State and local departments of transportation (DOTs) are being asked to solve ever more complex transportation problems and issues. Artificial Intelligence (AI) is being proposed and implemented to help address a number of these issues, such as improving safety, alleviating traffic congestion, assisting in real-time systems management, accommodating connected/automated vehicles, preserving the infrastructure, improving organizational efficiency, and customer service, among others. According to Gartner Information Technology Glossary (2021), AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions. At the same time, large amounts of both structured and unstructured data from various sources have become available for transportation applications.
A Transport Research International Documentation (TRID) literature search identified almost 100 papers on AI applications in transportation published in the Transportation Research Board’s (TRB) Transportation Research Record (TRR) in the last 5 years alone. However, almost all of these papers deal with very specific applications of AI. With the exception of the Transportation Research Circular E-C113: “Artificial Intelligence in Transportation” (2007) and Transportation Research Circular E-C168: “Artificial Intelligence Applications to Critical Transportation Issues” (2012), there is no strategic guidance that state and local DOTs can use to develop guidance, policies, and standards, and ensure a knowledgeable workforce that will enable them to effectively understand, develop, and apply AI solutions to improve their operations and to solve transportation problems. There is also a need to document and share current information on agency experiences with AI, including promising applications.
The objective of this research is to develop a research roadmap that identifies and prioritizes research needs that will provide state and local DOTs with a better understanding of AI, what activities are suited for AI, and the potential ways AI could be applied. The roadmap will build upon existing research and be informed by outreach to the transportation community. The focus of this research is on AI applications for state and local DOTs, but the research should also be relevant to a wide variety of research organizations beyond NCHRP. It should serve to generate additional research ideas, and encourage coordination among research agencies.
Proposers are asked to present a detailed plan for accomplishing the project’s objective. Proposers are expected to describe project plans that can realistically be accomplished within the constraints of available funds and contract time, including an indication of how proposed activities will make use of and build on available resources and engagement opportunities. The following description is intended to indicate NCHRP’s expectations and provide a framework for the research plan but is not meant to be restrictive on proposers’ thinking. Proposers must present their current thinking in sufficient detail to demonstrate their understanding of the issues and the soundness of their approach. NCHRP will require that the final products of this research be submitted in draft form for review and then revised to respond to review comments.
The research team is expected to have a fundamental understanding of AI (including natural language processing, machine learning, pattern recognition, knowledge representation, and other emergent practices) and have relevant experience and expertise with various transportation systems, agencies and modes. The roadmap should consider all aspects of the transportation enterprise, including but not limited to operations management, project delivery, customer service, and organizational administration.
In meeting the objective of this research project, the research plan should consider, at a minimum, the following:
- The current state of practice for using AI in state and local DOTs.
- Promising AI developments (tools, methods, technologies, platforms, etc.) in the larger transportation universe, and in other fields, that can be adapted to transportation applications for state and local DOTs.
- The transportation related problems that could be solved with AI, what are the benefits of incorporating AI to solve those problems, and how those benefits can be communicated.
- How state and local DOTs would evaluate proposed AI solutions, and how they would know they are ready to use AI. What would the prerequisites of using AI solutions be?
- The guidance, policies and/or standards that are needed to assist transportation agencies in successfully applying AI.
- Potential risks, limitations, and challenges of AI on transportation and transportation agencies, transportation modes, and transportation systems.
- The ethical, data security, and privacy challenges of AI, and how they can be addressed and overcome.
- The diversity, fairness, and equity implications of AI.
- Workforce development implications of AI, including preparing and training the workforce.
The roadmap shall be designed to systematically revisit research priorities to guide research and ensure its future relevancy. The roadmap shall include, but not be limited to, the following elements:
- A final report that documents the entire research effort, and the research findings and recommendations of the research team.
- The final report shall include a stand-alone executive summary and a brochure that briefly outlines the research findings and recommendations.
- A report of relevant active and completed research on AI that could benefit from focused implementation efforts at the state and local DOT level.
- A report on relevant current and forthcoming resources on AI and their applications that could be referenced by state and local DOTs.
- A research needs report that includes general descriptions of research that should be conducted within the next 5 years, with a minimum of 10 detailed research problem statements suitable for submittal to NCHRP or other funding sources.
- The research needs report should account for rapid changes in AI, and should provide a plan for how it will be maintained.
- A dissemination plan for the research roadmap that includes presentation materials aimed at state and local DOTs, relevant practitioner communities at TRB and AASHTO, and others, that simply and concisely explains why the roadmap and supporting recommendations are helpful, and how they can be used.
Status: Proposals have been received in response to the RFP. The panel will meet to select a contractor to perform the work.