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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".