Bike commuters contribution to balance shared bike systems during peak load

Gimon, Dmitry

doi:10.1109/isc2.2018.8656882

Cited by 11 publications

(8 citation statements)

References 2 publications

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“…We assumed that all e-scooters had the same maximum battery range in the same run and that the operation hours started with the same number of e-scooters at each of the 14 origins for each run. We simulated the proposed ABM six times using a maximum battery range of (35,45, and 60 km) and different numbers of e-scooters at each origin (50 and 100). The HI and the PSD were calculated every day at different combinations of the proposed centre agent rules.…”

Section: Psd(%) = Number O F Satis F Ied Trips Total Number O F Trips (2)mentioning

confidence: 99%

“…The characteristics of customers in renting rides from different stations have been evaluated. Moreover, the tendency of choosing an alternative station with fewer available bikes has been found among customers than preferred ones [ 35 ]. Dockless ride-sharing is getting superior with economic advantages in the bike-sharing market by implementing a remote mobile payment system and the emergence of big-data computation [ 8 , 15 , 36 , 37 , 38 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems

Elhenawy

Komol

Masoud

et al. 2021

Sensors

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Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment 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 is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. 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.

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Section: Psd(%) = Number O F Satis F Ied Trips Total Number O F Trips (2)mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems

Elhenawy

Komol

Masoud

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, the tendency of choosing an alternative station with less available bikes has been found among customers than preferred ones [35]. Dockless ride-sharing is getting superior with economic advantages in the bike-sharing market by implementing a remote mobile payment system and the emergence of big-data computation [8,15,[36][37][38].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Elhenawy¹,

Komol²,

Masoud³

et al. 2021

Preprint

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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.

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“…In the evening rush hour, a large number of shared bikes are silted up near subways and bus stations. In fact, the tidal phenomenon just deviates from the actual needs of users: in the morning rush hour, users' main demand is to find a car near the subway station [2,3]. During the evening rush hour, find a bus near the office building and return to the subway station.…”

Section: Introductionmentioning

confidence: 99%

Predicting Shared-Bike Routes with Geographic Information System and LSTM Algorithm

Wang¹,

Zhang²,

Zhan³

2022

Proceedings of the 2022 3rd International Conference on E-Commerce and Internet Technology (ECIT 2022)

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Shared bikes in red, orange, yellow and blue can now be seen everywhere in China's first-tier cities, Including Beijing, Shanghai, Guangzhou and Shenzhen. During the rush hour, because a large number of bikes are riding away, there is little reverse inflow, resulting in tidal dilemma. We collected over one million shared bike tracks and conducted data mining with our geographic information system to find out the rules of scientific parking of shared bikes. This project adopts a two-layer LSTM (Long and Short memory neural network) algorithm with spatial-temporal big data from Xiamen.

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Bike commuters contribution to balance shared bike systems during peak load

Cited by 11 publications

References 2 publications

A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems

A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems

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

Predicting Shared-Bike Routes with Geographic Information System and LSTM Algorithm

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