A Short-Term Traffic Flow Prediction Method Based on Kernel Extreme Learning Machine

2018 ◽  
Author(s):  
Yi-ming Xing ◽  
Xiao-juan Ban ◽  
Ruoyi Liu
Keyword(s):  
Traffic Flow ◽  
Extreme Learning Machine ◽  
Prediction Method ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

2015 ◽  
Vol 22 (2) ◽  
pp. 779-786 ◽  
Author(s):  
Meng Meng ◽  
Chun-fu Shao ◽  
Yiik-diew Wong ◽  
Bo-bin Wang ◽  
Hui-xuan Li
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Short Term ◽  
Two Stage ◽  
Traffic Flow Prediction ◽  
Flow Prediction
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