CHAPTER 3

Outreach Efforts

Introduction

There were two tasks in the course of the project that involved outreach to state and local DOTs as well as academic institutions. These tasks involved performing interviews and workshops. The outreach efforts were needed to understand the current state of practice within DOTs and metropolitan agencies, areas where AI may be applied, and their needs to support the implementation of AI. Both the interviews and workshops explored the themes and topics which are listed below.

• Current and future focus areas of transportation development at DOTs.
• Challenges in adopting AI-based solutions.
• Sustainable workforce and infrastructure development within DOTs for AI readiness.
• Readiness of AI, evaluation, and third-party collaborations.
• Future Scope for AI integration.

The chapter is organized in two parts. The first part covers the results from the interviews whereas the second part covers the workshop results. A detailed discussion on the methodology and results for interviews and workshops can be found in Appendix C and Appendix D respectively.

Part 1: Interviews

Eight state DOTs, including 29 individuals, participated in the interviews. The participating states represented northwest, south, and mid-east regions of the U.S. The individuals participating held positions in traffic operations, research and innovation, and IT departments within their DOTs.

Interview Results

The first section of the interview was meant to understand the key areas within transportation where state DOTs envisioned that AI applications could be useful or where they might incorporate AI to resolve certain transportation related concerns in the state (e.g., traffic management, pavement, road safety, etc.). The responses under this section are categorized into three sub-sections: 1) transportation focus areas which are a priority for state DOTs and where they perceive AI will be useful, 2) examples of areas in which AI is currently being incorporated within the DOTs, and 3) collaboration efforts with private sector or academia to implement AI methods. Results showed that DOTs were looking to integrate AI and machine learning functions in areas of traffic management, safety, mobility, asset management, infrastructure, Transportation Systems Management and Organization, multimodal transportation, and pavement. Though a few DOTs indicated that they have incorporated AI-based methods in areas of traffic management, pavement performance, work-zone safety, and incident detection. It was highlighted that there is an active collaboration between DOTs, academics, and private industry to understand the scope of AI and machine learning in transportation research as well as evaluate market ready products.

The second section of the interview focused on the challenges faced by transit agencies or the issues they anticipate in terms of decision-making and planning with AI. Nine challenges emerged during the discussions: 1) Education and Awareness, 2) Data Management, 3) Workforce Expertise in AI and ML, 4) Funding Limitation, 5) Role of Leadership, 6) Trust in Third-party products, 7) Maintaining Cybersecurity, 8) Employee Retention, and 9) Mistrust/Poor communication within DOT departments. Participants shared that there is lack of understanding about the differences between various AI technologies and their use in DOTs. DOTs also expressed concern that data management and availability was a challenge for them. DOTs need the ability to store and maintain all the data they collect as well as a way to incorporate various data sources as more becomes available. They also need to have a system in place to manage duplicate records, as they are receiving continuous or timelapse data versus a single snapshot of data. There is funding limitation which creates a barrier for the DOTs to evaluate the products that are readily available in the market and carry out big data analytic work. There is also difficulty in getting leadership on board with regards to AI adoption due to lack of knowledge about their benefits as well as the high cost of some of the technologies. But one of the biggest challenges that DOTs face is the level of workforce with expertise in AI and machine learning. DOTs also expressed that it is hard to retain employees with data science background.

The third section of the interview focused on the needs of DOTs in terms of workforce and infrastructure. During discussion, two key things under workforce and 14 suggestions under infrastructure need were brought forward by DOTs to successfully integrate AI operation in transportation research. According to participants, one of the key components is to have individuals with knowledge about AI methods working with the DOT. The current internal workforce lacks the necessary skillset. DOTs can contract some of this work out; however, the concern is that those outside individuals will not have the necessary institutional knowledge. Particular roles that the DOTs felt were needed in order to be successful are: 1) Data scientists, 2) Data analytics team to build algorithms, 3) Software development team, 4) Technology managers, 5) Subject matter experts in ML, and 6) Civil engineers. Apart from finding individuals, the other important thing is to have proper compensation to retain the workforce.

