Effect of Rainfall and Wet Road Condition on Road Crashes : A Critical Analysis
Bibliographic record
Abstract
Road crashes deserve to be a strategic issue for any country's public health and can lead to overall growth crisis, if not addressed properly. More than 90% of deaths on the world's roads occur in low and middle-income countries (21.5 and 19.5 per lakh of population, respectively) though they have just 48% of all registered vehicles. It is estimated that road traffic deaths will increase worldwide, from 0.99 million in 1990 to 2.34 million in 2020 (representing 3.4% of all deaths). India already accounts for about 9.5% of the total 1.2 million fatal accidents in the world. In 2007, 1.14 lakh people in India lost their lives in road mishaps-that's significantly higher than the 2006 road death figures in China, 89,455. One person dies at every 4.61 minutes in India for road crashes. Road deaths in India registered a sharp 6.1% rise between 2006 and 2007. The Planning Commission of India had assessed the social cost at 55,000 crore ( 550 billion) on account of road accidents in India. Road crashes are complex interaction of different parameters like road, vehicle, environment, human etc. Skidding of road vehicles is considered as one of the major causes of road accidents occurring all over the world. Skidding, caused by lack of tire-to-road friction, is one of the most important single causes of traffic accidents. This paper aims to critically analyze the weather and wet road related crashes. Exhaustive critical analysis of total 1928 number of road crashes from a large Indian metropolitan city has been presented in this paper. A range of statistical methods has been applied for the data analysis. Some novel new techniques of wet road crash analysis also developed and used in this study. It has been found that 12.8% of total crash took place in wet days. It has been noted that the value of Rain-Crash-Effect (RCEi) were positive for three months only. It is also interesting to note that monsoon months (June to August) have negative rain-crash-effect. A negative rain-crash-effect during monsoon months may be the results of extra care of drivers during rainy days, low vehicle speed due to traffic congestion and runoff effect. High positive values of RCEi during April, May and September months may be explained by dry spell effect. It has been found that only ‘large dry spell wet day’ has greater average crash rate than normal average crash rate. It is clear that dry spell has positive and significant effect over average rain-crash-index. An enhancement of the accident count and average rain-crash-index after a dry spell could be due to physical or psychological factors, e.g. the buildup of oil and dirt on the road surface or the slow mental realignment to wet conditions. Trend of the relationship of rainfall class and ‘Rain-Class-Crash-Rate’ (RCCRi) revealed that heavy rainfall reduced RCCRi than drizzling. Different probable physical and psychological reasons are discussed to analyze the rainfall class effect. In general, rainfall creates driving hazard. But rainfall hazard is complexly related with road crash and needs more specific and distinguished research rather than general approach to minimize rainfall related road crashes. It is expected that unique and specific findings of this research, differing from traditional rain-crash relationship will foster more guided future research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".