IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v589y2022ics0378437121008888.html
   My bibliography  Save this article

Weighted spatio-temporal taxi trajectory big data mining for regional traffic estimation

Author

Listed:
  • Dokuz, Ahmet Sakir

Abstract

The estimation of traffic conditions in cities is becoming essential to establish a sustainable transportation system and to help traffic management authorities plan the traffic of cities. Recently, taxi trajectory big datasets are being collected during taxi drivers are routing around the cities. Taxi trajectory datasets provide behavioral information about the city residents, urban flows of the taxi passengers, and infrastructure for traffic condition estimation. This study aims to estimate regional traffic velocity of New York City using New York taxi trajectory dataset. A new method is proposed that uses weighted spatio-temporal trajectory big data mining approach and scores each region of the cities in terms of traffic velocity. A new algorithm is proposed, namely Regional Traffic Velocity Estimation (RTVE) algorithm, which uses proposed regional spatio-temporal velocity estimation method and experimentally evaluated using New York taxi trajectory dataset. Experimental results show that each region in New York have different velocity and usage characteristics in terms of hourly and daily analyses. Also, borough-level analyses are performed that reveal knowledge about the boroughs of New York. The estimated regional traffic velocity of cities based on taxi trajectory datasets would provide a decision support system for decision-makers in terms of regional hourly and daily evaluation of cities with cost-free and widespread city traffic dataset.

Suggested Citation

  • Dokuz, Ahmet Sakir, 2022. "Weighted spatio-temporal taxi trajectory big data mining for regional traffic estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
  • Handle: RePEc:eee:phsmap:v:589:y:2022:i:c:s0378437121008888
    DOI: 10.1016/j.physa.2021.126645
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121008888
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2021.126645?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Disheng Yi & Yusi Liu & Jiahui Qin & Jing Zhang, 2020. "Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    2. Xiaoling Tao & Yang Peng & Feng Zhao & Peichao Zhao & Yong Wang, 2018. "A parallel algorithm for network traffic anomaly detection based on Isolation Forest," International Journal of Distributed Sensor Networks, , vol. 14(11), pages 15501477188, November.
    3. 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.
    4. Dandan Chen & Yong Zhang & Liangpeng Gao & Nana Geng & Xuefeng Li, 2017. "The impact of rainfall on the temporal and spatial distribution of taxi passengers," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-16, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Junjie & Hu, Cheng & Yang, Jiayu & Bai, Jun & Lee, Jaeyoung Jay, 2024. "Do traffic flow states follow Markov properties? A high-order spatiotemporal traffic state reconstruction approach for traffic prediction and imputation," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    2. Zeng, Jie & Xiong, Yong & Liu, Feiyang & Ye, Junqing & Tang, Jinjun, 2022. "Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Dokuz, Yesim & Dokuz, Ahmet Sakir, 2023. "Time-persistent regions discovery of taxi trajectory big datasets based on regional spatio-temporal velocity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    4. Xueting Zhao & Liwei Hu & Xingzhong Wang & Jiabao Wu, 2022. "Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems," Sustainability, MDPI, vol. 14(24), pages 1-23, December.

    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. Dokuz, Yesim & Dokuz, Ahmet Sakir, 2023. "Time-persistent regions discovery of taxi trajectory big datasets based on regional spatio-temporal velocity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    2. Tang, Jinjun & Bi, Wei & Liu, Fang & Zhang, Wenhui, 2021. "Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    3. 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).
    4. 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.
    5. 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).
    6. Chen, Li & Zheng, Linjiang & Xia, Li & Liu, Weining & Sun, Dihua, 2021. "Detecting and analyzing unlicensed taxis: A case study of Chongqing City," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    7. Alexandre B. Gonçalves, 2021. "Spatial Analysis and Geographic Information Systems as Tools for Sustainability Research," Sustainability, MDPI, vol. 13(2), pages 1-3, January.

    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:eee:phsmap:v:589:y:2022:i:c:s0378437121008888. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.