To fulfil infrastructure needs, DOTs require leadership who are willing to use AI. Participants also mentioned that they require operating systems that can continuously work with new data sources that are integrated into the system. They also mentioned the need to have a large storage capacity for data along with having one central system where data from all the sources could be stored. From the security and network side, participants shared that DOTs need a network of fiber cables to improve AI activities. Fiber networking is currently the fastest way to move data. DOTs also require strong network security to protect all the data collected for AI activities. Few participants shared the need for having a system and crew in place to ensure that all technology used for AI activities is serviced when needed and is in good working order to continue operations. Some emphasized that the infrastructure data should be interconnected so simulations of situations can be run to assist the ML. The ability to run simulations, can help DOTs to gain confidence in the AI decision process, since it will not be relying only on a handful of real-life scenarios but rather on millions of simulated situations from which it has learned and created solutions.

The final session of the interview focused on transportation areas where DOTs would like to see the integration of AI and ML. Thirteen different areas of future work were mentioned, with the most commonly mentioned work being the use of AI in predicting conditions for use in traffic management. DOTs are interested to see predictive capabilities for traffic patterns and congestion as well as a system which can execute decisions proactively. On similar lines, some of the DOT participants discussed that the future use of AI in incident management and detection work would enable them to respond more quickly to hazardous situations on the road, be it debris in the roadway or a crash. As per discussions AI could be useful to investigate near misses of VRU injuries or fatalities to identify high risk areas or use ML capabilities to advertise real time truck parking spot availability for drivers. Pavement condition monitoring could be an area where AI could be used for supervision. For example, using AI to determine how automated vehicles may impact pavement conditions. There were also topics mentioned that did not necessarily deal directly

with a transportation topic but rather with how the DOT is run and how AI could help DOT departments run more effectively.

Part 2: Workshops

Two workshops were conducted to collaborate with stakeholders in state and local DOTs to identify topics that could be incorporated into the AI Research Roadmap. The first workshop was designed to gain additional insight into the results discovered in the literature review and interviews and to discuss primary needs for research needed to advance AI in state and local DOTs. The second workshop focused on presenting the draft AI Research Roadmap ideas to the stakeholders and gathering their feedback on each of the ideas.

Workshop 1

Workshop 1 was divided into two sessions: the first session occurred on October 3, 2022, and the second session took place on October 12, 2022. Thirty individuals participated in the October 3, 2022 session and 26 participated in the October 12, 2022 session.

Workshop 1 Results

The first session of Workshop 1 focused on the current and future applications for AI. In discussing each of the research areas, participants highlighted specific applications where AI has been deployed and where AI may be useful in the future. Specifically, participants indicated that AI could be used to (1) manage data and to effectively organize DOT functions, (2) prioritize roadway maintenance operations, (3) improve the efficiency of winter road maintenance, (4) detect maintenance needs through assess condition monitoring, (5) management project resources and performance, (6) identifying crash modification factors, (7) examine data to manage recurring and non-recurring traffic congestion, and (8) improve pedestrian safety.

The second session focused on the potential risks, limitations, and challenges that transportation agencies expect while integrating AI into their operations. The discussions centered around five key challenges: (1) availability of data, (2) data security, (3) computing resources, (4) workforce, and (5) trust in AI. Participants discussed how a lack of quality data affects DOTs ability to train AI systems. Without quality data, AI programs are limited in application and may not provide benefit. Further, AI requires large amounts of data which requires new computing resources and creates significant needs for security measures to protect the data. DOTs need a knowledgeable workforce to understand the needed computing resources, management the AI programs, and protect the data. However, they struggle to recruit, train, and retain a workforce with the knowledge and skills to deploy AI. Finally, a lack of trust in AI limits its application. To include AI in transportation, it is probably best to create a paradigm or steps where, in the beginning, AI performs data summarization. At a second level, there may be diagnostics to show where the problems are. A third level could be predicting problems and having a human in the loop to address them. It’s after these steps that AI can be considered complete automation.

The third session focused on the organizational needs of DOTs to support the deployment of AI. Discussions included challenges with workforce development. Central to the use of AI within DOTs is a workforce that has an in-depth understanding of its operation and use. However, DOTs struggle to find qualified workers as the private sector is more appealing to new job applicants. This is especially true when considering a need for workers to both understand AI and the nuances and countermeasures associated with the transportation industry. One possible solution to this challenge was to work with universities to encourage interdisciplinary programs that include AI and transportation.

