A Review on Impact Analysis of Accident Using AI

In recent years, road collisions have become a global problem and have been classified as the 10th leading cause of death in the world. Due to the large number of road losses consistently, it has become a major problem in Bangladesh. It is totally unacceptable and sad to allow a citizen to kill in a road accident. The purpose is to show you how to extract logical data from a raw database and visualize it. The results show that hourly planning, day-to-day intelligence, lunar intelligence and year-round planning allow you to look at how road accidents change over time. Two types of road accidents have occurred in particular, and data analysis of road accidents have led to conclusions that will help reduce the number of accidents.


Introduction
It has a huge impact on the community due to road accidents where there are high cost of death and injury. In recent years, there has been an increase in research by researchers to determine the extent to which serious injuries are caused by motor vehicle accidents. Accurate and complete risk records are the basis of risk analysis [7], [8]. The effective use of accident records depends on other factors, such as data accuracy, record keeping, and data analysis [9], [10], [11], [13][14][15][16][17][18][19]. There are many methods used in this situation to study this problem. anticipate the seriousness of mishaps. Moreover, the outcomes have been utilized to contrast and attempted with bring up which programming language is better for information representation, information handling, Predictive Analytics, etc. [1].

Md. Farhan Labib et al [2019]
Thusly, to deal with this overpowered circumstance, an exact examination is required.
This examination paper has been never really car crashes all the more profoundly to decide the force of mishaps by utilizing AI approaches in Bangladesh. We additionally sort out those huge variables that clearly affect street mishaps and give some gainful proposals with respect to this issue. Investigation has been done, by utilizing Decision Tree, K-Nearest Neighbors (KNN), Naïve Bayes and AdaBoost these four regulated learning strategies, to group the seriousness of mishaps into Fatal, Grievous, Simple Injury and Motor Collision these four classifications. At long last, the best execution is accomplised [2]. and lessen the computational outstanding task at hand. Also, it has the great capacity in order and acknowledgment just as speculation contrasted and the model utilizing support vector machine alone [3].

Jamshid Sodikov et al [2018]
The paper audits, street auto collision information investigation and perception in R programming climate. The point is to tell the best way to extricate significant information from the crude data set and envision it. The outcomes uncovered that hour savvy, day insightful, month astute and year shrewd plots permitted seeing how street auto collisions change in timescale. Two sorts of streetcar crash predominantly occurred, such as type 1 (impact) and type 5 (impact with walker). The two kinds of streetcar crashes occurred in comparable sizes across all timescales. Perception and information examination of street auto collisions prompted make ends which would help lessen the quantity of accidents [4].

Problem Statement
The road accidents to lead to loss of human life and/or incapacitation. It was noted in road accidents, that most cases of results are death of people and people lose their loved ones. Some people prefer to proceed with a reimbursement claim. In these types of cases capturing the scene of the events with a photograph can be helpful. You can snap a few pictures of the accident which involve your car damages, bodily injuries etc., and serve as valid proof during claim settlement.

Problem Solution
Models will be made utilizing mishap information records which can assist with understanding the qualities of numerous sary answer can be acquired. This examination expects to feature the information of the most significance in a street auto collision and permit forecasts to be made. The outcomes from this strategy can be found in the following part of the report.

Conclusions
Traffic crashes are a major concern for communities, agencies, and policymakers and lead to countless deaths and injuries, with the need to conduct a comprehensive analysis aimed at understanding the relationship between the factors of impact and the effects of a collision. The impact of this study lies in the development of a model for analyzing road accident data and predicting the severity of road accident injuries Analyze type of accident. In this study, we introduced a new way to predict car crashes. Methodologically, we have shown a learning process embedded within a multivariate model that can be used to identify relationships between tested variables and traffic crashes. Future use of this method has the potential to provide insight into basic questions about road work and mobility and practical questions about combat measures.