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Title	Traffic Flow Prediction Method Based On Big Data Mining
ID_Doc	58587
Authors	Hu Y.; Lian X.
Year	2022
Published	Proceedings - 2022 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2022
DOI	http://dx.doi.org/10.1109/ICITBS55627.2022.00081
Abstract	To predict the state of urban short-term traffic flow in real time and accurately, this paper puts forward a feasible scheme to realize traffic flow prediction by big data mining technology. Combined with the characteristics of urban traffic flow, we process and extract the collected data based on wavelet. Then, the relevant knowledge in the field of data mining is used to find the hypothesis rules most similar to current traffic flow characteristics. We also discuss the feasibility of machine learning to mine data with similar change trend from massive data for short-term traffic prediction, and provide the specific solution process. Finally, a more optimized frequent set computing method and implementation scheme are proposed through MapReduce on the cloud computing platform. The test results of the prediction model show that the excavated congestion conduction rules can accurately analyze the traffic congestion and it can be used to provide guidance paths for vehicles in real time within a local range. © 2022 IEEE.
Author Keywords	big data; data mining; MapReduce; traffic flow

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