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.

[1]  Elisa del Galdo.  Internationalization and translation: some guidelines for the design of human-computer interfaces , 1990 .

[2]  R. X. Liu,et al.  Principal component regression analysis with SPSS , 2003, Comput. Methods Programs Biomed..

[3]  Gavriel Salvendy,et al.  Handbook of Human Factors and Ergonomics , 2005 .

[4]  David Shinar,et al.  Traffic sign symbol comprehension: a cross-cultural study , 2003, Ergonomics.

[5]  Aaron J. Marcus,et al.  Icon and symbol design issues for graphical user interfaces , 1996 .

[6]  Jakob Nielsen,et al.  Designing User Interfaces for International Use , 1990 .

[7]  R. R. Hocking.  Methods and Applications of Linear Models: Regression and the Analysis of Variance , 2003 .

[8]  Nick Hammond,et al.  People and computers VI , 1991 .

[9]  Robert E. Dewar.  CRITERIA FOR THE DESIGN AND EVALUATION OF TRAFFIC SIGN SYMBOLS , 1988 .

[10]  Tom Carey,et al.  Human-computer interaction , 1994 .

[11]  Ravindra S. Goonetilleke,et al.  Effects of training and representational characteristics in icon design , 2001, Int. J. Hum. Comput. Stud..

[12]  Marie-Pierre Bruyas,et al.  Ergonomic guidelines for the design of pictorial information , 1998 .

[13]  Michael Harris Bond,et al.  The Psychology of the Chinese people , 1986 .

[14]  Lyle F. Bachman.  Statistical analyses for language assessment , 2004 .

[15]  Jakob Nielsen,et al.  International user interfaces , 1993 .

[16]  Oscar de Bruijn,et al.  The effects of visual information on users' mental models: an evaluation of pathfinder analysis as a measure of icon usability. , 2001 .

[17]  Lin Yu.  A Study on Visual Angle Threshold of Traffic Sign , 2005 .

[18]  Neil Salkind,et al.  Using SPSS for Windows and Macintosh : Analyzing and Understanding Data , 2004 .

[19]  Hashim M. N. Al-Madani,et al.  Assessment of drivers' comprehension of traffic signs based on their traffic, personal and social characteristics , 2002 .

[20]  A. Colman,et al.  Optimal number of response categories in rating scales: reliability, validity, discriminating power, and respondent preferences. , 2000, Acta psychologica.

[21]  T. Schnell,et al.  Traffic Sign Luminance Requirements of Nighttime Drivers for Symbolic Signs , 2004 .

[22]  Mary Beth Rosson,et al.  Usability Engineering: Scenario-based Development of Human-Computer Interaction , 2001 .

[23]  Andy P. Field,et al.  Discovering Statistics Using SPSS for Windows: Advanced Techniques for Beginners , 2000 .

[24]  Martin B. Curry,et al.  Measuring symbol and icon characteristics: Norms for concreteness, complexity, meaningfulness, familiarity, and semantic distance for 239 symbols , 1999, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[25]  Michael S. Wogalter,et al.  Comprehension of Pictorial Symbols: Effects of Context and Test Method , 1998, Hum. Factors.

[26]  Yvonne Rogers,et al.  Icons at the Interface: Their Usefulness , 1989, Interact. Comput..

[27]  H Al-Madani.  Influence of drivers' comprehension of posted signs on their safety related characteristics. , 2000, Accident; analysis and prevention.

[28]  Kuo-shu Yang,et al.  Chinese personality and its change. , 1986 .

[29]  Harm J. G. Zwaga,et al.  Visual Information For Everyday Use : Design And Research Perspectives , 1999 .

[30]  M. J. Norušis,et al.  SPSS 13.0 Guide to Data Analysis , 2000 .

[31]  Mary F Lesch,et al.  Comprehension and memory for warning symbols: age-related differences and impact of training. , 2003, Journal of safety research.

[32]  J. Annett.  Subjective rating scales: science or art? , 2002, Ergonomics.