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.

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.

scholarly journals The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fan Hou ◽  
Yue Zhang ◽  
Xinli Fu ◽  
Lele Jiao ◽  
Wen Zheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Road Network ◽  
Short Term Memory ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Point Of Interest ◽  
Flow Prediction ◽  
Long Short Term Memory ◽  
On The Road

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

scholarly journals Traffic Flow Prediction Model Based on Drivers’ Cognition of Road Network

2020 ◽  
Vol 24 (7) ◽  
pp. 900-907
Author(s):  
Songjiang Li ◽  
◽  
Wen An ◽  
Peng Wang
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Road Network ◽  
Probability Model ◽  
Prediction Method ◽  
Path Selection ◽  
Peak Period ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

The traditional traffic flow prediction method is based on data modeling, when emergencies occur, it is impossible to accurately analyze the changes in traffic characteristics. This paper proposes a traffic flow prediction model (BAT-GCN) which is based on drivers’ cognition of the road network. Firstly, drivers can judge the capacity of different paths by analyzing the travel time in the road network, which bases on the drivers’ cognition of road network space. Secondly, under the condition that the known road information is obtained, people through game decision-making for different road sections to establish the probability model of path selection; Finally, drivers obtain the probability distribution of different paths in the regional road network and build the prediction model by combining the spatiotemporal directed graph convolution neural network. The experimental results show that the BAT-GCN model reduces the prediction error compared with other baseline models in the peak period.

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.

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.

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