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Study on the Relationship between Drivers’ Personal Characters and Non-Standard Traffic Signs Comprehensibility

Author

Listed:
  • Antoni Wontorczyk

    (Institute of Applied Psychology, Jagiellonian University, 30-348 Kraków, Poland)

  • Stanislaw Gaca

    (Department of Civil Engineering, Cracow University of Technology, 31-155 Kraków, Poland)

Abstract

Drivers’ incorrect perception and interpretation of the road space are among reasons for human errors. Proper road markings are elements improving perception of road space. Their effectiveness relies on traffic participants receiving the provided information correctly. The range of signs used is constantly expanding and unusual situations in traffic require use of non-standard signs or an unusual combination of existing standard signs. The aim of this study was to explore the level of comprehensibility of four different types of non-standard signs. The relationship between the level of comprehensibility of these signs and personality traits of the drivers was also studied. A total of 369 drivers were tested using a questionnaire to analyze the traffic signs comprehensibility and Five Factor Inventory (NEO-FFI). The obtained results indicate that symbolic signs, unlike symbolic and text ones, are much better comprehended by drivers. Men comprehend the significance of non-standard symbolic regulatory signs better than women. Higher level of comprehensibility of symbolic and text regulatory signs is shown by older, better educated drivers and professional drivers. The study found there is a link between personality traits of the driver and the comprehensibility of symbolic regulatory signs.

Suggested Citation

  • Antoni Wontorczyk & Stanislaw Gaca, 2021. "Study on the Relationship between Drivers’ Personal Characters and Non-Standard Traffic Signs Comprehensibility," IJERPH, MDPI, vol. 18(5), pages 1-19, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2678-:d:512200
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    References listed on IDEAS

    as
    1. Peter Barraclough & Anders af Wåhlberg & James Freeman & Barry Watson & Angela Watson, 2016. "Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-32, April.
    2. Taamneh, Madhar & Alkheder, Sharaf, 2018. "Traffic sign perception among Jordanian drivers: An evaluation study," Transport Policy, Elsevier, vol. 66(C), pages 17-29.
    3. Longyu Shi & Nigar Huseynova & Bin Yang & Chunming Li & Lijie Gao, 2018. "A Cask Evaluation Model to Assess Safety in Chinese Rural Roads," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
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    Cited by:

    1. Woochul Choi & Hongki Sung & Kyusoo Chong, 2023. "Impact of Illuminated Road Signs on Driver’s Perception," Sustainability, MDPI, vol. 15(16), pages 1-19, August.

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