A noise-immune Kalman filter for short-term traffic flow forecasting

2019 ◽  
Vol 536 ◽  
pp. 122601 ◽  
Author(s):  
Lingru Cai ◽  
Zhanchang Zhang ◽  
Junjie Yang ◽  
Yidan Yu ◽  
Teng Zhou ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Short Term ◽  
Traffic Flow Forecasting

Research of Short-Term Traffic Flow Forecast Method Based on the Kalman Filter

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Feng Chen ◽  
Yuanhua Jia ◽  
Wenjuan An ◽  
Na Zhang ◽  
Zhonghai Niu
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Forecast Method ◽  
Short Term ◽  
Traffic Flow Forecast ◽  
Flow Forecast

A Summary of Short-Term Traffic Flow Forecasting Methods

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Gang Chang ◽  
Yi Zhang ◽  
Danya Yao ◽  
Yun Yue
Keyword(s):  
Traffic Flow ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Forecasting Methods

scholarly journals Short-Term Traffic Flow Forecasting Model Based on GA-TCN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongji Zhang ◽  
Feng Sun ◽  
Ziwen Song ◽  
Xiaolin Wang ◽  
Yingcui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Absolute Error ◽  
Forecasting Model ◽  
Average Absolute Error ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

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