Incentive-driven transition to high ride-sharing adoption

Abstract

Ride-sharing -the combination of multiple trips into one -may substantially contribute towards sustainable urban mobility. It is most efficient at high demand locations with many similar trip requests. However, here we reveal that people's willingness to share rides does not follow this trend. Modeling the fundamental incentives underlying individual ride-sharing decisions, we find two opposing adoption regimes, one with constant and one with decreasing adoption as demand increases. In the high demand limit, the transition between these regimes becomes discontinuous, switching abruptly from low to high ride-sharing adoption. Analyzing over 360 million ride requests in New York City and Chicago illustrates that both regimes coexist across the cities, consistent with our model predictions. These results suggest that current incentives for ride-sharing may be near the boundary to the high-sharing regime such that even a moderate increase in the financial incentives may significantly increase ride-sharing adoption. RESULTS Contrasting ride-sharing adoptionCurrently, only a small fraction of people adopts ride-sharing even in high-demand situations, despite all its positive aspects [26]. For example, among more than 250 million ride-hailing requests served in New York City in 2019 less than 18% were requests for shared transportation [27]. Moreover, the city's ride-sharing activity varies strongly across different parts of the city, in particular at locations with a high number of ride-hailing requests (see Fig. 1): For instance, in the East Village and Crown Heights North the fraction of shared ride requests is relatively high, while it is low at both John F. Kennedy and LaGuardia airports, locations that would intuitively be especially efficient for sharing rides. Several other location throughout New York City as well as Chicago exhibit similarly contrasting

Spontaneous symmetry breaking in ride-sharing adoption dynamics

“…This bistability may lead to a loss in efficiency of ride-sharing services due to lower than expected adoption and is particularly relevant if only a small fraction of the potential user base adopts the service. Similar effects may partially underlie currently low ride-sharing adoption in metropolitan areas such as New York City [30].…”

“…While the qualitative, fundamental interactions are likely persistent also in real-world ride-sharing applications, additional influences from alternative mode choice options, matching strategies, or quickly evolving regulatory boundary conditions add to the dynamics [31,[33][34][35][36]. In particular, adoption patterns in real ride-sharing applications seem to be dominated by socio-economic heterogeneities and the interaction with other (public) transport modes [30,36]. While this observation suggests that the pattern formation dynamics found in the simplified model may not be dominant, the spatially heterogeneous willingness to share may result in higher difficulty to achieve large-scale ride-sharing adoption due to the inherent bistability of partial ride-sharing adoption patterns and full sharing adoption.…”

“…The decision of a ride-hailing user to request a single or a shared ride depends on the possible benefits of the two options [30,31,[33][34][35][36] (see Fig. 1).…”

“…Still, the combination of multiple trips into one vehicle potentially reduces the total distance driven compared to individual mobility options and makes ride-sharing a promising option to reduce congestion and emissions with growing urban mobility demands [21,[27][28][29]. However, ride-sharing can only improve urban mobility if ride-hailing users actually adopt sharing [30][31][32].…”

“…We analyze how interactions between users shape the adoption of ride-sharing compared to the adoption of single taxi rides. Based on a recently proposed game-theoretic model of ride-sharing adoption dynamics [30], we demonstrate how strategic interactions between the users give rise to symmetry breaking and pattern formation dynamics of the users' ride-sharing adoption, resulting from the incentive to avoid detours in shared rides. Moreover, the same interactions lead to bistability, potentially limiting the adoption of the ride-sharing service.…”