Measuring road safety performance and culture: A comparative study of 39 countries
Notice bibliographique
Résumé
Monitoring of road safety performance is essential to effectively address the global road safety problem. Consistent and accurate monitoring allows policymakers to assess the effectiveness of current safety measures, identify emerging risk factors, and develop targeted interventions. Different key performance indicators can be used to monitor road safety performance. In addition to the traditional road safety indicators based on the number of fatalities or injured people in road traffic crashes, complementary road safety performance indicators can be used in relation to vehicles, infrastructure or road users' behaviour. The E -Survey of Road Users' Attitudes (ESRA) is an online survey that aims to collect and analyse comparable data on road safety performance and traffic safety culture across the world. In its three editions (from 2015 to 2023) ESRA has included data from more than 120,000 road users from a total of 68 different countries. This paper focuses on data from the third edition of the ESRA survey (ESRA3), which was conducted in 2023 across 39 countries and includes answers from over 37,000 road users. The objectives are to provide an overview of the ESRA3 survey methodology and to present results related to several road safety topics, such as drink-driving, speeding, or distraction, across different types of road users: car drivers, pedestrians, cyclists, and moped riders/motorcyclists. It examines multiple dimensions of risky behaviours in traffic, including self-declared behaviours, personal acceptability of unsafe behaviours, and support for policy measures. Results show low acceptability of unsafe traffic behaviours like speeding, drink-driving, fatigued driving or using a mobile phone while driving a car – less than 5 % of respondents considered these behaviours acceptable. Notwithstanding the low acceptability, a high percentage of car drivers declared engaging in risky behaviours in traffic: speeding within built-up areas was declared by 37 % to 47 % of car drivers, using a mobile phone by 22 % to 32 %, fatigued driving by 18 % to 20 %, and driving under the influence of alcohol by 10 % to 14 %. As for vulnerable road users, distraction (reading messages/checking social media or listening to music through headphones) was the most declared risky behaviour by pedestrians, the non-use of helmet the most declared by cyclists, and speeding the most declared by moped riders and motorcyclists. Most respondents support policy measures to restrict risky behaviour. The ESRA survey offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety. • ESRA data helps global road safety monitoring and policy makers to tailor policy measures. • ESRA3 survey gathered data on road safety performance and traffic safety culture from 37,000 road users across 39 countries. • Minority accepts unsafe traffic behaviour and majority supports policy measures that limit risky behaviours in traffic. • Speeding (37–47 %), followed by mobile phone use (22–32 %) are the most frequently declared risky car driving behaviours. • Top risk behaviour of vulnerable road users: for pedestrians distraction, for cyclists no helmet, for moto riders speeding.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».