IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i12p6866-d576921.html
   My bibliography  Save this article

How Do Subway Signs Affect Pedestrians’ Wayfinding Behavior through Visual Short-Term Memory?

Author

Listed:
  • Haoru Li

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Jinliang Xu

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Xiaodong Zhang

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Fangchen Ma

    (School of Highway, Chang’an University, Xi’an 710064, China)

Abstract

Recently, subways have become an important part of public transportation and have developed rapidly in China. In the subway station setting, pedestrians mainly rely on visual short-term memory to obtain information on how to travel. This research aimed to explore the short-term memory capacities and the difference in short-term memory for different information for Chinese passengers regarding subway signs. Previous research has shown that people’s general short-term memory capacity is approximately four objects and that, the more complex the information, the lower people’s memory capacity. However, research on the short-term memory characteristics of pedestrians for subway signs is scarce. Hence, based on the STM theory and using 32 subway signs as stimuli, we recruited 120 subjects to conduct a cognitive test. The results showed that passengers had a different memory accuracy for different types of information in the signs. They were more accurate regarding line number and arrow, followed by location/text information, logos, and orientation. Meanwhile, information type, quantity, and complexity had significant effects on pedestrians’ short-term memory capacity. Finally, according to our results that outline the characteristics of short-term memory for subway signs, we put forward some suggestions for subway signs. The findings will be effective in helping designers and managers improve the quality of subway station services as well as promoting the development of pedestrian traffic in such a setting.

Suggested Citation

  • Haoru Li & Jinliang Xu & Xiaodong Zhang & Fangchen Ma, 2021. "How Do Subway Signs Affect Pedestrians’ Wayfinding Behavior through Visual Short-Term Memory?," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6866-:d:576921
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/12/6866/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/12/6866/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Steven J. Luck & Edward K. Vogel, 1997. "The capacity of visual working memory for features and conjunctions," Nature, Nature, vol. 390(6657), pages 279-281, November.
    2. Bin Lei & Jinliang Xu & Menghui Li & Haoru Li & Jin Li & Zhen Cao & Yarui Hao & Yuan Zhang, 2019. "Enhancing Role of Guiding Signs Setting in Metro Stations with Incorporation of Microscopic Behavior of Pedestrians," Sustainability, MDPI, vol. 11(21), pages 1-14, November.
    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. Yong Fang & Wenli Zhang & Hua Hu & Jiayi Zhou & Dianliang Xiao & Shaojie Li, 2022. "Adaptive Aging Safety of Guidance Marks in Rail Transit Connection Systems Based on Eye Movement Data," IJERPH, MDPI, vol. 19(2), pages 1-12, January.

    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. Aki Kondo & Jun Saiki, 2012. "Feature-Specific Encoding Flexibility in Visual Working Memory," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    2. Tullo, Domenico & Faubert, Jocelyn & Bertone, Armando, 2018. "The characterization of attention resource capacity and its relationship with fluid reasoning intelligence: A multiple object tracking study," Intelligence, Elsevier, vol. 69(C), pages 158-168.
    3. Jifan Zhou & Jun Yin & Tong Chen & Xiaowei Ding & Zaifeng Gao & Mowei Shen, 2011. "Visual Working Memory Capacity Does Not Modulate the Feature-Based Information Filtering in Visual Working Memory," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-10, September.
    4. Li, Qian & Huang, Zhuowei (Joy) & Christianson, Kiel, 2016. "Visual attention toward tourism photographs with text: An eye-tracking study," Tourism Management, Elsevier, vol. 54(C), pages 243-258.
    5. Yuri A. Markov & Igor S. Utochkin, 2017. "The Effect of Object Distinctiveness on Object-Location Binding in Visual Working Memory," HSE Working papers WP BRP 79/PSY/2017, National Research University Higher School of Economics.
    6. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. repec:plo:pcbi00:1004592 is not listed on IDEAS
    8. Ociepka, Michał & Kałamała, Patrycja & Chuderski, Adam, 2022. "High individual alpha frequency brains run fast, but it does not make them smart," Intelligence, Elsevier, vol. 92(C).
    9. repec:cup:judgdm:v:7:y:2012:i:3:p:254-267 is not listed on IDEAS
    10. Shaiyan Keshvari & Ronald van den Berg & Wei Ji Ma, 2013. "No Evidence for an Item Limit in Change Detection," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-9, February.
    11. Bin Zhu & Stephanie A. Watts, 2010. "Visualization of Network Concepts: The Impact of Working Memory Capacity Differences," Information Systems Research, INFORMS, vol. 21(2), pages 327-344, June.
    12. Liang Sun & Yao Xu & Sijing Teng & Bo Wang & Ming Li & Shanmin Ding, 2022. "Research into the Visual Saliency of Guide Signs in an Underground Commercial Street Based on an Eye-Movement Experiment," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    13. Wenting Jin & Ying Yao & Guichao Ren & Xiaohua Zhao, 2022. "Evaluation of Integration Information Signage in Transport Hubs Based on Building Information Modeling and Virtual Reality Technologies," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    14. Rock, Rufus & Strauss, Ilan & O'Reilly, Tim & Mazzucato, Mariana, 2024. "Behind the clicks: Can Amazon allocate user attention as it pleases?," Information Economics and Policy, Elsevier, vol. 69(C).
    15. D. Alexander Varakin & Jamie Hale, 2014. "Intentional Memory Instructions Direct Attention But Do Not Enhance Visual Memory," SAGE Open, , vol. 4(4), pages 21582440145, October.
    16. Haggar Cohen-Dallal & Isaac Fradkin & Yoni Pertzov, 2018. "Are stronger memories forgotten more slowly? No evidence that memory strength influences the rate of forgetting," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-18, July.
    17. Loic Matthey & Paul M Bays & Peter Dayan, 2015. "A Probabilistic Palimpsest Model of Visual Short-term Memory," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-34, January.
    18. Krieger, Florian & Zimmer, Hubert D. & Greiff, Samuel & Spinath, Frank M. & Becker, Nicolas, 2019. "Why are difficult figural matrices hard to solve? The role of selective encoding and working memory capacity," Intelligence, Elsevier, vol. 72(C), pages 35-48.
    19. Jochen Ranger & Jörg-Tobias Kuhn, 2013. "Analyzing Response Times in Tests With Rank Correlation Approaches," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 61-80, February.
    20. Yifei Suo & Bin Lei & Tianxiang Xun & Na Li & Dongbo Lei & Linlin Luo & Xiaoqin Cao, 2023. "Optimization Method of Subway Station Guide Sign Based on Pedestrian Walking Behavior," Sustainability, MDPI, vol. 15(17), pages 1-18, August.
    21. Jeanne Hagenbach & Rachel Kranton, 2023. "Competition, Cooperation, and Motivated Social Perceptions," SciencePo Working papers Main hal-03792554, HAL.
    22. Pais, Miguel Pessanha & Cabral, Henrique N., 2017. "Fish behaviour effects on the accuracy and precision of underwater visual census surveys. A virtual ecologist approach using an individual-based model," Ecological Modelling, Elsevier, vol. 346(C), pages 58-69.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:gam:jsusta:v:13:y:2021:i:12:p:6866-:d:576921. 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.