IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v42y2019i6p539-553.html
   My bibliography  Save this article

Map-matching poor-quality GPS data in urban environments: the pgMapMatch package

Author

Listed:
  • Adam Millard-Ball
  • Robert C. Hampshire
  • Rachel R. Weinberger

Abstract

Global Positioning System (GPS) data have become ubiquitous in many areas of transportation planning and research. The usefulness of GPS data often depends on the points being matched to the true sequence of edges on the underlying street network – a process known as ‘map matching.’ This paper presents a new map-matching algorithm that is designed for use with poor-quality GPS traces in urban environments, where drivers may circle for parking and GPS quality may be affected by underground parking and tall buildings. The paper is accompanied by open-source Python code that is designed to work with a PostGIS spatial database. In a test dataset that includes many poor-quality traces, our new algorithm accurately matches about one-third more traces than a widely available alternative. Our algorithm also provides a ‘match score’ that evaluates the likelihood that the match for an individual trace is correct, reducing the need for manual inspection.

Suggested Citation

  • Adam Millard-Ball & Robert C. Hampshire & Rachel R. Weinberger, 2019. "Map-matching poor-quality GPS data in urban environments: the pgMapMatch package," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(6), pages 539-553, August.
    • Handle: RePEc:taf:transp:v:42:y:2019:i:6:p:539-553
    DOI: 10.1080/03081060.2019.1622249
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2019.1622249
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081060.2019.1622249?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.

    Citations

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


    Cited by:

    1. Xia, Dawen & Jiang, Shunying & Li, Yunsong & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing, 2023. "An ASM-CF model for anomalous trajectory detection with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).

    More about this item

    Statistics

    Access and download statistics

    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:taf:transp:v:42:y:2019:i:6:p:539-553. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    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.