/Main Takeaways

As CVTS is used consistently over time, agencies can 

monitor how patterns and perceptions shift. The CVTS 

streamlines survey collection by eliminating the need for 

separate agencies to go through the full OMB process each 

time, reducing administrative costs and timelines for 

developing user surveys. Some examples of projects that 

have used the CVTS include: 

• Alaska: The BLM, USFS, USFWS, and the NPS

collaborated on a statewide survey to measure

visitor experience performance metrics. The

partners then incorporated the data into their 2020

LRTP update.

• White Mountain National Forest (WMNF): The

USFS collected information across several sites to

evaluate and improve visitor transportation and

recreation management for the WMNF.

• Beaver Lake: U.S. Army Corps of Engineers used

information gathered to update of the Beaver Lake

Master Plan and Shoreline Management Plan.

• Millennial and Baby Boomer Mobility Preferences:

U.S. Fish and Wildlife (FWS) administered a

survey to understand Millennial and Baby Boomer

interests and preferences regarding access and

circulation within FWS units in three western states

– California, Colorado, and Texas.

Looking Forward

The CVTS is a continuing effort. WFL, Volpe, and USFS 

continue to manage the CVTS generic clearance and 

support agencies interested in using it. Looking forward, 

the CVTS team would like to share more information on 

how agencies have used surveys through the CVTS to 

improve transportation plans and projects. The CVTS team 

would also like to provide a clearinghouse for CVTS survey 

data in the future.

Special thanks:

• Roxanne Bash, Transportation Planning Team

Lead, FHWA WFL

• Kenli Kim, Landscape Restoration and Ecosystem

Services Research National Program Leader,

USFS

• Margaret Petrella, Social Scientist, USDOT Volpe

Center

Quick-Reference Information

• Project Phase: System and Project Planning

• Agencies/Partners Involved: Federal Highway Administration, Bureau of Land Management, National Park Service,

U.S. Fish and Wildlife Service, U.S. Forest Service, Alaska Department of Transportation and Public Facilities

• Location: Alaska (statewide)

• Type(s) of Data Shared: Visitor transportation survey data

• Method of Data Sharing: OMB Generic Clearance, compendium of survey questions, coordinated survey data

collection

• Additional Resources: Collaborative Visitor Transportation Survey (CVTS)
1

Cape Cod Commission Outer Cape Bike and Pedestrian Master Plan

Interviewed Organization(s): Cape Cod Commission

Forming a Data Sharing Partnership 

In 2017, Cape Cod Commission (CCC) staff, in 

collaboration with the National Park Service Cape Cod 

National Seashore (NPS CCNS), completed the Outer 

Cape Bicycle and Pedestrian Master Plan (OCBPMP). This 

plan creates a framework for an interconnected bicycle and 

pedestrian network linking the towns of Wellfleet, Truro, 

and Provincetown with the Cape Cod Rail Trail (CCRT), 

CCNS, and other destinations within the three 

communities. There were numerous attempts over the 

years to initiate this project, but it did not formally begin 

until CCNS, the CCC, and representatives from the three 

regional towns received funding through the Federal 

Transit Agency Transit in Parks (FTA TRIP) program.

A partnership to share data was necessary as there were 

several gaps in data between the various stakeholders. For 

instance, NPS was able to provide cultural resource data 

within its boundaries, however for areas outside of these 

zones, the data was gathered from the Massachusetts 

Historical Commission’s GIS database. NPS and towns did 

not have road data (such as geometries or traffic counts) 

for regional roads, so the CCC provided their own collected 

data on bike and pedestrian traffic and collaborated with 

MassDOT. Necessary data to accomplish the project were 

dispersed among many different agencies and 

stakeholders, requiring efforts for collaboration.

Data Fields & Tools 

Data for the project included roadway data, GIS data of 

regional roads and paths, bike and pedestrian traffic data, 

as well as subjective information such as “roadway 

characteristics” collected from the general public at 

community meetings. CCC compiled data collected over 

the years of the project and provided GIS services, 

Lessons Learned

Lesson #1 – Establish efficient data digitization 

processes

Digitizing data was found to be a time-consuming 

process. Some digitizing needed to be done by GIS staff, 

particularly converting raw data into usable data for GIS. 

Another form of data that required digitization was the 

input of public comments from community meetings. 

Sometimes this information was based on hand drawn 

maps and handwritten notes. There is a need to find 

means to modify qualitative data into quantitative data in a 

practical and time-efficient manner.

Lesson #2 – Establish mechanism for continued 

collaboration

A challenge that the partnership team encountered was 

that there was no mechanism to keep the team 

collaborating. In an ideal data sharing partnership, there 

would be an implementation plan to keep the partnership 

going. For instance, this could be an MOU incorporated 

as a part of the process to help determine when the plan 

is completed. After an initial plan is created, those 

involved are often exhausted and therefore the project 

lacks a champion to keep it going. The project team 

acknowledged that having some mechanism to help 

achieve an agreement among involved parties would 

have been beneficial earlier in the collaborative process. 

In addition, rotating the leads of meeting can help keep 

things fresh and allows smaller towns and collaborating 

stakeholders to have more buy-in in initiatives.

FHWA Planning & Data Sharing 

Partnerships Case Study Series
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including sharing GIS shape files, which were used to layer

a shared web map.

Implementation Challenges 

Developing the scope of this data sharing partnership was 

a lengthy process, requiring nearly a year and involved 

both park land and non-park land involvement. 

Determining the responsibilities of various partners was a 

challenging process. One of the initial relationships 

established was between CCC and CCNS, collaborating 

under a cooperative agreement related to data sharing and 

planning. The partnership helped streamline the process of 

navigating the CCNS protocol for requesting and receiving 

data. This relationship was integral when interacting with 

local towns, particularly when pertaining to sensitive 

subject matter such as delegating responsibilities between 

NPS managing federal land and nearby towns facilitating 

non-park lands. 

The project team achieved success sharing data and 

garnering community buy-in when developing the Bike and 

Pedestrian Master Plan. Despite this, the team has noted 

challenges related to executing the concepts presented in 

the Master Plan, siting slow progress as far as 

implementation is concerned. CCC does not own any land 

for implementation. A primary difficulty in implementing the 

initiatives developed thanks to shared data resources is a 

lack of a “champion” to push initiatives forward toward 

successful implementation. The towns and their unique 

governance and predispositions have been the cause of 

slow progress. 

Results/Main Takeaways

CCC was able to serve as a community liaison between 

CCNS and local towns connected along the bicycle and 

pedestrian network of the Cape Cod Rail Trail. This 

relationship underscores the benefit of having a partner in 

the community that has the trust of the public to gain their 

participation/buy-in. Establishing the relationship early 

helps the set up for sharing information. In the context of 

developing the OCBPMP, CCC facilitated discussions 

between CCNS and local partners, fostering improved trust 

and cooperation among the team.

Looking Forward

The data shared among the partnership established a good 

foundation of support for planning and decision making by 

the steering committee. Since the project, base data layers 

have been updated; however, there is not a regularly 

updated data set on the bike routes. This project was 

generally singular in approach, and there have been no 

major changes since.

Special thanks:

• Steven Tupper, Director of Transportation

Program, Cape Cod Commission

• Sarah Korjeff, Planning staff, Cape Cod

Commission

• Martha Hevenor, Planning Staff, Cape Cod

Commission

Quick-Reference Information

• Project Phase: System and Project Planning

• Agencies/Partners Involved: Cape Cod Commission, National Park Service Cape Cod National Seashore

• Location: Cape Cod, MA

• Type(s) of Data Shared: Traffic volume data and cultural/environmental resource data

• Method of Data Sharing: GIS layers accessed via ArcGIS
1

Colorado Integrated Planning Project

Interviewed Organization(s): FHWA Central Federal Lands (CFL) Highway Division, Colorado Department of Transportation 

(CDOT), Grand Valley Metropolitan Planning Organization / Mesa County Regional Transportation Planning Office 

Forming a Data Sharing Partnership 

As part of the Colorado Integrated Planning Project, the 

Federal Highway Administration (FHWA) Central Federal 

Lands (CFL) Division convened a wide range of public land 

management agencies (PLMAs) and state and local 

transportation agencies to share information on project 

needs at regional and corridor scales. CFL established this 

partnership to determine where organizations had similar 

gaps in data and develop and create a communal platform 

to address these needs. Another goal of this data-sharing 

effort has been to support integrated planning to ensure 

state and local transportation agencies incorporate PLMA 

needs into their plans and programs, and vice versa. This 

can help agencies identify opportunities to collaborate on 

projects of mutual interest and can inform future funding 

and project prioritization. Through the Project, the CFL 

hosted a series of workshops and created an online GIS 

platform for local agencies to share standardized data on 

identified project needs.

Data Fields & Tools 

Several agencies found that their primary gaps were 

qualitative data, particularly data demonstrating why 

travelers are going to a place. The CFL workshop series 

brought PLMAs and state and local transportation agencies 

together to share information on travel needs and to 

document qualitative data in a standardized way on a 

shared online GIS platform. Partnering agencies were then 

able to use this shared and standardized data to 

collaboratively build long term transportation plans and 

other building projects. Agencies were able to provide data 

and information they had available, allowing different 

agencies at the local, state, and federal level to work 

together towards common goals.

Lessons Learned

Lesson #1 – Create a standard platform for

data sharing 

CFL led workshops to review partner datasets and 

teach partner agencies how to access shared 

datasets, particularly HPMS data from CDOT. CFL 

invited participating agencies to incorporate their 

data into a shared online GIS platform and provided 

training on how to use the GIS platform. Agencies 

then added their transportation network and project 

needs datasets. As a result, the partners shared 

data on 170 projects throughout the state, 

strengthening the network writ large.

Lesson #2 – Link data sharing to future 

planning, projects, and funding 

opportunities

The Colorado Integrated Planning Project provided 

an opportunity for agencies to align their long-range 

transportation plans and programs of projects. The 

partners also used the findings to identify projects of 

mutual benefit and develop strategies to pursue 

funding through grant programs and other funding 

sources. This is especially important for programs 

like the Federal Lands Access Program (FLAP), for 

which PLMAs are not eligible applicants. Nevertheless, 

working with state, local, and tribal partners to support 

their application process is imperative.

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Partnerships Case Study Series
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The Colorado Department of Transportation (CDOT) and 

CFL entered into a data sharing agreement at the start of 

the project. CDOT shared Highway Performance 

Monitoring System (HPMS) data from their road dataset, 

and the partners used this data, along with PLMA road 

data, to supplement gaps in agency datasets, as well as to 

refine existing programmed projects.

By sharing data sets and working together, the agencies 

also developed a greater understanding of their respective 

planning processes. For example, local MPOs learned 

about the priorities of federal partners and were thus able 

to adapt their long-range transportation priorities to better 

position themselves as competitive applicants for federal 

funding.

Implementation Challenges

There were no barriers regarding the direct exchange of 

data. The primary implementation challenge was 

determining where data gaps existed.

Results/Main Takeaways

Sharing and comparing datasets across agencies 

highlighted overlapping data needs. Partner agencies 

incorporated the shared data into statewide long-range 

transportation and MPO plans to better position themselves 

as competitive applicants for federal funding.

Looking Forward

Partner agencies integrated data and findings from the 

Colorado Integrated Planning Project into their respective 

long-range transportation plans and programs of projects. 

CFL is establishing a similar integrated planning project in 

Nevada.

Special thanks:

• Aaron Bustow, Statewide and Metropolitan

Transportation Planner, FHWA – Colorado Division

• William Haas, Metropolitan Transportation Planner,

FHWA – Colorado Division

• Elijah Henley, Planning Team Lead, FHWA CFL

• Jeff Sanders, Transportation Planner, FHWA CFL

• Erica Cole, Transportation Planner, National Park

Service

• Dean Bressler, Senior Engineer, Transportation

Planning, Grand Valley Metropolitan Planning

Organization / Mesa County Regional

Transportation Planning Office

• Ross Mittelman, Mesa County Public Health Trails

Coordinator

• Matt Muraro, Environmental Specialist/Regional

Planner, Colorado DOT

Quick-Reference Information

• Project Phase: Planning, Programming

• Agencies/Partners Involved: FHWA Central Federal Lands (CFL) Highway Division, Colorado Department

of Transportation (CDOT), Bureau of Land Management (BLM), National Park Service (NPS), U.S. Fish and

Wildlife Service (FWS), and Colorado Metropolitan Planning Organizations (MPOs).

• Location: Colorado

• Type(s) of Data Shared: HPMS Data

• Method of Data Sharing: Online GIS Platform

• Additional Resources: https://www.codot.gov/programs/your-transportation-priorities/regional-transportationplans
1

Bi-State Planning: Tahoe Regional Planning Agency 

Interviewed Organization(s): Tahoe Regional Planning Agency

Forming a Data Sharing Partnership 

California and Nevada established the Tahoe Regional 

Planning Agency (TRPA) in 1969 to protect harmony 

between ecology and transportation with the consent of 

Congress through a bi-state compact. The compact 

enables the formalized coordination of efforts for corridor 

level planning by identifying common goals for Federal, 

state, and local planning, as well as resource protection 

agencies and tribes in Lake Tahoe. The compact charges 

TRPA with establishing a regional plan to achieve 

environmental standards that emphasize the intersection of 

ecology, land use authority, and transportation, with a 

specific focus on access, recreation, and land use. TRPA’s 

approach to transportation planning includes making their 

transportation demand model pairs with their travel 

demand model to build a recreational model. 

Partner agencies within the compact share datasets and 

together determine data gaps that will address common 

goals. They regularly coordinate to understand each 

other’s challenges and work to find solutions via 

agreements, charters, and other compact activities. This 

supportive problem solving, and ongoing support helps the 

partners realize the long-term value of participation and 

managing the corridor. 

Data Fields & Tools 

As an increasingly popular tourist destination, there is an 

overarching need for visitation data for the Lake Tahoe 

area, such as trip origin data, to understand travel behavior 

and how it impacts transportation and land use planning in 

the region. The partner agencies provide their data to 

TRPA who then pull it into the clearinghouse. Topline data 

showing the travel connections between the jurisdictions is 

a starting point for sharing. Data cleaning by TRPA staff is 

often necessary once it is acquired and before it can be 

added to the clearinghouse. They recently outsourced the 

data cleaning and travel demand modeling so in-house staff 

can shift to data management and communications. 

The TRPA conducts a travel mode survey to collect data on 

travel behaviors at different areas of the lake. The survey 

generates information on zip code of residency and income 

ranges to get an idea of who the traveler is, if they’re a local 

or visitor, and where they’re coming from and going. Bicycle 

counters are spread throughout the lake area and are now 

required for new trails. Parking data was recently added to 

the clearinghouse dataset; however, it isn’t received 

regularly. Survey results are shared in the clearinghouse 

available to TRPA and its partners.1 

Lessons Learned

Lesson #1 – Focus on the question

Given the amount of data available across the 

transportation and resource protection sectors, it is 

critical to focus on the question at hand and the data 

that will answer it. Collecting and analyzing data 

beyond what is needed can become overwhelming and 

waste critical time and resources without addressing 

the original need.

Lesson #2 – Regular coordination

Partner agencies and the TRPA are in regular 

communication with one another to understand each 

agency’s needs work to ensure those needs are being 

met by the collaboration. In addition to regular 

meetings, TRPA hosts an annual summit to reaffirm 

their commitment to the compact.

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Implementation Challenges

TRPA has had difficulty determining which of hundreds of 

indicators and data sets will best address long-term needs. 

To keep the process manageable, TRPA works to identify 

the most critical needs long-term and then focuses efforts 

to obtain that data to power the dashboards for decisionmaking with planning level data. They have started to 

prioritize data that supports decision-making for climate 

change initiatives, such as forest fuel hazards, because 

climate impacts are a high-priority goal for many agencies 

in the area. 

Another challenge is concisely communicating what the 

data says to the public. Partner agencies often use different 

data measures to describe similar things. If the message is 

not clear, the public doesn’t understand what the agencies 

are trying to convey. TRPA convenes discussions to select 

a single data point to report on and not confuse the 

community. Ultimately, it helps the partners speak with one 

voice, especially when they are advocating for funding or 

legislative action. 

Results/Main Takeaways

The TRPA’s longevity and success is firmly rooted in the bistate compact and having committed partner agencies that 

engage with each other frequently. TRPA’s collaboration 

with partners maximizes efficiency, providing higher-quality 

results by identifying common data needs, gaps, and 

sources that are then pulled into a common location with 

joint access. Their work advances the region’s 

transportation goals, protects the lake and its resources, 

and supports the economy by ensuring recreational travel 

is captured in the data and decision-making. It establishes 

a clear mission for transportation and resource protection 

for Lake Tahoe.

Looking Forward

TRPA is focusing on what data best addresses the 

compact partners’ needs and provides the greatest support 

for decision-making in the most cost-effective way. This 

includes more focused datasets that TRPA can use 

repeatedly and power decision-making dashboards with 

planning level data. Data relevant to planning for climate 

change are resiliency are especially important to TRPA in 

this effort. 

TRPA is also connecting with planning agencies in the 

broader California and Nevada area to understand 

recreational travel in the area to better plan for and support 

the future generations and resources at Lake Tahoe. TRPA 

is working to better correlate weather patterns, gas prices, 

and economic disposable income to model travel in a better 

way than a long-term model that glosses over those 

impacts. 

Special thanks:

• Michelle Glickert, Principal Transportation Planner,

Tahoe Regional Planning Agency

• Julie Regan, Chief, External Affairs/Deputy

Director, Tahoe Regional Planning Agency

• Nick Haven, Division Manager, Long Range and

Transportation Planning Division, Tahoe Regional

Planning Agency

Quick-Reference Information

• Project Phase: Planning and Programming

• Agencies/Partners Involved: Tahoe Regional Planning Agency

• Location: Lake Tahoe Region, Nevada, and California

• Type(s) of Data Shared: Visitor survey results, air, soil, and water quality, land use, and transportation data

• Method of Data Sharing: Via email; building data clearinghouse for partnership

• Additional Resources: https://www.trpa.gov, https://www.trpa.gov/programs/maps,

https://gis.trpa.org/mapmaker/
1

MARAD and University of Arkansas TransMAP 

Interviewed Organization(s): U.S. DOT Maritime Administration, University of Arkansas, U.S. DOT University 

Transportation Center (UTC)

Forming a Data Sharing Partnership 

University of Arkansas academics and industry specialists 

teamed up with the U.S. DOT Maritime Administration 

(MARAD) to form a data sharing partnership to address the 

challenges of sourcing maritime freight data, overcoming 

barriers to accessing this specific data, and improving 

accessibility to data portals. MARAD and the University of

Arkansas established the Transportation and Maritime 

Analytics Partnerships (TransMAP) Hub project to create 

an open access online visualization platform to collect,

analyze, and disseminate transactional and dynamic

maritime freight data across multiple software platforms.

The project team made the datasets available on a realtime basis to government agencies, industry, and citizens

based on open-source data management software tools.

TransMAP houses valuable interagency information and is

an example of a project that successfully incorporated as a 

university research project as a mechanism for its upkeep 

and maintenance.

Data Fields & Tools 

The online tool, TransMAP Hub, pulls together maritime 

freight systems data that would otherwise be widely

dispersed and difficult to align for public users. The data 

are organized by categories, including vessel movement in 

and out of lochs, vessel type, as well as the frequency and 

types of commodities transported through a port. This data 

may be pertinent to researchers who seek to download and 

utilize data for assessment of local trade and maritime

transportation. MPOs and other transportation decision

makers at US ports can use TransMAP to understand 

freight systems and guide planning related to travel

patterns.

