Abstract
Ride-sharing services may substantially contribute to future sustainable mobility. Their collective dynamics intricately depend on the topology of the underlying street network, the spatiotemporal demand distribution, and the dispatching algorithm. The efficiency of ride-sharing fleets is thus hard to quantify and compare in a unified way. Here, we derive an efficiency observable from the collective nonlinear dynamics and show that it exhibits a universal scaling law. For any given dispatcher, we find a common scaling that yields data collapse across qualitatively different topologies of model networks and empirical street networks from cities, islands, and rural areas. A mean-field analysis confirms this view and reveals a single scaling parameter that jointly captures the influence of network topology and demand distribution. These results further our conceptual understanding of the collective dynamics of ride-sharing fleets and support the evaluation of ride-sharing services and their transfer to previously unserviced regions or unprecedented demand patterns.
Highlights
Traditional forms of human mobility from pedestrian to private car traffic and air travel exhibit a range of collective dynamical phenomena including freezing by heating, congestion, traffic flow oscillations, and perturbation spreading [1,2,3,4,5,6,7]
A mean-field analysis confirms this view and reveals a single scaling parameter that jointly captures the influence of network topology and demand distribution
Nonlinear dynamics and statistical physics have substantially contributed to identifying many of these phenomena and to providing a better understanding of the mechanisms underlying them as well as their relations to other collective dynamics [3,4,5,6,7,8,9,10,11,12]
Summary
Traditional forms of human mobility from pedestrian to private car traffic and air travel exhibit a range of collective dynamical phenomena including freezing by heating, congestion, traffic flow oscillations, and perturbation spreading [1,2,3,4,5,6,7]. We introduce a quantitative measure of efficiency of on-demand ride-sharing fleets based on their collective nonlinear dynamics and reveal its universal scaling across network topologies.
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