Near-miss accident analysis for traffic safety improvement at a ‘channelized’ junction with U-turn
Bibliographic record
Abstract
A near-miss is an unplanned event that can precede events in which a loss or injury could occur. Therefore, it is an indicator leading to an accident. Near-miss analysis does not look at what happened but look into what could have happened. Serious conflicts are the result of a breakdown in the interaction between the road user, environment and vehicle, which leads to traffic accidents. As the similarity between accidents and serious conflicts is striking, accidents can basically be prevented by isolating and handling the conflicts potentials. The Swedish Traffic Conflict analysis of near-miss accidents is adopted in this study to improve the traffic safety of a channelized junction with U-turn at LentengAgung, Jakarta. The junction is a two-way junction with a wide median and an island. The location has been indicated as an accident-prone location with high conflict rates. A set of traffic video-recordings employing a number of surveyors at different points of observations were carried out on-site to obtain real near-miss accidents. Prior to the survey, surveyors practiced judging vehicles running speeds until they reached a certain maximum error of judgment. Evasive actions such as braking, swerving and accelerating were recorded, and actions were classified into serious and non-serious conflicts based on the time-to-accidents and speeds. The results of the analysis show that almost all of the total recorded conflicts fall in the category of serious conflicts. An improvement scheme of the junction with reduced potential traffic conflicts is proposed, which can be expected to lower the accidents occurrences.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".