SOURCES SOUGHT NUMBER: 693JJ3-26-SS-0008

“Artificial Intelligence Transportation Systems Management and Operations Data initiative”

THIS IS A SOURCES SOUGHT NOTICE; BEING RELEASED PURSUANT TO

FEDERAL ACQUISITION REGULATION (FAR) PART 10: MARKET RESEARCH.

This sources sought notice is issued solely for informational, market research, and planning purposes only and does not commit the Government to any further contract actions (i.e., solicitation and/or contract). Respondents are advised that the United States Government will not pay for any information or administrative costs incurred in responding to this sources sought notice. Not responding to this sources sought notice does not preclude participation in any future Request for Proposals/Quotations (RFP/RFQ) that may be issued. Responses to this notice are not offers and will not be accepted by the U.S Government to form a binding contract.

The United States Department of Transportation (USDOT) / Federal Highway Administration (FHWA) is conducting market research to determine if there are any small business, academic institutions etc., capable of developing Artificial Intelligence (AI) Transportation Systems Management and Operations (TSMO) Data initiative for the Office of Safety and Operations Research and Development located at the Turner-Fairbank Highway Research Center in McLean, VA, in partnership with Intelligent Transportation System Joint Program Office.

An overview of the required services and objectives is provided below.

1. Introduction

The Federal Highway Administration (FHWA) is a part of USDOT. The FHWA Office of Safety and Operations Research and Development is dedicated to advancing the safety and efficiency of the nation’s highway transportation system through innovative research and development. Its mission is to develop and implement innovative solutions and advanced technologies to enhance the safety, efficiency, and reliability of the U.S. highway system.

The USDOT Intelligent Transportation System Joint Program Office’s (ITS JPO) AI Program seeks to advance next generation transportation systems and services for safer, and more efficient movement of people and goods through safe, secure, and responsible AI utilization. The overall mission of ITS JPO’s AI program is to strategically lead research, development, deployment, and evaluation of Intelligent Transportation Systems enabled or transformed by Artificial Intelligence, fostering collaboration across sectors, establishing best practices, and accelerating the adoption of impactful AI technologies to address critical transportation challenges and opportunities. The ITS JPO is charged with working collaboratively across USDOT modal administrations to coordinate, plan, and execute the AI Program. The program is carried out in cooperation with State and local governments and other public entities, industry representatives, private sector firms of the United States, the Federal laboratories, and institutions of higher education.

Purpose of Sources Sought Notice

Through this notice, the FHWA’s Office of Safety and Operations Research and Development (R&D) and ITS JPO seeks input from academia, government, business, and industry groups on developing innovative research for AI TSMO Data initiative, aiming to enhance the availability, quality, interoperability, and governance of the TSMO data for effectiveness, scalability, and trustworthiness of AI solution development. A draft Statement of Work (SOW) has been developed with intention of accomplishing the project within 12 months with two phases. The first phase (6 months) will screen AI models and data for at least two TSMO use cases and identify and prioritize gaps in various dimensions of data readiness to support those use cases.

The second phase (6 months) will develop a prototype to address some of the highest priority data gaps identified in phase one. Depending on multiple factors, an optional phase (six months) may involve further developing, testing, and supporting the adoption of the prototype. In addition to answers to the questions posed below, FHWA welcomes other input on the project.

2. Project Description

A key focus of the ITS JPO AI Program and FHWA HRSO is to develop enabling resources, tools and methods to support the implementation of AI in public sector transportation agencies.

Currently, public agencies seek to apply AI to TSMO functions such as congestion management, work zone analytics, incident detection, traveler information, freight operations, safety, etc.

However, the effectiveness, scalability, and trustworthiness of AI solutions depend on the availability, quality, interoperability, and governance of the underlying data. Persistent gaps in TSMO-related data collection, data management, metadata completeness, data integration, and data sharing are key limitations. A national-level gap assessment is needed to:

• Identify inconsistencies in TSMO data availability, formats, quality, and metadata across agencies and regions.

• Determine priority TSMO data resources, standards, and infrastructure investments needed to fully support AI-enabled TSMO applications.

• Provide a structured, evidence-based foundation for subsequent AI TSMO data activities.

The goal of this project is to conduct a comprehensive TSMO data gap assessment and produce priority recommendations that document the existing state of practice for TSMO data. For prioritized TSMO use cases, the project will explore innovative data-centric AI approaches to enhance the efficiency of data discovery and processing and recommend data benchmarking strategies, with the aim to support responsible AI models’ development and deployment tailored to specific TSMO functions and needs.

Specifically, this effort will:

• Define the public sector’s current state of practice in collecting, managing, integrating, and using TSMO data, focusing on AI models currently applied in TSMO programs and projects.

• Identify data, metadata, and interoperability requirements for AI-enabled TSMO use cases such as incident prediction, arterial performance analytics, freight corridor management, work zone risk detection, and winter maintenance optimization.

• Evaluate data barriers and risks that limit public agencies in implementing AI-driven TSMO applications, including issues related to data quality, governance, privacy, access, lineage, and TEVV readiness.

• Develop innovative approaches aiming to address these gaps and barriers for prioritized TSMO use cases.

The government may use the outputs from this work to support further AI and TSMO data efforts involving more use cases, including: the development of more TSMO benchmark data, the evaluation and comparison of various AI models, the integration of existing AI models used for TSMO use cases to Agentic AI where complex functions involving collaborative detection and decisions can be undertaken.

