Developing Data Literacy Competencies and Practices for State Transportation Workforce

State departments of transportation (DOTs) are undergoing a major transformation in how they collect, manage, and use data. Historically reliant on manual observations and field reports, DOTs now collect large and diverse datasets from traffic monitoring, asset condition assessments, maintenance records, freight compliance, Global Positioning System (GPS) probe data, light detection and ranging (LiDAR), drones, and video analytics. These technologies support more data-driven decisions related to infrastructure management, operations, and planning. The growing volume and diversity of transportation data have created significant challenges for integration, governance, and analysis. To address these issues, many DOTs are adopting standardized data formats and centralized governance structures that improve interoperability, reduce duplication, and support collaboration with external stakeholders. At the same time, data access has expanded across agencies, allowing planners, engineers, managers, and policy staff to work directly with increasingly complex datasets. Artificial intelligence (AI) and machine learning applications are accelerating this shift, particularly in areas such as traffic incident detection, pavement performance prediction, asset management, and safety analysis. However, many transportation professionals lack foundational competencies in data governance, statistical reasoning, ethical data use, visualization, and interpretation of analytical outputs. The shortage of qualified data-science personnel within public agencies further increases reliance on undertrained staff and external consultants. Communication gaps between technical teams and transportation practitioners also hinder effective implementation of data-driven tools and practices. The objective of this research is to improve data literacy within transportation agencies by identifying current skill gaps and workforce needs, evaluating data usage practices, and developing strategies to improve the ability of staff to collect, interpret, manage, and apply data effectively. This research will identify baseline competencies required for transportation data literacy; examine barriers related to training, governance, and organizational silos; evaluate the impacts of limited data-science staffing; and explore best practices for training, communication, and knowledge management. The study will develop actionable recommendations for tailored training, improved data governance, reduced reliance on external consultants, and stronger data-driven decision-making across transportation agencies.

• Record URL:
  • http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=6074
• Supplemental Notes:
  • Contract to a Performing Organization has not yet been awarded.

Language

• English

Project

• Status: Proposed
• Funding: $400,000.00
• Contract Numbers:

  Project 23-58

• Sponsor Organizations:

  National Cooperative Highway Research Program

  Transportation Research Board
  500 Fifth Street, NW
  Washington, DC  United States  20001

  American Association of State Highway and Transportation Officials (AASHTO)

  444 North Capitol Street, NW
  Washington, DC  United States  20001

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Project Managers:

  Mohan, Sid

• Start Date: 20260608
• Expected Completion Date: 0
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Best practices; Data analysis; Data collection; Governance; State departments of transportation; Training; Workforce
• Subject Areas: Administration and Management; Data and Information Technology; Education and Training; Transportation (General);

Filing Info

• Accession Number: 01992197
• Record Type: Research project
• Source Agency: Transportation Research Board
• Contract Numbers: Project 23-58
• Files: TRB, RIP
• Created Date: Jun 9 2026 5:01PM