Statistical Analysis and Inferential Characterization of Cases of Road Accidents in India: A Multivariate Data Science Perspective
Pakki Venkata Teja et al.
What the paper says
Road accidents and subsequent injuries and deaths are at alarming stage in India and the same is true for almost all part of the world with some variations in the cases. Continuous expansions of road networks, disproportionate increase in urbanization, enormous state of motorization and many other micro factors. Rising accidental deaths result into large number of losses of life especially between the age group of 15–50, imposes a great concern to all stake holders (from policy makers to common people). Road accidents are multi-causal events mainly categorized by human error, road resource optimization, effective policy formulation. As such we are motivated to set our objectives to compute the hotspot and coldspots to develop deep understanding of related characteristics. The second objective is to support the policy makers in optimal resource utilization needed to combat and evolve an effective strategy ranging from mass awareness campaign to technological innovations and developing the infrastructure to meet pre-and-post accident related challenges. To achieve the computational efficacy and drawing in-depth inferences we have implemented the software's namely MS Excel, saTScan and Python. The finding of the current work highlights southern states and northern states have disproportionality higher rate. We found cities like Coimbatore, Aurangabad, Chandigarh, Bangalore, Delhi, Chennai and Hyderabad as the urban hotspots based on computed values of the statistical likelihood ratio and risk ratio. We found them as the most likelihood clusters (primary hotspots). We found time series model model Holt-Winter method is the best model when compared to other exponential model.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.