A Study on Road Safety Audit and Black Spot Identification
Bibliographic record
Abstract
Abstract Road Safety Audit (RSA) is a formal procedure for assessing accident potential and safety performance of new and existing roads. Road safety audit is an efficient, cost effective and proactive approach to improve road safety. It is proved that RSA has the potential to save lives. The RSA was originated in Great Britain and is well developed in countries like UK, USA, Australia, New Zealand, Denmark, Canada, Malaysia and Singapore. Presently, it is at varying stages of implementation in developing nations like India, South Africa, Thailand and Bangladesh. Therefore, road safety audit appears to be an ideal tool for improving road safety in India. In this study, a rural road stretching from Rallaguda bridge to Vardhaman College of Engineering in Hyderabad city is chosen for road safety audit. Safety assessment is done using iRAP application by collecting road side features, midblock details, intersection features, vulnerable road users’ facilities, speed and flow details. The secondary objective is to find out the Black Spot locations within Rajiv Gandhi International Airport (RGIA) police station jurisdiction of Hyderabad city. Accident prone areas are identified by estimating Weighted severity and accident severity indices with the help of historical accident data. Finally, major causes of accidents and measures to improve safety of the chosen road section are suggested.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".