Transportation Network Companies (TNCs) such as Uber and Lyft are smartphone app-based ride-hailing services that have grown rapidly over the past decade. Such services match passengers with drivers using online enabled platforms. The launch of TNC services took place in 2009, when Uber (formerly known as UberCab) introduced the service in the San Francisco area [1]. Soon after, TNCs made their appearance in various other markets across the US, thus adding transportation options that competed or complemented available transportation services. The promise to save time, increase affordability and convenience, reduce stress, and the lack of need to own and use a personal automobile has been appealing to many customers who embraced TNC services, especially in large metropolitan areas. Among available TNCs in the US market, Uber is the market leader with 65% market share.

In addition to providing user benefits, TNCs were initially perceived as a solution for urban congestion. However, in several cities in the United States (US) where these companies operate, TNCs failed to deliver on this promise. In fact, recent studies from heavily congested cities in the US have reported that TNCs took over part of the transit ridership rather than promoting ridesharing among solo drivers. To make things worse, Uber- or Lyft vehicles waiting for rides contributed to increased Vehicle Miles Traveled (VMT) and urban pollution. In addition, TNCs have been involved in regulatory and policy challenges, mainly because of the controversial aggressive models of market entry and the pushback from regulated for-hire transport industry [2].

A number of recent studies explored the emerging trend of TNC services as a mode of transportation. A concise summary is available by Sisiopiku et al. in [3]. Shaheen (2018) discussed the recent cultural shift from the auto-dependency to shared mobility and the impact of such shift on the growth of ride-hailing services such as Uber and Lyft [4]. Several studies attempted to define TNC market characteristics using surveys. These studies showed great variations in their findings depending on the geographical locations and the surveyed user demographics. For instance, studies conducted in large metropolitan areas like Boston, Chicago, Los Angeles, New York, San Francisco, Seattle and Washington D.C. showed that the typical TNC user is 18 - 29 years of age and possesses an advanced degree [5]. However, TNC users in cities like Pittsburgh and Puget where predominantly 34 - 44 years old and holding Bachelors’ degrees [6] [7]. Cirella et al. (2017) examined the differences in travel mode choice between Millennials and Generation Xers in California using inputs from 2155 individuals. When compared to Gen Xers, Millennials were three times more likely to use Uber or Lyft [8]. With respect to older transportation users, Freund et al. (2020) suggested that door-to-door assistance service could increase the use of TNC service among 65+ years old population [9]. A survey of 380 TNC users in San Francisco conducted by Rayle et al. (2014) reported that 67% of responders used ridesourcing for social/leisure trips (bars, restaurants, concerts, friends/family visits) while only 16% used it for commuting purposes. Responders reported that their leaning towards TNCs was driven by the availability of a secure payment system with short wait time and 40% of TNC users in the San Francisco region reported using their private vehicle less due to the availability of on-demand mobility sharing services [10]. A national Pew Research Center survey of 4787 American adults in 2015 found only 15% of Americans had used ride-hailing apps, whereas one third had yet to even hear of them [11]. While the author did not find race or gender as influential factors in the use of these apps; age, education, income level and type of locale (i.e. urban, suburban, or rural) were all found to be strongly explanatory.

Overall, the literature review confirms that knowledge and utility of TNC services among travelers vary greatly in accordance to a vast array of socio-demographic variables, as with most new technology [11]. Moreover, systematic documentation of actual impacts of TNCs presence on the preferences and daily travel patterns of the transportation system users is still limited. This is due to the lack of availability of detailed data resulting from privacy concerns and resistance of TNC companies to share company data [12]. Thus, localized studies are of great value in order to document transportation users’ attitudes and preferences and identify socio-demographic variables that influence the use of TNC services.

In light of this need, the objective of this study was to examine the impact of transportation users’ choice preferences on the selection of ride-hailing services as a mobility option in the Southeast US. Using questionnaire responses from two different geo-locations in the Southeast (i.e., Birmingham, Alabama and Miami Beach, Florida) the study documented and compared preferences and attitudes toward TNC use as a travel mode of choice. The study considered demographic data in the analysis and interpretation of the survey findings and the identification of indicators that affect the use of TNCs at the study locations.

This study compared results from two surveys that collected and documented public perceptions related to ride-hailing services in two TNC markets; namely Birmingham, AL and Miami Beach, FL. The study builds on the authors’ earlier work [12] that used a questionnaire survey to understand the leading reasons and conditions that drive people towards the use of TNCs services in the Birmingham Metro Area.

Both surveys were developed using the Qualtrics Research Core tool in accordance with the Institute of Transportation Engineers Manual on Transportation Engineering (ITE) Studies guidelines [13]. Qualtrics LLC facilitated the identification and recruitment of survey participants and automated the data entry and management process. The research team obtained the survey responses from Qualtrics LLC and performed validation checks, data processing, and data analysis. All necessary approvals were obtained from the Institutional Review Board (IRB) for Human Use prior to conducting the surveys. For quality assurance, both questionnaires were pretested and refined prior to distribution.

The surveys sought to get information about users’ attitudes towards using TNCs along with detailed socio-demographic such as age, gender, education level, and employment type. The demographic data were categorized based on the US Census criteria. The survey also requested participants to report detailed trip information for a typical day (i.e., 24-hr travel diary) during a typical weekday including origin and destination of each trip, travel time, trip purpose and the travel mode used. Additionally, information related to vehicle ownership, alternate mode choices, and recommendations for future transportation improvements (including expansion of the TNC services) was solicited.

Participants were presented with simple multiple-choice questions with specified context and were asked to answer each question categorically based on the context. While some survey questions were identical or similar between the two study sites, others solicited inputs on issues of unique importance to each study site. For example, since Miami Beach Area is a popular tourist spot that is busy during the weekends, Miami Beach survey participants were asked to provide trip information for a typical weekend, in addition to a typical weekday. In addition, being an adult who is residing within the geographical area of interest was a requirement for participation in the Birmingham study, but not in the Miami Beach survey in order to allow for documentation of responses from tourists that visited the Miami Beach area for recreational purposes.

The collected responses were carefully checked and validated. After eliminating any surveys that included incomplete, duplicate, or irregular answers, 451 responses from the Birmingham area and 339 from the Miami Beach area were analyzed for a total of 790 surveys. It is important to note that out of the 339 respondents in Miami Beach Area, 71 (21%) were Miami Beach residents and the rest (79%) were visitors from the greater Miami area or out-of-city tourists. This allowed for examination of potential differences in the preferences and attitudes toward TNCs between residents and tourists in the Miami Beach case study. For a quick reference, Table 1 summarizes characteristics of both study locations along with information relevant to the two surveys.