1 Introduction

Transit ridership in the United States has been in decline over the last several years, with underlying causes ranging from service cuts to the advent of new mobility options (Graehler, Mucci, and Erhardt 2019; Mallet 2018). These new mobility options – including bikeshare, e-scooters, and ridehailing through Transportation Network Companies (TNCs) – might also play an important role in supporting transit operations if the relative strengths of transit and modern mobility systems can be successfully partnered (Shaheen and Chan 2016; Oostendorp and Gebhardt 2018; Shiv 2018). This may lead to reduced dependence on automobiles and associated environmental benefits (Hoehne and Chester 2017).

One particular area where a partnership between high-capacity, fixed-route transit and TNC operations has been desired is in supporting first mile / last mile operations in low-density suburban regions (Shaheen and Chan 2016; Alonso-González et al. 2018; Kang and Hamidi 2020). TNC operators are incentivized to operate in dense areas where many potential passengers are located (Wong, Hensher, and Mulley 2020), meaning they compete with transit where transit can be most successful. But regulations or partnerships that changed this incentive pattern could be highly beneficial to many transit riders (Ronald, Thompson, and Winter 2017; Deakin, Frick, and Shively 2010). For example, a transit agency might partner with a TNC to offer shared rides at a subsidized fare in low-density areas where fixed route transit services are ineffective or expensive. As these partnerships to offer microtransit services materialize through demonstration projects or permanent offerings, there is an important opportunity to observe and evaluate who is using the service and for what reasons. It is also valuable to understand how users perceive the effectiveness and convenience of these systems.

This paper presents an analysis of a preferences survey conducted immediately before and several weeks after the November 2019 launch of a microtransit service in south Salt Lake County, Utah by the Utah Transit Authority (UTA). Respondents to the survey indicated their awareness of and willingness ot use the microtransit service. This paper investigates the relationship between this expressed willingness and the demographic characteristics of these individuals –– particularly age, transit use frequency, and household size — influence these preferences.

The remainder of this section contains a brief review of previous and ongoing studies relevant to the question of demand for and use of microtransit services. We then describe the survey methodology for this study, including both the context of the UTA microtransit service as well as the survey instrument and collection strategy. The survey results in several dimensions are followed by a discussion of the limitations of the findings and associated opportunities for future research.

1.1 Findings from Other Systems

In the last few years a number of on-demand microtransit services have begun operations in many cities around the world. Given the dynamic nature of this space, the literature is not mature and numerous projects are under evaluation at the moment. However, some findings from early systems are available and are worthy of discussion. These articles were identified through a search of academic databases — particularly TRID (Transportation Research Board 2020), Scopus (Elsevier 2020), and Google Scholar (Google 2020) — using keywords including “microtransit” and “on-demand transit.” Citations within the returned articles were investigated as well.

A microtransit service in Helsinki, Finland known as “Kutsuplus” operated from 2012 to 2015 and has been the subject of a number of studies. Weckström et al. (2018) and Haglund et al. (2019) each conduct a comprehensive analysis of the system using rider questionnaires supplemented with GPS data points. The studies found that the system was used by a wide variety of individuals for a wide variety of trip purposes, and the typical trip length suggested it was being used less like a taxi service and more to supplement last-mile transit access. In many cases, it appeared as though Kutsuplus replaced walking and bicycle trips. The Weckström et al. (2018) research also asked respondents why they may have continued or discontinued using the service, revealing strong differences in response among different income groups. High-income individuals were more likely to cite long response times, while lower income groups were more likely to cite the fare or difficulties understanding the service, or even not being aware of its existence.

Alonso-González et al. (2018) examined a microtransit system in the Arnhem-Nijmegen region in the Netherlands. They develop a methodology to calculate the accessibility contributed by the microtransit system above and beyond that provided by the fixed route transit system, and their findings suggest the microtransit service substantively enhances the mobility of people in the region. In this study the authors use GPS trip data from the service and do not have access to the actual riders to understand their preferences or characteristics.

In 2016 Austin, Texas, introduced a TNC operated as a non-profit and called “RideAustin.” The unique corporate structure of this TNC encourages it to share data from the system with researchers, leading to a number of studies examining the trip patterns of its users. Komanduri et al. (2018) show that a high proportion of trips (60%) taken on RideAustin could have been completed with a single-seat transit ride. Wenzel et al. (2019) additionally used the same dataset to estimate the level of deadheading and concomitant energy expenditure on the system. Though these findings are important in terms of understanding the risks of microtransit services, it should be stressed that the RideAustin service was not explicitly designed to support transit operations. And although the RideAustin dataset does identify unique individual riders through a persistent mobile device ID, it does not disclose any demographic information on the riders and therefore cannot support an analysis of their characteristics or preferences.

König and Grippenkoven (2020) present a survey focused on determining preferences and attitudes towards demand-responsive transit use in two rural regions in Germany. A structural equations model of expressed preferences suggests that users’ attitudes are most powerfully driven by the expected performance of the system in terms of wait and travel time, and less materially by attitudes towards other public transit systems or social perspectives. This is valuable insight, but attitudes such as these are difficult to forecast for a population, and therefore difficult to incorporate into service planning exercises. The authors collected demographic characteristics of the survey respondents, but did not consider these characteristics in the statistical models.

The literature to this point has been greatly aided by the use of so-called Big Data: GPS records, rider transaction data, and the like. These data are well-suited to important research questions such as where and when the services pick up and drop off riders, the wait times experienced by the riders, and in some cases even the ability to construct multiple trip tours. But the literature is somewhat limited in its exploration of the actual users of these systems: who they are, why they are traveling, and why they chose to use this service. This information is critical when planning and forecasting the potential success or failure of these systems, in contrast to reporting observed service characteristics for a service already in operation. In this paper, we present the results of a rider survey designed to answer these questions in the periods immediately before and after the launch of a microtransit service.


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