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 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 A Short-Term Traffic Flow Reliability Prediction Method considering Traffic Safety

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shaoqian Li ◽  
Zhenyuan Zhang ◽  
Yang Liu ◽  
Zixia Qin
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Rapid Development ◽  
Prediction Method ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Real Time Traffic ◽  
De Algorithm ◽  
Flow Prediction ◽  
Bpnn Model

With the rapid development and application of intelligent traffic systems, traffic flow prediction has attracted an increasing amount of attention. Accurate and timely traffic flow information is of great significance to improve the safety of transportation. To improve the prediction accuracy of the backward-propagation neural network (BPNN) prediction model, which easily falls into local optimal solutions, this paper proposes an adaptive differential evolution (DE) algorithm-optimized BPNN (DE-BPNN) model for a short-term traffic flow prediction. First, by the mutation, crossover, and selection operations of the DE algorithm, the initial weights and biases of the BPNN are optimized. Then, the initial weights and biases obtained by the aforementioned preoptimization are used to train the BPNN, thereby obtaining the optimal weights and biases. Finally, the trained BPNN is utilized to predict the real-time traffic flow. The experimental results show that the accuracy of the DE-BPNN model is improved about 7.36% as compared with that of the BPNN model. The DE-BPNN is superior to the performance of three classical models for short-term traffic flow prediction.

scholarly journals Short-term traffic flow prediction based on cuckoo search-wavelet neural network

2021 ◽  
Author(s):  
Haibo Lv ◽  
Yuheng Kang ◽  
Zhou Shen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Cuckoo Search ◽  
Wavelet Neural Network ◽  
Urban Traffic ◽  
Prediction Algorithm ◽  
Step Size ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Abstract The nonlinear fluctuation and uncertainty that characterize urban traffic flow are well-known. An Improved Cuckoo Search-Wavelet Neural Network (ICS-WNN) prediction model for urban traffic flow is suggested in order to increase the accuracy of traffic flow predictions. After the original traffic flow data have been cleaned up and normalized, the traffic flow prediction network model is built by optimizing the wavelet neural network weights and wavelet shrinkage and translation factors based on the adaptive step size and discovery probability of the cuckoo algorithm, and then adding the neural network momentum factor. The traffic flow prediction network model is built in two stages. The results of the experimental simulations demonstrate that the ICS-WNN prediction algorithm has a better fit and accuracy than numerous common optimization prediction techniques, which is encouraging.

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.

Chaotic Time Series Prediction Algorithm for Lorenz System

2014 ◽  
Vol 513-517 ◽  
pp. 2412-2415
Author(s):  
Chen Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Lorenz System ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

Based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm for BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to generate better network initial thresholds and weights so as to compensate the random defects for the thresholds and weights of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series generated by Lorenz system. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so prove it is feasible and effective in the chaotic time series.

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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