Lessons Learned

Lesson #1 – Beta-test publicly available 

tools

The objective of TransMAP is to centralize public 

data into one place to improve data-informed 

maritime transportation planning. The partnership 

team benefited from initial “beta-testing” groups that 

sought out access to TransMAP for their own 

independent research and planning initiatives. The 

feedback from these groups was incorporated to 

improve Hub data collection and cleaning processes. 

Beta-testing the application ensured a high-quality 

tool for future users. future users.

Lesson #2 – Establish API standards

A significant problem with building data integration 

platforms is keeping them current. Many of the input 

data sources are formatted for human reading, but it 

is difficult and expensive to write and maintain 

software to integrate these documents 

(spreadsheets, pdfs, etc.) into the Hub. Websites like 

TransMAP rely on well-documented Application 

Programming Interfaces (API) to access these data 

sources as they update, use metadata to understand 

the data collection goals, make transformations to 

the data and data formats as they occur. While many 

of the sites use APIs to varying degrees, many do 

not, or the APIs are experimental and subject to 

change. Thus, established standards for APIs would 

allow for less expensive and more reliable data 

sharing.

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To develop the platform, project collaborators from the 

University of Arkansas and U.S. DOT MARAD delegated 

roles among industry experts and academic specialists. 

Industrial engineers provided industry expertise by scoping 

and assessing relevant data sources. They also 

maintained access to integral industry contacts and 

stakeholders. On the technical development side of the 

project, university academics and graduate students 

assumed the responsibility of aggregating data and 

integrating it into interactive maps and dashboards. 

Implementation Challenges 

Implementation of the program was time intensive. The 

MARAD and University of Arkansas partnership was 

established as a three-year project, where the first two 

years were dedicated to developing the TransMAP Hub. 

The third year focused on testing and validation of the Hub. 

The TransMAP development team is developing a user 

guide to assist users to navigate and use the system. A 

challenge of creating this data sharing platform has been 

establishing necessary modifications to provide a publicly 

accessible resource without jeopardizing sensitive 

information. For instance, information specifying exact 

routes and schedules of commodities shipments could 

result in a breach of national security, and thus it was 

imperative to omit this type of information. 

Results/Main Takeaways 

The TransMAP Hub centralized diverse datasets into one 

location. Even though all the data included in the Hub are 

free and publicly available, it would otherwise be difficult for 

individuals to find because it is so dispersed. Creating the 

Hub required collaboration and input from all partners. The 

partnership brought together industry experts with 

academic specialists who each learned from each other in

the process. Industry experts learned how to interact with 

the collected data and the academics learned from the 

specifics of maritime transportation data.

Looking Forward

The project is currently focused on the development and 

deployment of the TransMAP Hub, with the three-year 

partnership anticipated to culminate in September of 2022. 

However, there is interest to extend the project for a 

second phase to include enhanced data dashboards and 

analytics. An example of how to extend the project for a 

second phase could include increasing the scope of the 

TransMAP Hub to include other modes of transportation 

data, which would enhance its applicability. This would 

require partnerships with other modal agencies who could 

provide usable data access.

Special thanks:

• Jackson Cothren, Director, Center for Advanced

Spatial Technologies, University of Arkansas

• Heather Nachtmann, Ph.D., Associate Dean for

Research, College of Engineering, Director,

Maritime Transportation Research and Education

Center, University of Arkansas

• Travis Black, Acting Director for the Office of Ports

and Waterways and lead for Port Infrastructure

Development Planning Program of the US

Department of Transportation, Maritime

Administration (MARAD)

Quick-Reference Information

• Project Phase: Project Design and Environmental Review

• Agencies/Partners Involved: U.S. DOT Maritime Administration, University of Arkansas, U.S. DOT

University Transportation Center

• Location: National

• Type(s) of Data Shared: Physical and socioeconomic data related to multimodal transportation for visualizing

human geography, area-value data, and travel times

• Method of Data Sharing: TransMap, a centralized online tool

• Additional Resources: https://castuofa.github.io/transmap/
1

Mississippi National River Recreation Area Paddle Share Program

Interviewed Organization(s): Mississippi Park Connection, National Park Service, Volpe Center

Forming a Data Sharing Partnership 

Mississippi National River Recreation Area (Mississippi 

NRRA) was created in 1988 to help facilitate recreational 

access to the Mississippi River; however, the National Park 

Service (NPS) did not have a formal program to support 

nonmotorized boating access to the river until 2016. The 

Mississippi Park Connection (MPC), a 501c3 non-profit 

partner of the Mississippi NRRA, has a common interest in 

river education and recreational use with the Mississippi 

NRRA (along with share office space). Beginning in 2014, 

MPC established relationships between NPS and other 

organizations interested in supporting a paddle share 

program. In addition to establishing the locations for paddle 

share stations, the partnership provided a mechanism for 

engaging with vendors to supply kayaks and lockers and 

operate the service. MPC manages the vendors and, 

together with the municipal and park district partners, owns 

the equipment, which NPS is unable to do because of 

limitations around maintenance costs and liability. 

Mississippi NRRA collects the use data and analyzes it for 

trends to help the partners manage the system’s 

performance, inform schedule and route changes, and 

demonstrate how paddle share meets the program goals. 

For instance, the Minneapolis Park and Recreation Board 

uses the shared data to learn whether people with a 

Minneapolis zip code use the paddle share system, which 

helps validate the Park Board’s involvement in the 

program. MPC is also able to use the data to advocate for 

funding subsidies based on the results.

Data Fields & Tools 

The data includes information about when paddle share 

users reserve kayaks, where and when they pick the boats 

up, where the boats are returned, and approximately how 

long they were out. The data is shared through a portal 

(called Checkfront) set up by the operating vendor. Data 

from the portal can be downloaded to a spreadsheet and 

requires little data cleaning. MPC, NPS, and the vendor 

can look at the number of reservations for upcoming days, 

right-size on-duty staff, and communicate to user groups 

when there are reservations available to encourage more 

use. NPS analyzes the data and provides the results to 

MPC. During the off-season, data from the previous year of 

operation is reviewed to inform decisions about modifying 

Lessons Learned

Lesson #1 – Good working relationships

Relationships between staff are vital to creating and 

establishing planning and data sharing partnerships 

between organizations. Good staff communication and 

coordination across agencies combined with positive 

data demonstrating the success of the partnership 

program fosters trust and confidence in the outcomes 

and continued collaboration.

Lesson #2 – Details in contracting

When working with a partner organization or vendor 

through an agreement or contract, it is critical from the 

outset to articulate the data points to be shared, the 

frequency of data reporting, the points of contact for 

sending and receiving data, and how they will be used, 

as well as the responsibilities of each signatory group. 

Mississippi NRRA and MPC realized that they should 

have been more explicit about their data needs and 

reporting requirements after the contract with the 

vendor was signed.

FHWA Planning & Data Sharing 

Partnerships Case Study Series
2 

operations, such as adding more boats or reservation

opportunities to the busiest stations. Nice Ride, the local

bike share that is operated by Lyft, co-locates bike share 

stations next to paddle share stations wherever possible 

and shares its use data from these stations. The Nice Ride 

data helps MPC and NPS understand the multimodal

nature of paddle share trips.

Implementation Challenges

Initially, there were challenges with the amount of data that

the vendor would share with MPC and NPS. The initial

agreement did not specify data reporting outside of monthly

invoices. The parties were able to come to an agreement

on data sharing by repeatedly asking the vendor to provide 

back-end access to the reservation system software. Since 

paddle share is the vendor’s top customer, the vendor

eventually provided this access.

NPS is constrained in its data collection through visitor 

surveys by the Paperwork Reduction Act and rules about 

collecting any data that contains personally identifiable 

information. When possible, it is better for NPS to leave the 

collection of data that contains this information to its 

partners. Surveys are an important tool in gauging the 

satisfaction of paddle share users. A subsidiary of MPC, 

Mississippi River Paddle Share LLC, was able to collect 

annual post-trip survey data to share with the partnership. 

The remaining challenge is consistent funding revenue. 

The paddle share program partners are all non-profit

organizations or local governments. Modest annual

subsidies from each of the partners helped cover any gap 

between expenses and revenue from paddle share user

fees. Operating costs have been increasing, especially as

the kayaks and other equipment age. At the same time,

grants have been shrinking. The partners are taking a

strategic look at how to balance funding and costs.

Results/Main Takeaways

The paddle share system has expanded over the years and 

the partnership has been successful due to continued 

collaboration and support amongst the partner 

organizations. The paddle share uses data to inform costeffective changes that sustain the program year after year. 

The data demonstrate high system usage and sufficient 

revenues to justify the program’s continuation. Mississippi 

NRRA benefits from the partnership by fulfilling its mission 

to increase recreational access to the river; MPC benefits 

from higher visibility and a greater ability to gain support for 

its other programs.

Looking Forward

For improving the data sharing and planning aspect of the 

partnership, Mississippi NRRA and MPC would like the 

vendor to improve its reservation interface, enable more 

customizable reports, and allow for real-time weatherrelated and emergency shutdowns, as well as information 

on invoicing.

As the paddle share program continues, partnering with 

local institutions, such as the University of Minnesota Twin 

Cities, to spread the word about the program may attract 

more users and make the program more financially 

resilient. Many people who engage with the University and 

even metro area residents do not realize the Mississippi 

River is close by, it is part of the National Park Service, nor 

that there is paddle share nearby to enjoy. 

Special thanks:

• Katie Nyberg, Executive Director, Mississippi Park

Connection

• Karen Katz, Outdoor Recreation Planner,

Mississippi NRRA

• Ben Rasmussen, Public Lands Team Lead, U.S.

DOT Volpe Center

Quick-Reference Information

• Project Phase: Operations and Maintenance

• Agencies/Partners Involved: Mississippi Park Connection, National Park Service; secondary: Three Rivers

Park District, City of Brooklyn Park, City of St. Paul, Minneapolis Park and Recreation Board, Mississippi

Watershed Management Organization, Nice Ride/Lyft

• Location: Minneapolis and St. Paul metro area, Minnesota

• Type(s) of Data Shared: User data

• Method of Data Sharing: Data portal, regular in-person, and virtual meetings

• Additional Resources: https://www.nps.gov/miss, https://parkconnection.org, https://www.paddleshare.org
1

National Park Service National Capital Area Regional Count Program

Interviewed Organization(s): National Park Service, National Capital Region

Forming a Data Sharing Partnership 

The National Park Service National Capital Area (NPS 

NCA) engages with multiple partners on planning and data 

sharing. NPS NCA is part of the National Capital Planning 

Commission (NCPC), the regional Metropolitan Planning 

Organization (MPO), which meets monthly to discuss the 

current and upcoming projects, as well as any issues 

related to them. The Commission members also include 

representatives from other Federal agencies, District of 

Columbia DOT (DDOT), Virginia DOT (VDOT), Maryland 

DOT (MDOT), and some presidential appointees. 

NPS NCA established a cooperative agreement to conduct 

a coordinated count program throughout the region. The 

goal is to install more counters, collect better data, and 

store it in a database available to participants. The partner 

agencies benefit from the economies of scale in trail 

counters, the technical support for maintaining them, and 

the resulting processed data. NPS NCA has a partnership 

with DDOT focused on traffic counts and has also 

proactively engaged with the NCPC to coordinate trail data 

collection and analysis. DDOT places temporary traffic 

counters on federal roads and shares the data with NPS. 

NPS shares any traffic data it collects with DDOT, as well. 

The two agencies also coordinate on projects that entail 

road closures and other construction phases that impact 

traffic flows. Regarding trails, NPS has more than 50 miles 

of trails that connect to other trails within the region. The 

centralized approach to trail count data facilitates continued 

data collection despite regular staff turnover at public lands 

agencies, especially for staff with specialized skills in data 

counting technology and analysis.

Data Fields & Tools 

DDOT and NPS share traffic counts and related data 

through the Highway Capacity Software (HCS), which 

applies the methodologies documented in the Highway 

Capacity Manual (HCM). The Federal Lands Highway Field 

Operations Technical Support Center (FOTSC) collects 

traffic data for NPS that can be used for traffic studies upon 

request, and then shared through the HCS. The transfer of 

traffic data is usually a single transaction based on 

individual project need rather than a consistent schedule.

Lessons Learned

Lesson #1 – Cross-agency relationships

Relationships across agencies are critical to 

establish sustainable, long-term collaboration. Interagency relationships must be maintained with regular 

communication and coordination of projects 

impacting each partner.

Lesson #2 – Collaborate on data collection and 

management

Maintaining data counters in good working order 

requires consistent monitoring and a specific skill set 

to troubleshoot problems. Data management and 

processing are also required to apply the information 

in decision-making. These tasks pose funding and 

bandwidth challenges for many agencies. 

Collaborating on collection and access to data can 

distribute the costs over multiple agencies and allow 

all to benefit from economies of scale.

FHWA Planning & Data Sharing 

Partnerships Case Study Series
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The regional trail count data is pulled directly from the 

counters into a database that is managed through a 

contract with the University of North Carolina (UNC), which 

manages technical work done by Virginia Tech and 

Portland State University Transportation Research and 

Education Center (TREC). While TREC is responsible for 

maintaining the counters in addition to processing the data 

in the database, NPS first established the counters and has 

access to the database. 

Implementation Challenges

Maintaining good relationships is a challenge as staff 

change across agencies. In the scope of the National 

Capital Region, the NCPC must work with the Federal 

Government which owns much of the land in Washington, 

DC. There are many actors involved, including the NPS

regional office, NPS units, DDOT, and other members of

the NCPC.

Another challenge is maintaining the funding to collect 

data. DDOT and other smaller agencies have funding and 

staff constraints limiting their ability to maintain their own 

counter program, let alone bear the cost of increasingly 

advanced counter technology. NPS has worked to 

establish a 5-year agreement with UNC, funded through 

FHWA for research, for UNC to conduct research and 

manage the regional trail count data collection efforts by 

Virginia Tech and TREC. 

Looking Forward

The longer-term goal for NCA is to issue an annual report 

with information beyond raw counts about the number of 

trail users. The goal is to translate raw collected data into 

performance measures, such as crash reduction and

emissions reductions, to communicate regional data trends 

across several sectors, such as public health, environment, 

and safety.

Special thanks:

• David Daddio, National Capital Area Regional

Transportation Manager

Quick-Reference Information

• Project Phase: System and Project Planning

• Agencies/Partners Involved: National Park Service (NPS), District of Columbia Department of Transportation

(DDOT), National Capital Planning Commission (NCPC), Metropolitan Washington Council of Governments

(MWCOG)

• Location: Transportation networks and recreation trails in the greater DC metro area

• Type(s) of Data Shared: Traffic count data, trail count data, project plans and schedules

• Method of Data Sharing: Database downloads, digital file transfers, Regional Transportation Data

Clearinghouse (RTDC), coordination meetings

• Additional Resources: https://www.ncpc.gov/; https://www.mwcog.org;

https://rtdcmwcog.opendata.arcgis.com;

https://www.mwcog.org/file.aspx?&A=X1FahAYWSbbzUZJeicVZ7SznncgkG4CQ0pQ9QDUYRNw%3D
1

Oregon Coast Trail Action Plan

Interviewed Organization(s): Oregon Parks and Recreation Department, Association of Oregon 

Counties, Oregon Solutions, Federal Highway Administration (FHWA)

Forming a Data Sharing Partnership 

The Oregon Coast Trail Action Plan is a regional 

collaborative planning effort to identify and inventory 

existing conditions, plan for improvements and 

realignments, and plan for long-term maintenance and 

governance of the Oregon Coast Trail. The Action Plan 

partnership includes the Association of Oregon Counties, 

Oregon Parks and Recreation Department, and Oregon 

Solutions. Partners compiled existing geospatial trail data 

with existing facilities and ownership data to determine 

baseline conditions. They also regularly engage with the 

public, tribes, and local elected officials to identify areas 

where the trail needs safety improvements. Qualitative 

information from the public engagement is regularly 

converted to geospatial data and added to the existing 

database, which is then used for long-term decision making 

and governance.

Once existing conditions are inventoried, several products 

will be developed: Declarations of Cooperation between 

trail segment owners for the North, Central, and South 

Segments and a Declaration of Cooperation for the overall 

trail’s long-term governance. The Declarations of 

Cooperation will be incorporated into a final planning 

document that will include, to the degree possible, 

conceptual drawings and cost estimates to advance future 

design and construction to address trail gaps.

Data Fields & Tools 

The Oregon Coast Trail Action Plan has a wide range of 

data inputs. The majority is geospatial data followed by 

natural and cultural resource data. There is also qualitative 

through-hikes data from other land management agencies, 

information from public engagement, and hiking books. The 

team is establishing a framework to accurately capture 

word-of-mouth data in a format that is meaningful to the 

Action Plan. 

Lessons Learned

Lesson #1 – Establish data storage and access 

plan

The Oregon Coast Trail Action Plan partnership 

recommends establishing a protocol for how to store 

data, who can access the data, and how they will 

access it. After several staffing changes over the 

course of the project, partners have learned that it is 

imperative to establish a development framework to 

ensure the project moves forward regardless of staff 

turnover.

Lesson #2 – Identify tasks based on individual 

skillsets

It is helpful to identify and complement other partners’ 

strengths and regularly communicate with partners. 

Team members can identify who will complete each 

task based on their skillset. Individual agencies may be 

best positioned to assume tasks based on a specific 

data focus or audience. This ensures everyone is on 

the same page while maximizing the team’s efficiency 

without repeating work.

FHWA Planning & Data Sharing 

Partnerships Case Study Series
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The Action Plan team has a contract with Parametrix, an

environmental planning and engineering firm, to collect, 

clean, house, and disseminate data through ArcGIS Online. 

Parametrix cleaned historical data where trail alignments 

differ from existing conditions, as well as gathered 

additional data for trail segments without existing data.

Once clean, Parametrix added the data to ArcGIS Online. 

The ArcGIS online dataset is then used by Action Plan 

members to explore different alignments and layers and 

make governance and maintenance decisions. Following 

the Action Plan’s completion, the ArcGIS data will be 

migrated to Oregon Parks and Recreation Department 

(OPRD) systems.

Implementation Challenges

In many cases, acquiring the most up-to-date data requires

going to specific locations on the trail to “ground-truth” it, or 

walk the trail and validate the exact route and condition. 

This requires more time and resources to collect that data.

Results/Main Takeaways

Currently, the team is still collecting data that will be used 

for all aspects of the project lifecycle. The data collection 

and analysis from the Oregon Coast Trail partnership has 

been critical to developing the Action Plan and has also 

provided baseline information for other projects. The Action 

Plan will be used to set goals and priorities for the groups 

involved, identify gaps and funding opportunities for the 

trail, and prioritize gaps based on usership and cost.

Looking Forward

The Oregon Coast Trail Action Plan hopes to identify types 

of users of the trail, what they are using it for, the length of 

hikes, and other measures to better understand how the 

trail is used. This will allow the team to better support 

facilities and focus those efforts on the most used areas. 

The Action Plan will set the path for a complete, 

sustainable long-term Oregon Coast Trail.

Special thanks:

• Cole Grisham, FHWA, Transportation Planner

• Peter Dalke, Oregon Solutions, Project Manager

• Paul Reilly, Oregon State Parks, Program

Coordinator

• Andy Smith, Association of Oregon Counties,

County Solutions Director

Quick-Reference Information

• Project Phase: System and Project Planning

• Agencies/Partners Involved: Oregon State Parks, Association of Oregon Counties, Oregon Solutions, and

FHWA

• Location: Oregon Coastal Counties

• Type(s) of Data Shared: Geospatial, natural and cultural resources

• Method of Data Sharing: ArcGIS Online

• Additional Resources: https://highways.dot.gov/federal-lands/projects/or/dot-2018-3; https://orsolutions.org/

osproject/oregon-coast-trail
1

Oregon Department of Transportation Regional Integrated

Transportation Information System

Interviewed Organization(s): Oregon Department of Transportation

Forming a Data Sharing Partnership 

Oregon Department of Transportation (ODOT) purchased a 

Regional Integrated Transportation Information System 

(RITIS) enterprise license to collect and house data on 

traffic speeds and recreational routes for planning needs. 

