IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i23p6870-d293634.html
   My bibliography  Save this article

Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points

Author

Listed:
  • Tong Zhou

    (School of Geographical Sciences, Nantong University, Nantong 226007, China
    Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
    Key Laboratory of Virtual Geographical Environment, MOE, Nanjing Normal University, Nanjing 210046, China)

  • Xintao Liu

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China)

  • Zhen Qian

    (School of Geographical Sciences, Nantong University, Nantong 226007, China)

  • Haoxuan Chen

    (School of Geographical Sciences, Nantong University, Nantong 226007, China)

  • Fei Tao

    (School of Geographical Sciences, Nantong University, Nantong 226007, China
    Key Laboratory of Virtual Geographical Environment, MOE, Nanjing Normal University, Nanjing 210046, China)

Abstract

This paper proposes a novel method for dynamically extracting and monitoring the entrances of areas of interest (AOIs). Most AOIs in China, such as buildings and communities, are enclosed by walls and are only accessible via one or more entrances. The entrances are not marked on most maps for route planning and navigation in an accurate way. In this work, the extraction scheme of the entrances is based on taxi trajectory data with a 30 s sampling time interval. After fine-grained data cleaning, the position accuracy of the drop-off points extracted from taxi trajectory data is guaranteed. Next, the location of the entrances is extracted, combining the density-based spatial clustering of applications with noise (DBSCAN) with the boundary of the AOI under the constraint of the road network. Based on the above processing, the dynamic update scheme of the entrance is designed. First, a time series analysis is conducted using the clusters of drop-off points within the adjacent AOI, and then, a relative heat index ( R H I ) is applied to detect the recent access status (closed or open) of the entrances. The results show the average accuracy of the current extraction algorithm is improved by 24.3% over the K-means algorithm, and the R H I can reduce the limitation of map symbols in describing the access status. The proposed scheme can, therefore, help optimize the dynamic visualization of the entry symbols in mobile navigation maps, and facilitate human travel behavior and way-finding, which is of great help to sustainable urban development.

Suggested Citation

  • Tong Zhou & Xintao Liu & Zhen Qian & Haoxuan Chen & Fei Tao, 2019. "Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points," Sustainability, MDPI, vol. 11(23), pages 1-20, December.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6870-:d:293634
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/23/6870/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/23/6870/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    2. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    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. Zheng, Linjiang & Xia, Dong & Zhao, Xin & Tan, Longyou & Li, Hang & Chen, Li & Liu, Weining, 2018. "Spatial–temporal travel pattern mining using massive taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 24-41.
    5. Liang, Xiao & Zheng, Xudong & Lv, Weifeng & Zhu, Tongyu & Xu, Ke, 2012. "The scaling of human mobility by taxis is exponential," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 2135-2144.
    6. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
    7. Shixiong Jiang & Wei Guan & Zhengbing He & Liu Yang, 2018. "Measuring Taxi Accessibility Using Grid-Based Method with Trajectory Data," Sustainability, MDPI, vol. 10(9), pages 1-16, September.
    8. Carlos A. Guerra & Su Yun Kang & Daniel T. Citron & Dianna E. B. Hergott & Megan Perry & Jordan Smith & Wonder P. Phiri & José O. Osá Nfumu & Jeremías N. Mba Eyono & Katherine E. Battle & Harry S. Gib, 2019. "Human mobility patterns and malaria importation on Bioko Island," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    9. Shutian Zhou & Guofang Zhai & Yijun Shi, 2018. "What Drives the Rise of Metro Developments in China? Evidence from Nantong," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    10. Arthur Getis & J. Keith Ord, 2010. "The Analysis of Spatial Association by Use of Distance Statistics," Advances in Spatial Science, in: Luc Anselin & Sergio J. Rey (ed.), Perspectives on Spatial Data Analysis, chapter 0, pages 127-145, Springer.
    11. Rongxiang Su & Zhixiang Fang & Ningxin Luo & Jingwei Zhu, 2018. "Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps," Sustainability, MDPI, vol. 10(4), pages 1-24, April.
    12. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. He, Zhengbing, 2020. "Spatial-temporal fractal of urban agglomeration travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    2. Cai, Hua & Zhan, Xiaowei & Zhu, Ji & Jia, Xiaoping & Chiu, Anthony S.F. & Xu, Ming, 2016. "Understanding taxi travel patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 590-597.
    3. Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2022. "Revealing mobility pattern of taxi movements with its travel trajectory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    4. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    5. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    6. Li, Ze-Tao & Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2023. "Exploring the topological characteristics of urban trip networks based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    7. Zhitao Li & Xiaolu Wang & Fan Gao & Jinjun Tang & Hanmeng Xu, 2024. "Analysis of mobility patterns for urban taxi ridership: the role of the built environment," Transportation, Springer, vol. 51(4), pages 1409-1431, August.
    8. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    9. Jincheng Wang & Qunqi Wu & Feng Mao & Yilong Ren & Zilin Chen & Yaqun Gao, 2021. "Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
    10. 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.
    11. Xu Mengqiao & Zhang Ling & Li Wen & Xia Haoxiang, 2017. "Mobility Pattern of Taxi Passengers at Intra-Urban Scale: Empirical Study of Three Cities," Journal of Systems Science and Information, De Gruyter, vol. 5(6), pages 537-555, December.
    12. Fu, Xin & Xu, Chengyao & Liu, Yuteng & Chen, Chi-Hua & Hwang, F.J. & Wang, Jianwei, 2022. "Spatial heterogeneity and migration characteristics of traffic congestion—A quantitative identification method based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    13. Oshan, Taylor M., 2020. "Potential and pitfalls of big transport data for spatial interaction models of urban mobility," OSF Preprints gwumt, Center for Open Science.
    14. Yanyan Chen & Zheng Zhang & Tianwen Liang, 2019. "Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    15. Pengyu Zhu & Jiarong Li & Kailai Wang & Jie Huang, 2024. "Exploring spatial heterogeneity in the impact of built environment on taxi ridership using multiscale geographically weighted regression," Transportation, Springer, vol. 51(5), pages 1963-1997, October.
    16. Ruixi Dong & Fengying Yan, 2021. "Revealing Characteristics of the Spatial Structure of Megacities at Multiple Scales with Jobs-Housing Big Data: A Case Study of Tianjin, China," Land, MDPI, vol. 10(11), pages 1-20, October.
    17. Roya Etminani-Ghasrodashti & Shima Hamidi, 2019. "Individuals’ Demand for Ride-hailing Services: Investigating the Combined Effects of Attitudinal Factors, Land Use, and Travel Attributes on Demand for App-based Taxis in Tehran, Iran," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    18. Jiang, Jincheng & Xu, Zhihua & Zhang, Zhenxin & Zhang, Jie & Liu, Kang & Kong, Hui, 2023. "Revealing the fractal and self-similarity of realistic collective human mobility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    19. Zhang, Shen & Tang, Jinjun & Wang, Haixiao & Wang, Yinhai & An, Shi, 2017. "Revealing intra-urban travel patterns and service ranges from taxi trajectories," Journal of Transport Geography, Elsevier, vol. 61(C), pages 72-86.
    20. Maxime Lenormand & Miguel Picornell & Oliva G Cantú-Ros & Antònia Tugores & Thomas Louail & Ricardo Herranz & Marc Barthelemy & Enrique Frías-Martínez & José J Ramasco, 2014. "Cross-Checking Different Sources of Mobility Information," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6870-:d:293634. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.