Short-term Traffic Flow Prediction Based on NPCA-PSR-IGM (1,1) Combined Model of Multi-dimensional Space-time

Lisheng YIN; He GAO; Shuaikang WEI; Shuangchen SUN; Yigang HE

doi:10.11999/JEIT200026

Volume 43 Issue 4

Apr. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(4): 1035-1041

Lisheng YIN, He GAO, Shuaikang WEI, Shuangchen SUN, Yigang HE. Short-term Traffic Flow Prediction Based on NPCA-PSR-IGM (1,1) Combined Model of Multi-dimensional Space-time[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1035-1041. doi: 10.11999/JEIT200026

Citation:

Lisheng YIN, He GAO, Shuaikang WEI, Shuangchen SUN, Yigang HE. Short-term Traffic Flow Prediction Based on NPCA-PSR-IGM (1,1) Combined Model of Multi-dimensional Space-time[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1035-1041. doi: 10.11999/JEIT200026

Citation:

Lisheng YIN, He GAO, Shuaikang WEI, Shuangchen SUN, Yigang HE. Short-term Traffic Flow Prediction Based on NPCA-PSR-IGM (1,1) Combined Model of Multi-dimensional Space-time[J]. Journal of Electronics & Information Technology, 2021, 43(4): 1035-1041. doi: 10.11999/JEIT200026

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Short-term Traffic Flow Prediction Based on NPCA-PSR-IGM (1,1) Combined Model of Multi-dimensional Space-time

doi: 10.11999/JEIT200026

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Funds: The National Natural Science Foundation of China (51577046, 61673153), The Key Grant Project of Chinese Ministry of Education (313018), Anhui Provincial Science and Technology Foundation of China (1301022036)

Received Date: 2020-01-05
Rev Recd Date: 2020-08-22

Available Online: 2020-09-17

Publish Date: 2021-04-20

Abstract

Abstract

In view of the nonlinear and chaos of urban short-term traffic flow sequence, this article proposes a combined prediction model based on multi-dimensional spatio-temporal Nonlinear Principal Component Analysis (NPCA) and Phase Space Reconstructed (PSR) Improved Gray Model (IGM(1,1)) in order to improve its forecast accuracy. First, the data correlation NPCA algorithm is used to reduce the spatial and temporal dimensions of multi-dimensional traffic flow sequences, while preserving the main traffic flow data that affects the predicted points, so as to improve the accuracy of the modeling. Phase space reconstruction amplifies the subtle features inside the traffic flow, so that its internal laws can be fully displayed, and improve further the prediction accuracy. Finally, the gray model combined with the improved background value is adapted to the characteristics of linearity, non-linearity and less required data. Short-term traffic flow is predicted. The experimental results show that the average relative error of the NPCA-PSR-IGM (1,1) combination prediction model is 3.12% smaller than that of the NPCA-PSR-GM (1,1) combination prediction model, and its standard deviation is relative to the PCA-PSR-IGM (1,1) combination prediction model has dropped from 15.7091 to 2.0589. At the same time, compared with the latest prediction model, the combined prediction model also improves the prediction accuracy and achieves a better prediction effect.

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References(15)

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