RITIS is a data aggregation and dissemination platform for 

solving transportation problems, from the Center for 

Advanced Transportation Technology Laboratory at the 

University of Maryland.

Data on ODOT’s RITIS platform is available to everyone in 

the state working in public transportation. RITIS serves as 

a data sharing platform, where ODOT inputs data in RITIS 

and becomes accessible to all ODOT users on the 

platform. It supports a broad range of tools and features 

and allows agencies to easily collaborate and share data. 

This enables better decision making and higher quality 

projects across the state.

Data Fields & Tools 

ODOT’S RITIS platform houses a wide range of data 

across the state. Examples include state highway traffic 

volume data, incident and weather data, traffic signal 

locations, and congestion data and associated calculations, 

such as hours of delay, cost of delay, and contributions to 

delay. RITIS performs its own calculations from inputted 

data, eliminating any possible inconsistencies or errors 

from people completing their own calculations.

The RITIS enterprise platform has data format 

requirements, which requires ODOT to clean some data to 

have it added to the platform. There are no barriers for 

users to access and download data once it is on the 

platform. ODOT has training resources from RITIS 

available on their website for users, and plan on visiting 

agencies throughout the state to increase awareness of the 

tool and its benefits.

Implementation Challenges

ODOT has faced challenges determining how users are 

using the data in RITIS. RITIS is available to all ODOT 

employees, State of Oregon public agencies, and 

consultant or university staff performing work for a public 

agency in the State of Oregon. Non-ODOT RITIS accounts 

require an organization to sign an INRIX data use 

agreement to establish a new account for that organization. 

Users must fill out a login application and be approved by 

ODOT staff to gain access to RITIS.

Lessons Learned

Lesson #1 – Learn from peers

RITIS has been available for many years longer than 

ODOT’s use of the system, which has enabled ODOT 

staff to learn from other state that have a more mature 

working knowledge of the platform. ODOT’s access to 

RITIS serves as an example of how a state agency can 

make transportation data available to its partners, 

which includes land managers. As the primary contract 

holder to RITIS, ODOT allowed for other agencies, 

particularly those that are smaller or less resourced, to 

utilize data under ODOT’s contract without the burden 

of high start-up costs.

FHWA Planning & Data Sharing 

Partnerships Case Study Series
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While staff in various agencies in Oregon can use RITIS, it 

is their consultants accessing the data, which makes it 

difficult for ODOT to gauge who the “real” users are, 

identify what future data inputs would best serve its users, 

and which additional groups to include.

Results/Main Takeaways

RITIS has saved time and resources by having all the data 

ODOT and other agencies need in one easy-to-find 

location with many calculations already completed. ODOT 

has used RITIS to plan transit programs, especially in 

recreational districts that have experienced recent surges 

in population growth and tourism. The data has helped 

ODOT determine who is travelling on Oregon roads and 

their trip start and end points. This information and RITIS’ 

calculations allow ODOT to tell road users the best time to 

visit destinations to avoid congestion and manage parking. 

Additionally, RITIS has dramatically increased data access 

to users across the state, allowing them to answer 

questions they were unable to with only their agency’s 

data. Furthermore, it ensures consistency across projects 

from various agencies because all calculations and 

necessary indices are preloaded into the tool.

Looking Forward

ODOT has a license for RITIS until 2030. They plan to visit 

agencies throughout the state to increase awareness of 

RITIS and its benefits. Project efficiency, quality, and 

decision making will increase statewide as more agencies 

use the tool, and reduce costs associated with making 

individual data purchases.

Special thanks:

• Thanh Nguyen, Oregon Department of

Transportation, Senior Transportation System

Analyst

• Chi Mai, Oregon Department of Transportation,

Transportation Systems Analysis Engineer

Quick-Reference Information

• Project Phase: Evaluation and Reporting

• Agencies/Partners Involved: University of Maryland (RITIS owner), Oregon Department of Transportation,

various State of Oregon public agencies

• Location: Oregon

• Type(s) of Data Shared: Traffic volumes and speeds, incident data, recreational routes

• Method of Data Sharing: Enterprise license to database

• Additional Resources: https://ritis.org; https://www.oregon.gov/odot/Data/Pages/RITIS.aspx
1

Collecting Multi-Jurisdictional Road Stream Crossing Data in Maine 

Interviewed Organization(s): U.S. Fish and Wildlife Service Forming a Data Sharing Partnership 

The Maine Stream Crossing Survey partnership formed in 

2006 to collect and disseminate statewide data on stream 

barriers and barriers to fish passage throughout the state. 

The reason this group formed in Maine was to focus on 

improving habitat for the Atlantic salmon, an endangered 

species whose remaining habitat in the United States is 

almost entirely within the state of Maine. The partnership 

included the following partners: 

• U.S. Fish and Wildlife Service (FWS)

• Maine Department of Transportation

• Maine Department of Marine Resources, Inland Fisheries,

and Wildlife

• Maine Department of Environmental Protection

• Maine Forest Service

• Atlantic Salmon Federation

• The Nature Conservancy (TNC)

The partnership began in 2006 with a pilot study on fish 

passage in the Lower Penobscot River area. They 

developed their own data collection protocols and 

conducted a survey of stream crossing data to prioritize 

improvements at crossings. The team then expanded data 

collection to all public roads and some private roads1

throughout the state, which it maintains in a database and 

disseminates through the Maine Stream Habitat online 

viewer. This dataset includes asset management and 

ecological information on road stream crossing structures, 

such as culverts and aquatic organism passage structures. 

Data Fields & Tools 

TNC manages the partnership, including directing data 

collection, managing staff and volunteers, coordinating 

with private landowners, land managers, and public 

entities. 

Additionally, TNC created a data use agreement for 

private landowners to increase their level of comfort with 

the data collection process. USFWS hosts and populates 

the stream crossing database. The database has 26,000 

records that represent road stream crossings and 

includes associated resource and asset data attributes. 

The stream crossing database feeds into the Maine 

Stream Habitat online viewer, which presents stream 

crossing data 

Lessons Learned

Lesson #1 – Develop common goals among 

partners 

When creating a partnership with a myriad of groups 

with differing goals and capacity, it is important to 

focus first on the areas where the most parties have 

a shared interest. For example, the Maine Stream 

Crossing Survey partners found success by first 

working together on shared interests, like improving 

data on road conditions and aquatic habitat quality. 

Lesson #2 – Identify realistic goals for the 

partnership 

To be successful, partnerships should be conscious 

of their capacity and careful to set realistic goals.

FHWA Planning & Data Sharing 

Partnerships Case Study Series
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and a network analysis tool. The viewer has layers such as 

stream crossings, fish passage barriers, priority habitats for 

various species, water features, and watersheds. The data 

available for each stream crossing includes potential 

impacts on aquatic species, watershed information, and 

other detailed crossing information. The viewer highlights 

the data that is most relevant and beneficial to the public 

users, but additional information is available to partners in 

the database.

Implementation Challenges

This partnership has encountered several challenges and 

Partner engagement was a significant reoccurrence. This 

project requires the participation of a wide range of 

agencies and private entities, including some whose 

primary mission is not environmental protection. It has 

been important to communicate the value of this dataset to 

a wide range of partners so that they support the project. 

The partnership has also had to build trust with private 

landowners and show them the value of this data for their 

land management. 

Additive to the partner engagement aspect, it has been 

difficult to maintain consistent funding for this partnership, 

which is necessary to keep the dataset current and to 

maintain the data-sharing site.

With regards to developing data protocols, the partnership 

needed to establish an agreed-upon data dictionary and 

data collection methodology that would meet the partners’ 

specific needs related to ecology and asset management. 

The project developed a data collection protocol that has 

since become a model for other ecology data collection 

efforts in New England and other locations in the U.S.

Results/Main Takeaways

The stream crossing database and online viewer combines 

infrastructure and habitat information to identify high priority 

crossing sites for repair or replacement and areas at risk of 

flooding. This partnership benefits private and public users, 

such as municipalities, road owners, and other 

stakeholders. The database has also resulted in the 

development of a bond fund from the Maine Department of 

Environmental Protection to competitively fund crossing 

replacements. The Maine Department of Marine Resources 

has also used the results of this partnership to help set 

restoration and recovery goals. The Maine Stream 

Connectivity Work Group convenes to coordinate with 

partners on data collection, updates, and general work 

progress.

Looking Forward

The project partners would like to make the stream 

crossing database as accurate and up to date as possible 

and have a consistent host for the online viewer, as 

multiple partners have hosted the online viewer over the 

years. The partnership is also interested in incorporating 

LiDAR data in the database, as more imagery becomes 

available. Additionally, the project would significantly 

benefit from funding to support local rural road data 

collection.

Special thanks:

• Alex Abbott, GIS Analyst and Stream Restoration

Specialist, U.S. Fish and Wildlife Service

Quick-Reference Information

• Project Phase: System and Project Planning; Maintenance and Operations

• Agencies/Partners Involved: U.S. Fish and Wildlife Service, Maine Department of Transportation, Maine

Department of Marine Resources, Maine Department of Environmental Protection, Maine Forest Service, Atlantic

Salmon Federation

• Location: Maine (statewide)

• Type(s) of Data Shared: Culvert and stream crossing data

• Method of Data Sharing: Data dictionary, database, and online web viewer

• Additional Resources: Maine Stream Habitat Viewer; Maine Road-Stream Crossing Survey Manual
1

Wasatch Front Regional Council Active Transportation and Congestion 

Management Programs

Interviewed Organization(s): Wasatch Front Regional Council, Utah Geospatial Resource Center, Utah Department of Transportation

Forming a Data Sharing Partnership 

Utah has a strong network of public organizations sharing 

geospatial information system (GIS) mapping data that is 

coordinated by the Utah Geospatial Resource Center 

(UGRC). The UGRC is the map technology coordination 

office for the state of Utah. It is a common hub for the Utah 

Department of Transportation (UDOT) Planning 

Department, the Wasatch Front Regional Commission 

Metropolitan Planning Organization (WFRC MPO), and 

other local and state-level partners public agencies to 

share and access updated GIS data. UGRC serves as the 

coordinator among the agencies using the data and have 

the responsibility to improve the quality of collected and 

shared data and ensure consistency across projects. 

UGRC created a linear referencing system for projects 

across the state. All planned projects involving Utah 

Department of Transportation (UDOT) are based on this 

linear referencing system, which is regularly updated if 

roadway geometry changes. Federal land management 

agencies (FLMAs) have not been engaged in coordination 

outside one-off data requests.

Data Fields & Tools 

State- and local-level GIS layers represent most data 

shared in the hub. State-level GIS layers include roads, 

address points, and boundaries. The local-level roadway 

centerline data has been especially useful for many 

projects because it has detailed information on roadway 

lanes, bike infrastructure, sidewalks, signal counts, 

pedestrian delay, and collisions. Other data sets include 

public lands boundaries, which are maintained by the Trust 

for Public Land, and aerial imagery that are available on 

the Wasatch Front Regional Council website.

UGRC previously housed all the data; however, they 

have shifted to a distributed system. WFRC stewards 

data for their area and shares the data with other groups 

to further disseminate it. They also house state-level 

data on their website and index it with UGRC to 

maximize its audience. UGRC conducts quality control 

on data they receive, cleaning the data as needed. 

There are also some datasets where UGRC collects 

public input to crowdsource existing conditions. UGRC 

has an internal central multi-user database for the 

centerline data. Several years ago, 

Lessons Learned

Lesson #1 – The project champion

UGRC has established a strong statewide network of 

partners for sharing high quality geospatial data. The 

office provides a consistent, reliable centerline 

dataset that is used at all levels of government in the 

state. Strong partners, such as WFRC and UDOT, 

provide support to UGRC by being stewards of data 

relevant to their constituents.

Lesson #2 – Efficiency in common baselines 

The linear referencing system that UGRC developed 

and distributed is used statewide. As new data is 

incorporated into the system, UGRC has quality 

control to ensure it references the correct geometry 

and imports into maps correctly. Until there is an 

active trusted exchange of accurate info, agencies 

are going to use their own datasets. Combining data 

from disparate sources often leads to compatibility 

issues that can be difficult to been resolve.

FHWA Planning & Data Sharing 

Partnerships Case Study Series
2 

the agencies worked together to expand the data to 

include existing and planned bike facilities. UDOT updated 

linear referencing system and WFRC can log in and 

update data. UGRC ensures data from all the counties is 

accurate and reliable before publishing it.

Implementation Challenges 

An authoritative and correct data source is key to efficient

and effective transportation planning. If there is no 

coordination to bring the databases closer together, project

staff first must validate the information, which is very time 

consuming. Eventually, the people who pick up the data 

later must determine which database is correct with little 

information on where to start. UDOT referenced an 

experience regarding active transportation plans in which 

planners had to retain and refer to multiple datasets

throughout the project. It reduces efficiency and can lead to 

recurring issues.

Federal agencies have been minimally involved in the data 

sharing process, which has led to data gaps resulting in 

challenges with implementation on projects that intersect or 

border federal land. Federal agencies in the state aren’t

using this referencing system, so when they do share data 

with UDOT or others in the state there are some 

inconsistencies in the geometry.

It has been challenging to find the right person within the 

FLMA to engage. UGRC and UDOT have been trying to 

put together a federal user group to foster better

communication with those other agencies. It’s currently 

most active with U.S. Forest Service, Natural Resources 

Conservation Service, and the Farm Service Agency. 

Some agencies are more willing to participate than others. 

The project team achieved success sharing data and 

garnering community buy-in when developing the Bike and 

Pedestrian Master Plan. Despite this, the team has noted 

challenges related to executing the concepts presented in 

the Master Plan, siting slow progress as far as

implementation is concerned. CCC does not own any land 

for implementation. A primary difficulty in implementing the 

initiatives developed thanks to shared data resources is a 

lack of a “champion” to push initiatives forward toward 

successful implementation. The towns and their unique 

governance and predispositions have been the cause of 

slow progress.

Results/Main Takeaways

The partnership has ensured consistent high-quality data 

across the state. Organizations across the state, like 

planning agencies and police departments, rely on these 

data sets, which increases overall data efficiency and 

consistency. Local agencies can streamline project 

development because Wasatch Front Regional Council 

ensures high-quality and comprehensive input data.

Looking Forward

This is an enduring data sharing effort. UGRC is continuing 

to identify data stewards to ensure the data is maintained in 

the long term while continually working to improve datasets 

to ensure its high quality. As the data sharing network in 

Utah has matured, many data consumers became data 

contributors under the guidance of UGRC. It will be 

important for UGRC, WFRC, UDOT, and other proponents 

to communicate the benefits of data sharing in effort to 

institutionalize contributions to a common database. In the 

future, they would like to increase coordination with Federal 

agencies to determine data accuracy and ensure data 

exchanges moving forward.

Special thanks:

• Bert Granberg, Analytics Director, Wasatch Front

Regional Council

• Matt Peters, Director, Utah Geospatial Resource

Center

• Stephanie Tomlin, Central Planning Modeling,

Data, and GIS Program Manager, Utah

Department of Transportation

Quick-Reference Information

• Project Phase: System and Project Planning

• Agencies/Partners Involved: Utah Geospatial Resource Center, Utah Department of Transportation, Wasatch Front

Regional Council

• Location: Wasatch Front Region, UT

• Type(s) of Data Shared: Roadway lanes, bike and pedestrian infrastructure, signal counts, pedestrian delay, collisions

• Method of Data Sharing: Housed on Utah Geospatial Resource Center website

• Additional Resources: https://wfrc.org; https://gis.utah.gov; https://www.udot.utah.gov/connect;
Appendix 2: Memo 1 - Background and Literature Review
Prepared by the U.S. DOT Volpe Center March 24, 2021

Finding a Win-Win: Planning and DataSharing Partnerships between 

Governments and Public Land 

Management Agencies 

Memorandum1: Background and Literature Review 

March 24, 2021
Prepared by the U.S. DOT Volpe Center March 24, 2021

Table of Contents

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

Definition of Key Terms........................................................................................................... 1

Background and Context............................................................................................................. 3

Legislative Context................................................................................................................. 4

Common Motivations for Data Sharing ........................................................................................10

Common Data Sharing Challenges...............................................................................................11

Staff Capacity .......................................................................................................................12

Data Quality .........................................................................................................................13

Data Compatibility and Comparability ......................................................................................13

Effort and Cost......................................................................................................................14

Data Governance ..................................................................................................................15

Data Privacy and Security .......................................................................................................15

Leadership Buy-in..................................................................................................................16

Data Management and Sharing...................................................................................................16

Managing Data Internally .......................................................................................................16

Preparing for a Data Sharing Partnership..................................................................................18

Building Data Sharing Partnerships ..........................................................................................20

Summary of Previous Transportation Agency and PLMA Planning and Data Sharing Efforts.................22

Federal Lands Collaborative Long-Range Transportation Plans.....................................................22

Transportation Working Groups..............................................................................................24

Other Examples of Transportation Agency and PLMA Joint Studies and Collaboration .....................24

Conclusion and Research Gaps....................................................................................................25
Prepared by the U.S. DOT Volpe Center 1 

Introduction

This memorandum summarizes a review of the literature about planning and data-sharing partnerships 

between transportation agenciesand Public Land Management Agencies (PLMAs). This literature review 

supports the Federal Highway Administration’s (FHWA) Finding a Win-Win: Planning and Data-Sharing 

Partnerships between Governments and Public Land Management Agencies research study. The 

objectives of this research project are to: 

1. Identify examples of cross-agency coordination between PLMAs and transportation agencies

that results in process efficiencies, cost savings, and better transportation system delivery

and/or management. These examples are intended to focus on corridor-scale collaboration.

2. Develop a suite of tools and contexts to aide PLMAs and State Departments of Transportation 

(DOTs) in sharing data and improving coordination for better transportation systems.

To conduct this literature review, the U.S. Department of Transportation’s Volpe National 

Transportation Systems Center (Volpe Center) conducted a scan of Federal laws, regulations, and 

guidance that pertain to planning and data-sharing partnerships between transportation agencies and 

PLMAs; research documents; and other Federal, State, and local documentation (e.g., case studies, 

guides, reports). Appendix A: Bibliography lists each resource referenced in this document. 

Memorandum 1 also identifies gaps in the existing literature that will inform the content of

Memorandum 2: Study Methodology. 

This memorandum will be updated throughout the course of the Study as the project team identifies 

additional relevant literature. The final literature review will be included as part of the Study’s final 

report.

Definition of Key Terms

The research team provides the following definitions as applied to this Study. 

