Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Novel Crowdsourcing Model for Micro-mobility Ride-sharing Systems

Mohammed Elhenawy
,
Mostafizur Komol
,
Mahmoud Masoud
,
Shi Qiang Liu
* ORCID logo ,
Huthaifa Ashqar
ORCID logo ,
Mohammed Hamad Almannaa
ORCID logo ,
Hesham A. Rakha
ORCID logo ,
Andry Rakotonirainy
Version 1 : Received: 11 May 2021 / Approved: 12 May 2021 / Online: 12 May 2021 (13:48:39 CEST)

A peer-reviewed article of this Preprint also exists.

Elhenawy, M.; Komol, M.R.; Masoud, M.; Liu, S.; Ashqar, H.I.; Almannaa, M.H.; Rakha, H.A.; Rakotonirainy, A. A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems. Sensors 2021, 21, 4636. Elhenawy, M.; Komol, M.R.; Masoud, M.; Liu, S.; Ashqar, H.I.; Almannaa, M.H.; Rakha, H.A.; Rakotonirainy, A. A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems. Sensors 2021, 21, 4636.

Abstract

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfillment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.

Keywords

Micro-mobility; Ride-sharing; Agent-based modelling; Crowdsourcing

Subject

Engineering, Control and Systems Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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