The guessability of traffic signs: Effects of prospective-user

factors and sign design features

Author: Annie W.Y.Ng Alan H.S. Chan

Accident Analysis and Prevention 39(2007) 1245-1257

Speaker: Yang-Kun Ou

Purpose

• The purpose of this experiment want to demonstrated that characteristics of the users and the features of the signs themselves are involved in effective communication of traffic sign messages to prospective-users

Reference

• According to McDougall et al. (1999), instead of considering sign features that are icon features like familiarity, concreteness, complexity, meaningfulness, and semantic distance are of the central concern in sign and icon research.

Reference

• Laughery and Wogalter (1997) showed that experience with incidents was not related to accurate knowledge of hazards and behaviours– it was expected that people with traffic incident ex

perience may not have better awareness

Method

• Subjects– 19 man and 22 woman (Hong Kong Chinese

engineering undergraduates)– No driving license– They are voluntarily participated in this

experiment– The age from 18 to 27(median=22.5)– No color vision deficiencies

Apparatus

• Minolta luminance meter was used to measure the luminance levels

• Personal computer

• 17-in CRT

• Comfortable chair

• Voice recorder (LG VP-988)

Questionnaire and sign feature evaluation sheet

• A questionnaire with nine closed-ended questions was designed to capture personal particulars

• The first question was about intention to become a driver in the next 3 years, subjects to tick yes, no, or not sure.

Questionnaire and sign feature evaluation sheet

• The follow seven questions is very simple and required the subjects to tick yes or no– Example: car game experience in the last 12

months

• The final question was about gender requiring them just to tick the male or female box

Questionnaire and sign feature evaluation sheet

• The participants were asked to give subjective ratings between 0 and 100 points (0 = very unfamiliar, 100 = very familiar)– familiarity– Concreteness– Simplicity– Meaningfulness – Semantic closeness

Stimuli• All the signs were fitted into 7cm x 7cm squar

es

• Viewing distance of 60 cm (subtending 6.67度 )

• The lowest and highest luminance of the scenes in the testing stimuli were 25.4 and 176.5cd/m2

• 6.95 亮度對比

A sample of Mainland China traffic signs and verbal labels used in this experiment

Procedure

• All subjects were screened for red-green deficiency prior to the experiment

• Each qualified subject was tested individually in two sessions on two different days– The guess test for traffic signs was conducted

first(40min.)– The quantification of traffic sign features(1 hr)

• Randomized for the two sessions for all subjects

Session one: guessing test of traffic signs

• Five practice trials with Hong Kong traffic signs were given prior to the random testing with the 120 signs.

• The subject was required to guess its intended meaning within 10 s.

• If the guessing response had been started but could not be completed within the time allowed then the oral response for the sign was recorded for further analysis

Session one: guessing test of traffic signs

• The subject did not provide a response after the allowed time had elapsed, or the answer for the sign could not be clearly heard or understood

• The process was repeated until the guessing for all signs was finished

• To avoid fatigue, three 1-min breaks were given after testing 42 signs

• Each participant took about 5 min to fill out the questionnaire on prospective-user factors at the end of the guessing test

Session two: traffic sign feature evaluation

• The meanings of the terms familiarity, concreteness, simplicity, meaningfulness, and semantic closeness at the beginning of this session

• After practicing with the five Hong Kong traffic signs, the 120 test signs were randomly presented on the screen

Rating instructions and meanings of traffic sign features

Session two: traffic sign feature evaluation

• give subjective ratings for four rating means on the evaluation sheet

• gave a rating for semantic closeness of the verbal label with the sign, after which label and sign disappeared

• The process was repeated until the rating for all of the 120 signs

• A 1-min rest was given after every 42 signs rated

Result• Guessability

– Descriptive statistics of guessability score for signs in the six categories

The signs with minimum and maximum guessability score

Variability of guessability score

• To determine if there were any signs with variability of guessability score very different from other signs

• long time parking or stopping prohibited (P28; 235.40%) …

The signs with extremely large coefficients of variation for guessability score

Relationships between prospective-user factors and guessing performance

• With the exception of sign information receiving experience and Mainland China visit experience, all the factors were adequately normal (Shapiro-Wilk’s test, p > .05)

• Variances at all the levels were equal (Levene’s test, p > .05) for all the nine factors.

Relationships between prospective-user factors and guessing performance

• First, the results of Kruskal–Wallis test showed a significant main effect

• Second, two way interaction effects were considered

Relationships between prospective-user factors and guessing performance

Traffic sign features• The descriptive statistics for the rating of traffic sign features

for the six categories of signs

Signs with lowest and highest ratings on familiarity, concreteness, simplicity,

meaningfulness, and semantic closeness

Signs with extremely large coefficients of variation in rating of sign features

Pearson correlation coefficients amongst traffic sign features and guessability score

Relationships amongst traffic sign features and guessability score

• Correlation analysis was conducted to test the hypotheses that guessing scores will be higher for familiar signs, concrete signs, simple signs, meaningful signs, and for signs with higher semantic closeness ratings.

Relationships amongst traffic sign features and guessability score

• A multiple regression model was developed for the guessability scores of the 120 signs used, with traffic sign features as the independent variables

• Predicted guessability score (%) = −13.166 − 0.134 familiarity − 0.114 concreteness + 0.418 simplicity − 0.0325 meaningfulness + 0.01018 (semantic closeness)2

Conclusion

• The findings may serve as a useful guide for interface designers to design and evaluate icons across various types of consumer and safety products

• This study will provides useful information and recommendations for designing more user-friendly traffic signs and effective ways of using them