The guessability of traffic signs: effects of prospective-user factors and sign design features.
This experiment investigated the relationships between the characteristics of prospective-users of traffic signs (people who will use the signs in the future) and the guessability of traffic signs, and also examined the effects of sign design features on the guessability of traffic signs. Forty-one Hong Kong Chinese subjects guessed the meanings and rated the sign features of 120 Mainland Chinese signs. Contrary to expectation, cycling experience and previous experience with sign information had no effect on sign guessing. Males and females with similar education level had similar guessing performance. Previous experience of visiting Mainland China was a significant predictor of guessing performance. Family ownership of a vehicle was associated with guessing performance for subjects who intended to become a driver and for those with car game experience. Subjects who claimed to pay attention to traffic signs in daily life performed better at sign guessing than those who did not. Traffic incident experience did not seem to enhance awareness of, or knowledge about, traffic signs. Guessability of a sign varied with the five design features of; familiarity, concreteness, simplicity, meaningfulness, and semantic closeness of the sign. Semantic closeness was the best predictor of guessability score, followed by familiarity, meaningfulness, concreteness, and simplicity. In order to design more user-friendly traffic signs and effective ways of using them, it is suggested that designers develop and evaluate signs according to the relative importance of the five sign features.
Ng
Annie W Y
AW
Department of Manufacturing Engineering, and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong.
Chan
Alan H S
AH
D000293:Adolescent; D000328:Adult; D002681:China; D003142:Communication; D032882:Comprehension; D004779:Environment Design; D005260:Female; D006801:Humans; D008123:Location Directories and Signs; D008297:Male; D018986:Motor Vehicles; D010364:Pattern Recognition, Visual