基于XGBoost的公路交通流短期预测

Jiawei Cao, Gang Cen, Yuefeng Cen, Weifeng Ma
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引用次数: 4

摘要

随着城市智能交通系统的快速发展,交通流的短期预测越来越受到人们的重视。由于缺乏交通流的特征和合适的模型,对交通流的准确预测面临着很大的挑战。提出了一种基于梯度极值上升的短期交通流预测模型。通过与传统预测模型的比较,实验结果表明了该模型的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Short-Term Highway Traffic Flow Forecasting Based on XGBoost
With the rapid development of urban intelligent transportation system, the prediction short-term of traffic flow attracts more and more attention. As the lack of the characteristics of traffic flow and appropriate models, the accurate predction of the traffic flow are facing a big challenge. A short-term traffic flow prediction model based on extreme gradient rise is proposed in this paper. The experiment results reveal the superiority of the modle by comparing with the traditional prediction model.
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