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Smart city article details

Title	E-Scooter Sharing: Leveraging Open Data For System Design
ID_Doc	21537
Authors	Ciociola A.; Cocca M.; Giordano D.; Vassio L.; Mellia M.
Year	2020
Published	Proceedings of the 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2020
DOI	http://dx.doi.org/10.1109/DS-RT50469.2020.9213514
Abstract	With the shift toward a Mobility-as-a-Service paradigm, electric scooter sharing systems are becoming a popular transportation mean in cities. Given their novelty, we lack of consolidated approaches to study and compare different system design options. In this work, we propose a simulation approach that leverages open data to create a demand model that captures and generalises the usage of this transportation mean in a city. This calls for ingenuity to deal with coarse open data granularity. In particular, we create a flexible, data-driven demand model by using modulated Poisson processes for temporal estimation, and Kernel Density Estimation (KDE) for spatial estimation. We next use this demand model alongside a configurable e-scooter sharing simulator to compare performance of different electric scooter sharing design options, such as the impact of the number of scooters and the cost of managing their charging. We focus on the municipalities of Minneapolis and Louisville which provide large scale open data about e-scooter sharing rides. Our approach let researchers, municipalities and scooter sharing providers to follow a data driven approach to compare and improve the design of e-scooter sharing system in smart cities. © 2020 IEEE.
Author Keywords	data driven optimization; demand model; electric vehicle; open data; scooter sharing

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