CHAPTER 1

Introduction

Uncrewed (or unmanned) aircraft systems (UAS) were first invented more than 100 years ago and, throughout most of their history, have been used in military applications. Over the last decade, UAS have been widely adopted by state departments of transportation (DOTs) and other transportation or emergency response agencies to be used as a tool in a variety of ways.

The FAA Modernization and Reform Act of 2012 defined the term unmanned aircraft as “an aircraft that is operated without the possibility of direct human intervention from within or on the aircraft” (49 United States Code [U.S.C.] 40101 § 331). This law goes on to define unmanned aircraft systems to mean “an unmanned aircraft and associated elements (including communication links and the components that control the unmanned aircraft) that are required for the pilot in command to operate safely and efficiently in the National Airspace System (NAS)” (49 U.S.C. 40101 § 331). Small UAS have been defined in regulations to mean an unmanned aircraft weighing less than 55 pounds. This research is primarily focused on state DOT implementation of small UAS.

Advanced air mobility (AAM) is an inclusive umbrella term that encompasses new enabling technologies that allow for new air transport use cases. NASA introduced the term AAM in the spring of 2020:

In October 2022, the AAM term was legally defined in the AAM Coordination and Leadership Act, which directed the USDOT to form an AAM Interagency Working Group tasked with delivering an innovative and safe regulatory framework for AAM integration. This law defines AAM as a “transportation system that transports people and property by air between two points in the United States using aircraft with advanced technologies including electric aircraft or electric vertical takeoff and landing aircraft (eVTOL), in both controlled and uncontrolled airspace” (49 U.S.C. 40101 [2022] § 2).

AAM encompasses a variety of use cases for eVTOL aircraft and electric or hybrid-electric aircraft performing a short takeoff and landing. AAM includes UAS cargo delivery (small and large), UAM, regional air mobility (RAM), and other use cases, such as air ambulance, medical transport, and emergency response.

UAM is a subset of AAM and is defined by the FAA as “a safe and efficient aviation transportation system that will use highly automated aircraft that will operate and transport passengers or cargo at lower altitudes within urban and suburban areas” (FAA 2022a). Although UAM refers to air transportation within an urban environment, RAM refers to the larger regional connectivity

Research Approach

The methodology used to conduct this research and collect data for the development of the Guide was a mixed-method approach that included reviewing literature, using a survey instrument, and conducting two focus groups. Additionally, during the course of this research project, the large language model (LLM) ChatGPT was released and refined as its popularity and utility grew. The research team acknowledges the limitations of LLMs and acknowledges the use of this LLM as a brainstorming tool and as an instrument to develop different approaches to presenting data in digestible formats for the reader. This LLM was not used to generate citations. The following sections provide an overview of each core research activity.

Literature Review

The research team initiated this research by conducting a thorough literature review. This review was completed using a multi-step process. Through the lens of the identified research objectives, literature was collected using various databases and selected keywords and phrases. After gathering a broad range of academic journal articles, the research team also searched industry white papers, press releases, and media articles, which were included in addition to scholarly research literature because of the nascent nature of the topic. The research team then evaluated the quality and relevance of all collected literature compared to the research objectives and proceeded to thoroughly read and analyze over 50 relevant pieces of literature. The literature review concluded with a synthesis of information that laid the foundation for the other research activities.

Survey

Following a gap analysis of the literature review, the research team developed and distributed a survey in March 2023. The survey was distributed to government agencies, industry organizations, and academic institutions to better understand the implementation of UAS and AAM.

The survey resulted in a total of 144 stakeholder respondents, 40% representing government agencies, 34% representing industry, and 26% representing academia. Among government respondents, a majority (72%) represented state DOTs, followed by state, regional, or local planning organizations (16%), and State Divisions or Departments of Aeronautics (10%). Finally, 2% of respondents represented air traffic control.

Among industry groups, 47% represented service providers, 31% passenger air mobility, and 22% represented UAS original equipment manufacturers (OEMs). The survey received 35 responses from individuals representing academia. Nearly two-thirds of respondents represented four-year universities, while 31% represented two-year colleges.

Focus Groups

Upon analysis of the survey results, another gap analysis was completed in preparation for conducting focus groups to address the identified gaps. The use of focus groups is a long-established and proven qualitative research method (Morgan and Spanish 1984; Mashuri et al. 2022); however, as with any research methodology, it has strengths and limitations. Focus groups are time- and cost-efficient ways to collect data. Focus group discussions can create a group synergy that can enable the group to work together and share profound insights into complicated topics (SIS International Research 2021). When properly designed, focus groups can produce rich qualitative data sets for analysis. Focus groups can also serve as a valuable platform to understand how something should change or progress (Gibbs 1997). Due to the evolving nature of UAS operations at state DOTs and the nascent nature of AAM, focus groups were the best methodology to collect the necessary data to develop the Guide for maturing UAS and AAM capabilities.

Potential limitations to focus groups include a lack of anonymity among participants, which could influence their responses. A similar challenge is that the moderator can have a strong effect on the overall feeling of the focus group, which could also influence participants’ responses. The third potential limitation is assembling enough people to establish a representative sample of the larger population group.

