The Combined Short-Term Forecasting Approach to Traffic Flow Based on Non-Parametric Regression

2007 ◽  
Author(s):  
Xiao-li Zhang ◽  
Guo-guang He
Keyword(s):  
Traffic Flow ◽  
Short Term ◽  
Parametric Regression ◽  
Short Term Forecasting ◽  
Non Parametric

scholarly journals A Deep Neural Network Based on Classification of Traffic Volume for Short-Term Forecasting

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Bai ◽  
Yehua Chen
Keyword(s):  
Traffic Flow ◽  
Urban Traffic ◽  
Fine Tuning ◽  
Traffic Prediction ◽  
Short Term ◽  
The Road ◽  
Deep Architecture ◽  
Proposed Model ◽  
Short Term Forecasting

This paper developed a deep architecture to predict the short-term traffic flow in an urban traffic network. The architecture consists of three main modules: a pretraining module, which generates initialized weights and provides a rough learning of the features firstly with the training set in an unsupervised manner; a classification module, which performs the data classification operation through adding the logistic regression on top of the pretrained architecture to distinguish the traffic state; and a fine-tuning module, which predicts the traffic flow with supervised training based on the initialized weights in the first module. The classification module provides the fine-tuning modules with two classified datasets for more accurate forecasting. Furthermore, both upstream and downstream data are utilized to improve the prediction performance. The effectiveness of the proposed model was verified by the traffic prediction of the road segments of Nanming District of Guiyang. And with the comparison analysis over the existing approaches, the proposed model shows superiority in short-term traffic prediction, especially under incident conditions.

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