Critical Analysis of Road Crashes and a Case Study of Wet Road Condition and Road Crashes in an Indian Metropolitan City
Bibliographic record
Abstract
Road traffic crashes kill 1.2 million people each year and injure 50 million worldwide. Nearly 8.5% of total fatal accidents per year takes place in India. This issue is creating a huge socio-economic toll globally. Various studies revealed that the total number of people killed in road crashes in regions of the third world countries continued to increase, whereas in the developed nations there has been a steady decrease for the last two decades. This paper aims to critically discuss the road accidents in view of the cause, effect and mitigation means with special emphasis on some technical interventions in the vehicles. Exhaustive review of weather and wet road related crashes have also been carried out as part of the study. Authors commented on the global estimation of the socio-economic impact of road crashes. In 2000, economic impact of road accidents for low and middle income countries had been estimated as 65-100 billion US$ per year, more than the total developmental assistance they received yearly. Total average impact of road crashes in 2006 has been estimated to 901 billion US$ to the world. The estimated average percentage share of road crash to the world GDP is 1.87% for the year 2006. Considering the estimated 2007-08 GDP (nominal) of India as 1.249 trillion US$, total road crash cost has been predicted as 1480 billion rupees (37.5 billion US$). Critical analysis of wet road driving conditions due to rainfall and 1966 number of road crashes from a large Indian metropolitan city has also been presented in this paper. It has been found that nearly 17% of total crash took place in wet days. It has been noted that the value of rain-crash-effect were positive for five months and none of them was monsoon month. 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 values of rain-crash-effect during January, April and May months may be explained by dry spell effect. It is clear that dry spell has positive and significant effect over average rain-crash-index. Shift from ‘no dry spell’ to ‘small dry spell’ (1-5 dry days) increased the average rain-crash-index by 23.3% and shift from ‘small’ to ‘large dry spell’ (>5 dry days) increased the average rain-crash-index by 115.7%. 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 build-up 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’ revealed that heavy rainfall reduced ‘rain-class-crash-rate’ than drizzling or light rainfall. 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 and will be instrumental to enforce some specific protective regulations and traffic precautions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".