CHAPTER 4

Research Roadmaps

This section summarized the proposed research roadmap implementation. The complete implementation plan is shown in Appendix E. The implementation plan provides recommendations on how to best put the research products developed as part of NCHRP 23-12 into practice.

The proposed implementation plan outlines the strategies aimed at enhancing awareness of the project. It identifies potential stakeholders, including national and state organizations. These stakeholders are envisioned to play a crucial role not only in shaping the research roadmap but also in disseminating the information garnered throughout the project. The plan also underscores the development of supplementary educational materials to bolster awareness. Additionally, it proposes the establishment of a depository and methodologies for identifying and quantifying the impacts associated with these implementation action areas. These areas of action exhibit variances in terms of implementation levels, timelines, and the potential requirement for additional funding.

The implementation plan described herein encompasses the following: (1) activities already executed as part of the project, (2) activities slated for completion prior to the project’s conclusion, and (3) activities set to be finalized after the project concludes.

The project team has identified five action areas for the dissemination plan:

1. Awareness of the research conducted as part of the NCHRP 23-12 project.
   1. Project Workshops
   2. Coordination with TRB Committees
   3. Dissemination of findings through conferences
   4. Distribution of final deliverables
2. Identification of potential stakeholders
   1. State and Local DOT’s, AASHTO Committees, USDOT, Academia, TRB Committees, Professional and Non-Profit Organizations (i.e., National Association of Counties (NACo), Institute of Transportation Engineers (ITE))
3. Development of educational materials
   1. A 10 minute Buy-in Presentation
   2. A 45 minute Stakeholder Presentation
   3. A webinar Tailored for practitioners, the objective of this webinar is not only to create awareness of the research, but also to encourage collaboration of the different entities.
4. Creation of a Depository
   1. Establish a process for maintaining and updating the roadmap, transforming it into a dynamic and valuable living document. At the present moment no funding has been allocated for these tasks.
5. Identification of Performance Measures
   1. The number of copies of the report disseminated though TRB and follow-up inquiries.
   2. The number of hits for report downloads on the TRB website.
   3. The number of contacts and the type of contacts acquired by the TRB and the members of the research team.

1. 4. The requests for actual presentations of the material and follow-up calls from presentations completed by the team.
   5. The number of research problem statements selected for project development.

Research Problem Statements

During the workshops participants provided input regarding project ideas to be included in the roadmap for the different priorities’ areas identified. Those areas include: (1) workforce development, (2) infrastructure development, (3) readiness and evaluation of AI, (4) challenges in adopting AI, (5) current practices and prioritization, external collaboration, (6) and equity, policy & planning as shown in Table 5.

Table 5. Final research problem statements, objectives and areas.

(1) workforce development and infrastructure development, (2) readiness and evaluation of AI, (3) challenges in adopting AI, (4) current practices and prioritization, (5) external collaboration, and (6) equity, policy & planning

Implementation Schedule

The actual implementation schedule of the proposed projects elements will depend on specific priorities of states and federal agencies, available funding levels through state and federal sources and activities from partnering agencies Priorities may also be impacted by other aspects such as advances in technology.

The proposed research roadmap and implementation plan comprehensively address a wide spectrum of AI applications for transportation needs. The foundation of this plan rests on the assumption that in addition of NCHRP, there will be collaborative efforts among entities like AASHTO, USDOT, State and local DOT’s, academic institutions, the industry, and other nonprofit and professional organizations. This orchestrated cooperation stands as key for the successful execution of the initiatives outlined in this plan. This collaborative synergy not only holds paramount importance for the triumph of the current plan but also for the sustained integration and evolution of AI across diverse transportation undertakings.

These potential partnerships can involve funding, leadership, support (such as data collection and provision), or assistance in implementation and outreach. Some projects may also receive strong backing from one or more states. While this may result in a more confined scope, it is anticipated that the outcomes will hold substantial value for nationwide dissemination.

Table 6 details on potential partnerships, projected budget, and estimated duration for each research problem statement outlined in this roadmap. Throughout the interviews and workshops, it was noted that several ongoing projects align with FHWA research activities.

Table 6. Problem statement potential partnerships and expected budget and duration of each research problem statement proposed.

Research Roadmap Proposed Timeline

The actual implementation schedule for the proposed problem statements will be contingent on the specific priorities and available funding of the involved agencies.

To establish guidance regarding the implementation timeline, participants were tasked with prioritizing each idea based on its needs and potential benefits. Additionally, they were instructed to rank each of the research problem statements according to the likelihood of receiving funding. Ideas were categorized into

two tracks: (1) workforce, infrastructure needs, readiness, and evaluation, and (2) current practices of AI in transportation and challenges faced by DOTs. Scores for priority rankings and funding likelihood were standardized for each set of research problem statements. The pavement evaluation research problem statement included post-workshop rankings in response to the identified need for a more specific and mature technical evaluation.

