Car drivers' road safety performance: A benchmark across 32 countries
Bibliographic record
Abstract
The road safety performance of a country and the success of policy measures can be measured and monitored in different ways. 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 last-mentioned can be based on data from roadside surveys or from questionnaire surveys. However, results of such surveys are seldom comparable across countries due to differences in aims, scope, or methodology. This paper is based on the second edition of the E-Survey of Road Users' Attitudes (ESRA), an online survey carried out in 2018, and includes data from more than 35,000 road users across 32 countries. The objective is to present the main results of the ESRA survey regarding the four most important risky driving behaviours in traffic: driving under the influence (alcohol/drugs), speeding, mobile phone use while driving, and fatigued driving. The paper explores several aspects related to these behaviours as car driver, such as the self-declared behaviours, acceptability and risk perception, support for policy measures, and opinions on traffic rules and penalties. Results show that despite the high perception of risk and low acceptability of all the risky driving behaviours analysed, there is still a high percentage of car drivers who engage in risky behaviours in traffic in all the regions analysed. Speeding and the use of a mobile phone while driving were the most frequent self-declared behaviours. On the other hand, driving under the influence of alcohol or drugs was the least declared behaviour. Most respondents support policy measures to restrict risky behaviour in traffic and believe that traffic rules are not being checked regularly enough, and should be stricter. The ESRA survey proved to be a valuable source of information to understand the causes underlying road traffic crashes. It offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".