Raspberry PI Based Road Accident Analysis using Data Mining

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Prediction Of The Cause Of Accident And Accident Prone Location On Roads Using Data Mining Techniques

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Description

ABSTRACT

Data mining has been proven as a reliable technique to analyse road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. KNN is one of the popular data mining techniques that identify the correlation in various attributes of road accident with the using of pre-defined data sets. 

EXISTING SYSTEM

  • In the existing system, there is no possibility of the earlier prediction.
  • Sensors are only use to detect the roadside problems and accident causes.
  • The data mining concepts were not used.

PROPOSED SYSTEMS

  • In our system, here we used the data mining process to predict the accidental causes.
  • The location based predictions with using of GPS
  • The efficient KNN algorithm will be usedpredict the datamining process.

BLOCK DIAGRAM

Raspberry PI Based Road Accident Analysis using Data Mining

HARDWARE REQUIREMENTS

  • Raspberry Pi 3
  • GPS
  • Alarming device

SOFTWARE REQUIREMENTS

  • Operating system : Raspbian jessie
  • Programming language: Python 2.7
  • Data mining algorithm: K-Means Nearest neighbour

REFERENCES

[1] https://en.wikipedia.org/wiki/Pradhan_Mantri_Gram_Sadak_Yojana.

[2] Maizatul Akmar Ismail, Tutut Herawan, Ashish Dutt, “A Systematic Review on Educational Data Mining,” IEEE, 2017.

[3] Uwe Becker, Geltmar von Buxhoeveden, “Comparison of Traffic Incident Data in Individual and Public Transport,” in International Conference on Systems and Informatics , 2016, pp. 1067-1071.

[4] Williamjeet Singh, Dheeraj Khera, “Prediction and Analysis of injury Severity in traffic systemusing data mining technique,” National Journal of Computer Applications, 2015.

[5] Sarbajit Bhattacharyya, Mrinal Roy, Pinak Paul , Rupanjan Chakraborty, “Accident Analysis and the Suggestion of an Accident Prediction Model for Guwahati city,” Internatinal Journal of Innovative research in Science,Engineering and Technology, 2015.

[6] R.P. Kulkarni, S.U. Bobade, M.S. Patil, A.M. Talathi, I.Y.Sayyad, S.V.Apte, R.R.Sorate, “Identification of Accident Black Spots on National Highway 4,” , 2015.

Additional information

Weight 1.000000 kg

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