Table 7 shows the recommended timeline following the prioritization and analysis. We also considered the dependencies between the projects. In this section, the specific project is indicated by project number, P1 to P11. P1 (case studies), P3 (workforce need), P5 (vulnerability), and P11 (data management) have been identified as the first phase of projects. These four projects will investigate four crucial aspects of AI implementations. The case study project will highlight the best practices at DOTs for using AI. The workforce project represents the most imminent need for DOTs to understand the current need and strategies to develop a sustainable workforce. P5 and P11 will provide information about potential risk in AI applications and the data management infrastructure. All four of these projects will develop the backbone of an AI-based implementation plan.

P2 (toolbox), P4 (funding strategy), and P6 (multimodal transportation) can leverage the findings from P1 and start concurrently 1 year after P1 has begun. P2 can also leverage the findings from P5 listing potential vulnerabilities. These three projects are recommended as the second phase, along with one other project, P8 (NLP). With the recent revolution in large language models (LLMs), NLP-based implementations can play a crucial role at DOTs. However, LLMs are going through a transition, and more fundamental research in the AI community is required before their true potential and associated cost will be understood. Therefore, we suggest waiting another year before starting the project.

The third phase will comprise three projects: P7 (pavement), P9 (industry collaborations), and P10 (sharable data). The pavement project is a standard implementation-based project that will leverage P1, P2, and P4, which will demonstrate the successful cases, selection of technology, and key funding strategy. P9 deals with collaborations with industry. As industry is playing a critical leading role in developing implementable solutions using AI, DOTs should have a strategy to include them in their solution. The outcome from Phase 1 projects, P1 (case studies), P3 (workforce need), P5 (vulnerability), and P11 (data management), will help P9. Additionally, P9 and P4 can run concurrently to help with the funding strategy. Finally, P10 (sharable data) can leverage from P11 (data management) and P5 (vulnerability).

Table 7. Research roadmap timeline to show the temporal relations between the projects. For more details on dependencies, please refer to the text.

Challenges Affecting Potential Implementation

Barriers and challenges for accelerating the adoption of AI by state and local governments were identified by the interviews, workshops, and literature review. It is important to note that most of these challenges are similar to the challenges encountered to adopt AI generally and are not specific to transportation (https://rosap.ntl.bts.gov/view/dot/66971/dot_66971_DS1.pdf)

Major challenges in the implementation of AI include:

• Trust of AI capabilities
• Integration with existing systems
• Lack of sufficient high quality and relevant data
• Supporting funding and technology needs
• Potential Bias at current infrastructure and policies
• Level of workforce expertise
• Model Extrapolation

To mitigate the impacts of potential challenges, the research team created a risk register covering the main risks, identifying the risk probability, impact, and mitigation scores and a list of mitigation strategies (See Appendix F)

Workforce Development

Issues related to having a skilled AI workforce was mentioned by the states as one of the major hurdles to overcome in order to accelerate the integration of AI methodologies into transportation-related applications. This skilled workforce can originate within the state and local DOTs, the industry, or the academy and, in general, hinges on a comprehensive training strategy aimed at both the broader workforce and specifically targeted at state and local DOT personnel.

Based on the state interviews, workshops, and literature and state-of-the-art analysis, the research team has identified workforce challenges that the states have already encountered or anticipate encountering when implementing AI strategies.

Furthermore, several agencies mentioned the expectations from upper management that the agency as a whole support the application of AI technologies to create better solutions to transportation problems. However, in doing so, management tend to underestimate the need for resources in general, and the workforce in particular. This level of workforce expertise within DOTs related to AI and ML restricts states’ abilities to promote AI projects.

DOTs agree there are benefits to promoting and finding trustworthy partners in universities and private industry. There is also agreement on the need for a strong state DOT counterpart workforce throughout the process. State DOTs have previously faced crosscutting workforce challenges. Strategies to address these challenges include:

• Working with partner agencies and other stakeholders (e.g., contractors, industry partners, and the general population);
• Establishing working groups focused on workforce development activities; and
• Supporting employees as they pursue the acquisition of new skills.

To maximize the benefits associated with the identification and implementation of transportation-related AI projects, there is a need to identify the required workforce skills, education, and training. In the near term, this could include identifying the necessary skills, developing skills courses, and making the courses available to DOT personnel. In addition, DOTs should encourage employee participation at national and local AI-related forums and encourage peer-to-peer knowledge transfer within and between DOTs. For the optimization of outcomes arising from the conceptualization and execution of AI-driven projects in the transportation realm, it is paramount to define the essential proficiencies, educational requisites, and training pathways. In the short term, this could involve identifying pertinent competencies, formulating educational modules, and facilitating access to these resources for DOT personnel. Furthermore, active participation of DOT employees in national and local AI-focused platforms should be actively encouraged, fostering the exchange of insights and know-how both within and between different state DOTs.