A Real Time Neural Network Learning Approach for Traffic Forecasting

摘要：

Reliable and accurate short-term traffic forecasting system is crucial in supporting any Intelligent Transportation System. The past two decades have witnessed many forecasting models being developed, yet none of them could consistently outperform the others under various traffic conditions. To deal with the nonlinearity and non-stationarity of dynamic traffic process, a real time neural network learning approach is taken and a traffic flow mode based forecasting method is presented. Results obtained from case study indicate the proposed approach can enhance adaptability of short-term traffic forecasting and has the advantages of better flexibility and transferability.

关键词： traffic forecasting real time learning flow modes

作者: Jiasong Zhu Hao Zheng

作者单位: Department of Transportation Engineering Faculty of Civil Engineering, Shenzhen University Shenzhen, Department of Urban Planning Faculty of Architecture and Urban Planning, Suzhou University of Scienc

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Network Technology(ICCNT 2011)(2011第三届IEEE计算机与网络技术国际会议)

会议地点: 太原

会议语种:英文

页码: 279-283

在线出版日期: 2011-02-26（万方平台首次上网日期，不代表论文的发表时间）