Transportation Agency/Organization

For the purposes of this Study, “transportation agency” or “transportation organization” refers to 

agencies with jurisdiction, planning, funding, ownership, or management responsibility for 

transportation systems (e.g., roads, trails, transit, marine, or aviation systems). These could include 

system owners or operators. Typical transportation agencies or organizations include: 

• State Departments of Transportation (DOT)

• Metropolitan Planning Organizations (MPO)

• Regional transportation planning organizations (RTPOs, rural counterpart to MPOs)

• Local Public Agencies (LPA), such as city or county governments

• Tribal governments

• Transit agencies

• Marine or aviation management agencies

1

1 For more information on tribal transportation planning, see FHWA’s research project on Transportation Planning 

in Tribal Communities (in progress). https://highways.dot.gov/federal-lands/ott/study 
Prepared by the U.S. DOT Volpe Center 2 

• U.S. Department of Transportation agencies: Federal Highway Administration (FHWA), Federal

Transit Administration (FTA), and other modal administrations

Public Land Management Agency

A Public Land Management Agency (PLMA) is any public agency that manages land for public access and 

use. These uses can include recreation, resource protection, and economic uses, such as resource 

extraction or energy production. PLMAs include federal land management agencies, and parks and 

conservation agencies managed by states, regional governments, counties, or municipalities. PLMAs 

may own and manage transportation systems within their boundaries. Typical PLMAs include: 

• U.S. Department of the Interior Agencies:

o Bureau of Land Management (BLM)

o National Park Service (NPS)

o U.S. Bureau of Reclamation (BOR)

o U.S. Fish and Wildlife Service (FWS)

• U.S. Army Corps of Engineers (USACE)

• U.S. Department of Agriculture, U.S. Forest Service (USFS)

• Independent Federal Agencies (IFA) that manage public lands (e.g., Presidio Trust, Tennessee

Valley Authority)

• State parks, forests, and other state land management agencies

• Regional, county, and municipal parks and land management agencies

Public Lands Transportation Stakeholders

There are organizations that are not captured by transportation agency/organization and PLMA above 

that are invested in the successful management of transportation systems to access public lands. These 

can include:

• Nonprofit advocacy organizations

• Private companies doing business within the transportation planning or engineering sectors

• Academic institutions conducting transportation research

• National Oceanic and Atmospheric Administration (NOAA)2

Planning and Data-Sharing Partnerships in Planning and Decision-making Processes

This research project focuses on how transportation agencies and PLMAs share and use data to inform 

transportation planning and decision-making processes. Transportation agencies and PLMAs may have 

different data sharing needs for different phases. These partnerships may inform any stage of the 

transportation project lifecycle, including:

• System and project planning

• Project selection and programming

• Design and environmental review

• Construction

2 NOAA is a member of the Interagency Visitor Use Management Council in addition to the Bureau of Land 

Management, Forest Service, National Park Service, U.S. Army Corps of Engineers, and U.S. Fish and Wildlife 

Service. The council serves to raise awareness of and commit to proactive, professional, and science-based visitor 

use management on federally managedlands and waters. https://visitorusemanagement.nps.gov/
Prepared by the U.S. DOT Volpe Center 3 

• Operations and maintenance

• Performance management and reporting

The types of data that agencies may share includes a wide range of data related to agency goals at each 

stage of the project lifecycle. These data types may include: 

• Traffic volume (current traffic patterns, historic trends, and forecasted) 

• Visitation and usage (including trail use and transit ridership)

• Traffic and parking congestion

• Transportation safety and incident data

• Asset condition, ownership, and maintenance

• Environmental data (resources, resilience)

• Demographic and economic data

Background and Context 

The primary purpose of transportation is to connect goods and people with destinations – whether to 

support recreational, commercial, or industrial trips. Transportation systems of all modes create 

connections across the landscape, connecting origins and destinations across land ownership 

boundaries. As such, transportation agencies – whether landowners, road owners, or transportation 

planning and funding organizations – manage different aspects of transportation networks. 

Transportation organizations, PLMAs, and other stakeholders need to coordinate on decision-making to 

successfully operate seamless connections across jurisdictions. Because transportation management 

decisions are increasingly data-driven to maximize the impact of limited funds, data sharing among 

partners becomes an important part of successful collaboration. 

In general, recreational travel to the country’s Federal public lands has been increasing for the past 

several years, 3,4 and this trend is expected to continue for public lands in both urban and rural areas in 

the coming decades. Increasing visitation means increased use of the transportation system 

infrastructure that gets visitors both to our national parks, forests, refuges; state parks; and other 

recreational sites and lets them travel within these public lands. Transportation agenciesand PLMAs

share a common interest in ensuring the public enjoys safe, efficient access to and through public lands. 

Visitation to and resource management activities on these lands also generate local, regional and state 

economic benefits. At the same time, the traveling public is not concerned with the jurisdictional 

boundaries, and instead wants to get from their residence to their desired outdoor recreation site by 

the mode of their choosing as quickly and easily as possible. As a result, transportation agenciesand 

PLMAs are exposed to the impacts of congestion and crowding, which can adversely affect efficient 

access, economic activity, safe travel, and the visitor experience. 

Planning collaboration can help transportation agencies and PLMAs identify travel trends, needs, and 

projects of mutual benefit to improve transportation access for the traveling public. Sharing data is an 

 

3 US Bureau of Land Management. “Table 4-1: Estimated Recreational Use of Public Lands Administered by the 

BLM.” Public Land Statistics, 2010-2019. Last retrieved 3/31/2021. https://www.blm.gov/about/data/public-landstatistics

4 US National Park Service. Annual Visitation Summary Reports 2010-2019. Last retrieved 3/31/2021. 

https://irma.nps.gov/STATS/Reports/National
Prepared by the U.S. DOT Volpe Center 4 

important part of transportation collaborations, as it helps organizations identify problems, make datadriven cases for funding and other decisions, and evaluate the effectiveness of plans, projects, and 

management practices, as well as potential impacts. Data sharing partnerships can also help PLMAs and 

transportation agencies improve conditions related to common goals, such as transportation safety, 

congestion management, visitor experience and mobility, resource protection, and sensitive 

environmental features. 

Legislative Context

There are several laws, regulations, and executive orders pertaining to PLMA and transportation agency 

planning and decision-making processes. The primary legislation governing transportation decisionmaking for PLMAs and transportation agencies is the surface transportation authorization under Title 23 

of the U.S. Code (USC). The most recent authorizations were the Moving Ahead for Progress in the 21st

Century (MAP-21) Act of 2012 and the Fixing America’s Surface Transportation (FAST) Act of 2015, which 

authorizedseveral programs and requirements regarding transportation planning, programming, and 

performance management, and other decision-making. These provisions are summarized in this section.

Metropolitan, Rural and Statewide Transportation Planning and Programming

Metropolitan and Statewide Transportation Planning are governed by 23 USC Section § 134 

(Metropolitan transportation planning) and § 135 (Statewide transportation planning). 5 23 USC § 134 

and § 135 require MPOs and State DOTs to develop the following: 

• Long-Range Transportation Plans(LRTPs): 23 USC § 134 and § 135 require MPOs and State

DOTs to develop long-range transportation plans (LRTPs) that cover their respective planning

areas. 23 USC § 135 requires State DOTs to develop LRTPs with a minimum 20-year forecast

period for all areas of the State that provides for the development and implementation of the

intermodal transportation system of the State. 23 USC § 134 requires MPOs to develop LRTPs,

often referred to as Metropolitan Transportation Plans (MTPs), for their planning areas. State

DOTs and MPOs typically update their LRTPs every 4 to 5 years, depending on applicable

requirements. LRTPs are multimodal planning documents, covering all transportation modes

and require both public involvement and consultation with other transportation organizations

within the planning area, including State DOTs, MPOs, nonmetropolitan planning organizations,

transit agencies, tribal governments, and federal land management agencies (FLMAs). RTPOs are

designated to develop transportation improvement programs and long-range plans for nonmetropolitan areas. Federal transit law (49 USC. § 5301 et seq) and the final rule on Statewide

and Nonmetropolitan Transportation Planning and Metropolitan Transportation Planning spell 

out provisions for the organizations.

• Transportation Improvement Programs (TIPs) and Statewide Transportation Improvement 

Programs (STIPs): 23 USC § 134 and § 135 require MPOs and State DOTs to develop

Transportation Improvement Programs (TIPs) and Statewide Transportation Improvement 

Programs (STIPs), respectively. TIPs and STIPs are documents containing lists of projects

programmed for funding that are consistent with the MPO’s or State DOT’s current LRTP and

contribute to achieving the LRTP’s goals. TIPs and STIPs have 4-year time horizons and are

updated every 4 to 5 years. During TIP and STIP development, MPOs and State DOTs must

5 23 USC Section 134, 135.
Prepared by the U.S. DOT Volpe Center 5 

conduct public involvement and consult with other transportation organizations within the 

planning area, including State DOTs, MPOs, nonmetropolitan planning organizations, transit 

agencies, tribal governments, and FLMAs. In non-metropolitan rural areas, Federal planning law 

(49 USC § 5304) requires each state to cooperate with local officials to develop a long-range 

statewide transportation plan and STIPs.

FAST Act Planning Factors 

The FAST Act established several national planning factors, or issues for State DOTs and MPOs to 

consider in their LRTPs and TIPs. These planning factors are summarized below: 6 

A. Economic vitality;

B. Transportation Safety (motorized and non-motorized);

C. Transportation Security (motorized and non-motorized);

D. Accessibility and mobility of people and freight;

E. Environment;

F. Integration and connectivity (across jurisdictions and modes);

G. System management and operation;

H. Preservation of the existing transportation system;

I. Resiliency and reliability; and 

J. Travel and tourism.

Items (I) and (J) above are new planning factors in the FAST Act. Although many of these planning 

factors are also relevant to PLMAs, factor J – travel and tourism – places a new emphasis on 

understanding recreational travel demand in the planning process and is an area best addressed 

through coordination between transportation agencies and PLMAs. Because this is a relatively new 

planning factor, guidance on how to address it in LRTPs and TIPsis still being developed. 

Transportation Performance Management

FHWA defines Transportation Performance Management (TPM) as a strategic approach that uses system 

information to make investment and policy decisions to achieve national performance goals. 7 In 2012, 

MAP-21 created new requirements for the U.S. Department of Transportation (USDOT)to generate a 

series of rulemakings establishing performance measures and targets for seven national goals: 8

1. Safety - To achieve a significant reduction in traffic fatalities and serious injuries on all public

roads

2. Infrastructure Condition - To maintain the highway infrastructure asset system in a state of

good repair

3. Congestion Reduction - To achieve a significant reduction in congestion on the National

Highway System

4. System Reliability - To improve the efficiency of the surface transportation system

6 23 USC Section 134(h)(1), 135(d)(1).

7 FHWA. 2019. Transportation Performance Management website: 

https://www.fhwa.dot.gov/tpm/about/tpm.cfm 8 23 USC 150(b)
Prepared by the U.S. DOT Volpe Center 6 

5. Freight Movement and Economic Vitality - To improve the national freight network, strengthen 

the ability of rural communities to access national and international trade markets, and support 

regional economic development

6. Environmental Sustainability - To enhance the performance of the transportation system while 

protecting and enhancing the natural environment

7. Reduce Project Delivery Delays - To reduce project costs, promote jobs and the economy, and 

expedite the movement of people and goods by accelerating project completion through 

eliminating delays in the project development and delivery process, including reducing 

regulatory burdens and improving agencies' work practices

The USDOT published rulemakings for national performance measures and targets for the national goals

in 2016. These rules require State DOTs and MPOs to submit the specified performance management 

data for national reporting, with the first performance period beginning January 1, 2018, and ending 

December 31, 2021. The TPM statutes and regulations are available on the FHWA TPM webpage. 9 

TPM creates a data sharing framework for a set of nationally consistent performance measures. For 

many transportation agencies, TPM has created a need for agencies to increase their capacity to collect, 

analyze, and share data. TPM has also created opportunities for increased collaboration as agencies 

collect and share comparable data. FHWA’s TPM Toolbox includes capacity building resources to help 

agencies develop their TPM programs, including chapters on “External Collaboration and Coordination” 

and “Data Management.”10

Performance-Based Planning and Programming

In 2012, MAP-21 also placed new emphasis on performance-based planning and programming (PBPP) by 

requiring the use of performance management elementsin planning and programming documents 

including LRTPs and TIPs/STIPs. 11 The FAST Act built upon these requirements in 2015. PBPP refers to 

the application of performance management elements within transportation planning and programming 

processes to achieve desired performance outcomes for the multimodal transportation system. This 

includes a range of activities undertaken by a transportation agency with other agencies, stakeholders, 

and the public. PBPP attempts to ensure that transportation agencies make decisions based on their 

ability to meet desired goals. 12 PBPP is how transportation agencies implement TPM. 13

As shown in Figure 1, PBPP links each phase of the project life cycle – including planning, programming, 

implementation, and evaluation – with performance data to articulate an agency’s goals, objectives, 

performance measures, and targets; analyze how investment decisions will achieve the agency’s 

objectives; and evaluate outcomes during and after implementation. 

 

9 FHWA. 2019. TPM Statutes and Regulations website: https://www.fhwa.dot.gov/tpm/about/how.cfm. 10 FHWA. 2020. TPB Toolbox. https://www.tpmtools.org/ 11 23 USC Section 134, 135.

12 Federal Highway Administration. 2013. Performance Based Planning and Programming Guidebook. Washington, 

DC. https://www.fhwa.dot.gov/planning/performance_based_planning/pbpp_guidebook/ 13 FHWA. 2020. Transportation Performance Management / Performance-Based Planning and Programming 

Implementation Workshop Series. 

https://www.fhwa.dot.gov/planning/performance_based_planning/workshops/tpm_interim_report/ 
Prepared by the U.S. DOT Volpe Center 7 

Figure 1: PBPP Framework (Source: FHWA, 2013, Performance Based Planning and Programming Guidebook)

One benefit of PBPP is that it allows transportation agencies to communicate their goals and evaluate 

decisions based on data with other agencies, stakeholders, and the public. FHWA’s Performance Based 

Planning and Programming Guidebook provides the following lessons for effective implementation of 

PBPP related to data sharing and collaboration: 14 

• Use measures that matter. Rather than identifying hundreds of measures, it is often 

preferable to identify a limited set of key measures to best support goals and objectives, 

guide investment decisions, and evaluate progress.

• Engage the public and stakeholders. Public engagement is critical to identify the issues that 

residents care about most. In addition, keep the public and stakeholders in mind when 

developing measures to ensure that they are easy to understand and resonate.

• Coordinate and collaborate broadly. Effective PBPP involves coordination within agencies 

and across agencies so the State DOTs, MPOs, nonmetropolitan planning organizations, and 

transit agencies are coordinated in the development of goals, objectives, performance 

measures, and targets. It also involves coordination with a wide range of partners, including 

local governments, the business community, freight communities, law enforcement, 

economic development, and others.

 

14 Federal Highway Administration. 2013. Performance Based Planning and Programming Guidebook. Washington, 

DC. https://www.fhwa.dot.gov/planning/performance_based_planning/pbpp_guidebook/
Prepared by the U.S. DOT Volpe Center 8 

• Provide context for performance results. A recent trend in performance management has 

been to develop dashboards and other data visualization techniques. These tools are helpful 

for communicating data; however, using a simplified approach to reporting data could create 

a risk for misinterpretation. It is important to tell a story and combine data with an 

explanation of performance results.

Although FHWA’s PBPP Guidebook does not discuss collaboration with PLMAs specifically, the adoption 

of PBPP by agencies throughout the U.S. provides new opportunities for data sharing and collaboration 

between transportation agencies and PLMAs. FHWA has developed a wide range of resources related to, 

summarized in Table 1. 

Table 1: Summary of USDOT PBPP Resources

Title Date Description

Performance-Based Planning 

and Programming Guidebook15 2013

The Guidebook has been designed to help 

transportation agencies and partner organizations 

understand: the key elements of a PBPP process and 

the relationship of these elements within existing 

planning and programming processes. The Guidebook

highlights effective practices to help transportation 

agencies in moving toward a performance-based 

approach to planning and programming.

Model Long-Range 

Transportation Plans: A Guide 

for Incorporating PerformanceBased Planning 16

2014

This Guidebook informs transportation agencies and 

their planning partners about effective practices for 

incorporating performance-based planning into the 

development of a long range transportation plan.

Performance-Based Planning for 

Small Metropolitan Areas17 2014

This report provides insights on effective practices in 

performance based planning by MPOsthat plan for 

Urbanized Areas with populations less than 200,000. It 

presents key themes from interviews with small MPOs 

and DOT partners across the country and includes two 

case studies of small MPOs that are currently leaders 

in implementing performance based planning: 

Chittenden County Regional Planning Commission 

(Vermont) and Thomas Jefferson Planning District 

Commission (Virginia).

 

15 FHWA. 2013. Performance Based Planning and Programming Guidebook. Washington, DC. 

https://www.fhwa.dot.gov/planning/performance_based_planning/pbpp_guidebook/

16 FHWA. 2014. Model Long-Range Transportation Plans: A Guide for Incorporating Performance-Based Planning. 

Washington, DC. https://www.fhwa.dot.gov/planning/performance_based_planning/mlrtp_guidebook/ 17 FHWA. 2014. Performance-Based Planning for Small Metropolitan Areas. Washington, DC. 

https://www.fhwa.dot.gov/planning/performance_based_planning/small_mpo_report/ 
Prepared by the U.S. DOT Volpe Center 9 

Title Date Description

Performance-Based Planning 

and Programming: A Report to 

Congress18

2017

This Report to Congress covers the overall 

effectiveness of PBPP as a tool for guiding 

transportation investments and the effectiveness of 

the PBPP processes of State DOTs and MPOs. 

PBPP Case Studies19

2011, 

2015, 

2019

This collection of case studies represents varied 

approaches to PBPP by transportation agencies. 

Agencies featured include: Florida DOT, Minnesota 

DOT, Utah DOT, Washington State DOT, Delaware 

Valley Regional Planning Commission, Lewis Clark 

Valley MPO, Maricopa Association of Governments, 

Metropolitan Transportation Commission, Portland 

Metro Council, Rockingham MPO, San Diego 

Association of Governments, and Wasatch Front 

Regional Council.

Example Practices for 

Performance-Based Planning 

and Programming 20

2020 This report shares how transportation agencies are 

using their LRTPs and STIPs/TIPs to implement PBPP.

Federal Land Management Agency Transportation Planning and Programming

23 USC § 201 requires FHWA’s Office of Federal Lands Highway, in consultation FLMAs, to implement 

transportation planning procedures for Federal lands “that are consistent with the planning processes” 

required for MPOs and State DOTs under 23 USC § 134 and 135. 

For long-range planning documents, FLMAs typically develop LRTPs at the national level. NPS and FWS 

have also developed regional LRTPsand, in some cases, unit-level LRTPs. FLMAs also develop other unitlevel or site plans and planning studies that have transportation components, such as a portfolio 

planning approach (NPS)21, Comprehensive Conservation Plans (FWS), Forest Plans (USFS), and Travel 

and Transportation Management Plans (BLM). Although FLMA LRTPs consider unique public lands 

contexts, many of the common FLMA goals overlap with common State DOT and MPO goals. Common 

goals include safety, resource protection/environmental sustainability, mobility/congestion 

management, asset management, visitor/user experience, and economic opportunity. 22 

For programming, each FLMA has a process to develop its program of projects for inclusion into its TIP in 

collaboration with FHWA Office of Federal Lands. FHWA Office of Federal Lands also develops a TIP that 

 

18 FHWA and FTA. 2017. Performance-Based Planning and Programming: A Report to Congress. Washington, DC. 

https://www.fhwa.dot.gov/planning/performance_based_planning/resources/report_to_congress_2018/ 19 FHWA. 2019. Performance-Based Planning and Programming Case Studies: 

https://www.fhwa.dot.gov/planning/performance_based_planning/case_studies/ 20 FHWA. 2020. Example Practices for Performance-Based Planning and Programming. Washington, DC. 

https://www.fhwa.dot.gov/planning/performance_based_planning/resources/example_practices/. 21 NPS may still complete General Management Plans (GMPs), but only when needed. 

https://parkplanning.nps.gov/planningProgram.cfm 22 Published FLMA LRTPs are available on the FHWA Federal Lands Planning Program website: 

https://highways.dot.gov/federal-lands/programs-planning/lrtps. 
Prepared by the U.S. DOT Volpe Center 10

includes all federal projects it has stewardship and oversight responsibility for, including FLMA projects. 