3. Specific Information Requested

Please provide answers to the following questions:

a. Experience Working with TSMO Data

• Have you worked with TSMO-related datasets from public agencies (e.g., roadway sensors, incident logs, work zone data, probe data, CV/AV datasets, freight data)? Please describe.

• What issues or limitations have you identified in these datasets (e.g., quality, completeness, coverage, metadata gaps, access constraints, format inconsistence)?

• Have you worked with TSMO-relevant data standards (e.g., WZDx (Work Zone Data Exchange), TMDD (Traffic Management Data Dictionary), MIRE (Model Inventory of Roadway Elements), GTFS (General Transit Feed Specification), CV (Connected Vehicle) data standards)? If so, describe challenges and solutions.

• Have you worked with metadata? Please describe your experience with metadata standards, schemas, and management tools

b. AI training Data Preparation, Management, and Interoperability

• Have you developed any AI/Machine Learning (ML) or automated approaches or algorithms for preparing data for AI model training data preparation, such as o automated labeling, embedding, transforming, anomaly detection, o augmenting, synthetic data generation, active learning, o fusion and harmonization across multidisciplinary data across multiple sources.

• How effectively did these approaches improve the efficiency, scalability, or trustworthiness of your AI model development?

• Describe your approaches to enhancing data completeness, data lineage, interoperability across agencies, systems, or regions, and documentation to support responsible AI development.

• Have you developed tools or pipelines for large-scale data discovery? Please describe.

c. AI Model Development Evaluation

• Have you developed and implemented AI or ML models for any TSMO use cases? If so, please describe the AI technologies and datasets you used. In addition, explain how you identify and validate data sources, assess data quality, and evaluate the performance of your AI models.

• Have you evaluated and compared AI/ML models for some use cases? How do you ensure the datasets you used are representative, unbiased, and include diverse edge cases.

d. Project Management and Collaboration Experience

• Describe your organization’s experience working with diverse organizations, such as small businesses, universities, state DOTs, and local traffic operators. Please describe the collaboration and management.

• What methods do you use to gather, validate, and synthesize information from diverse data owners or regions?

• Describe your organization’s experience supporting multi-stakeholder efforts involving federal, state, or local transportation agencies.

4. Instructions for the SOURCES SOUGHT NOTICE Responses:

Responses shall be submitted electronically to the e-mail addresses below:

EMAIL: Vivian.Riboli@dot.gov and Amalia.Rodezno@dot.gov

Subject Line: AI TSMO Data Initiative - Sources Sought Notice Response

Responses shall be submitted on or before February 20, 2026; Noon ET

Regarding format and content:

• White paper/thesis format on 8 ½ x 11-inch paper. Pages that exceed this size, such as foldouts, are not allowed.

• The document can be either a PDF or Word format.

• Narrative text must be printed using a font size no less than 12-point font.

• Tables are permitted. Text in tables may be smaller than 12-point but must be legible.

• Page margins must be a minimum of 1-inch top, bottom, and each side.

• Page numbers may be located within 1-inch margin.

• Page Limits: The number of pages should not be more than 10 pages single sided. The page limit includes all narratives, figures, tables, appendices and all other ancillary materials with the exception of the following.

• Exceptions on Page Limits: The following items do NOT count against the page limitation:

o Document covers;

o Cover letters;

o Title pages;

o Divider pages;

o Tables of contents;

o List of acronyms;

• Proprietary information submitted in response to this sources sought notice will be protected from unauthorized disclosure as required by the Federal Acquisition Regulation (FAR). All proprietary markings should be clearly delineated. The respondent shall identify where data is restricted by proprietary or other rights and mark it accordingly.

The cover page shall contain:

• Company name

• Primary Point of Contact

• Phone Number and Email Address

• UEI/Cage Code

• NAICS Code(s)

• Business Size

• Socio Economic Category (i.e., HBCU, Women Owned Small Business, Veteran Owned

Small Business, etc.)

• Federal Supply Schedule (FSS) or OASIS Contract Number and SIN, if applicable.

Technical Ability: Respondents shall submit a capabilities statement package to include an overall description of the proposed project and/or any product solution(s) and provide technical data and a demonstrated ability for those areas identified. Please ensure the following information is included:

1. The respondents should provide their relevant experience in developing similar projects, examples of previous work, qualifications, past projects, and client references for completing the project.

2. Resources available such as corporate management and key personnel to be assigned to tasks under this effort to include professional qualifications, and specific experience of such personnel.

3. Please note that under a Small-Business Set-Aside, in accordance with FAR 52.219-14, the small business prime must perform at least 50% of the work themselves in terms of the cost.

4. Company's ability to begin performance upon contract award.

Note: Responses may include links or web addresses that demonstrate evidence of your technical expertise (i.e.: final reports, technical Journals, peer reviewed papers, etc.).

4.1 Sources sought notice Response Contact

Respondents to this notice shall designate a primary and one alternate point of contact within the company (Name, Title/Position, Address, Email, and Telephone).

4.2 Clarification of sources sought notice Responses

FHWA reserves the right to seek additional information from respondents identified with unique solutions that are determined to be beneficial to FHWA. To fully comprehend the information contained within a response to this notice, there may be a need to seek further clarification from respondents. This clarification may be requested in the form of brief verbal communication by telephone; written, electronic communication; or a request for a presentation of the response to a specific FHWA group or groups.

5. Next Steps:

The responses will be reviewed within 14 days of receipt. A subsequent Request for Proposals (RFP) will be issued after completion of the review.