Human Factors Evaluation of New Pictograph-Based Messages for Bilingual Variable Message Signs
Bibliographic record
Abstract
In response to requirements of the French Language Services Act of 1989, the Ontario Ministry of Transportation (MTO) was required to implement bilingual messages on a new generation of Variable Message Signs (VMS). The goal of this study, carried out in collaboration with IBI Group, for MTO, was to develop a pictograph-based solution for bilingual VMS, by means of applying a human factors test and analysis procedure. The first stage of the study involved the development of pictograph concepts using a public consultation process with anglophone and francophone drivers, a jurisdictional scan, and an internal team design charette. In the consultation process, participants were asked to create colour drawings, using symbols and as few words as possible, to describe messages such as traffic congestion, lane blockage, road closure, border delay, and severe winds. The jurisdictional scan explored symbols used by other transportation agencies. Using findings from the public consultation and the jurisdictional scan, the internal team design charette was carried to create design concepts that would be carried forward for comprehension evaluation. In the second stage of the study, ten message sets related to traffic operations (congestion management, blockages and closures), travel time and safety were tested for comprehension using static images of signs shown (in a roadway context) for four seconds on a desktop computer. Each message set included the currently used English message and at least one pictograph-based bilingual alternative. Following the second stage, design decisions were made. Revised and new messages were tested in the third phase. In the third stage of the study nine message sets related to traffic operations (congestion management, blockages, and closures) were tested. Each message set included the currently used English message and one pictograph-based alternative. In addition, eight pictograph-based safety messages were tested for comprehension. The TAC recommended level of comprehension for guide sign messages is 75% (1). Overall, in the final stage of the study, text traffic messages scored 75% or above for 5 of the 7 messages and the pictographic message alternative scored this high for only 2 of the 9 traffic messages. However, even absent any feedback to the participants, there was a clear learning process for these novel graphic messages: by the end of the test, 5 of the 9 pictograph messages had a mean comprehension of 75% or over and 2 messages came very close to this level of comprehension, at 74%. The remaining two messages scored 55 to 58% overall, and improved to 65 to 70% by the end of the test. A statistical comparison showed no significant difference between text and pictograph messages for messages first seen at the end of the test.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".