Traffic violation detection using Map/Reduce

Reducing transportation problems regarding safety, traffic and consequently the human, social and economic impacts is one of the main challenges of human societies. The incremental increase in driving violations and their harmful effects are one of the most important social issues in different countries. Multimedia/unstructured data has grown dramatically over the last few years. The share of big data and behavior detection in this regard are more likely to be addressed by controlling the mechanized violation of drivers. In the current system, control is carried out by human sources (police or operator). The purpose of this research is to control the mechanized violation of drivers based on distributed architecture and the Map / Reduce programming model that reduce processing time (CPU time) with Hadoop. In order to investigate and test the proposed system, information about cameras available in the city has been obtained from the Traffic Control Center of Yazd city in Iran. The results indicate that the processing time for a big data using Hadoop and executing the program only with a single slave-node decreases by more than 87% relative to the implementation of the program sequentially. Also, system performance increases by increasing the number of data nodes by more than 75%. The algorithm presented in this article without 24-hour operator is able to recognize inappropriate and violation behavior of drivers. Therefore, if the operator is not present, the driver's behavior will not be out of sight.