Road accident Analysis and classification using Machine Learning

Models are created using accident data records which can help to understand the characteristics of many features like driver’s behaviour, over speed, mobile usage, sleeping conditions. This can help the users to compute the safety measures which is useful to avoid accidents. It can be illustrated how statistical method based on directed graphs, by comparing two scenarios based on out-of-sample forecasts. The model is performed to identify statistically significant factors which can be able to predict the probabilities of crashes and injury that can be used to perform a risk factor and reduce it.

Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
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Description

Road accident Analysis and classification using Machine Learning

Abstract:

There are many inventories in the automobile industry to design and build safety measures for automobiles, but traffic accidents are unavoidable. There is a huge number of accidents prevailing in all urban and rural areas. Patterns involved with different circumstances can be detected by developing accurate prediction models which will be capable of automatic separation of various accidental scenarios. This cluster will be useful to prevent accidents and develop safety measures. We believe to acquire maximum possibilities of accident reduction using low-budget resources by using some scientific measures.Road accident Analysis and classification using Machine Learning


 

INTRODUCTION:

There is a huge impact on society due to traffic accidents where there are great costs of fatalities and injuries. In recent years, there is an increase in research attention to determine the significant effect of the severity of driver’s injuries which is caused due to road accidents. Accurate and comprehensive accident records are the basis of accident analysis. The effective use of accident records depends on some factors, like the accuracy of the data, record retention, and data analysis. There are many approaches applied to this scenario to study this problem.

A recent study illustrated that the residential and shopping sites are more hazardous than village areas. as might have been predicted, the frequencies of the casualties were higher near the zones of residence possibly because of the higher exposure. A study revealed that the casualty rates among the residential areas are classified as relatively deprived and significantly higher than those from relatively affluent areas.


Existing System:

Many research studies focused solely on identifying the fundamental factors that cause road crashes. From these studies, it was noticed that human factors have the most significant impact on accident risk. The basic factors that influence road safety directly related to the driver are i.e., driving behavior, driver’s perception of traffic risks, and driving experience. Drivers involve frequently in attitudes that cause road safety issues. Many of these attitudes are dynamic, conscious rule violations, while others are the result of errors due to less driving experience, momentary mistakes, inattention, or failure to perform a function, the latter often related to age. These behaviors often contribute to traffic collisions. Besides risky driver behavior, bad driving practices and poor knowledge along with disrespect for road and safety regulations are the obvious problems.


Disadvantage:

  1. The study investigated that the task of driving can be easy or difficult depending on the momentary task demand of driving and the driver’s skill to control his/her vehicle correctly.
  2. To examine the relationship between driving behavior and the number of traffic accidents in each country, regression analyses were performed by using a forward stepwise procedure.

PROPOSED SYSTEM AND METHODOLOGY:

Models are created using accident data records which can help to understand the characteristics of many features like driver’s behavior, over speed, mobile usage, sleeping conditions. This can help the users to compute the safety measures which is useful to avoid accidents. It can be illustrated how statistical method based on directed graphs, by comparing two scenarios based on out-of-sample forecasts. The model is performed to identify statistically significant factors which can be able to predict the probabilities of crashes and injury that can be used to perform a risk factor and reduce it.

Here the road accident study is done by analyzing some data by giving some queries which are relevant to the study. The queries like what is the most dangerous time to drive, what fractions of accidents occur in rural, urban, and other areas. What is the trend in the number of accidents that occur each year, do accidents in high-speed limit areas have more casualties and so on? These data can be accessed using a Microsoft excel sheet and the required answer can be obtained. This analysis aims to highlight the data of the most important in a road traffic accident and allow predictions to be made. The results from this methodology can be seen in the next section of the report.


Advantage:

  1. There is evidence that driving impairments are related to the time of day, loss of sleep or sleep inertia, prolonged work, and minor illness.
  2. It is important, therefore, to assess the frequency with which people drive when they are potentially fatigued because of this range of risk factors.

    Road accident Analysis and classification 3
    Road accident Analysis and classification 3


System Architecture:

Road accident Analysis and classification


Hardware and Software Requirements:


Hardware:

  1. Windows 7,8,10 64 bit
  2. RAM 4GB

Software:?

  1. Python 2.7
  2. Anaconda Navigator

Python’s standard library

  • Pandas
  • Numpy
  • Sklearn
  • tkMessageBox
  • matplotlib

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