期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：88
期号：3
出版社：Journal of Theoretical and Applied
摘要：With the wide variety of motion sensors that traffic information can come from many research has been reserved for the development of traffic forecast, which in turn increases the shipping routes, traffic management, urban planning, etc. The most important challenge is to predict how traffic based on predictive models based on historical data traffic in real time, which may differ from historical data and change over time. In this system can learn new context of the current online traffic situation (or context) in real time, most effectively formed using a predictive historical data traffic model is intended to predict the future of the current situation. If traffic in real time, distributed environment enters the bloodstream space efficiently adapt to assess the effectiveness of each significant predictor different situations. We can show you the way, and short-term and long-term performance guarantees (STEP), our algorithm is designed in accordance with the algorithm works well in situations where there are no real signs (for ex. Traffic Ready) or later. We proposed an algorithm called Extraction and Processing of situation Spatiotemporal traffic using SVM algorithm with Big data By using the proposed framework, a context in which the most important is to predict the traffic by monitoring the movement of vehicles, which can further reduce the complexity of the request and inform the trade-policy. Our experience with real data in real-time circumstances indicates that the proposed approach is superior to existing solutions.
