Mining GPS Data for Traffic Congestion Detection and Prediction
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

Research Paper | Information Technology | India | Volume 4 Issue 9, September 2015

Mining GPS Data for Traffic Congestion Detection and Prediction

Suhas Prakash Kaklij

GPS data is available in the large amount, also for the devices having GPS a large amount data is being collected over time. The mining of this huge data is endorsed in discovery of the areas which face regular traffic congestion. User will have prior awareness of such locations which guide in deciding whether or not to go for that route. Avoidance of such routes will also assist in reduction of congestion of such locations. Also detected that the work which has been carried out till now in this field do not provide very precise and relevant results. The reason behind this is the no proper algorithm are selected and distinguished between on road and off road traffic. To deal with all this we proposed this system. This system will be structured and applied over GPS data i.e. data coming from devices like mobile phones, tablets, on board units etc. In the technique used in this system, these GPS data will be first cauterized using the K-means clustering algorithm. The clusters obtained are filtered out. On further processing these clusters a mining method of Naive bayes algorithm is used for mining for traffic Congestion detection and prediction

Keywords: Traffic Congestion Detection, Traffic Jam Prediction, Traffic Tracking and Tracing, Data Mining

Edition: Volume 4 Issue 9, September 2015

Pages: 876 - 880

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How to Cite this Article?

Suhas Prakash Kaklij, "Mining GPS Data for Traffic Congestion Detection and Prediction", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SUB158203, Volume 4 Issue 9, September 2015, 876 - 880

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