Special Session: Monitoring Road Safety Attitudes & Performance the ESRA Approach
Notice bibliographique
Résumé
This session will provide insights on the ESRA approach of monitoring road safety attitudes and safety performance, on a global level. It especially addresses researchers and policy makers who are interested in using representative online surveys in road safety monitoring. Furthermore, potential partners will have the chance to ask questions on participation in the ESRA network. ESRA (E-Survey of Road users’ Attitudes) is a global cross-national initiative in currently 38 countries. The aim of the project is to provide scientific support to road safety policy by generating comparable national data on the current road safety situation. Using a uniform sampling method, an identical questionnaire and uniform programming of the questionnaire, allows for full comparability among the countries. The objective of this session is to provide an overview on the project: motivation, objectives, methodology, and key results. The different speakers will highlight examples of extracting results on regional, national and thematic level: Uta Meesmann (ESRA coordinator; Vias institute, Belgium): motivation, objectives, methodology and recent key results on regional level. Ward Vanlaar (ESRA2 core group partner; TIRF, Canada): comparison of national- and regional results with respect to mobile phone use (Europe, Canada, and USA). Sangjin Han (ESRA2 core group partner; KOTI, Republic of Korea): comparison of national results of the Republic of Korea with European results (benchmarking). Gerald Furian (ESRA1_2 core group partner; KfV, Austria): extracting thematic results from ESRA and combining them with external data sources, here exemplified with CARE accident data. Uta Meesmann (ESRA coordinator; Vias institute, Belgium): brief overview of the structure of the ESRA network and the possibilities to join this initiative (next wave ESRA2 - 2019). The session will close with a discussion on using representative online survey in monitoring road safety attitudes and performance. Furthermore, potential new partners will have the chance to ask questions on joining this network. Background and motivation: Monitoring road safety attitudes and performance Trends in road safety performance and the success of policy measures can be monitored using road safety indicators. Important data sources to assess the road safety situation are accident statistics, road side surveys, and questionnaire surveys. The latter, in particular if they are conducted online, are a relatively inexpensive way to obtain indicators on safety culture and road users’ behaviour, but they rely on self-declared information which might be prone to factors such as social desirability in responses. A main advantage of questionnaire surveys is that they can provide insights into socio-cognitive determinants of behaviour, such as attitudes, perceived social norm, risk perception, or existing habits. Socio-cognitive factors can help to understand the underlying motivations of certain behaviour (e.g. Ajzen, 1991; Rosenstock, 1974; Rogers, 1975; Vanlaar and Yannis, 2006). It is tempting to use such indicators based on questionnaire surveys for benchmarking purposes. However, the results of national surveys are seldom comparable across countries because of differences in the aims, the scope, the methodology, the questions used, or the sample population being surveyed. Therefore, the European Commission initiated the European project SARTRE (Social Attitudes to Road Traffic Risk in Europe; homepage: www.attitudes-roadsafety.eu/) in 1991. A common questionnaire and study design was developed and face to face interviews were conducted among a representative sample of the national adult population. Four editions of the SARTRE survey were launched (1991, 1996, 2002, 2010). In the first three editions of the SARTE project, surveys were directed only to car drivers. In the fourth edition, the target group was extended to ‘powered two wheelers’, pedestrians, cyclists and users of public transport (Cestac and Delhomme, 2012). This SARTRE4 survey in 2010, was the last large-scale measurement of social attitudes towards road traffic risk in Europe. Since then, there was a lack of comparable and reliable data on road safety attitudes and behaviour within Europe. Hence, in 2015, the Vias institute (formerly Belgian Road Safety Institute) launched the ESRA initiative (E-Survey of Road users’ Attitudes; homepage: www.esranet.eu).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,109 | 0,063 |
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 source (Gemma direct ou Codex distillé), 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 ».