State DOT’s then incorporate the FHWA Office of Federal Lands TIP into their respective STIPs.

Although FHWA’s TPM requirements do not apply to FLMAs, 23 USC § 201 statesthat FLMAs shall, to 

the extent appropriate, implement safety, bridge, pavement, and congestion management systems. 23

The performance measures that State DOTs and MPOs are required to report under TPM requirements 

are not required for FLMAs, and in many cases they would not be feasible for FLMAs or meaningfully 

characterize their transportation systems. (For example, a performancemeasure focused on pavement 

condition is less useful for a PLMA with mostly unpaved roads.) However, TPM may provide 

opportunities for FLMAs to better understand transportation agencies’ data, as well as opportunities for 

FLMAs to provide similar data for cross-jurisdictional planning and collaboration with PLMAs. 

State and Local Public Lands Transportation Planning and Programming

There is limited literature on transportation planning and programming for state and local PLMAs, and 

outside of Federal funding programs there are no nationwide requirements for PLMA and transportation 

agency coordination. However, many states have laws and regulations pertaining to non-federal PLMAs. 

These include land use and transportation planning requirements. In addition, PLMAs may work with 

State DOTs and MPOs to provide inputs into their travel demand modeling. 

Common Motivations for Data Sharing 

Possible Outcomes of Data Sharing

Public agencies collect data to understand the function of their services and potential areas of 

improvement. When transportation agencies and organizationsshare data, their partnersand other 

users of the data all benefit. The following section is an overview of the possible benefits of data sharing 

in transportation planning. 

In the case of TPM, regulations require data collection to quantify final performance measures. 24 It is 

imperative to use specific standards for data sharing to meet established national performance goals

because the data collected isshared and comparedacross different agencies. This establishes a 

motivation for involved agencies to cooperate and abide by established requirements for data collection 

and sharing.

According to Data Sharing Guidance for Public Transit Agencies, sharing data among public agencies 

provides the following benefits to agencies and their partners: 25

• Improving efficiency. Sharing data can spark innovation and supporting research, allowing for 

better overall service. 

• Promoting cost effectiveness. By sharing and utilizing third party or private sector assistance for 

data analysis, agencies can operate more efficiently. 

 

23 23 USC § 201. 24 TPM Regulations Webpage: https://www.fhwa.dot.gov/tpm/about/regulations.cfm

25 National Academies of Sciences, Engineering, and Medicine. 2020. Data Sharing Guidance for Public Transit 

Agencies – Now and in the Future. Washington, DC: The National Academies Press. 

https://doi.org/10.17226/25696. 
Prepared by the U.S. DOT Volpe Center 11

• Supporting improved customer information. Understanding the general usage patterns and 

needs of customers can optimize services.

• Establishing greater transparency of agency services. By increasing awareness of services, 

agencies can foster improved engagement with customers.

• Supporting performance management. Benchmarking assists agencies to review and improve 

their overall performance.

When public organizations engage in data sharing partnerships, they must consider several factors such 

as whether they want to share their data, with whom, and if they decide to share, the best model to 

utilize. Benefits such as increased transparency and potential for innovation are primary motivators for 

engaging in data partnerships. When agencies engage in such partnerships, inter-agency cooperation 

and understanding, public perceptions, and civic engagement are enhanced. 26 

Data Sharing Benefits to Transportation Decision-Making Processes

Data sharing can also support transportation agencies, PLMAs, and other stakeholders to improve 

transportation decision-making throughout the project lifecycle. For example, data sharing can help 

agencies better understand the needs and priorities of partner organizations, which can help identify 

opportunities for collaboration during planning, programming, design and environmental review, project 

implementation, and evaluation. This, in turn, can lead to project funding opportunities or project 

delivery efficiency improvements, and ultimately to a more seamless transportation network for the 

traveling public. 

Recognizing that data needs vary from phase to phase and between users, this Study could examine 

how the desired outcomes outlined above can be achieved in each of the following transportation 

phases:

• System and project planning

• Project selection and programming

• Design and environmental review

• Construction

• Operations and maintenance

• Performance management and reporting

The Eco-Logical approach is an example of how such motivations influence desired outcomes. In the 

approach, both “Region System Framework” (REF) and the “Regional Ecosystem Infrastructure 

Development Framework” (REIDF) describe the outcome of integrating conservation priorities, data, and 

plans with transportation and infrastructure data and plans. 27 

Common Data Sharing Challenges

The literature surrounding data sharing identifies a number of challenges collaborating organizations 

may face. While the literature does not address data sharing challenges in the context of public lands 

management, the information from other transportation and related disciplines may be applicable. 

 

26 Ibid. 27 National Academies of Sciences, Engineering, and Medicine. 2014.Manager’s Guide to the Integrated Ecological 

Framework. Washington, DC: The National Academies Press. https://doi.org/10.17226/22423; 1/12/21
Prepared by the U.S. DOT Volpe Center 12

These relate to processes from collecting data through keeping shared data updated. One report 

established that data challenges are simultaneously technical and institutional in nature. 28 This section 

identifies what the literature says about the different types of challenges.

Staff Capacity 

Successful data collection and management is contingent on staff resources, capacity, and coordination. 

One Transit Cooperative Research Program (TCRP) report notesthat when individual groups collect and 

manage data they may inadvertently withhold this information from teams even within the same 

organization. A lack of communication may leave teams unaware of how their collected and managed 

data could hold utility for similar or separate teams. 29 Different parts of the same organization may not

be aware that relevant data they need already exists within their own organization or partner 

organizations. A centralized data repository or catalog provides a more effective way to manage data 

collection, management, and distribution processes. The efficiency carries through to responding to 

public requests for information relevant to public agencies. 

Despite the efficiencies to be gained, only one transit agency surveyed by the TCRP research team 

responded that it has an information management and governance group established to handle outside 

data requests. 30 The lack of a dedicated data management group in transit agencies is indicative of a 

larger problem – agencies do not have the staff capacity and related resources to put dedicated teams in 

place. Part of this can be attributed to leadership not recognizing the importance of data 

management, 31 which will be discussed further below.

Additionally, staff turnover impacts the continuity of data sharing capacity and partner relationships, 

especially when there is no overlap between the outgoing and incoming staff. Ensuring the continuity of 

knowledge regarding data management is critical for data governance and data management 

sustainability. 32 A recent report reviewing data management practices in Midwestern DOTs assessed the 

participating agencies on the maturity level of their data strategy and governance, including a question 

on whether their staff transition processes maintain data management knowledge and expertise. On a 

scale of one to five, with five being the most mature, the highest score for staff transition processes was 

a three.

 

28 National Academies of Sciences, Engineering, and Medicine 2019. Developing National Performance 

Management Data Strategies to Address Data Gaps, Standards and Quality. Washington, DC: The National 

Academies Press. http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_920NPM.pdf 12/18/2020 29 National Academies of Sciences, Engineering, and Medicine, Data Sharing Guidance for Public Transit Agencies 

Now and in the Future, 11-14. 30 National Academies of Sciences, Engineering, and Medicine 2020. Data Sharing Guidance for Public Transit 

Agencies Now and in the Future. Washington, DC: The National Academies Press. https://doi.org/10.17226/25696

12/17/2021

31 BMC Public Health. 2014; 14:1144. https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-14-

1144 12/17/2021 32 Nextrans USDOT Region V Regional University Transportation Center. 2017. A Synthesis of Data Management 

Practices in the Midwestern DOTs. Project No. 166UWY2.2. Last accessed 2/3/2021. 

https://www.purdue.edu/discoverypark/nextrans/assets/pdfs/166UWY2.2_Summary%20and%20Final%20Technic

al%20Report.pdf
Prepared by the U.S. DOT Volpe Center 13

Data Quality

Data quality control is essential to sharing reliable data between organizations. Databases and 

dashboards used to share information within an organization and externally are only as good as the data 

stored within them. Several reports identified data completeness as a challenge related to quality. Data

completeness refers to whether the information collected contains all data points relevant to its 

application, including consideration of whether the collection method is inclusive of all users. For 

example, smart phone data is an emerging big data source; however, not every person has a smart 

phone, especially among low income populations. If portions of the population are not represented in 

the data, they may not be accounted for in decision-making that impacts them. Checking for data 

completeness requires staffing, processes, and tools that can identify potential issues. 33

The data collected needs to be relevant to the planning and decision-making at hand. The data collected

may be a by-product of a different project rather than a dedicated effort. In this situation, the data may 

not capture valuable details that would be covered by a targeted collection. For instance, weekday 

commuter data is not useful to understanding weekend visitation to public lands. Additionally, limited 

data coverage could result from using old collection methods that could include biases. The NCHRP 

report on performance management data found that organizations tend to rely on available data 

instead of finding new types of data that is better fit for decision-making. 34 If the older data includes

biases that may not be immediately apparent, it continues to impact results until the method is 

correctedand new data is collected. 

Finally, poor data quality may lead an organization not to trust it and, therefore, unwilling to share it, 

further challenging data collaboration.

Data Compatibility and Comparability

Organizations collect transportation data across a variety of metrics and methods. Documentation of the 

data is also varied, which impacts the ability to cross reference data sets. A group of researchers looking 

at barriers to sharing data related to public health identified several technical challenges that are listed 

in the table below. 35

 

33 National Academies of Sciences, Engineering, and Medicine 2019. Leveraging Big Data to Improve Traffic 

Incident Management. Washington, DC: The National Academies Press.

http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_920NPM.pdf12/18/2020 34 Ibid. Page 5 35 van Panhuis, WG, Paul, P, Emerson, C, Grefenstette, J, Wilder, R, Herbst, AJ, et al. A systematic review of barriers 

to data sharing in public health. BMC Public Health. 2014; 14: 1144. 

https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-14-1144 
Prepared by the U.S. DOT Volpe Center 14

Table 2: Technical challenges that inhibit data sharing 

Technical Issue Example

Data collection not accessible/ 

retrievable

Data stored on individual computers instead of a common 

network

Data terminology differences Nomenclature and coding differences do not align

Limited formatting Data collected and maintained in hard copy form and not 

digitized

Lack of metadata and standards

Without metadata to describe data within the set, other 

organizations may not be able to identify what data they 

need.

Source: BMC Public Health. 2014; 14: 1144

According to several reports, some transportation sectors have taken steps to standardize data 

collection and analysis processes. A recent example of standardized data sharing is the use of a general 

transit feed specification (GTFS). GTFS establishes a common data framework for transit agencies to 

share route and schedule information that external parties can use to develop web applications and 

other uses. The Federal Transit Administration (FTA) established the National Transit Database (NTD) to 

collect various data points from operators based on their size. A significant challenge to establish

standards across a broad range of transportation organization is that adoption is not uniform. A strong 

coalition with champions is needed to get enough buy-in for a substantial shift in process. 

Looking at the emergence of big data sets from a range of sources, NCHRP researchers referenced a

need to improve data collection and communication infrastructure. When referring to the ability of 

Traffic Management Centers (TMC) the report stated: “New capabilities will be needed for data 

acquisition, communications bandwidth from the roadside to the TMC, computing hardware, software, 

data storage and management systems, decision support subsystems, and data sharing and 

dissemination systems.” 36

Effort and Cost

Establishing proper data collection and management efforts in an organization takes substantial effort 

and funding. Staff need to establish goals and objectives for data use and management and be 

empowered to implement a plan that attains them. The investment in data collection and management 

should be a long term commitment that continues beyond staff changes. The TCRP report states, “[S]taff

turnover can make it difficult to ensure that progress in data management is sustainable. Establishing a 

staff member or team that is dedicated to data management is an important step in addressing these 

challenges.”37 The relationships between sharing partners should also withstand staff turnover. 

Data collection is labor intensive, and organizations may not have the staff capacity to collect enough 

data. As an alternative to data collection, public organizations purchase private datasets from 

companies that collect data on transportation systems, but these datasets can also be costly. These 

agreements require negotiating data sharing agreements, which can be complicated. The agreements 

 

36 National Academies of Sciences, Engineering, and Medicine 2020. Framework for Managing Data from Emerging 

Transportation Technologies to Support Decision-Making. Washington, DC: The National Academies Press. Pg 39. 

http://www.trb.org/Publications/Blurbs/181365.aspx 12/18/2021 37 National Academies of Sciences, Engineering, and Medicine, Data Sharing Guidance for Public Transit Agencies 

Now and in the Future, 11-14. Page 50.
Prepared by the U.S. DOT Volpe Center 15

can include provisions about accuracy and use. When there is shared interest in purchasing the same 

data set, state and local agencies can coordinate effortsfor cost sharing.

Data Governance

Data is constantly being updated and refined. Even if a road segment location data remains the same, 

data related to its condition may be updated by the operating agency. An organization can maintain its 

datasets with regular updates; however, once a data set is shared, later updates may not transmit and 

the partner’s information remains the same. 

The literature reviewed did not discuss the need for data partnerships to establish agreed upon update 

protocols and commitments. While it was not mentioned directly, organizations sharing data must be 

able to understand the data opportunities and limitations they receive. They should also keep each 

other informed on scheduled updates, roles and responsibilities, and the contact for when questions or 

errors arise.

New data from new sources are also a challenge. NCHRP Report 282 establishes that new big data sets 

from emerging sources cannot be well managed using traditional data handling approaches that most 

organizations have now. 38 Traffic Management Centers need improved capabilities for “data acquisition, 

communications bandwidth from the roadside to the TMC, computing hardware, software, data storage 

and management systems, decision support subsystems, and data sharing and dissemination systems.” 

Challenges also exist when there are discrepancies in data collection standards between agencies. If 

there are divergent standards, or inconsistencies in data collection practices, the data may not be 

compatible and no longer provide value for respective agencies.

Data Privacy and Security

Data privacy is an enormous issues in an increasingly connected world. Most transit agency 

representatives interviewed for the TCRP Report 213 identified privacy protection as a major concern. It 

requires effort to remove the Personal Identifiable Information (PII) from data sets; however, it is 

integral to insuring public trust with the public. Research on data sharing in public health indicated two 

categories of barriers to data sharing are political and legal. Distrust of the government may make 

people resistant to having data collected about them. In response, elected officials may ask for

restrictive policies to address their constituents’ concerns. An agency may also be wary of allowing other 

organizations that it doesn’t fully trust the ability to link to its data or system. Legally, transportation 

organizations that collect or use data must be careful regarding data ownership and privacy controls. 

Data sharing between organizations must address privacy issues. The partner organizations must assess 

the risk of specific data and identify methods for protecting PII when data is transferredbetween them. 

Partners should work together to establish a balance between the need and benefits of open data 

sharing and the risks of data containing PII.

 

38 National Academies of Sciences, Engineering, and Medicine 2020. Framework for Managing Data from Emerging 

Transportation Technologies to Support Decision-Making. Washington, DC: The National Academies Press.

http://www.trb.org/Publications/Blurbs/181365.aspx 12/18/2021
Prepared by the U.S. DOT Volpe Center 16

Some data sets can include information that can be compiled to establish details on where specific 

infrastructure lacks security or the location of people in an area, making them susceptible to physical or 

cyberattack.

Leadership Buy-in

The leader of an organization directs its priorities and investment. Leadership support of data collection, 

management, and sharing is critical given the resources it requires to operate effectively. However, 

“Leadership often does not fully understand the value of big data, modern data management, or the 

eminent need to ready for it.” 39 Without a data sharing champion at the top of an organization, it can be 

very hard for staff to obtain the resources they need for their planning and decision making. The 

literature identified some specific reasons that leadership does not embrace data and data sharing. 40, 41, 

42 

• Lack of trust in the data, especially from external sources

• Operational focus does not allow for collection and analysis of emerging mobility technologies

• Limited funding to distribute and choose to keep data and data sharing efforts to just meet the 

federal standards

• Lack of understanding about how good data can support transportation planning and informed 

decision making, coupled with limited time to learn more about the possibilities.

Data Management and Sharing 

There is a significant body of literature on the internal management and external sharing of data across 

industries, including the transportation industry. However, little has been written about these processes 

as they pertain to data sharing between PLMAs and other transportation organizations. This section 

summarizes some of the key findings from other industries or transportation entities that are relevant to 

the focus of this research.

Managing Data Internally

A recent NCHRP study defined data management as “the practice of organizing and maintaining data 

and data processes to meet ongoing information lifecycle needs.” 43 Data management practices have 

evolved as technological advancements enabled a dramatic increase in the volume of data available to 

 

39 National Academies of Sciences, Engineering, and Medicine 2020. Framework for Managing Data from Emerging 

Transportation Technologies to Support Decision-Making. Washington, DC: The National Academies Press.

http://www.trb.org/Publications/Blurbs/181365.aspx 12/18/2021 40 Ibid. page 61 41 National Academies of Sciences, Engineering, and Medicine, Data Sharing Guidance for Public Transit Agencies 

Now and in the Future, 27. 42 van Panhuis, WG, Paul, P, Emerson, C, Grefenstette, J, Wilder, R, Herbst, AJ, et al. A systematic review of barriers 

to data sharing in public health. BMC Public Health. 2014; 14: 1144.

https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-14-1144 43 National Academies of Sciences, Engineering, and Medicine 2020. Framework for Managing Data from Emerging 

Transportation Technologies to Support Decision-Making. Washington, DC: The National Academies Press.

https://doi.org/10.17226/25965http://www.trb.org/Publications/Blurbs/181365.aspx
Prepared by the U.S. DOT Volpe Center 17

and used by organizations. Some organizations use a data management plan to ensure these practices 

maximize funding, time, resources, and meeting the organization’s goals and requirements. 44

The body of literature includes several different ways of defining the activities that fall under data 

management. In a synthesis of data management practices in Midwestern DOT’s, the management 

practices most relevant to this Study include: 

• Data Strategy and Governance—the planning, policies, and principles that determine how data 

is used at the organization, as well as those responsible for managing and making decisions 

about the data.

• Data Life-cycle Management—managing data from collection to archiving. This includes 

creating data catalogs or dictionaries to track available data, as well as managing regular update 

cycles to ensure the data is current.

• Data Architecture and Integration—standardizing data referencing methods and other key 

linkages across datasets, including for geospatial data.

• Data Collaboration—coordinating within and outside of the organization to maximize utility and 

avoid duplication.

• Data Value—ensuring the quality and availability of the organization’s data. 45

The following maturity model prescribing ratings for data management practices at a DOT is applicable 

more broadly to other organizations that collect and manage transportation data: 46

Table 3: Data Management Maturity Model (Source: Teresa Adams et al, Capability Maturity Levels for Data Management 

Assessment, (NEXTRANS Center and University of Wisconsin-Madison, 2017), 11, table 3)

Maturity Level Rating Score Description

Initial 1 Ad hoc and event driven, success due to heroic efforts of 

individuals

Developing 2 Recognized need for improvement, pilot initiatives underway

Defined 3 Defined and documented processes not yet stabilized or widely 

socialized

Functioning 4 Implemented processes – operating and adding value

Sustained 5 Evaluated and improved processes, sustained over time

An organization that has clear procedures for collecting, processing, and documenting its own data will 

more easily be able to develop data sharing partnerships. One of the most common reasons PLMAs 

initially form data sharing partnerships is to partner on a specific project for which a non-park entity has 

or can apply for funding. PLMAs often partner with local organizations to apply for mutually beneficial 

transportation improvements, such as to a road connecting to a park or a shuttle system that serves a 

 

44 Backlund Jarquín, P., MPH. (n.d.). Data Sharing: Creating Agreements In support of community-academic 

partnerships. Retrieved January 8, 2021, from http://trailhead.institute/wpcontent/uploads/2017/04/tips_for_creating_data_sharing_agreements_for_partnerships.pdf, 7. 45Adams, T. (n.d.). A Synthesis of Data Management Practices in the Midwestern DOTs. Retrieved January 8, 2021, 

from 

https://www.purdue.edu/discoverypark/nextrans/assets/pdfs/166UWY2.2_Summary%20and%20Final%20Technic

al%20Report.pdf, 11-13. 46 Ibid.
Prepared by the U.S. DOT Volpe Center 18

park and a surrounding community. PLMAs and partners may start with an “initial” maturity level, as 

they share information ad hoc related to a project. These projects can be leveraged to develop a more 

consistent sharing partnership.

