research-article

SParking: a win-win data-driven contract parking sharing system

Authors:

Tian HeAuthors Info & Claims

UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

Pages 596 - 604

https://doi.org/10.1145/3410530.3414588

Published: 12 September 2020 Publication History

Abstract

With a rapid growth of vehicles in modern cities, searching for a parking space becomes difficult for drivers especially in rush hours. To alleviate parking difficulties and make the most of urban parking resources, contract parking sharing services allow drivers to pay for parking under the consent of owners, reaching a win-win situation. Contract parking sharing services, however, have not yet been prevailingly adopted due to the dynamic parking time which leads to uncertainties for sharing. Thanks to the Internet of things technique, most of modern parking lots record vehicles' fine-grained parking data including entry and exit timestamps for billing purposes. Leveraging the parking data, we analyze and exploit available vacant contract parking spaces. We propose SParking, a <u>s</u>hared contract <u>parking</u> system with a win-win data-driven scheduling. SParking consists of (i) a parking time prediction model to exploit reliable periods of free parking spaces and (ii) an optimal scheduling model to allocate free parking spaces to drivers. To verify the effectiveness of SParking, we evaluate our design on seven-month real-world parking data involved with 368 parking lots and 14,704 parking spaces in Wuhan, China. The experimental results show that SParking achieves more than 90% of accuracy in parking time prediction and the average utilization rate of contract parking spaces is improved by 35%.

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Cited By

Cheng MZhou ZZhang BWang ZGan JRen ZFeng WLyu YZhang HDiao X(2024)Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00261(2546-2555)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00261
Zhao BWang XZhang TShi RXu FMan FChen ELi YLi YSun TChen X(2024)Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home ordersHumanities and Social Sciences Communications10.1057/s41599-024-03068-411:1Online publication date: 8-May-2024
https://doi.org/10.1057/s41599-024-03068-4
Xiang CZhou YDai HQu YHe SChen CYang P(2023)Reusing Delivery Drones for Urban CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2021.312721222:5(2972-2988)Online publication date: 1-May-2023
https://doi.org/10.1109/TMC.2021.3127212
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Index Terms

SParking: a win-win data-driven contract parking sharing system
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing

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cover image ACM Conferences

UbiComp/ISWC '20 Adjunct: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

September 2020

732 pages

ISBN:9781450380768

DOI:10.1145/3410530

General Chairs:
Monica Tentori
CICESE, Mexico
,
Nadir Weibel
UC San Diego
,
Kristof Van Laerhoven
University of Siegen, Germany
,
Program Chairs:
Gregory Abowd
Georgia Tech
,
Flora Salim
RMIT, Australia

Copyright © 2020 ACM.

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Published: 12 September 2020

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China National Key R&D Program
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province

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UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers

September 12 - 17, 2020

Virtual Event, Mexico

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Cheng MZhou ZZhang BWang ZGan JRen ZFeng WLyu YZhang HDiao X(2024)Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00261(2546-2555)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00261
Zhao BWang XZhang TShi RXu FMan FChen ELi YLi YSun TChen X(2024)Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home ordersHumanities and Social Sciences Communications10.1057/s41599-024-03068-411:1Online publication date: 8-May-2024
https://doi.org/10.1057/s41599-024-03068-4
Xiang CZhou YDai HQu YHe SChen CYang P(2023)Reusing Delivery Drones for Urban CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2021.312721222:5(2972-2988)Online publication date: 1-May-2023
https://doi.org/10.1109/TMC.2021.3127212
Xiang CYang PXiao FFan XXiang CYang PXiao FFan X(2023)Airborne Sensing Application: Reusing Delivery DronesMulti-dimensional Urban Sensing Using Crowdsensing Data10.1007/978-981-19-9006-9_6(151-189)Online publication date: 24-Mar-2023
https://doi.org/10.1007/978-981-19-9006-9_6
Ling TZhu XZhou XWang S(2021)ParkLSTM: Periodic Parking Behavior Prediction Based on LSTM with Multi-source Data for Contract Parking SpacesWireless Algorithms, Systems, and Applications10.1007/978-3-030-86130-8_21(262-274)Online publication date: 25-Jun-2021
https://dl.acm.org/doi/10.1007/978-3-030-86130-8_21

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