The final session of Workshop 1 was to better understand where DOTs struggle to evaluate, implement, and identify useful AI. When asked, participants indicated that they had difficulties in determining if an AI program was ready for implementation. Leadership in DOTs did not always have a clear understanding of

AI. There was a need to standardize data to measure and understand the maturity of AI, and to better understand why particular AI programs were effective. Overall, the participants felt their organizations could benefit from increased collaboration both internally and externally to see how other people are using AI to solve big issues and to increase organizational knowledge about AI to better understand how to determine the readiness of AI.

Workshop 2

Workshop 2 took place on March 7, 2023. Twenty-three individuals participated in the workshop. Participants included personnel from state and local DOTs, the Federal Highway Administration, the American Association of State Highway and Transportation Officials, and project panel members.

Workshop 2 Topics

Workshop 2 focused on 14 potential AI Research Roadmap ideas that were developed based on results from the literature review, interviews, and Workshop 1. These 14 Roadmap ideas align with seven research areas: workforce development, infrastructure development, readiness and evaluation of AI, challenges in adopting AI, current practices and prioritization, external collaboration, and equity, policy & planning.

1. Conducting case studies of successful implementation of AI programs in state DOTs.
2. Developing a roadmap for successful collaboration with Industry partners providing AI based solutions.
3. Use of blockchain and AI in dot research (asset management)
4. Roadmap to create sharable, reliable sources of datasets.
5. Development an Equity plan for AI ingestion across DOTs
6. Develop research plan to include AI in the less explored transportation research field.
7. Develop a guidebook to understand the vulnerability and security concerns for the AI based solutions.
8. Framework to process and manage data collected by DOTs.
9. Integration of Artificial Intelligence based methods in Multimodal Transportation Planning
10. Explore natural language processing-based methods can help solve problems at DOTs.
11. Workforce Needs and Development to Prepare Transportation Agencies for the Application of Existing and Emerging Artificial Intelligence Approaches
12. Implementable Funding Strategies for Artificial Intelligence Opportunity Applications for State and Local DOTs
13. Toolbox to Guide the Selection and Deployment of Artificial Intelligence Technologies in State and local DOT’s.
14. Outreach and Awareness of Artificial Intelligent applications to accelerate the adoption of AI mechanisms by States and Local DOT

Workshop 2 Results

The team presented each of the 14 draft AI Research Roadmaps to gather the participants’ comments, feedback, and recommendations. After presenting each of the ideas, participants were asked to prioritize the ideas related to workforce and infrastructure, and readiness of AI implementation. The results are shown in Table 2.

Table 2. Prioritize the ideas from one to six for workforce and infrastructure needs, readiness, and evaluation of AI programs.

Participants were then asked to prioritize the ideas related to current practices of AI within transportation and challenges in adopting AI-based solutions. The results are shown in Table 3.

Table 3. Prioritize ideas from one to eight for current practices of AI in transportation and challenges faced by DOTs.

Finally, participants were asked to rank all 14 draft AI Research Roadmap ideas based on the likeliness of them receiving funding on a scale from 1 to 7, where 1 was not likely and 7 was extremely likely. The results are shown in Table 4.

Table 4. Rank the Roadmap ideas based on the likeliness of receiving funding.

Conclusion

This task attempted to investigate the research needs to support the integration of AI tools and resources across all levels of DOTs in the United States. The first workshop focused on gathering data to inform the research needs to further the use of AI within DOTs. Much of the discussion focused on where AI may be useful and some of the major barriers and challenges faced by DOTs in the application of AI. Results from polls showed that 40% of participants expressed challenges in availability and quality of data, 40% of participants stated that DOTs lack awareness about the use of AI, and 70% of participants expressed that they lacked the workforce to execute AI applications in their projects.

The second workshop focused on gathering feedback on the draft AI Research Roadmap ideas. Results from the discussions and polls show that participants were looking for case studies that provide examples of how AI has been incorporated into different organizations’ practices and why was it used. Many participants agreed that Roadmap ideas that focused on creating educational materials regarding the adoption of AI programs and toolkits to select appropriate technologies would be very useful. Few participants showed interest in ideas that focused on defining datasets that could be shared across DOTs for AI analysis and use as well as for creating standardized data management practices.

Overall, the results from both workshops tell a story that representatives from state and local DOTs as well as regional transportation agencies are looking into AI-based solutions to address problems within transportation. To achieve that goal, participants’ first priorities were to understand and learn from some of the successful cases of AI integration, to have access to educational materials to spread awareness, and to have a guidebook to identify which resources would best fit their needs. fulfill.