Traffic Flow Forecasting Based on Chaos Neural Network

2010 ◽  
Vol 20-23 ◽  
pp. 1236-1240 ◽  
Author(s):  
Yuan Yuan Zhang ◽  
Shi Song Yang ◽  
Qing Cai ◽  
Peng Sun
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Traffic Flow ◽  
Traffic Engineering ◽  
Intelligent Transportation System ◽  
Real Data ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Term Forecast ◽  
Saturation Phase

Traffic flow forecasting has become an emphasis question for discussion in traffic engineering domain and one kernel study in Intelligent Transportation System. After ensuring the traffic flow has the characteristic of chaos, traffic flow real data have been used to reconstruct phase space. Calculate the saturation phase space embedding dimension and maximal Lyapunov exponent. By above all, a chaos neural network model is constructed, which can make high precision short-term forecast for the nonlinear big-lagged system even by imperfect and variation inputs. At last, a forecasting example provides that the traffic flow forecasting based on chaos neural network is validity and feasibility.

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.

Applying Artificial Intelligence to Short-Term Traffic Flow Forecasting

2012 ◽  
Vol 433-440 ◽  
pp. 5214-5217
Author(s):  
Hai Huang
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Optimization Algorithm ◽  
Fuzzy Neural Network ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fuzzy Neural ◽  
Low Efficiency

Short-term traffic flow forecasting has a high requirement for the responding time and accuracy of the forecasting method because the result is directly used for instant traffic inducing. Based on the introduction of the fuzzy neural network model for short-term traffic flow forecasting together with its detailed procedures, this paper adopt the particle swarm optimization algorithm to train the fuzzy neural network. Its global searching and optimization algorithm helps to overcome the shortcomings of the traditional fuzzy neural network, such as its low efficiency and “local optimum”. A case study is also given for the PSO algorithm to train the fuzzy neural network for traffic flow forecasting. The result shows that the average square error is 0.932 when the PSO algorithm is put to use for the network training, which is 3.926 when the PSO is not used. Thus result is more accurate and it requires less time for the training procedures. It proves this method is feasible and efficient.

Short-Term Traffic Flow Forecasting of Road Based on Spline Weight Function Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 695-698
Author(s):  
Dai Yuan Zhang ◽  
Jian Hui Zhan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Weight Function ◽  
Traffic Flow ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Forecasting Method

Traditional short-term traffic flow forecasting of road usually based on back propagation neural network, which has a low prediction accuracy and convergence speed. This paper introduces a spline weight function neural networks which has a feature that the weight function can well reflect sample information after training, thus propose a short-term traffic flow forecasting method base on the spline weight function neural network, specify the network learning algorithm, and make a comparative tests bases on the actual data. The result proves that in short-term traffic flow forecasting, the spline weight function neural network is more effective than traditional methods.

A hybrid approach for short-term traffic flow forecasting based on similarity identification

2021 ◽  
pp. 2150212
Author(s):  
Wenjun Li ◽  
Si Chen ◽  
Xiaoquan Wang ◽  
Chaoying Yin ◽  
Zhaoguo Huang
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Moving Average ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Gray Relation Analysis

Short-term traffic flow forecasting is a key component of intelligent transportation system, yet difficult to be forecasted reliably, and accurately. A novel hybrid forecasting model is proposed by combining three predictors, namely, the autoregressive integrated moving average (ARIMA), back propagation neural network (BPNN) and support vector regression (SVR). First, it is assumed that all previous intervals can have influence on the predicted interval and then the entropy-based gray relation analysis method is applied to analyze the correlation and determine the length of time constrain window. Second, an improved Euclidean distance is employed to identify the similarity. Furthermore, the rank-exponent method is utilized to rank the results according to the similarity and fuse the predicted values of the predictors. Finally, a numerical experiment is implemented, which indicates that the performance of forecasting results is superior to the conventional ones.

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