Short-term traffic flow forecasting model based on Elman neural network

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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A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics

Applied Intelligence ◽

10.1007/s10489-017-1095-9 ◽

2017 ◽

Vol 48 (8) ◽

pp. 2429-2440 ◽

Cited By ~ 6

Author(s):

Hong Zhang ◽

Xiaoming Wang ◽

Jie Cao ◽

Minan Tang ◽

Yirong Guo

Keyword(s):

Time Series ◽

Traffic Flow ◽

Forecasting Model ◽

Short Term ◽

Model Based ◽

Traffic Flow Forecasting

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Short-Term Intersection Traffic Flow Forecasting

Sustainability ◽

10.3390/su12198158 ◽

2020 ◽

Vol 12 (19) ◽

pp. 8158

Author(s):

Wenrui Qu ◽

Jinhong Li ◽

Lu Yang ◽

Delin Li ◽

Shasha Liu ◽

...

Keyword(s):

Neural Network ◽

Traffic Flow ◽

Traffic Management ◽

Parametric Modeling ◽

Neural Network Modeling ◽

Short Term ◽

Trip Planning ◽

Elman Neural Network ◽

Traffic Flow Forecasting ◽

Short Period

The intersection is a bottleneck in an urban roadway network. As traffic demand increases, there is a growing congestion problem at urban intersections. Short-term traffic flow forecasting is crucial for advanced trip planning and traffic management. However, there are only a handful of existing models for forecasting intersection traffic flow. In addition, previous short-term traffic flow forecasting models usually were for predicting roadway conditions in a very short period, such as one minute or five minutes, which is often too late given that a driver may well be approaching the bottleneck already. Being able to accurately predict traffic congestions in about half-hour advance is very critical for advanced trip planning and traffic management. To fill this gap, this research develops a two-layer stacking model for intersection short-term traffic flow forecasting by integrating the K-nearest neighbor (KNN) and Elman Neural Network modeling methods. It was developed using the 24-h cycle by cycle traffic data collected at a signalized intersection in Jinan, China. The developed model is evaluated by applying it to the same intersection for forecasting the short-term traffic conditions in a different set of days. The prediction performance of this model was compared with four other models developed using some existing non-parametric modeling and machine learning methods, including clustering, backpropagation (BP) neural network, KNN, and Elman Neural Network. The results of this study indicate that the proposed model outperforms other existing models in terms of its prediction accuracy.

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