Road accident analysis using machine learning

Machine Learning has become an integral part of our daily life. It is applied almost everywhere nowadays, whether it be medical sciences or lane detection that is very useful for automatic self-driving cars. Not only that, but machine learning can also be used to prevent road accidents.

Objective:

Tons of accidents occur every day on every highway. So, what about creating a model that will help prevent accidents? We can prevent an accident by creating an accurate model with the help of the patterns of the accidents taking place in urban and rural areas. We can make a cluster of different patterns of accidents and can take necessary safety precautions based on that. But for that, we need an accurate record of road accidents that have taken place in the past few years. According to research, it is stated that residential areas are more prone to accidents. So, we can cluster the areas like that.

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Concepts Used:

1. Python programming language basics (preferably Python 3 or above)
2. Basics of machine learning algorithms

Software Needed:

Anaconda – Jupyter or you can just simply download Jupyter notebook from your cmd terminal. Just type pip install -jupyter notebook and you are good to go.

What Modules We Will Be Using:

1. Numpy
2. Pandas
3. Import matplotlib.pyplot as plt

Implementations:

1. Collect data records. Data plays the most vital role here. We will have to collect accurate data about past accidents. There are many features like driver characteristics (drunk, rash driver etc), what was the condition of the roads, were there any lights on the road, weather condition and so on. These features will help us to create an accurate model.
2. Create a graph out of these features. The graph will show you the intensity of the accidents where it is spiked. This can predict the crashes and casualties on the highway. This can be used to create risk factors and safety measures will be taken on that.
3. Now calculate the fraction of accidents took place in urban and rural areas. Make a graph out of it.
4. Now we will make a graph of time. What is the most dangerous time to drive? This will help us to take special precaution during that time of the day or that time of the year.
5. Make a correlation between speed and accident. Research says that high-speed areas have more accidents.
6. Make a correlation between age and accident. Make a graph out of this. Research says that people aged between 18-20 are involved in more accidents.
7. Lights on the roads are a very important part of the safety drive. So, you have to create a correlation between light conditions on-road and the number of accidents on the road. 

Summary

You can follow this implementation and it is likely that you will get an accurate result. However, results depend on the accuracy of the data collected or provided as input.

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Road accident analysis using machine learning