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Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles

Author

Listed:
  • Chengming Li

    (Chinese Academy of Surveying and mapping, Beijing 100830, China)

  • Zhaoxin Dai

    (Chinese Academy of Surveying and mapping, Beijing 100830, China)

  • Weixiang Peng

    (China University of Geosciences (Wuhan Campus), Wuhan 430074, China)

  • Jianming Shen

    (Chinese Academy of Surveying and mapping, Beijing 100830, China)

Abstract

Location-based service (LBS) technologies provide a new perspective for the analysis of the spatiotemporal dynamics of urban systems. Previous studies have been performed using data from mobile communications, public transport vehicles (taxis and buses), wireless hotspots and shared bicycles. However, corresponding analyses based on shared electric bicycle (e-bike) have not yet been reported in the literature. Data cleaning and extraction of the origin-destination (O-D) are prerequisites for the study of the spatiotemporal patterns of urban systems. In this study, based on a dataset of a week of shared e-bike GPS data in the city of Tengzhou (Shandong Province), sparse characteristics of discontinuities and nonuniformities of the GPS trajectory and a lack of riding status are observed. Based on the characteristics and the actual road, we proposed a method for the extraction of O-D pairs for every trajectory segment from continuous and stateless trajectory GPS data. This method cleans the incomplete and invalid trajectory records, which is suitable for sparse trajectory data. A week of shared e-bike GPS data in Tengzhou is scrubbed and, by the sampling method, the extraction accuracy of 91% is verified. We provide preliminary cleaning rules for sparse trajectory shared e-bike data for the first time, which are highly reliable and suitable for data mining from other forms of sparse GPS trajectory data.

Suggested Citation

  • Chengming Li & Zhaoxin Dai & Weixiang Peng & Jianming Shen, 2019. "Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles," Sustainability, MDPI, vol. 11(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1429-:d:212040
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    References listed on IDEAS

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    1. Yu Liu & Chaogui Kang & Song Gao & Yu Xiao & Yuan Tian, 2012. "Understanding intra-urban trip patterns from taxi trajectory data," Journal of Geographical Systems, Springer, vol. 14(4), pages 463-483, October.
    2. Jie Huang & David Levinson & Jiaoe Wang & Jiangping Zhou & Zi-jia Wang, 2018. "Tracking job and housing dynamics with smartcard data," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(50), pages 12710-12715, December.
    3. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    4. Cui, JianXun & Liu, Feng & Janssens, Davy & An, Shi & Wets, Geert & Cools, Mario, 2016. "Detecting urban road network accessibility problems using taxi GPS data," Journal of Transport Geography, Elsevier, vol. 51(C), pages 147-157.
    5. Elisabete Correia & Helena Carvalho & Susana G. Azevedo & Kannan Govindan, 2017. "Maturity Models in Supply Chain Sustainability: A Systematic Literature Review," Sustainability, MDPI, vol. 9(1), pages 1-26, January.
    6. Kou, Zhaoyu & Cai, Hua, 2019. "Understanding bike sharing travel patterns: An analysis of trip data from eight cities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 785-797.
    7. Yang, Xu-Hua & Cheng, Zhi & Chen, Guang & Wang, Lei & Ruan, Zhong-Yuan & Zheng, Yu-Jun, 2018. "The impact of a public bicycle-sharing system on urban public transport networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 246-256.
    8. Zhang, Yongping & Mi, Zhifu, 2018. "Environmental benefits of bike sharing: A big data-based analysis," Applied Energy, Elsevier, vol. 220(C), pages 296-301.
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    Cited by:

    1. Yixiao Li & Zhaoxin Dai & Lining Zhu & Xiaoli Liu, 2019. "Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    2. Hsiao-Hsien Lin & Chih-Chien Shen & I-Cheng Hsu & Pei-Yi Wu, 2021. "Can Electric Bicycles Enhance Leisure and Tourism Activities and City Happiness?," Energies, MDPI, vol. 14(23), pages 1-27, December.

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