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Recently, there has been growing interest in monitoring the road traffic data. Most of the work focused on real-time traffic data. In this paper, we propose an Extrac system which extracts trend of traffic congestions from historical traffic data and answers the queries about the trends. In Extrac system, we first convert the historical traffic data into(More)
Due to the growing number of collected traffic data, big data technology enables us to obtain in-depth analysis of road traffic data. For better understanding for traffic behaviors, the analyzed data can be provided in a timeline view. In this paper, we formally define a timeline model for traffic data and propose an algorithm for constructing a timeline(More)
We present a novel system called Extrac for querying a large database of road traffic information. Such traffic data are collected from an ITS (Intelligent transportation systems) center of Busan and represents speed values of all road segments of Busan for every 5 minutes. Extrac stores the collected traffic data into a No SQL document database and(More)
The escalation of traffic congestion in urban cities has urged many countries to use intelligent transportation system (ITS) centers to collect historical traffic sensor data from multiple heterogeneous sources. By analyzing historical traffic data, we can obtain valuable insights into traffic behavior. Many existing applications have been proposed with(More)
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(More)
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