A Novel approach for Computing Congestion degree of Road Traffic using MapReduce Framework
In today’s world due to growing population and migration of humans in the urban area, pressure on cities road and road traffic environment has increased exponentially, which leads to traffic jam situation, waiting on squares, growth in fuel consumption, and increase in travel time from source to destination respectively. Hence there is a need for an effective traffic management system to address the problem of urban area road traffic. The biggest challenge is the collection of road traffic data from various sources such as sensors and video surveillance camera and processed it in Hadoop Distributed File System (HDFS). In this paper, we have proposed the novel approach of congestion degree computation using the MapReduce framework in the HDFS. The proposed approach is divided into three part as 1) Efficient framework for road traffic data acquisition using the video camera, 2) Collection of traffic information from road traffic video surveillance camera and 3) Process the traffic data in the HDFS using the MapReduce framework. First, the road traffic data from a video is processed to identify a number of the vehicle, type of vehicle on the road and the speed of vehicles using vehicle details extraction algorithm. Second, the extracted information from video is stored in HDFS using two levels of MapReduce function that can be used to count the number of vehicles and compute the congestion degree for that road. Experimental results show that the proposed method successfully process the road traffic data and compute the congestion degree for efficient traffic management.