Is There A Limit To Adoption Of Dynamic Ridesharing Systems? Evidence From Analysis Of Uber Demand Data From New York City

Abstract

Recent technological advances have paved the way for new mobility alternatives within established transportation networks, including on-demand ride hailing/sharing (e.g., Uber, Lyft) and citywide bike sharing. Common across these innovative modes is a lack of direct ownership by the user; in each of these mobility offerings, a resource not owned by the end users’ is shared for fulfilling travel needs. This concept has flourished and is being hailed as a potential option for autonomous vehicle operation moving forward. However, substantial investigation into how new shared modes affect travel behaviors and integrate into existing transportation networks is lacking. This paper explores whether the growth in the adoption and usage of these modes is unbounded, or if there is a limit to their uptake. Recent trends and shifts in Uber demand usage from New York City were investigated to explore the hypothesis. Using publicly available data about Uber trips, temporal trends in the weekly demand for Uber were explored in the borough of Manhattan. A panel-based random effects model accounting for both heteroscedasticity and autocorrelation effects was estimated wherein weekly demand was expressed as a function of a variety of demographic, land use, and environmental factors. It was observed that demand appeared to initially increase after the introduction of Uber, but seemed to have stagnated and waned over time in heavily residential portions of the island, contradicting the observed macroscopic unbounded growth. The implications extend beyond already existing fully shared systems and also affect the planning of future mobility offerings.

Publication Date

1-1-2018

Publication Title

Transportation Research Record

Volume

2672

Issue

42

Number of Pages

127-136

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1177/0361198118788462

Socpus ID

85050375034 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85050375034

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