The research team developed an overall strategy to mitigate the first two noted limitations. The research team and focus group moderators established an inclusive and open environment to foster the sharing of diverse thoughts and opinions by developing semi-structured, non-biased questions, and using poll questions, the meeting chat function, and whiteboards. These various tools allowed participants to share their thoughts in ways in which they were most comfortable.

The moderators were subject matter experts and knowledgeable on each of the topics discussed within the focus group, and they approached the focus group meetings with the recognition that each member had valuable contributions to make. The moderators strived to remain unbiased while leading the discussion of the predetermined questions to draw out the views of each participant, and they continued to encourage participation through the various platforms throughout the focus group meetings.

The research team mitigated the third noted limitation by expanding the invitation list for the focus group. This list was developed to include people with varying experience across each stakeholder group (e.g., participation from well-established state DOT UAS programs and from newer, less-established state DOT UAS programs). The list of invitees was developed with the goal that at least four participants would represent each stakeholder group. Because the main audience of the Guide is expected to be state DOTs, this stakeholder group was purposely designed to have a larger representation.

The predetermined questions used in the focus group meetings were designed as semi-structured questions. Semi-structured questions are written in an open-ended format and designed to encourage participants to freely share their thoughts. Semi-structured questions allow for focused questioning while still giving the focus group moderator the flexibility to dive deeper into relevant topics that emerge during the discussion (Adeoye-Olatunde and Olenik 2021). The semi-structured method is ideal for exploring participants’ thoughts on complex open-ended topics and yields a rich data set.

The research team designed the focus group questions in an unbiased way, avoiding the use of leading questions. The neutral questions were developed to target the identified research gaps and extract data from the participants to address these gaps. Focus group best practice is to use engagement, exploration, and exit questions to maximize participation throughout the course of the focus group meeting (Then et al. 2014). Engagement questions are used to establish the baseline topic and warm up the participants. Exploration questions are designed as the core questions for the focus groups and are expected to take the most time. Exit questions are used to check if anything was missed and to provide an opportunity for participants to share last-minute thoughts. The research team designed the flow of questions for each research topic to follow this sequence.

Focus group participants were chosen primarily from the established survey participant list. This list of invitees was expanded in some of the categories to ensure the stakeholder group was adequately represented. These stakeholder groups included:

• State DOTs and local transportation agencies.
• State divisions of aeronautics.
• Multimodal transportation planning organizations.
• UAS OEMs.
• Passenger air mobility OEMs.
• UAS/AAM service providers.

Adequate representation of at least four participants from each stakeholder group was the goal when seeking representation for participation in the focus group. When using focus groups as a research methodology, it is best practice to invite more participants than the established goal because participants may need to withdraw for various reasons. The research team invited more people per stakeholder group and contacted potential participants individually to explain the scope and goals of the focus group to secure commitments for the anticipated two meetings.

Both focus group meetings were held virtually and recorded, which assisted with notetaking and provided recorded transcripts for analysis. There were 54 attendees at the first focus group meeting on July 5, 2023. Forty-four percent of the attendees were from state DOTs, 17 percent were from the three industry stakeholder groups, four individuals were from the planning stakeholder group, and six individuals were from academia. In addition to having each stakeholder group represented by at least four people, the research team met the goal of having a range of experience in UAS and AAM. The second focus group meeting was attended by 49 individuals on August 4, 2023. In general, stakeholder representation and experience levels at the second meeting were similar to those at the first focus group meeting.

The focus groups provided a great deal of information, and recordings from both focus group meetings were transcribed using transcription software and then analyzed. Thematic analysis was then used to analyze the meeting transcripts and the data from the meeting chats and whiteboards. This approach included properly formatting the transcripts, meeting chats, and whiteboards for the coding process. This process resulted in 134 formatted pages of data. Once the data were formatted, each entry was thoroughly read and coded, resulting in 390 total codes that were processed for additional analysis. Qualitative data coding of transcripts can occur in a variety of ways. Coding can refer to the assignment of numbers to various words or phrases, or a system of abbreviations or words and short phrases to describe bits of data. In qualitative coding, there are two categories of code: a priori codes and empirical codes. A priori codes are codes that are defined prior to conducting any data collection and are typically related to categories that the researcher expects to have confirmed. Empirical codes are codes created after data collection while analyzing the data set (Gibson and Brown 2009).

Using empirical codes with an inductive approach ensures that themes can be derived from the participants and the collected data. A deductive (a priori) approach applies a predetermined framework and seeks confirmation within the data. To allow new ideas or themes to emerge from the data set, the research team employed an empirical coding system during the thorough thematic analysis of the collected data.

Stakeholder Feedback Workshop

In addition to the mixed-method research approach outlined earlier, once the draft Guide was completed, the research team held a stakeholder feedback workshop. This workshop consisted of 15 primary participants from state DOTs, academia, and industry who reviewed the draft Guide prior to meeting for a full one-day workshop. The workshop was held in person in May 2024 and provided an opportunity for the collection of quality feedback on the draft Guide, which was subsequently edited and refined to include feedback received from the workshop.