scholarly journals A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hailong Zhu ◽  
Yawen Xie ◽  
Wei He ◽  
Chao Sun ◽  
Kaili Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Prediction Method ◽  
Time Correlation ◽  
Rule Base ◽  
Intelligent Transport ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Forecasting Method ◽  
Belief Rule Base

As an important part of a smart city, intelligent transport can effectively reduce energy consumption and environmental pollution. Traffic flow forecasting provides a reliable traffic dispatch basis for intelligent transport, and most of the existing prediction methods only predict a single saturation or speed and do not use the saturation and speed in a unified way. This paper proposes a new traffic flow prediction method based on RNN-GCN and BRB. First, the belief rule base (BRB) is used for data fusion to obtain new traffic flow data, then the recurrent neural network (RNN) and graph convolution neural network (GCN) model is used to obtain the time correlation of the traffic data, and finally, the traffic flow is predicted by the topology graph. The experimental results show that the method has a better performance than ARIMA, LSTM, and GCN.

An Approach for Short Term Traffic Flow Forecasting Based on Genetic Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2866-2869 ◽  
Author(s):  
Lei Yang ◽  
Wei Dong Dai
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Nonlinear Function ◽  
Prediction Method ◽  
Short Term ◽  
Forecast Performance ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Flow Prediction ◽  
Genetic Neural Network

In this paper, genetic neural network is applied to forecast the short-term traffic flow and traffic guidance. Because of the factors of time correlation and spatial correlation, we construct the short-term traffic flow forecasting model using back-propagation neural network that has the function of arbitrary nonlinear function approximation. In order to find proper initial values of the neural network weights and threshold quickly, a combination of neural network prediction method is presented. This method utilizes genetic algorithm to choose the initial weights and threshold, and uses L-M algorithm to train sample, which can enhance the global convergence rate. Trained network is used for short-term traffic flow prediction with mean square error as the forecast performance evaluation. The results show that the performance of genetic neural network is better than a separate BP neural network for short-term traffic flow prediction.

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.

scholarly journals Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

2014 ◽  
Vol 26 (5) ◽  
pp. 393-403 ◽  
Author(s):  
Seyed Hadi Hosseini ◽  
Behzad Moshiri ◽  
Ashkan Rahimi-Kian ◽  
Babak Nadjar Araabi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Weather Conditions ◽  
Traffic Data ◽  
Data Loss ◽  
Traffic Lights ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Flow Prediction ◽  
Forecasting Method

Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

An Approach for Short Term Traffic Flow Forecasting Based on Fuzzy Logic Control

2013 ◽  
Vol 336-338 ◽  
pp. 438-441
Author(s):  
Wei Dong Dai
Keyword(s):  
Fuzzy Logic ◽  
Traffic Flow ◽  
Fuzzy Logic Control ◽  
Time Correlation ◽  
Output Curve ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Logic Control ◽  
Flow Prediction

In this paper, fuzzy logic control is applied to forecast the short-term traffic flow and traffic guidance. Because of the factors of time correlation and spatial correlation, we construct the short-term traffic flow forecasting model using fuzzy logic control that can handle non-linear plant behavior. In order to find a feasibility way of traffic flow prediction, we deal the combination of time correlation traffic value and space correlation traffic value as the input variables. Considering the real condition, we use triangular and trapezoid membership function to design the belongings relationship. Five fuzzy rules are applied in the control. Last, we use fuzzy logic toolbox to simulate the short term traffic flow forecasting basing on the fuzzy logic control. The system input/output curve result shows that this method can have a good performance for short-term traffic flow prediction.

Short-term traffic flow prediction based on 1DCNN-LSTM neural network structure

2020 ◽  
pp. 2150042
Author(s):  
Yihuan Qiao ◽  
Ya Wang ◽  
Changxi Ma ◽  
Ju Yang
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Spatial Information ◽  
Short Term Memory ◽  
Prediction Method ◽  
Traffic Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
The Past ◽  
Flow Prediction

In the past decade, the number of cars in China has significantly raised, but the traffic jam spree problem has brought great inconvenience to people’s travel. Accurate and efficient traffic flow prediction, as the core of Intelligent Traffic System (ITS), can effectively solve the problems of traffic travel and management. The existing short-term traffic flow prediction researches mainly use the shallow model method, so they cannot fully reflect the traffic flow characteristics. Therefore, this paper proposed a short-term traffic flow prediction method based on one-dimensional convolution neural network and long short-term memory (1DCNN-LSTM). The spatial information in traffic data is obtained by 1DCNN, and then the time information in traffic data is obtained by LSTM. After that, the space-time features of the traffic flow are used as regression predictions, which are input into the Fully-Connected Layer. In the end, the corresponding prediction results of the current input are calculated. In the past, most of the researches are based on survey data or virtual data, lacking authenticity. In this paper, real data will be used for research. The data are provided by OpenITS open data platform. Finally, the proposed method is compared with other road forecasting models. The results show that the structure of 1DCNN-LSTM can further improve the prediction accuracy.

Prediction for Traffic Flow of BP Neural Network based on DE Algorithm

2013 ◽  
Vol 671-674 ◽  
pp. 2951-2955
Author(s):  
Yue Hou ◽  
Hai Yan Li
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Computing Time ◽  
Prediction Method ◽  
Optimal Solution ◽  
Prediction Algorithm ◽  
Traffic Flow Prediction ◽  
De Algorithm ◽  
Real Traffic

A prediction algorithm for traffic flow prediction of BP neural based on Differential Evolution(DE) is proposed to overcome the problems such as long computing time and easy to fall into local minimum by combing DE and neural network . In the algorithm, DE is used to optimize the thresholds and weights of BP neural network, and the BP neural network is used to search for the optimal solution. The efficiency of the proposed prediction method is tested by the simulation of real traffic flow. The simulation results show that the proposed method has higher precision compared with the traditional BP neural network，so prove it is feasible and effective in the practical prediction of traffic flow.

scholarly journals A Traffic Flow Prediction Method Based on Road Crossing Vector Coding and a Bidirectional Recursive Neural Network

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1006 ◽  
Author(s):  
Zhao ◽  
Zhao ◽  
Bai ◽  
Li
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Prediction Method ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Vector Coding ◽  
Road Intersections ◽  
Spatio Temporal ◽  
Traffic Guidance ◽  
And Control

Aiming at the problems that current predicting models are incapable of extracting the inner rule of the traffic flow sequence in traffic big data, and unable to make full use of the spatio-temporal relationship of the traffic flow to improve the accuracy of prediction, a Bi-directional Regression Neural Network (BRNN) is proposed in this paper, which can fully apply the context information of road intersections both in the past and the future to predict the traffic volume, and further to make up the deficiency that the current models can only predict the next-moment output according to the time series information in the previous moment. Meanwhile, a vectorized code to screen out the intersections related to the predicting point in the road network and to train and predict through inputting the track data of the selected intersections into BRNN, is designed. In addition, the model is testified through the true traffic data in partial area of Shen Zhen. The results indicate that, compared with current traffic predicting models, the model in this paper is capable of providing the necessary evidence for traffic guidance and control due to its excellent performance in extracting the spatio-temporal feature of the traffic flow series, which can enhance the accuracy by 16.298% on average.

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