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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
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    Citations

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    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.

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