Fourier transform approach for inverse dimension reduction method

2018 ◽  
Vol 30 (4) ◽  
pp. 1049-1071
Author(s):  
Jiaying Weng ◽  
Xiangrong Yin
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Zhao ◽  
Satish V. Ukkusuri ◽  
Jian Lu

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.


2010 ◽  
Vol 60 (11) ◽  
pp. 1100-1114 ◽  
Author(s):  
Gerard L.G. Sleijpen ◽  
Peter Sonneveld ◽  
Martin B. van Gijzen

2014 ◽  
Vol 34 (2) ◽  
pp. 0233001 ◽  
Author(s):  
何颂华 He Songhua ◽  
刘真 Liu Zhen ◽  
陈桥 Chen Qiao

Sign in / Sign up

Export Citation Format

Share Document