Analysis of Children’s Traffic Behaviour at Signalized Crosswalks as a Precondition for Safe Children Routes Design: A Case Study from Croatia
Bibliographic record
Abstract
Children pedestrians represent road users with some specifics because of which it is important to study and take into account their traffic behaviour when traffic infrastructure is designed. Design should ensure and enhance their traffic safety because for decades, traffic accidents have been among the first few causes of children and adolescent mortality. Pedestrian speed is one of the important inputs when pedestrian infrastructure, especially crosswalks, is designed. On corridors where children are expected on a daily basis as independent pedestrians, the infrastructure should be adjusted to their characteristics and needs. The results of a study conducted in two Croatian cities of a similar size but of different urban and traffic conditions are presented in this paper. This study aimed at establishing and analysing children’s pedestrian speed while crossing the signalized crosswalk in the buffer area of elementary schools, mostly on primary roads in the school vicinity. Children aged 5–15 were observed, and accordingly V15, V50, and V85 speeds were established on the basis of altogether 600 measurements. Speed was established for children walking individually, in a group and supervised by adults, and of a different age, and based on their gender, the impact of infrastructural elements on their speed in traffic was also analysed. Significant differences were found between children’s speed measured in similar conditions in analysed cities and between some of the analysed groups. This fact proves that when improving conditions for children’s independent movement, it is important to consider their specifics in order to ensure safe design adjusted to children’s needs and limitations. As design speed in this paper, 15 percentile speed (V15) is considered. Suggestions on how to establish children pedestrian speed for design of routes regularly used by school children are proposed as well as some inputs elicited from the study done in Croatia are presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 0.000 |
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".