Preparing for a Data Sharing Partnership

While the scope of this literature review does not include detailed discussion of each of these data 

management tasks, they help ensure that any data shared is authoritative, and has sufficient context to 

ensure it is used and communicated properly. 47

Figure 2: Data Sharing Process (Source: National Academies of Sciences, Engineering, and Medicine, Process of Preparing Data 

for Sharing, (Washington, DC: The National Academies Press, 2020), 25, figure 4). 

Beyond these general best practices for data management, there are specific steps an agency can take 

to prepare staff and data for a data sharing partnership:

Identify dedicated, data-focused staff

As with many organizational functions, it is important to have dedicated staff with the responsibility and 

resources to manage data. Such staff may include IT specialists, whose role involves guiding data 

management efforts along with agency processes. GIS staff also may serve to benefit data management, 

as much data requires geospatial interpretation. Based on the size of the organization, this could be a 

full-time staff member, or a division with several people. For particularly small organizations, such as a 

small transit agency, these roles may be filled by staff at partner organizations. These individuals should 

have skills in database administration, maintenance, analytics, and data privacy. 

The organization should also include a legal staff person or division that is equipped to interpret 

legislation related to data storage and sharing. This staff person or division should understand data 

 

47 National Academies of Sciences, Engineering, and Medicine. 2020. Data Sharing Guidance for Public Transit 

Agencies – Now and in the Future. Washington, DC: The National Academies Press. 

https://doi.org/10.17226/25696; 27.
Prepared by the U.S. DOT Volpe Center 19

security laws, data breach notification laws, information disclosure (public records) laws, exemptions, 

and tort laws regarding mishandling of data.

It is also important that data management staff coordinate closely with other staff at the partner 

organization who collect and work with data. The staff responsible for data management should 

establish clear processes for standardizing, sharing, and communicating about data across the 

organization. While there are specific staff focused on data management, everyone at the organization 

should understand their role. 48

Establish goals and objectives to be achieved through data sharing partnerships.

An agency considering a data sharing partnership should clearly define the data and analysis needs it is 

trying to achieve through a data sharing partnership.

Data management staff at the organization should work with other staff and leadership to establish the 

goals for the data analysis and what data is needed to meet those goals. From there, they need to 

determine how much of the goal can be met with data the organization already has, as well as the data 

gaps. The organization should evaluate whether a data sharing partnership could fill the gaps more 

efficiently and effectively than collecting the data itself. Some aspects to consider when choosing which 

path to pursue are the decisions that will be made based on the data and analysis, the frequency with 

which the data needs to be updated, whether the organization has the capacity to meet these needs, 

and whether a partner organization already collects the data. An organization might begin by 

inventorying its existing data management and sharing processes, and assess any necessary changes to 

data collection and governance. A review of the decisions the data informs and any outcomes of not 

sharing the data will also set parameters of a data sharing partnership.

The organization should develop protocols for cataloging data, data sharing, privacy protection, public 

records requests, and other protocols. This can also include ensuring the organization’s own data 

quality, coverage, processing, and documentation. 49

Select data tools

Organizations need to consider what capabilities a data reporting tool needs to meet their goals. Does it 

integrate data from multiple sources? Does it simply allow users to input and view the data, or does it 

allow for analysis? Does it output data reports? Organizations may have to use multiple tools to reach all 

of their needs. Organizations also need to determine whether they purchase these tools or build them 

in-house. 50

Establish data standards

There are a number of existing data standards for transportation data, such as those for reporting 

congestion, safety, and pavement condition. Organizations may also adopt their own data formats that 

they document in a data dictionary or catalog. In developing a data sharing agreement, partners may 

 

48 Ibid, 11-14. 49 Ibid, 11-14. 50 National Academies of Sciences, Engineering, and Medicine. 2019.Management and Use of Data for 

Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies 

Press. https://doi.org/10.17226/25462; 38-42.
Prepared by the U.S. DOT Volpe Center 20

agree to a common format or standard to facilitate sharing. In other cases, partners may derive more 

use from working with raw, non-standardized data. 51

Publish data

The type of data sharing model and audience will determine how an organization publishes its data, 

what data is shared, and to what degree it is aggregated. Agencies may have different formatsfor data 

sharing for internal staff, external partners, and the general public, based on its intended use for each 

audience. For example, the agency may have a data tool that all internal staff can access, upload select 

data from that tool to a shared server with a partner, and share that information to the public via 

reports or dashboards. 52

Evaluate and select data sharing models

Organizations should select data sharing models based on the benefits an organization hopes to achieve 

from data sharing and the costs and risks the organization is trying to minimize. 53

TCRP Research Report 213 provides an overview of two major categories of data sharing models: 54

1. Public data sharing (open data)includes online data repositories, dashboards, or public reports. 

It may also include developing publicly available standard data feeds. An example might be a 

State DOT publishing public dashboards with transportation safety or congestion data that 

PLMAs can use to identify safety and congestion issues in gateway communities. 

2. Private data sharing includes sharing data between partners by a private agreement, which may 

include purchasing the data. This could also include developing shared repositories, standard 

data feeds, or Application Programming Interface (API), which allows organizations to share 

content and data across different software platforms. An example could be a nearby transit 

agency sharing its more granular ridership data with a PLMA than it publishes publicly. 

Building Data Sharing Partnerships

Building data-sharing partnerships does not subscribe to a set formula. These steps may happen 

concurrently and iteratively, and thus follow no prescribed order. The following section reviews 

common steps involved in the process of building these partnerships.

Identify a potential data-sharing partner

Before any partnerships are established, an organization will first establish the needs that any applicable 

data sharing could address. When an organization identifies an effort or project that would benefit from 

data sharing, it is best to identify partners early in order to define the goals and approach collectively. 

Building a strong partnership can help ad hoc data sharing become a longer-term partnership with 

multiple mutually beneficial projects.

 

51 Ibid, 43-45. 52 Ibid, 46-49. 53 Ibid, 11-14. 54 Ibid, 15.
Prepared by the U.S. DOT Volpe Center 21

Engage with a partner’s organizational leadership

Before engaging with a potential partner’s leadership to establish a partnership, it is important to learn 

as much as possible in advance about the organization. This includes, as available, their history of data 

sharing, past data sharing partnerships, barriers identified, and the organization’s data use guidelines. 

In initial discussions with leadership, it is important to clearly understand the priorities of each 

organization, and what apprehensions they have about sharing data. The conversation should 

determine the resources each organization might be able to provide to support the data sharing 

partnership, particularly where there may be opportunities to overcome barriers. For example, the 

organization sharing the data might not have the resources to clean the data, but the recipient 

organization might be able to do this on their own time.

Having identified the key elements of a data sharing partnership, the organizations can develop a 

memorandum of understanding (MOU) or other agreement for data sharing. 55

The following list includes examples of the type of content that might be included in a data sharing 

agreement: 56

• Description of the information

• Scale of the data

• Funding and resource requirements

• Privacy, security, copyright, and other legal requirements

• Ownership of the data

• Storage of the data (short- and long-term)

• Access of the data by other parties, partners, or the public

• Approval process

• Roles and responsibilities for data management and analysis

• Training needs and requirements

• Release of the data or findings

• Timeline for agreement

Engage with a partner’s data management team

Once a data sharing agreement is established, or as it is being drafted, the organizations should 

coordinate between their respective data management teams to identify available data, expertise at 

each organization, roles and responsibilities, potential technical issues (e.g., inadequate servers, 

obsolete data sources, quality or limitations of data). Ideally, the teams should identify opportunities to 

reduce the burden on the organization sharing the data. For example, an organization could request full 

data tables rather than asking them to extract specific fields.

If possible, it may be useful to review a sample of the data to more definitively identify what data will be 

valuable to share and what barriers exist to data completeness, usability, etc. 

 

55 Wiehe, et al. (2018). A Solutions-Based Approach to Building Data-Sharing Partnerships. EGEMS (Washington, 

DC), 6(1), 20. https://doi.org/10.5334/egems.236; 4-7. 56Backlund Jarquín, P., MPH. (n.d.). Data Sharing: Creating Agreements In support of community-academic 

partnerships. Retrieved January 8, 2021, from http://trailhead.institute/wpcontent/uploads/2017/04/tips_for_creating_data_sharing_agreements_for_partnerships.pdf, 12-14.
Prepared by the U.S. DOT Volpe Center 22

As the data management teams define the process for and barriers to data sharing, it may be necessary 

to adjust the approach and amend the data sharing agreement.

Engage with a partner’s legal team

Prior to the step of meeting with the partner organization’s leadership, an organization may work with 

its own internal legal team to identify templates for data use agreements and anticipate potential 

barriers. 

Depending on the anticipated complexity of the agreement or significant barriers, the organization may 

choose to include its internal legal team in conversations with the partner’s legal team. The legal team 

can bring a sample data use agreement to initial meetings with partner’s legal team to articulate scope 

of the data sharing effort and potential risks. This includes a discussion of specific data use guidelines, 

particularly those that protect individual privacy and where data may need to be aggregated before 

sharing. 57

Summary of Previous Transportation Agency and PLMA Planning and 

Data Sharing Efforts

Since 2010, there have been several examples of transportation agencies and PLMAs collaborating and 

sharing data to support transportationdecision-making that could inform this Study. These include: 

• FLMA Collaborative LRTPs

• Transportation Working Groups for ongoing collaboration

• Transportation agency / PLMA joint studies and plans

• TIP/STIP collaboration

• Collaborative project implementation

This section summarizes examples of these collaborative efforts. 

Federal Lands Collaborative Long-Range Transportation Plans

FLMAs develop LRTPs that are similar to those developed by State DOTs and MPOs. Federal lands LRTPs 

establish a vision with goals, objectives, and strategies for managing FLMA transportation systems. They 

typically have a planning horizon of 20 years or more and may be developed at the national, regional, or 

unit-level. 58 These plans should include outreach and consultation with other agencies and stakeholders, 

including other FLMAs, FHWA, state and local transportation agencies, tribal governments, and the 

public. In two regions of the U.S., FLMAs have enhanced this outreach by developing the plans 

collaboratively, including a range of FLMAs, FHWA, State DOTs, and other local partners in the 

development of the plan. These plans span jurisdictional boundaries, considering the network of 

transportation systems that provide access to and within public lands. 

 

57 Wiehe, et al. 2018. A Solutions-Based Approach to Building Data-Sharing Partnerships. EGEMS (Washington, 

DC), 6(1), 20. https://doi.org/10.5334/egems.236; 4-7. 58 FHWA, Office of Federal Lands Highway. 2020. Long Range Transportation Plans (LRTPs) website. 

https://highways.dot.gov/federal-lands/programs-planning/lrtps 
Prepared by the U.S. DOT Volpe Center 23

Alaska Federal Lands Collaborative LRTPs

In Alaska, FHWA’s Western Federal Lands Highway Division Office (WFL) convened the FLMAs in Alaska 

to develop a regional Collaborative LRTP. 59 This plan – the first Collaborative LRTP in the U.S. and one of 

the first Federal lands LRTPs – was developed through the active involvement of the BLM, NPS, USFS, 

FWS, FHWA, Alaska Department of Transportation and Public Facilities (DOT&PF), Alaska Municipal 

League, and WFL. Through this collaboration, the project team developed a common set of goals, 

objectives, and strategies, and they shared data to develop a common analysis of baseline conditions 

and trends. 

Through the process of developing the Alaska CLRTP, the FLMAs, FHWA, and their partners identified 

data gaps and developed implementation actions to address them. For example, the group conducted a 

Collaborative Visitor Transportation Survey (CVTS) to fill data gaps regarding visitation; conducted a 

multimodal transportation safety study to enhance their baseline understanding of traveler safety in and 

to Federal lands in Alaska; and worked to develop a common understanding of asset management data 

from each agency. They also developed mechanisms for sharing project data, such as a shared GIS 

platform to provide updated data on each agency’s program of projects. 60 

In 2020, the Alaska partners published an update to their Collaborative LRTP. 61 This plan developed an 

updated set of baseline conditions, based partly on the progress in developing and sharing data the 

group had accomplished in implementing the 2012 plan. The 2020 plan also reflected new legislation 

and agency guidance to provide an updated vision, goals, objectives, strategies, and potential 

performance measures to guide the FLMAs and their local transportation partners in Alaska for the next 

20 years. 

Pacific Northwest Collaborative LRTP

In 2019, a collaborative team of FLMAs, FHWA, and State and local transportation agencies published 

the Pacific Northwest Federal Lands Collaborative Long-Range Transportation Plan. 62 This plan was the 

product of a similar collaborative planning effort in Oregon and Washington. The agencies who 

developed the plan included: BLM, USFS, FWS, USACE, FHWA, Oregon Department of Transportation 

(ODOT), Washington State Department of Transportation (WSDOT), Oregon Association of Counties, and 

the Washington County Road Administration Board. Similar to the Alaska Federal Lands Collaborative 

LRTP, the Pacific Northwest Federal Lands Collaborative LRTP aggregated data from each FLMA and 

State and local partners to develop baseline conditions for transportation systems across jurisdictions 

that provide access to and within Federal lands in Oregon and Washington. The plan also developed a 

 

59 Bureau of Land Management, National Park Service, U.S. Forest Service, U.S. Fish and Wildlife Service, Alaska 

Department of Transportation and Public Facilities, and Federal Highway Administration. 2012. Alaska Federal 

Lands Long Range Transportation Plan. https://highways.dot.gov/federal-lands/programs-planning/lrtps/alaskacollaborative-lrtp. 60 Fix, et al. 2018. Collaborative Visitor Transportation Survey: Results from Summer 2016 Alaska Survey.

http://volpe-public-lands.s3-website-us-east1.amazonaws.com/flma_lrtp_cvts/documents/AK%20CVTS%20FINAL%20REPORT.pdf. 61 Bureau of Land Management, National Park Service, U.S. Forest Service, U.S. Fish and Wildlife Service, Alaska 

Department of Transportation and Public Facilities, and Federal Highway Administration. 2020. Alaska Federal 

Lands Long Range Transportation Plan: 2020-2040. 62 Bureau of Land Management, National Park Service, U.S. Forest Service, U.S. Fish and Wildlife Service, U.S. Army 

Corps of Engineers. 2019. Pacific Northwest Federal Land Management Agency Long-Range Transportation Plan. 

https://highways.dot.gov/federal-lands/programs-planning/lrtps/pac-nw-collaborative-lrtp 
Prepared by the U.S. DOT Volpe Center 24

common set of goals, objectives, strategies, and potential performance measures to guide future 

planning collaboration, data collection, and data sharing partnerships to support plan implementation.

Transportation Working Groups 

In both Alaska and the Pacific Northwest, the multiagency planning teams that developed Federal Lands 

Collaborative LRTPs continue to collaborate as Transportation Working Groups (TWGs). These TWGs 

focus on implementing the Collaborative LRTPs, sharing data and conducting joint studies, and 

coordinating on project programming. They also work to identify projects of mutual benefit and 

opportunities to leverage multiple funding sources to achieve project efficiencies. These TWGs arose to 

continue long-term collaborative efforts between the partners, including data needs and shared system 

information. 

In Alaska, the TWG meets monthly via teleconference and holds an annual project coordination 

meeting, which is typically in person. During these project coordination meetings, participating agencies 

provide updates on their upcoming TIPs and STIPs and discuss opportunities for coordination on project 

programming as well as planning. The annual project coordination meetings encourage informationsharing by using the STIP as the basis for the FLMA-related projects discussed and potential future 

opportunities for coordination. Alaska DOT&PF presentations on statewide LRTP activities have also 

helped the FLMAs better understand the State’s transportation planning process. 63 

In the Pacific Northwest, the Collaborative LRTP planning team has also continued to meet to 

collaborate on LRTP implementation, data initiatives, and programming. 

In Arizona and New Mexico, similar multi-agency groups have been meeting regularly for several years 

to coordinate on long-range planning, project selection, and design along highway corridors through 

Federal lands. Although they are not formalized as TWGs, these multi-agency collaborative relationships 

can help PLMAs and transportation agencies share data to inform transportation decisions. 64 

Other Examples of Transportation Agency and PLMA Joint Studies and Collaboration

In addition to the Collaborative LRTPs and TWGs above, PLMAs and transportation agencies around the 

country have developed partnerships for data sharing. Some examples include: 

FLMA Membership on MPO Policy Boards and Technical Committees

Relationships among FLMAs and MPOs can take many forms, depending on the presence of FLMA lands 

in a particular region. The Lake Tahoe region in California and Nevada and the National Capital Region in 

the District of Columbia, Maryland, and Virginia both have large FLMA presences; the U.S. Forest Service 

manages a large portion of the land near Lake Tahoe, and the National Park Service manages land and 

facilities in the National Capital Region. Both of the MPOs serving these regions have formalized the 

 

63 FHWA. 2018. “Implementation of Alaska’s Long-Range Transportation Plan through Annual Project Coordination 

Meetings.” Memorandum, part of “Federal Lands Highway-Federal-Aid Division Planning Coordination: Examples 

of Coordination in Action” series. 64 FWHA. 2018. “State Department of Transportation Annual Coordination Meetings with Federal Land 

Management Agencies.” Memorandum, part of “Federal Lands Highway-Federal-Aid Division Planning 

Coordination: Examples of Coordination in Action” series.
Prepared by the U.S. DOT Volpe Center 25

relationships with their corresponding FLMAs by inviting them to serve as members of their policy 

boards and technical committees. 65 

In the case of the Tahoe Regional Planning Agency, the U.S. Forest Service’s Tahoe Basin Management 

Unit serves as a voting member to the Tahoe MPO. The FS’s membership on the Tahoe MPO has helped 

to improve coordination between the FS and the other non-FLMA MPO members. In its 2017 Regional 

Transportation Plan, the MPO describes how a “bundled” approach to corridor planning, which requires 

significant coordination among a host of partners, results in cost savings by grouping projects by 

geographic area. In particular, the State Route 89 Recreation Corridor Improvement Project, which the 

U.S. Forest Service is leading in close coordination with several other MPO member agencies, will 

improve access to Federal lands as well as within the region.

In the Washington, DC, area, the Metropolitan Washington Council of Governments’ (MWCOG’s) 

Transportation Planning Board serves as the MPO. The National Park Service’s National Capital Area 

manages many parks, parkways, and facilitiesin the region, and therefore is an important partner for 

many topics, including transportation. The National Capital Regional Office is an ex officio (non-voting) 

member of the Transportation Planning Board. The National Capital Region is complex, as it involves two 

States plus the District of Columbia. The MPO and the NPS National Capital Area have used this 

relationship to identify opportunities for collaboration both at the long-range planning level and at the 

project level. This is particularly important since several NPS units, specifically the Baltimore-Washington 

Parkway and the George Washington Memorial Parkway, are themselves major elements of the regional 

transportation network.

Colorado: Planning and Programming Data Sharing Pilot

In Colorado, transportation agencies and PLMAs recently completed a pilot to share planning and 

programming data to better inform existing plans and funding programs. The goal of the pilot was to 

better collect multi-agency data to feed into existing planning efforts – such as Colorado DOT’s LRTP 

update – and funding programs related to Federal lands transportation, such as the Federal Lands 

Transportation Program and the Federal Lands Access Program. FHWA’s Central Federal Lands (CFL) 

Division Office facilitated this pilot study and created standardized templates for each partner to assess 

and communicate their transportation infrastructure needs along multi-agency planning corridors. 66 

Conclusion and Research Gaps

Based on the literature reviewed, data sharing research applications can be split into two possible 

groups: 

1. Governance: The body of literature, applications, and case studies focused on establishing and 

maintaining data-sharing relationships. These are the institutional mechanisms that support 

data sharing between organizations.

