Detection of Traffic Violation Crime Using Data Mining Algorithms

S. Umadevi and S. Nirmala Sugirtha Rajini

In general, the criminalization has been significantly increased for past few years. Therefore, the use of technology is an essential for making the business work easier by providing several activities. Analysis of criminal investigation using data mining has created an important factor for understanding and predicting the activities of the criminals. The crime get classifies into various types but in this paper, the discussion is about traffic violation crime. The traffic violation crime occur while the driver breaks law which happen in vehicle driving on all kinds of roadways. The increase of light vehicles number in the cities have build high traffic volume and also signifies the crime of traffic violation has become more common that create severe damage to the property and high accident rate which have cause danger to the people’s life. In order to resolve these issues, the technique of data mining have utilized in several machine learning algorithm for extracting awareness from large data volume and even discovered the trends and pattern for traffic violation crime. The K-means clustering technique is used for tracing the region of accident, where the activities of crime have occurred. In other hand, KNN classifier is utilized for identifying the criminal behavior by assisting of observation in past crimes and identifying same crime activity but in case of no previous information get discovered then this crime is consider as a new sample crime which get added to the crime dataset. Hence, this study discuss about the detection and investigation of crimes and the behavior of the criminals using K-means clustering and KNN classifier in the traffic violation crimes.

Volume 11 | 09-Special Issue

Pages: 982-987

DOI: 10.5373/JARDCS/V11/20192660