Quantifying the benefits of vehicle pooling with shareability networks

By Paolo Santi, Giovanni Resta, Michael Szell, Stanislav Sobolevsky, Steven H. Strogatz, Carlo Ratti. Proceedings of the National Academy of Sciences of the United States of America, 2013

349 citations recorded by the source corpus.

Significance Recent advances in information technologies have increased our participation in “sharing economies,” where applications that allow networked, real-time data exchange facilitate the sharing of living spaces, equipment, or vehicles with others. However, the impact of large-scale sharing on sustainability is not clear, and a framework to assess its benefits quantitatively is missing. For this purpose, we propose the method of shareability networks, which translates spatio-temporal sharing problems into a graph-theoretic framework that provides efficient solutions. Applying this method to a dataset of 150 million taxi trips in New York City, our simulations reveal the vast potential of a new taxi system in which trips are routinely shareable while keeping passenger discomfort low in terms of prolonged travel time. Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network, which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.

Subfields: Automotive Engineering, Marketing, Transportation

View Paper: https://www.semanticscholar.org/paper/bd8ec6c74512d5e9f68cc1ecb71b770628afc756

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Citations in the corpus

The optimal pricing strategy for two-sided platform delivery in the sharing economy (2017, positive): “For instance, Santi et al. (2014) claim that the cumulative trip length could be reduced by roughly 40 percent when using ride sharing like Uber as opposed to using traditional taxis.”
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment (2017, methodology): “Our approach decouples the problem by ﬁrst computing feasible trips from a pairwise shareability graph (5) and then assigning trips to vehicles.”
The Passions and the Interests: Unpacking the 'Sharing Economy' (2016, background): “By contrast, all the reservations in three boroughs (Queens, Staten Island, and the Bronx) brought hosts revenue of $12 million — less than 3% of the New York City total.”
Human mobility: Models and applications (2017, background): “Apart from this, some of the many aspects studied on intra-urban mobility relate to the typical distance scales of movement [63]; measures of travel duration [340]; effects of population size on the structure and dynamics of mobility [105, 341]; the effect of city topology on the displacements of it”
Addressing the minimum fleet problem in on-demand urban mobility (2018, background): “The type of shareability network introduced here is profoundly different from the type studied previously: it models the sharing of vehicles, whereas previous networks 7–9 modelled the sharing of rides.”