 

65 FHWA. 2018. “Federal Land Management Agency Membership on Metropolitan Planning Organization Policy 

Boards and Technical Committees.” Memorandum, part of “Federal Lands Highway-Federal-Aid Division Planning 

Coordination: Examples of Coordinationin Action” series. 66 FHWA. 2020. Colorado Planning Pilot Close-out Report (DRAFT).Washington, D.C.: US Department of 

Transportation.
Prepared by the U.S. DOT Volpe Center 26

2. Data-driven Decision-making: The body of literature, applications, and case studies focused on 

data needs, inputs, outputs, tools, and decisions by lifecycle phase. These are the actual data 

and tools needed to inform specific decisions.

The research team identified a wide body of literature addressing both data governance and data-driven 

decision-making. However, there are limited resources addressing both data governance and decisionmaking in the context of transportation systems and PLMAs. In addition, the project team identified the 

following research gaps: 

• The USDOT has developed a wide range of guidance and research regarding data sharing and 

decision-making in the context of State DOTs and MPOs, particularly regarding legislative 

requirements for Transportation Performance Management and Performance-Based Planning 

and Programming. However, there are limited resources specific to a PLMAs or collaboration 

between transportation agencies and PLMAs. 

• There is substantial literature on PLMA transportation planning and programming, but there is 

very limited literature on data sharing between transportation agencies and PLMAs. 

• Almost all of the literature reviewed on PLMAs pertain to FLMAs. There is very limited literature 

on how state and local PLMAs conduct transportation planning, collaborate with transportation 

agencies, or share data. 
Appendix 3: Memo 2 - Methodology
Finding a Win-Win: Planning and DataSharing Partnerships between 

Governments and Public Land 

Management Agencies

Memorandum2: Methodology

May 4, 2021
Prepared by the U.S. DOT Volpe Center 1

Table of Contents

Table of Contents ...................................................................................................................... 1

Introduction.............................................................................................................................. 2

Research Questions.................................................................................................................... 3

What types of data do PLMAs and transportation agencies use to support planning on public lands?. 3

How do PLMAs and state/local transportation partners use data to inform decision-making? ........... 3

What are the characteristics of successful public lands information/data sharing examples?............. 4

What are the barriers to effective data sharing between PLMAs and transportation agencies?.......... 4

What do PLMAs and transportation agencies need to share data more effectively?......................... 4

Proposed Collection Methods...................................................................................................... 5

Literature Review................................................................................................................... 5

Technical Research Panel (TRP)................................................................................................ 5

Case Studies.......................................................................................................................... 6

Case Study Selection Criteria ................................................................................................ 7

Case Study Assumptions.......................................................................................................... 9

Compliance with the Paperwork Reduction Act....................................................................... 9

Travel Restrictions.............................................................................................................10

Recent Changes to Planning Processes..................................................................................10

Transportation Planning Process as a Starting Point................................................................10

Schedule and Next Steps............................................................................................................10

Appendix A: Transportation Planning Data ...................................................................................11
Prepared by the U.S. DOT Volpe Center 2

Introduction

This memorandum summarizes the proposed methodology for collecting and analyzing data and 

information in support of an applied research study titled, Finding a Win-Win: Planning and DataSharing Partnerships between Governments and Public Land Management Agencies. 

The specific goals of this project are to:

• Identify examples of information coordination between Public Land Management Agencies 

(PLMAs) and transportation partners that result in process efficiencies, cost savings, and more 

effective transportation system delivery and/or management.

• Develop a suite of tools, resources, and usage guidelines to aid PLMAs and their partners in 

improving data coordination for better transportation systems.

This memorandum describes:

• The research questions to be addressed; 

• The information and data needed; 

• The stakeholder entities and individuals from whom to gather the information and data;

• The proposed methods to collect the information and data; 

• Assumptions that will inform the methodology; and 

• The general timeframe for the Data Collection and Analysis task (Task C). 

This methodology is based in part on the information documented in Memorandum 1: Background and 

Literature Review(March 2021). 1

Definition of Key Terms

The research team provides the following definitions as applied to this Study. These terms are defined in 

additional detail in Memorandum 1: Background and Literature Review. 2

Transportation Agency/Organization

For the purposes of this Study, “transportation agency” or “transportation organization” refers to 

agencies with jurisdiction, planning, funding, ownership, or management responsibility for 

transportation systems (e.g., roads, trails, transit, marine, or aviation systems). Examples may include 

State Departments of Transportation (DOT), Metropolitan Planning Organizations (MPO) ,and other local 

or agency related divisions focusing on transportation issues. 

Public Land Management Agency

A Public Land Management Agency (PLMA) is any public agency that manages land for public access and 

use. These uses can include recreation, resource protection, and economic uses, such as resource 

extraction or energy production. PLMAs include federal land management agencies, as well as parks and 

conservation agencies managed by states, regional governments, counties, or municipalities. PLMAs 

may own and manage transportation systems within their boundaries. 

 1 Federal Highway Administration. 2021. “Finding a Win-Win: Planning and Data-Sharing Partnerships between 

Governments and Public Land Management Agencies. Memorandum 1: Background and Literature Review.” 

https://highways.dot.gov/federal-lands/programs-planning/studies/pads. 2 Ibid. 
Prepared by the U.S. DOT Volpe Center 3

Research Questions

Accurate and up-to-date data is critical for transportation planning; however, the types of data available 

and the process for accessing them are not always clear, especially when applied to transportation 

systems that provide access to or within public lands. This research project aims to fill some of these 

gaps, and considers both the data and the process of sharing data.

In order to focus this research on planning and data sharing partnerships, the study team developed 

high-level research questions:

• Why do PLMAs and transportation agencies share data?

• How do PLMAs and transportation agencies share and use data?

• What are the challenges to data sharing?

These high-level questions served as the basis for the literature review, and the project team will further 

explore them in later phases of this project. To address these questions, the project team has developed 

the following, more specific research questions: 

• What types of data do PLMAs and transportation agenciesuse to support planning on public 

lands?

• How do PLMAs and transportation agencies use data to inform decision-making?

• What are the characteristics of successful PLMA and transportation agency data sharing

examples?

• What are the barriers to effective data sharing between PLMAs and transportation agencies?

• What do PLMAs and transportation agencies need to share data more effectively? 

This memo expands upon each of these high-level research questions below.

What types of data do PLMAs and transportation agencies use to support planning on 

public lands?

Establishing data sharing partnerships requires determining which data must be, could be, or may not 

need to be shared for transportation planning on public lands. Once planners identify the data sets, they 

are better positioned to determine whether they exist, who has them, and the quality of the data. 

This research project will explore:

• The primary types of transportation data for transportation planning in a public lands context; 

and 

• Additional types of transportation data or transportation-relateddata that could be beneficial 

when applied to this process.

How do PLMAs and state/local transportation partners use data to inform decisionmaking? 

For this study, the team will review how PLMAs and their federal, state, and local agency partners

approach transportation decisions across the entirety of the transportation project lifecycle (e.g., 

planning, project selection, design, operations, maintenance, etc.). Although this research project 

focuses primarily on using data sharing to support transportation planning, agencies’ decision-making 
Prepared by the U.S. DOT Volpe Center 4

processes typically apply across project phases. As a result, the project team will also consider data 

sharing throughout the transportation project lifecycle.

The research project will explore:

• State and local planning processes relevant to PLMAs;

• PLMA planning processes relevant to state and local transportation agencies;

• Differences and similarities in the goals for transportation data sharing between PLMAs and 

transportation agencies;

• Differences and similarities in transportation data access and analysis between these groups;

• Stage of the planning process or lifecycle process that data is shared; and

• Additional stakeholders relevant to PLMA, state, and local transportation planning processes.

What are the characteristics of successful public lands information/data sharing 

examples?

The literature review informed common practices for establishing data sharing partnerships. This 

research study builds on that background by identifying practices specific to successful examples of 

sharing transportation data between PLMAs and partners.

The research project will explore:

• Motivationsfor data sharing partnerships;

• The communication and coordination efforts to initiate and build data sharing partnerships;

• The mechanisms, methods, or platforms agencies use to share transportation data; and

• Outcomes of data sharing partnerships, such as expected benefits, realized benefits, and 

unanticipated failures. 

What are the barriers to effective data sharing between PLMAs and transportation 

agencies?

This part of the research will focus on what barriers exist to effective data sharing between PLMAs and 

transportation agencies. This will build on existing literature on barriers to effective data sharing 

partnerships, and use information from case studies to identify barriers specific to a public lands 

context.

The research project will explore:

• At what stage in the data sharing process agencies face challenges; 

• Challenges associated with sharing particular types of data;

• Challenges experienced by particular types of agencies in sharing data;

• Differencesin IT infrastructure or data storage between agencies;

• How agencies overcame or attempted to overcome any identified barriers; and 

• What resources agencies think would be helpful to overcome barriers.

What do PLMAs and transportation agencies need to share data more effectively?

This part of the research will focus on what resources and support PLMAs and transportation agencies 

need to engage in more successful data sharing partnerships. It will explore best practices and lessons 

learned from case studies, as well as feedback from the research panel and other sources.
Prepared by the U.S. DOT Volpe Center 5

The research project will explore:

• How to establish and maintain the mechanisms for data sharing (working groups, agreements, 

shared platforms, etc.);

• Existing opportunities to standardize transportation data to better share across agencies;

• Resources (e.g., funding, staff training, shared platforms) needed for data sharing partnerships; 

and 

• The role regional and national agencies could play in facilitating data sharing.

Proposed Collection Methods 

This study requires strategic and targeted use of literature (including PLMA and transportation agency

publications, academic journals, and related documents), conversations with planning staff and 

technical experts, and analysis of existing data and tools.

Literature Review

The study began by investigating the why, how, and what high-level questions identified in the research 

statement and introduction section above. Using those questions as a framework, the study team 

conducted a literature review. The review process included key word searches in various databases and 

websites, including the Transportation Research Information Database (TRID), Western Transportation 

Institute resources, Federal Geographic Data Committee (FGDC), Transportation Research Board 

publications, U.S. Department of Transportation publications, FHWA plans and studies, State DOT and 

local agency research related to transportation on public lands, university research reports, and 

academic journals.

Through the literature review, the project team gatheredinformation on data sharing partnerships with 

other types of organizations and the available tools to support these processes. The final literature 

review includes sections on the purpose and benefits of data sharing; challenges to data sharing; data 

management sharing processes; and a summary of previous PLMA and transportation agency planning 

and data sharing efforts.

Technical Research Panel (TRP)

The researchteam assembled a technical research panel (TRP) to ensure the study purpose, approach, 

findings, and deliverables align with project goals and public land management needs at key project 

milestones. The research team will facilitate up to eight TRP meetings throughout the research process. 

The TRPconsists of participants from the following organizations or types of organizations: 

• Bureau of Land Management

• Bureauof Reclamation

• City planning and/or department of transportation

• County planning

• Economic development agencies or organization

• FHWA Federal-Aid Division Office

• FHWA Office of Tribal Transportation and/or Bureau of Indian Affairs

• National Park Service

• Nonprofit parks advocacy or research organization
Prepared by the U.S. DOT Volpe Center 6

• Private planning or engineering practitioner

• State Department of Transportation

• Regional planning organization

• State or local parks and/or other land management department

• United States Forest Service

• University researchers

• United States Army Corps of Engineers

• United States Fish and Wildlife Service 

The TRPis integral to the study, as participants have direct experience in the planning and data sharing 

processes that are the subject of this study. The TRP’sprimary purpose is to help guide the research 

process, and provide input and insight on the research team’s progress. 

The TRPwill also be involving in identifying and selecting case studies. Members will help connect the 

research team to resources or contacts for case studies.

Database Research and Scan for Additional Data Sharing Examples

Beyond the literature review and recommendations from the TRP, the research team will scan for 

additional examples of data sharing between PLMAs and partners in various databases and 

transportation groups. Examples include:

• FHWA Road Safety Audits

• FHWA Transportation Management Area (TMA) Certification Reviews

• Relevant TRB committees

• Relevant research conducted by University Transportation Centers

Case Studies

The research team will also reach out to stakeholders to gather the information and data for case

studies. These case studies will highlight how public lands management agencies and transportation 

agencies are using data sharing partnerships to achieve shared goals. These case studies will help 

examine best practices for partnerships to coordinate information/data sharing for planning, delivering, 

and managing transportation systems on and near public lands.

The methodology for conducting the case studies is as follows:

1. Establish case study selection criteria

2. Identify candidate case studies through database scans, TRPworkshop, and broad “Call for Case 

Studies”

3. Develop final case study list per selection criteria

4. Develop case study method, format, and evaluation

5. Conduct outreach to case study subjects

6. Conduct case study discussions

a. Conduct interviews and workshops, including up to 10 site visits (as possible)

b. Review background materials

c. Map planning and data sharing processes

d. Conduct up to 10 site visits (as possible)

7. Synthesize cross-case study findings for Research Panel review
Prepared by the U.S. DOT Volpe Center 7

The information gatheredfrom case studies may include, but is not limited to:

• Identification of common challenges that initiate data coordination efforts;

• Formation and maintenance of agency relationships;

• Data needs and innovations;

• Agencies involved;

• Tools and data platforms used;

• Application of data to project planning, selection, design, implementation, lifecycle 

management, and evaluation;

• Application of data to agency and user needs and travel patterns, including adaptive 

management of travel demand and public lands use;

• Lessons learned; and

• Results of collaborative data sharing efforts.

Case Study Selection Criteria

The TRP helped identify and define criteria for selecting case studies that cover the full range of 

examples discussed below.

Multi-Agency Involvement

The case studies should include a variety of types of agency involvement, such as: 

• One-to-one data sharing examples; 

• Data sharing that include three or more agencies; 

• Open data sharing with an unspecified number of agencies; and 

• Publicly-shared data. 

These data-sharing relationships primarily include traditional transportation and public lands 

stakeholders, but they also may include other agencies, such as state-level environmental agencies,

universities/research entities, non-profits, and private data providers. The research will cover the 

different data sharing policies and processes each of these types of agencies have, and how that 

influences their data sharing efforts.

Variety of Types of Data

The case studies should include a range of types of data, including not only traditional transportation 

data related to safety, traffic volume, transportation assets, or road ownership and maintenance, but 

also on cross-cutting topics like environment, resiliency, and equity. The examples should include 

multiple modes of travel. The case studies should include examples of sharing data that is easy to 

collect, as well as data that is less common or needs to be collected by innovative methods. The case 

studies should include both public and proprietary data. Finally, the case studies should include data in 

different forms, such as tabular, geospatial, longitudinal, and historic data.

Replicability

In order for case studies to be useful, they need to be replicable by other agencies. Case studies should 

provide an easy-to-follow roadmap for other agencies to implement similar data sharing efforts, with 

consideration given to agency policies, mechanisms for partnering, governance, project context, and 

costs. Case studies should highlight how agencies overcame similar challenges, and how identified gaps 

could be filled to better facilitate data sharing. 
Prepared by the U.S. DOT Volpe Center 8

Diversity of Circumstances and Contexts

Case studies should apply across public lands with a variety of circumstances and contexts, such as:

• Examples in rural, suburban, and urban areas;

• Examples that are local and regional in scale;

• Examples with limited staff capacity and with trained staff;

• Examples that span a range of costs, including consideration of cost effectiveness;

• Examples that consider the range of transportation needs to and within public lands, including 

recreation, economic development, and community connectivity; and 

• Examples with different agency goals for data sharing.

Different Project Phases

The case studies should include examples across different project phases, including those identified in

Figure 1, which shows a generalized version of the transportation planning process. This process will be 

used as a starting point in case study discussions to understand agencies’ transportation decisionmaking processes. Case study examples will include a focus on how data-driven decision making feeds 

into all elements of this process.
Prepared by the U.S. DOT Volpe Center 9

Figure 1: Generalized Transportation Planning Process

Case Study Assumptions

The project teammakes the following assumptions related to the case study methodology.

Compliance with the Paperwork Reduction Act

The Paperwork Reduction Act (PRA) is a law that governs how the Federal government collects 

information from the public. This research is subject to the PRA, and the Volpe Center may need to seek 

Office of Management and Budget (OMB) approval for any survey or structured interviews of nonfederal PLMAs and transportation partners. The PRA does not apply to collecting information from staff 

at Federal agencies.
Prepared by the U.S. DOT Volpe Center 10

Travel Restrictions

Due to restrictions on travel related to the Covid-19 pandemic, there will be limited opportunities for 

representatives of the research teamto conduct site visits or attend in-person meetings. The project 

team will only travel if and when USDOT travel guidance allows travel and it is safe for both the project 

team and meeting participants. If necessary, the project team will hold meetings virtually in lieu of 

travel. 

Recent Changes to Planning Processes

PLMAs and transportation partners may have recently modified their planning processes or their use of 

tools and resources due to impacts from recent events, including the Covid-19 pandemic and climate 

disasters. The Volpe Center will seek to understand how the planning processes and use of 

tools/resources have changed based on these impacts, and what additional tools/resources might help 

PLMAs and transportation partnersin times of transition.

Transportation Planning Process as a Starting Point

This research focuses on data sharing partnerships between PLMAs and partners. The research assumes 

a “one-to-many” relationship, discussing how PLMAs can connect with various partners to achieve

shared transportation goals. Within the PLMA context, the researchprimarily focuses on transportation 

planning but also considers data sharing to inform decisions throughout the transportation project 

lifecycle. Agencies have different flow charts to illustrate the transportation project lifecycle, or the 

transportation planning process. Figure 1 includes a generalized version of this process that is used as a 

basis for this research. 

Schedule and Next Steps

The research study’s Data Collection and Analysis task includes three subtasks. The subtasks are listed 

below with their respective proposed timeframes and the initial next steps within each subtask.

• Data Collection: March 2021 to September 2021

o Conduct case studies, with the following interim deliverables:

 List of case study criteria and call for case studies

 Matrix for case study selection

 List of selected case studies

 Case study key questions and approach

 Field reports from each case study (including any resources shared by 

participants)

 Draft case study chapter, including case study write-ups and preliminary 

findings/themes

• Data Coordination and Data Sharing Toolbox: July 2021 to October 2021

o Develop a toolbox with the following information: 

 Data sharing forums, platforms, and practices;

 Partnership structures and governance;

 Plans, studies, or project delivery tools;

 Existing tools, benefits and barriers, and how to use them in different settings, 

such as the Congestion Management Toolkit (NPS) and INSTEP (NPS);
Prepared by the U.S. DOT Volpe Center 11

 Emerging data sources, such as mobile location data and Bluetooth traffic and 

visitor trip data collection;

 Data sharing platform tools, which may be used primarily outside of PLMA 

context currently or perhaps outside of transportation altogether;

 Gap analysis for tools that do not currently exist and the context for their use;

and

 Application and context of tools identified, including clarifying contextsfor tools 

and resources needed to operate tools.

• Findings: September 2021 to December 2021

o Summarize key findings and other recommendations based on the data collection and 

data coordination/data sharing toolbox tasks.

o Identify data coordination and sharing approaches that are most successful, how data 

coordination can be more effective, what steps can be taken to further advance 

mutually-beneficial coordination, and a best practices framework for state PLMAs and 

partners to adapt into their existing planning and project delivery processes.

Appendix A: Transportation Planning Data

Data that is critical to the transportation planning process are captured in Table 1. This list will be 

updated to reflect additional information gathered during the research process.

