Traffic congestion on road networks is characterized by slower speeds, longer trip times and increased vehicular queuing. We all are observing that vehicle volume is increasing day by day exponentially, but in comparison with it, the road infrastructure is not. It leads to ever increasing traffic congestion. Different technologies are used to detect traffic congestion and making congestion management more efficient. For that, lots of techniques have been found out. In this paper, the author has studied the Radio Frequency Identification technique (RFID) which is used for controlling traffic congestion. The data created from it has to be processed and then used by the different traffic control systems on the road. Processing of the data may create a problem due to large data volumes. Hence author has suggested to use the Hadoop Architecture to address this problem. By using Map Reduce framework, the data can be available at many sites as Map Reduce architecture uses parallel processing paradigm at its core. The goal is to implement a system that would trace the travel time of individual cars as they pass the roadside readers, create an average trip time and then making that information available to the different toll centers. For this, the author has suggested using Hadoop architecture and by using Map Reduce Framework the data can be processed at different centers in a distributed manner.

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Rfid Hadoop Map Reduce Traffic