Spatiotemporal Traffic Prediction using Semantic Traffic Analytics and Reasoning(STAR) With Big Data Environment
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 2
Abstract
In Urban Mobility Report, delays due to heavy traffic costing Americans $78 billion in the form of 4.2 billion lost hours and 2.9 billion gallons of wasted fuel. In addition, 2/3 of traffic delays are caused not by recurring congestion but by point-based spontaneous congestion due to traffic incidences. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. The real-time traffic situation to the most effective predictor constructed using historical data, thereby self-adapting to the dynamically changing traffic situations. Also includes in proposed with the distributed scenarios with the global traffic prediction.
Authors and Affiliations
L. Pravin Kumar, Ramesh Krishnan
Keywords
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