Table 1: Data Use by Type

Data Type(s)3 Example of Uses Source(s)

Road network Spatial Origin-destination pairs Departments of 

transportation (all levels)

Intersection nodes Spatial Modeling

Crash data Spatial, Tabular Road Safety Audits Law enforcement agencies

Demographic Spatial, Tabular Population density, Census, state and local 

agencies

Bus stop nodes Spatial Transit Network 

analysis

Transit agencies, DOTs

Traffic volumes Tabular Measure congestion FHWA, state and local DOTs

Train station 

nodes (all rail)

Spatial Transit network 

analysis

Transit agencies, DOTs

 3 Integrated Land and Water InformationSystem 

(http://ilwis.itc.utwente.nl/wiki/index.php/Basic_concepts:_Spatial_and_tabular_data) 
Prepared by the U.S. DOT Volpe Center 12

Data Type(s)4 Example of Uses Source(s)

Watershed Spatial Geometric design Environmental management 

agencies

Floodplain Spatial Infrastructure 

resiliency

Natural resource or 

environmental management 

agencies

Bridges Spatial Modeling Departments of 

transportation (all levels)

Culverts Spatial, Tabular Infrastructure 

resiliency

Departments of 

transportation (all levels)

Trails Spatial, Tabular Non-vehicle networks Natural resource or 

environmental management 

agencies

Bicycle Routes Spatial, Tabular Non-motorized vehicle 

networks

DOT (All Levels)

Pavement 

condition

Spatial, tabular Long range planning Departments of 

transportation (all levels)

Asset location Spatial Long range planning Managing agencies

Deferred 

Maintenance

Tabular Long range planning Managing agencies

 4 Integrated Land and Water InformationSystem 

(http://ilwis.itc.utwente.nl/wiki/index.php/Basic_concepts:_Spatial_and_tabular_data) 
Appendix 4: Memo 3 - Data Collection
Finding a Win-Win: Planning and DataSharing Partnerships between 

Governments and Public Land 

Management Agencies

Memorandum 3: Data Collection

February 28, 2022
Prepared by the U.S. DOT Volpe Center 1

Table of Contents

Table of Contents..........................................................................................................................................1

Abbreviations................................................................................................................................................2

Introduction ..................................................................................................................................................3

Case Study Process........................................................................................................................................3

Discussion Questions................................................................................................................................4

Interest and Discussions ...........................................................................................................................4

Summary of Findings.....................................................................................................................................5

Motivations for Data Sharing....................................................................................................................5

Mechanisms for Data Sharing...................................................................................................................5

Results from Planning and Data Sharing Partnerships .............................................................................6

Suggested Tools for Supporting Data Sharing Partnerships.....................................................................7

Schedule and Next Steps...............................................................................................................................7

Appendix A: Case Study Questions...............................................................................................................9

Appendix B: Meeting Schedule...................................................................................................................10
Prepared by the U.S. DOT Volpe Center 2

Abbreviations

The abbreviations for the following terms appear in this report:

CVTS Collaborative Visitor Transportation Survey

LTRPA Lake Tahoe Regional Planning Authority 

MARAD United States Maritime Administration

MOU Memorandum of Understanding

NPS National Park Service

OCBPMP Outer Cape Bike and Ped Master Plan

ODOT Oregon Department of Transportation

PLMA Public Land Management Agencies

RITIS Regional Integrated Transportation Information System (RITIS)

TRB Transportation Research Board

TRP Technical Research Panel

USFS United States Forest Service

UTC University Transportation Center
Prepared by the U.S. DOT Volpe Center 3 

Introduction 

This memorandum summarizes the data collection methodology, findings, and next steps in support of 

an applied research study titled, Finding a Win-Win: Planning and Data-Sharing Partnerships between 

Governments and Public Land Management Agencies. 

The specific goals of this project are to:

• Identify examples of information coordination between Public Land Management Agencies 

(PLMAs) and transportation partners that result in process efficiencies, cost savings, and more 

effective transportation system delivery and/or management. 

• Develop a suite of tools, resources, and usage guidelines to aid PLMAs and their partners in 

improving data coordination for better transportation systems.

This memorandum describes:

• How the study team selected case studies were selected;

• How case studies discussions were conducted;

• The motivations for planning and data-sharing partnerships in the case studies; 

• The mechanisms the partnerships used;

• The results of the partnerships; and

• The general timeframe for finalizing the case study documents and developing tools for the 

toolkit. 

Case Study Process

As outlined in Technical Memo 2,1 the planned approach for the data collection task in the research 

project involved a literature review (documented in Technical Memo 1), a review of technical report 

databases, and a set of case studies. While the database search did not yield much information, the 

result itself is an indication of why data sharing partnerships was identified as a research need. 

The study team followed the planned approach for developing case studies to highlight how public lands 

management agencies and transportation agencies are using data sharing partnerships to achieve 

shared goals. The study team developed a set of criteria for evaluating and selecting the nominated 

examples, which were documented in Technical Memo 2. They included: 

• Multi-agency involvement, 

• Variety of data types, 

• Diversity of contexts, 

• Project phase, 

• Replicability, and 

• Readiness.

The team requested case study suggestions from the Technical Research Panel (TRP) and released a 

“Call for Case Studies,” which was distributed through Transportation Research Board (TRB) committees,

1 Federal Highway Administration. 2021. “Finding a Win-Win: Planning and Data-Sharing Partnerships between 

Governments and Public Land Management Agencies. Memorandum 2: Methodology.” 

https://highways.dot.gov/federal-lands/programs-planning/studies/pads. 
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relevant newsletters, the University Transportation Centers (UTC), the project website, and the research 

team’s professional networks. The call was sent on May 3, 2021, with a response requested by June 4, 

2021. The effort resulted in 24 nominations. The study team applied the selection criteria and sorted the 

case studies into the following groups:

1. Highly recommended: Cases that meet the selection criteria well, including stage of

transportation lifecycle, geographic diversity, and general applicability.

2. Potential: Cases that meet many of the “highly recommended” criteria but did not make the

cut.

3. Not recommended: Cases that do not appear to meet selection criteria as well as cases in the

other two categories.

The TRP provided input on the rankings, leading to the final tally of 16 highly recommended, four

potential, and four not recommended. The study team began reaching out to the case study points of 

contact to gauge interest in participation and schedule discussions. The discussions were scheduled 

between September 20, 2021, and January 7, 2022. 

Discussion Questions

The study team developed a set of 23 questions to guide each discussion. The team designed these 

questions to answer the high-level research questions presented in Technical Memo 2, which were:

• What types of data do PLMAs and transportation agencies use to support planning on public

lands?

• How do PLMAs and transportation agencies use data to inform decision-making?

• What are the characteristics of successful PLMA and transportation agency data sharing

examples?

• What are the barriers to effective data sharing between PLMAs and transportation agencies?

• What do PLMAs and transportation agencies need to share data more effectively?

The questions were grouped into four sections:

1. Data sharing partnership formation,

2. Data fields and tools,

3. Results, and

4. Looking forward.

The questions were not shared with the meeting participants in advance of the meeting, which were 

conducted virtually using Microsoft Teams. The full set of questions are in Appendix A. 

Interest and Discussions

All but one of the case study candidate points of contact were willing to participate in the case study 

discussion process, presented as one to two one-hour calls. The River Network did not participate; they 

felt their position as peer group organizers was not a good example of a data sharing partnership

between transportation and public lands agencies. Two other discussions were unable to be scheduled. 

The study team facilitated discussions for the following case studies (see Appendix B for the list of 

meeting dates and times): 

1. Alaska Collaborative Visitor Transportation Survey (CVTS)
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2. Alaska Transportation Working Group

3. Colorado Integrated Planning Project

4. Lake Tahoe Regional Coordination

5. Maine Stream Crossing Survey

6. United States Maritime Administration (MARAD) and University of Arkansas TransMAP Hub

7. Mississippi National River and Recreation Area Paddle Share program 

8. Mount Rainier Visitor Use Management Planning

9. National Park Service (NPS) Pilot Data Collection Partnerships

10. NPS National Capital Area Data Collection Partnership (originally grouped with the NPS 

partnerships above but separated due to unique aspects)

11. Oregon Coast Trail Action Plan

12. Oregon Department of Transportation (ODOT) Regional Integrated Transportation Information 

System (RITIS)

13. Outer Cape Cod Bike and Ped Master Plan

14. Wasatch Front Regional Council Regional Data Sharing Partnerships

The study team initially scheduled discussion meetings for one hour; however, the team quickly realized 

it was not enough time for the full discussion of topics. The team extended the remaining initial calls to 

90 minutes and scheduled follow up calls with participants that had shorter initial meetings.

The study team sent the meeting notes to the participants for review and confirmation after the 

meetings were completed. The notes will be used to develop the final case study documents.

Summary of Findings

The study identified the following common themes from the case studies. The study team will document 

each case study in more depth in the Final Report. 

Motivations for Data Sharing

The public lands and transportation agencies in the case studies expressed some common motivations 

for planning and data sharing partnerships. These included: 

• Identifying shared priorities, missions, goals, and objectives;

• Developing a common understanding of how projects from each agency interact with other 

agencies’ existing or planned efforts;

• Leveraging additional data to make project planning and analysis more robust;

• Analyzing travel patterns, transportation networks, and gaps at a regional scale; 

• Addressing challenges that cross jurisdictional boundaries, such as congestion and 

environmental protection; 

• Overcoming institutional barriers to data sharing, such as firewalls, incompatible technologies, 

and inefficient one-off data transfers; and 

• Working together on a particular task, such as a project of mutual benefit.

Mechanisms for Data Sharing

The case studies illustrate a variety of types of data sharing partnerships and mechanisms for data 

sharing. These include: 
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• Informal data sharing partnerships: These partnerships typically arise out of a shared desire to

share data for particular plans or projects, but they are not governed by any formal agreements,

such as Memoranda of Understanding (MOUs) or formal data sharing protocols. The benefit of

informal partnerships is that they require less effort to establish. However, they may be best

suited to simple data sharing partnerships.

• Formal data sharing partnerships: These are partnerships that are formalized through

agreements, such as MOUs, between partner entities. These MOUs may establish what data

partners share, data management protocols, privacy and access, and other considerations. An

informal data sharing partnership may become formalized over time.

• Different geographic scales: The case study examples were at a variety of scales, including local

(e.g., in the vicinity of a public lands unit and local community), regional, state-wide, or national.

The case study entities identified the importance of the following factors for success: 

• Building relationships and trust among partners: Case study participants described

relationship-building among partners as a means of achieving effective partnerships and as a

result of those partnerships. The case study agencies also stressed the importance of

establishing a partner as a “first-among-equals” who is the lead point of contact in coordinating

and implementing the partnership.

• Explaining the value of data sharing partnerships: The case study participants described the

importance of communicating the importance of data sharing partnerships to agency leadership

and the public.

• Using common technologies or platforms for processing and accessing the data: It is important

for all partners in a data sharing partnership to have access to the same technologies and

platforms. In some cases – including the Oregon Coast Trail Action Plan and the Colorado

Integrated Planning Pilot – partners achieved this with commonly available platforms, such as

online mapping software. In other cases, such as the ODOT Regional Integrated Transportation

Information Systems, partners use more complex platforms developed and maintained by thirdparty vendors. In either case, it is important for partners to have access to the data platforms

and the training and resources needed to analyze the data.

Results from Planning and Data Sharing Partnerships 

It is important to communicate the impacts of good data sharing partnerships in transportation planning 

engaging public lands. Positive results garner continued support within the participating agencies and 

provides an example for others to follow. Based on the case study discussions, the study team identified 

four outcomes of data sharing partnerships that demonstrate the benefits of these partnerships.

1. Coordinated plans and projects: Partnering and sharing data with other agencies that have

planned studies and projects enables the partners to see where there is possible overlap. By

working together on the Outer Cape Pedestrian and Bicycle Master Plan (OCPBMP), NPS and the

local towns had a better understanding of where each other’s projects are happening and how

they intersect. The Oregon Coast Trail Action Plan had a similar outcome.

2. Enhanced awareness of partner goals and projects: The Colorado Integrated Planning Project

brought together representatives from local stakeholders, metropolitan planning organizations

(MPOs), state agencies, and Federal agencies when each was undergoing, or about to start, long

range transportation plans. The development of an integrated map helped visualize where
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different projects were planned, the proximity of projects to each other’s, and spurred 

discussion about the purpose of each project. The communication made the agencies and 

groups aware of where and why the various projects are occurring and has the potential to 

facilitate coordination for project implementation.

3. Identified common priorities and objectives: The Lake Tahoe Regional Planning Authority

(LTRPA) has established a compact with the United States Forest Service (USFS, owns 78 percent

of land in basin), six local jurisdictions, Tahoe city, private agencies, and other stakeholders. The 

LTRPA works on corridor planning with their partners by identifying common goals. They try to 

understand what each partner has to deal with in order manage their facilities and incorporate 

the understanding into via agreements and chartering exercises that work for everyone.

4. Coordinated project delivery: Time and financial efficiencies are possible when agencies share 

information and schedules to coordinate their planning transportation projects. For example, 

the Alaska Transportation Working Group described project efficiencies through regular project 

coordination to identify projects of mutual interest and opportunities to coordinate construction 

projects along similar timeframes. 

Suggested Tools for Supporting Data Sharing Partnerships

The discussions concluded by asking participants what, if any, tools would help facilitate the creation 

and longevity of data sharing partnerships. The responses ranged from universal but adaptable 

templates to information sharing on legal requirements and best practices.

• Templates for developing data sharing agreements,

• User-friendly data visualization tools,

• Tracking of best management practices,

• High-level overview of how to prioritize key data points,

• Synopsis of laws and regulations governing data/ownership/controls Such as privacy 

regulations, cloud-based storage.

• Supporting data user groups (geographic or topic) to discuss best practices and new 

developments, and

• Communication of funding opportunities for piloting data collection collaboration.

The study team will incorporate these suggestions into the tool development in the next phase of the 

project.

Schedule and Next Steps

The research study’s Data Collection and Analysis task includes three subtasks. The subtasks are listed 

below with their respective proposed timeframes and the initial next steps within each subtask. The 

remaining subtask for the data collection task is to draft and finalize the case study write-ups. 

• Data Collection: August 2021 to March 2022

o Conduct case studies, with the following interim deliverables:

 Draft case study chapter, including case study write-ups and preliminary 

findings/themes

• Data Coordination and Data Sharing Toolbox: March to July 2022

o Develop a toolbox with the following information: 

 Data sharing forums, platforms, and practices;
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 Partnership structures and governance;

 Plans, studies, or project delivery tools;

 Existing tools, benefits and barriers, and how to use them in different settings,

such as the Congestion Management Toolkit (NPS) and INSTEP (NPS);

 Emerging data sources, such as mobile location data and Bluetooth traffic and

visitor trip data collection;

 Data sharing platform tools, which may be used primarily outside of PLMA

context currently or perhaps outside of transportation altogether;

 Gap analysis for tools that do not currently exist and the context for their use;

and

 Application and context of tools identified, including clarifying contexts for tools

and resources needed to operate tools.

• Findings and Final Report: July to September 2022

o Summarize key findings and other recommendations based on the data collection and

data coordination/data sharing toolbox tasks.

o Identify data coordination and sharing approaches that are most successful, how data

coordination can be more effective, what steps can be taken to further advance

mutually-beneficial coordination, and a best practices framework for state PLMAs and

partners to adapt into their existing planning and project delivery processes.
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Appendix A: Case Study Questions

Data Sharing Partnership Formation

1. Could you provide a brief overview of the project (or partnerships, etc.), including how it 

formed, for what purpose, and who is involved?

2. What data did you need and how did you determine who had that data?

3. What are the designated roles of the parties involved?

4. How did you govern your data sharing relationship(s) (policy board, intergovernmental 

agreement, project team, etc.)?

5. What have been the main benefits to the data sharing partnership? 

6. Did you come across any organizational barriers in sharing data? If so, how did you overcome 

them?

Data Fields and Tools

7. What types of data were shared and how was it used? Is there a regular exchange of data or was 

it a single time transaction?

8. Was the data already in a form that could be used, or did you have to do additional cleaning and 

analysis to use it? 

9. How did you determine which parties were responsible for cleaning and analyzing the data?

10. What specific tools or platforms are used to share data?

11. Was additional training, funding, or other resources needed to implement the data sharing 

partnership?

Results

12. Did receiving this data allow you to do anything you could not have done without the data?

13. How is the data used to achieve goals, support the planning process, or advance specific 

projects? Consider all aspects of a project lifecycle, from project planning, selection, design, 

implementation, lifecycle management, and evaluation.

14. How is the data used to address agency and user needs and travel patterns, including adaptive 

management of travel demand and public lands use?

15. How would you recommend other public lands with similar goals establish data sharing

partnerships?

16. Once established, have there been challenges or barriers in maintaining the data sharing 

partnership?

17. Are there any other results and lessons learned of the data sharing efforts that haven’t already 

been mentioned?

18. In your experience, are there any unique challenges that public land agencies face regarding 

partnerships and data sharing that are different than the typical transportation planning and 

management processes?

19. Do you believe the goals and methods for transportation within public lands are different from 

those of transportation agencies? If so, how? 

Looking Forward
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20. Did the data sharing partnership achieve its goals (short-term and long-term)? Is there anything

you would change or like to be able to do moving forward?

21. What other groups might you like to partner with to share transportation data, or what other

types of transportation data would you like to access?

22. What tools would you like to see as a result of this study to support you in developing and

maintaining data sharing partnerships?

23. Do you have any recommendations for other public lands or transportation partners we should

reach out to about data sharing efforts?

Appendix B: Meeting Schedule

Case Study Interviewees Date (and follow-up discussion

dates if applicable)

Cape Cod Commission (Outer 

Cape Bike and Ped Master Plan 

OCBPMP)

Steven Tupper, Sarah Korjeff, 

Martha Hevenor

9/28/21 (12/3/21)

MARAD and University of 

Arkansas TransMAP

Heather Nachtmann, Travis 

Black, Jackson David

10/1/21

NPS Pilot Data Collection 

Partnerships

Rachel Collins, Joe Regula, Erica 

Cole, David Daddio

10/7/21 (12/2/21 discussion

with David Daddio regarding DC 

National Mall projects)

Wasatch Front Regional Council Bert Granberg, Matt Peters, 

Stephanie Tomlin

10/13/21 (12/20/21)

Oregon Coast Trail Action Plan Andy Smith, Paul Reilly, Peter 

Dalke

10/14/21 (1/5/22)

Collaborative Visitor 

Transportation Survey

Roxanne Bash, Kenli Kim, 

Margaret Petrella

10/19/21 (12/16/21)

Regional Integrated 

Transportation Information 

System (RITIS)

Thanh Nguyen, Chi Mai 10/20/21

Maine Stream Crossing Survey Alex Abbott 10/22/21

Tahoe Regional Planning Agency Michelle Glickert, Julie Regan, 

Nick Haven

10/29/21

Mississippi National River and 

Recreation Area Paddle Share 

Program

Katie Nyberg, Karen Katz, Ben 

Rasmussen 

11/8/21

Colorado Integrated Planning 

Project

Aaron Bustow, Erica Cole, Dean 

Bressler, Bill Haas, Elijah Henley, 

Matt Muraro, Ross Mittelman, 

Jeff Sanders, Rachel Peterson

12/17/21 (2/15/22)

Alaska Transportation Working 

Group

Amy Thomas, Paul Escamilla, 

Eric Taylor, Roxanne Bash, Kevin 

Doniere, Troy Civitillo, Curt 

Fortenberry

1/7/22 (2/4/22)
Appendix 5: Memo 4 - Data Coordination and Sharing Toolbox