Spatiotemporal Traffic Prediction using Semantic Traffic Analytics and Reasoning(STAR) With Big Data Environment

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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  • EP ID EP21622
  • DOI -
  • Views 252
  • Downloads 5

How To Cite

L. Pravin Kumar, Ramesh Krishnan (2016). Spatiotemporal Traffic Prediction using Semantic Traffic Analytics and Reasoning(STAR) With Big Data Environment. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 4(2), -. https://europub.co.uk/articles/-A-21622