e-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing

Ashutosh Timilsina; Simone Silvestri

doi:10.1109/MASS58611.2023.00051

e-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing

Ashutosh Timilsina
, Simone Silvestri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

There is growing interest in deploying the sharing-economy-based business model to energy systems, with modalities like peer-to-peer (P2P) energy trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). This paper exploits the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. We employ theoretical concepts of online spatial crowdsourcing, reinforcement learning, and reverse auction to devise this novel platform. Experimental results using real data from New York City taxi trips and energy consumption show that e-Uber performs close to the optimum and outperforms a state-of-the-art approach.

Original language	English
Title of host publication	Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023
Pages	359-365
Number of pages	7
ISBN (Electronic)	9798350324334
DOIs	https://doi.org/10.1109/MASS58611.2023.00051
State	Published - 2023
Event	20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023 - Toronto, Canada Duration: Sep 25 2023 → Sep 27 2023

Publication series

Name	Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

Conference

Conference	20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023
Country/Territory	Canada
City	Toronto
Period	9/25/23 → 9/27/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

This work is supported by the NSF grant EPCN-1936131 and NSF CAREER grant CPS-1943035.

Funders	Funder number
National Science Foundation Arctic Social Science Program	EPCN-1936131, CPS-1943035

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Online spatial crowdsourcing
V2G
combinatorial multi-armed bandit
energy-sharing
personalized recommendation
ride-sharing

ASJC Scopus subject areas

Control and Optimization
Modeling and Simulation
Instrumentation
Artificial Intelligence
Computer Networks and Communications
Information Systems and Management

Access to Document

10.1109/MASS58611.2023.00051

Cite this

Timilsina, A., & Silvestri, S. (2023). e-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing. In Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023 (pp. 359-365). (Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023). https://doi.org/10.1109/MASS58611.2023.00051

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title = "e-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing",

abstract = "There is growing interest in deploying the sharing-economy-based business model to energy systems, with modalities like peer-to-peer (P2P) energy trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). This paper exploits the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. We employ theoretical concepts of online spatial crowdsourcing, reinforcement learning, and reverse auction to devise this novel platform. Experimental results using real data from New York City taxi trips and energy consumption show that e-Uber performs close to the optimum and outperforms a state-of-the-art approach.",

keywords = "Online spatial crowdsourcing, V2G, combinatorial multi-armed bandit, energy-sharing, personalized recommendation, ride-sharing",

author = "Ashutosh Timilsina and Simone Silvestri",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023 ; Conference date: 25-09-2023 Through 27-09-2023",

year = "2023",

doi = "10.1109/MASS58611.2023.00051",

language = "English",

series = "Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023",

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Timilsina, A & Silvestri, S 2023, e-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing. in Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023. Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023, pp. 359-365, 20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023, Toronto, Canada, 9/25/23. https://doi.org/10.1109/MASS58611.2023.00051

e-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing. / Timilsina, Ashutosh; Silvestri, Simone.
Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023. 2023. p. 359-365 (Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Timilsina, Ashutosh

AU - Silvestri, Simone

PY - 2023

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N2 - There is growing interest in deploying the sharing-economy-based business model to energy systems, with modalities like peer-to-peer (P2P) energy trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). This paper exploits the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. We employ theoretical concepts of online spatial crowdsourcing, reinforcement learning, and reverse auction to devise this novel platform. Experimental results using real data from New York City taxi trips and energy consumption show that e-Uber performs close to the optimum and outperforms a state-of-the-art approach.

AB - There is growing interest in deploying the sharing-economy-based business model to energy systems, with modalities like peer-to-peer (P2P) energy trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). This paper exploits the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. We employ theoretical concepts of online spatial crowdsourcing, reinforcement learning, and reverse auction to devise this novel platform. Experimental results using real data from New York City taxi trips and energy consumption show that e-Uber performs close to the optimum and outperforms a state-of-the-art approach.

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KW - energy-sharing

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KW - ride-sharing

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Timilsina A, Silvestri S. e-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing. In Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023. 2023. p. 359-365. (Proceedings - 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023). doi: 10.1109/MASS58611.2023.00051

e-Uber: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this