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E-Sharing: Data-driven Online Optimization of Parking Location Placement for Dockless Electric Bike Sharing | IEEE Conference Publication | IEEE Xplore

E-Sharing: Data-driven Online Optimization of Parking Location Placement for Dockless Electric Bike Sharing


Abstract:

The rise of dockless electric bike sharing becomes a new urban lifestyle recently. More than just the first-and-last mile, it offers a new modality of green transportatio...Show More

Abstract:

The rise of dockless electric bike sharing becomes a new urban lifestyle recently. More than just the first-and-last mile, it offers a new modality of green transportation. However, in addition to the traditional re-balance and overcrowding problems, it also brings new challenges to urban management and maintenance. Due to the safety risks of batteries, customers are regulated to park at designated locations, which potentially causes dissatisfaction and customer loss. Meanwhile, service providers should charge those scattering low-energy batteries in time. To address these issues, we propose E-sharing, a two-tier optimization framework that leverages data-driven online algorithms to plan parking locations and maintenance. First, we balance the user dissatisfaction and the number of parking locations by minimizing their sum. To account for real-time dynamics while not losing track of the historical optimality, we propose an online algorithm based on its near-optimal offline solution. Second, we develop an incentive mechanism to motivate users to aggregate low-battery bikes together, saving the cost of bike charging. Our experiment based on the public dataset demonstrates that the online algorithm can minimize the cost from the conflicting objectives and incentive mechanism further reduces the maintenance cost by 47%.
Date of Conference: 29 November 2020 - 01 December 2020
Date Added to IEEE Xplore: 23 February 2021
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Conference Location: Singapore, Singapore

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Citations are not available for this document.

I. Introduction

Dockless bike-sharing has redefined the short-term bicycle rental business in China, and quickly expanded globally. With a GPS-based mobile app, users enjoy the flexibility to park almost anywhere. Being a green solution to the first-and-last mile problem, bike-sharing is promising to reshape the car-centric urban transportation in the future smart cities. Users can pick up a bike, ride for a while and drop at anywhere they want. Studied in [1], an average ride usually lasts within three miles. However, what if users want to ride extra miles? Those "human-powered" bicycles barely satisfy such emerging demands, especially for senior riders with physical limitations, or on hilly terrain like San Francisco. To fill this market niche, new start-up companies begin to offer an electric boost to the bicycles, i.e., the dockless electric bike sharing (E-bike sharing) [2]. As shown in Fig. 1, companies like XQBike [3], Lime [4], Qee Bike [5] and Bird Scooter [6] are quickly spreading regionally in the U.S., Europe and China. Powered by electricity, cyclists can enjoy an effortless longer ride, reach their destinations in the shortest route and save the time waiting for crowded transportation during rush hours.

Cites in Papers - |

Cites in Papers - IEEE (7)

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1.
R. Dhanasekar, L. Vijayaraja, R. Premkumar, Dhanushkanna B, Sudhesh Kumar E, Nitheshkumar V, "E-bike using wireless charging and split battery with renewable energy", 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), pp.1-5, 2024.
2.
Chun-An Yang, Shih-Chieh Chen, Yi-Hsuan Peng, Yu-Wen Chen, Jian-Jhih Kuo, Ming-Jer Tsai, "Near-Optimal Battery Swapping Algorithm in Dockless Electric Bike Sharing Systems", ICC 2024 - IEEE International Conference on Communications, pp.5329-5334, 2024.
3.
Yuhan Yin, Fei Mei, Junzhe Huang, Zhiming Feng, Ze Ouyang, Yazhao Yin, Jianyong Zheng, "Bi-level Programming of Siting and Sizing for Shared Electric Bike Based on Niche Particle Swarm Algorithm", 2023 IEEE International Conference on Power Science and Technology (ICPST), pp.585-589, 2023.
4.
Rania Swessi, Zeineb El Khalfi, Imen Jemili, Mohamed Mosbah, "Free-Floating Micro-mobility Smart Redistribution Using Spatio-temporal Demand Forecasting", 2023 IEEE Vehicular Networking Conference (VNC), pp.73-80, 2023.
5.
Anna Mariya Joy, Aravind Ravi, Jestin Roy, Gayathri V Karthikeyan, Thomas P Rajan, "IoT Enabled Electronic Security & Remote Monitoring Solution For Electric Bikes", 2022 IEEE International Power and Renewable Energy Conference (IPRECON), pp.1-6, 2022.
6.
Pengzhan Zhou, Xin Wei, Cong Wang, Yuanyuan Yang, "k-Level Truthful Incentivizing Mechanism and Generalized k-MAB Problem", IEEE Transactions on Computers, vol.71, no.7, pp.1724-1739, 2022.
7.
Yi Yang, Yaoming Zhou, Tanmoy Kundu, Suxiu Xu, "Dynamic Vehicle Routing for Battery Swapping in an Electric Bike-sharing System", 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp.678-682, 2021.

Cites in Papers - Other Publishers (1)

1.
Yu Liu, Joshua Comden, Zhenhua Liu, Yuanyuan Yang, "Online Resource Provisioning for Wireless Data Collection", ACM Transactions on Sensor Networks, vol.18, no.1, pp.1, 2022.
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References

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