Scalable extraction of timeline information from road traffic data using MapReduce

Due to the increasing number of vehicles in recent years, traffic congestion problem is a common issue for residents of metropolises. For a better understanding of traffic congestion, the analyzed data from big data technology can be provided as timeline information. However, a scalability problem would occur when we convert raw traffic data into the timeline information due to the volume and complexity of traffic data. In this paper, we present two MapReduce-based approaches which extract the timeline information of road traffic data. By utilizing the distribute processing strategy, we can resolve the scalability problem. We propose an iterative approach of MapReduce as a baseline approach and a single iteration approach as an efficient solution. The single iteration easily extended to support various and/or complex analytic queries by providing proper codes at a reducer. We validate experimentally our MapReduce-based approaches on real traffic dataset from a Busan Intelligent Transport System (ITS) center.