Analyzing the Effect of Autonomous Ridehailing on Transit Ridership: Competitor or Desirable First-/Last-Mile Connection?

Abstract

Ridehailing services (e.g., Uber or Lyft) may serve as a substitute or a complement—or some combination thereof—to transit. Automation as an emerging technology is expected to further complicate the current complex relationship between transit and ridehailing. This paper aims to explore how US commuters’ stated willingness to ride transit is influenced by the price of ridehailing services and whether the service is provided by an autonomous vehicle. To that end, a stated preference survey was launched around the US to ask 1,500 commuters how they would choose their commute mode from among choices including their current mode and other conventional modes as well as asking them to choose between their current mode and an autonomous mode. Using a joint stated and revealed preference dataset, a mixed logit model was developed and analyzed. The results show that ridehailing per se might not be a significant competitor to transit, especially if it is integrated with transit as a first-/last-mile service. The total share of transit (transit-only riders plus those who use transit in connection with first-/last-mile ridehailing) remains substantially flat as set against conventional ridehailing services, even if ridehailing fares decrease. On the other hand, when the ridehailing price is significantly reduced by automation, our analysis suggests a decline in total transit ridership and an increase in ridehailing, especially for solo ridehailing. Also, it was found that autonomous pooled ridehailing might not be as appealing to commuters as autonomous solo ridehailing.

Impacts of ride-hailing on energy and the environment: a systematic review

“…The authors also find the cost of waiting for transit around three times higher than waiting for ride-hailing vehicle. Unlike the above studies [34], find that in the US, the total share of transit is not very sensitive to the introduction of ridehailing, even as ride-hailing cost decreases, suggesting limited competition between the two modes.…”

“…One empirical study, [34], utilizes a choice experiment choice experiment to investigate AVs, or driverless vehicles, which can substantially lower ridehailing cost. They find a larger increase in ride-hailing share when the ride-hailing vehicles are autonomous, and an associated decrease in transit.…”

Strangers On This Road We Are On: A Literature Review of Pooling in On-Demand Mobility Services

“…Some studies have found that exclusive-ride products are more popular than shared-ride products such as ridesplitting or microtransit services, particularly for commuter trips or trips with an increased perception of the importance of being on time to the destination. Khaloei et al ( 25 ) developed a mixed logit model using data from a stated- and revealed-preference survey conducted in the U.S.A. and found that commuter trips in an autonomous ridehailing environment would likely result in mostly solo ridehailing trips. Abkarian et al ( 26 ) studied Chicago’s TNC trip data from November 2018 to December 2019 and found that shared rides amounted to 20%–30% of all ridesourcing trips throughout the day (implying that ridesplitting choice was not dependent on trip purpose).…”

“…Automation in ridesourcing and pooling services may also lower passenger fare costs as driver roles are eliminated from the vehicles, thereby making shared rides more attractive for riders that value the price of service more highly in their travel choices ( 54 , 55 ). If prices for ridesourcing trips substantially drop following the introduction of automation, there may be increased use of exclusive-ridesourcing trips (rather than pooled trips) and a decrease in public transit trips, thereby increasing net VMT and emissions ( 25 ).…”

Beyond Traditional TOD: Integrating Multiuse Paths and Bike Share into Public Transit to Address the First/Last Mile Issue

“…Research on FLM has focused considerably on the bicycle road network, distribution of transit stations, distribution of bike share stations, integration with different forms of transit, and various types of barriers to increasing bike share ridership [5][6][7]. This study contributes to the literature in two important ways.…”