Topology dependence of on-demand ride-sharing
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(2020) 5:49
Applied Network Science
RESEARCH
Open Access
Topology dependence of on-demand ride-sharing Debsankha Manik1,2 and Nora Molkenthin1,2* *Correspondence: [email protected] 1 Max Planck Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen, Germany 2 Chair for Network Dynamics, Institute for Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), Technical University of Dresden, 01069 Dresden, Germany
Abstract Traffic is a challenge in rural and urban areas alike with negative effects ranging from congestion to air pollution. Ride-sharing poses an appealing alternative to personal cars, combining the traffic-reducing ride bundling of public transport with much of the flexibility and comfort of personal cars. Here we study the effects of the underlying street network topology on the viability of ride bundling analytically and in simulations. Using numerical and analytical approaches we find that system performance can be measured in the number of scheduled stops per vehicle. Its scaling with the request rate is approximately linear and the slope, that depends on the network topology, is a measure of the ease of ridesharing in that topology. This dependence is caused by the different growth of the route volume, which we compute analytically for the simplest networks served by a single vehicle.
Introduction The increasing demand for mobility in modern urban, suburban and rural areas presents a wide range of ecological and logistic challenges. While urban areas struggle with traffic jams, air pollution and parking space shortages (Guerreiro 2018; NYC Department of Transportation 2018), rural areas are often unable to provide accessible and frequent public transport. The recent rise of the sharing economy (Belk 2014; Cohen and Kietzmann 2014; Kamargianni et al. 2016; Greenblatt and Shaheen 2015) has brought up ride-sharing as a possible answer to all of these problems. Ride-sharing poses an appealing alternative to personal cars, combining the traffic-reducing ride bundling of public transport with much of the flexibility and comfort of personal cars (Spieser et al. 2014; Zhang and Pavone 2016; Barbosa et al. 2018; Macharis and Keseru 2018; Vazifeh et al. 2018). Intelligent ondemand ride-sharing services are hoped to reduce the ecological footprint associated with individual mobility by dynamically bundling rides together, reducing the amount of vehicles necessary for the same number of rides (Tachet et al. 2017; Santi et al. 2014; Sorge et al. 2015; Sorge 2017). However, the complex behaviour of such dynamic dial-a-ride problems (DARP) (Berbeglia et al. 2010) is not yet fully understood. Recent studies have examined the dynamical behaviour of specific ride-sharing strategies analytically (Herminghaus 2019; © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to th
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