Special Session: Monitoring Road Safety Attitudes & Performance the ESRA Approach
Bibliographic record
Abstract
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).
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.109 | 0.063 |
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".