IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i20p10976-d659668.html
   My bibliography  Save this article

The Relationship of the Information Quantity of Urban Roadside Traffic Signs and Drivers’ Visibility Based on Information Transmission

Author

Listed:
  • Kun Liu

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

  • Hongxing Deng

    (School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China)

Abstract

For the lack of quantitative basis of traffic sign combination information, this paper established a model of information quantity of urban road traffic signs by analyzing the driver’s information processing and the visual recognition of traffic signs combined with theories of informatics. It used various analytical methods to build a model of the relationship between the traffic sign information quantity (TSIQ) and the driver’s visual recognition. Based on factors, the relationship between the TSIQ and the driver’s visual recognition was studied and analyzed to provide a reference for the design of urban traffic sign layout information. The results show that the TSIQ can explain 61% of the driver’s recognition time (DRT). The more information the road traffic sign conveys, the longer DRT will be. The TSIQ’s threshold is 642 bits, and exceeding this value will cause information overload. Different influence factors have a certain impact on drivers’ visual recognition distance (VRD). The male VRD is shorter than the female. The VRD of the young driver is larger than the old driver. The VRD of a novice driver is longer than an experienced driver, while the visual recognition sign of an experienced driver is shorter.

Suggested Citation

  • Kun Liu & Hongxing Deng, 2021. "The Relationship of the Information Quantity of Urban Roadside Traffic Signs and Drivers’ Visibility Based on Information Transmission," IJERPH, MDPI, vol. 18(20), pages 1-14, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10976-:d:659668
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/20/10976/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/20/10976/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Lei Han & Zhigang Du & Shoushuo Wang & Ying Chen, 2022. "Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety," IJERPH, MDPI, vol. 19(16), pages 1-23, August.
    2. Qin Zeng & Yun Chen & Xiazhong Zheng & Shiyu He & Donghui Li & Benwu Nie, 2023. "Optimization of Underground Cavern Sign Group Layout Using Eye-Tracking Technology," Sustainability, MDPI, vol. 15(16), pages 1-32, 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:jijerp:v:18:y:2021:i:20:p:10976-:d:659668. 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: 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.