Road Accident Analysis Using Machine Learning

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Road Accident Analysis Using Machine Learning

Platform : Python

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There are many inventories in in automobile industries 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 an accurate prediction models which will be capable of automatic separation of various accidental scenarios. These 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.



There is a huge impact on the society due to traffic accidents where there is a great costs of fatalities and injuries. In recent years, there is a increase in the researches attention to determine the significantly affect the severity of the drivers injuries which is caused due to the 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 is 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 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.


Sachin Kumar et al. [1] , used data mining techniques to identify the locations where high frequency accidents are occurred and then analayze them to identify the factors that have an effect on road accidents at that locations. The first task is to divide the accident location into k groups using the k-means clustering algorithm based on road accident frequency counts. Then, association rule mining algorithm applied in order to find out the relationship between distinct attributes which are in accident data set and according to that know the characteristics of locations.

S. Shanthi et al. [2] proposed data mining classification technology based on gender classification, in which RndTree and C4.S use AdaBoost Meta classifier to provide high-precision results. From the Critical Analysis Reporting Environment (CARE) system provided by the Fatal Analysis Reporting System (FARS) used by the training data set.

Tessa K. Anderson et al. [3] proposed a method of identifying high-density accident hotspots, which creates a clustering technique that determines that stochastic indices are more likely to exist in some clusters, and can therefore be compared in time and space. The kernel density estimation tool enables the visualization and manipulation of density-based events as a whole, which in turn is used to create the basic spatial unit of the hotspot clustering method.

The severity of damage occurring during a traffic accident is replicated using the performance of various machine learning paradigms, such as neural networks trained using hybrid learning methods, support vector machines, decision trees, and concurrent mixed models involving decision trees and neural networks. The experimental results show that the hybrid decision tree neural network method is better than the single method in machine learning paradigms.


Software: Anaconda – Jupyter.

Language: Python3

Modules Used:

  • numpy
  • pandas
  • from import scatter_matrix
  • import matplotlib.pyplot as plt


Models are created using accident data records which can help to understand the characteristics of many features like drivers behavior, roadway conditions, light condition, weather conditions and so on. 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 is 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 Microsoft excel sheet and the required answer can be obtained. This analysis aims to highlight the data of the most importance 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.


Fraction of Accidents Occurred in Urban, Rural and Other (Na) Areas

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the fraction of accidents occurred in all areas can be calculated by

urban_acci = len(acci[acci[‘Urban_or_Rural_Area’]==1])

rural_acci = len(acci[acci[‘Urban_or_Rural_Area’]==2])

na_acci = len(acci[acci[‘Urban_or_Rural_Area’]==3])

total_acci = urban_acci + rural_acci + na_acci


urban_pct = urban_acci*1.0 / total_acci * 100

rural_pct = rural_acci*1.0 / total_acci *100

na_pct = na_acci*1.0 / total_acci * 100


The Most Dangerous Time To Drive 

Road Accident Analysis Using Machine Learning 2

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Number Of Accidents That Occur Each Year

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Accidents In High-Speed-Limit Areas Have More Casualties

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The Number Of Car Accidents Drop Off With Age

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No Of Accidents Occurred As Per Light Conditions

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[1] Sachin Kumar, Durga Toshniwal, “A data mining approach to characterize road accident locations”, J. Mod. Transport. (2016) 24(1):62–72.

[2] S. Shanthi and Dr. R. Geetha Ramani, “Gender Specific Classification of Road Accident Patterns through Data Mining Techniques”, IEEE-International Conference on Advances in Engineering, Science and Management (ICAESM -2012) March 30, 31, 2012.

[3] Tessa K. Anderson, “Kernel density estimation and K-means clustering to profile road accident hotspots”, Accident Analysis and Prevention 41 (2009) 359–364.


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