Short-term Traffic Flow Prediction Based on Deep Learning

摘要：

　　The accuracy of short-term traffic flow prediction is affected by two factors: one is the accuracy of data collection,and another is model selection.During data collection,aiming at the high cost and low precision of the traditional use of fixed equipment to collect traffic flow data,this paper proposes an algorithm model combining convolutional neural network and support vector regression,and designs an input matrix considering the influence degree of the road segment.The example is proved that the proposed algorithm model is better than the ARIMA and SVR model,and it is an effective traffic flow prediction method.

关键词： Short-term traffic flow prediction Traffic flow extraction Convolutional neural network Support vector regression

作者: Dong-mei ZHAI Chao-hui SHI Hong ZHAO

作者单位: School of SoftWare,Beijing Jiaotong University,100044,China

会议类型: 国际会议

会议名称: 2020 2ND INTERNATIONAL CONFERENCE ON ADVANCED CONTROL, AUTOMATION AND ARTIFICIAL INTELLIGENCE(ACAAI 2020)(2020年第二届高级控制、自动化和人工智能国际会议)

会议地点: 武汉

会议语种:英文

页码: 181-188

在线出版日期: 2020-01-12（万方平台首次上网日期，不代表论文的发表时间）