scholarly journals Traffic flow prediction method based on bidirectional linear recurrent neural network

2021 ◽  
Author(s):  
Ruizhi Xu ◽  
Zhou Shen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Recurrent Neural Network ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Convergence Speed ◽  
Video Data ◽  
Particle Swarm Algorithm ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Abstract In order to improve the rationality and effectiveness of intelligent traffic control and management on urban roads, a bidirectional linear recurrent neural network-based traffic flow prediction method is proposed from the perspective of spatial and temporal characteristics of traffic flow. The method effectively combines the characteristics of fast and accurate bilinear polynomial solution and dynamic calibration of recurrent neural network, and adopts particle swarm algorithm to realize the dynamic pruning process of redundant neurons and weights, which improves the convergence speed and prediction accuracy of the algorithm. The algorithm is trained and experimented with video data, and a comparative analysis is conducted. The results show that the method can achieve accurate prediction of road traffic flow, the traffic flow prediction accuracy reaches more than 90%, meeting the data accuracy requirements of actual traffic management and control, and the convergence speed of the algorithm has also been significantly improved.

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

scholarly journals Short-term Traffic Flow Prediction Based on Genetic Artificial Neural Network and Exponential Smoothing

2020 ◽  
Vol 32 (6) ◽  
pp. 747-760
Author(s):  
Changxi Ma ◽  
Limin Tan ◽  
Xuecai Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Prediction Accuracy ◽  
Feedforward Neural Network ◽  
Short Term ◽  
Combined Model ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Artificial Neural

In order to improve the accuracy of short-term traffic flow prediction, a combined model composed of artificial neural network optimized by using Genetic Algorithm (GA) and Exponential Smoothing (ES) has been proposed. By using the metaheuristic optimal search ability of GA, the connection weight and threshold of the feedforward neural network trained by a backpropagation algorithm are optimized to avoid the feedforward neural network falling into local optimum, and the prediction model of Genetic Artificial Neural Network (GANN) is established. An ES prediction model is presented then. In order to take the advantages of the two models, the combined model is composed of a weighted average, while the weight of the combined model is determined according to the prediction mean square error of the single model. The road traffic flow data of Xuancheng, Anhui Province with an observation interval of 5 min are used for experimental verification. Additionally, the feedforward neural network model, GANN model, ES model and combined model are compared and analysed, respectively. The results show that the prediction accuracy of the optimized feedforward neural network is much higher than that before the optimization. The prediction accuracy of the combined model is higher than that of the two single models, which verifies the feasibility and effectiveness of the combined model.

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.

Short-Term Traffic Flow Prediction Model of Wavelet Neural Network Based on Mind Evolutionary Algorithm

2018 ◽  
Vol 32 (12) ◽  
pp. 1850041 ◽  
Author(s):  
Liqiang Xu ◽  
Xuedong Du ◽  
Binguo Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Evolutionary Algorithm ◽  
Traffic Flow ◽  
Prediction Accuracy ◽  
Wavelet Neural Network ◽  
Short Term ◽  
Connection Weight ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This paper introduces mind evolutionary algorithm (MEA) into the application of short-term traffic flow prediction, and proposes a short-term traffic flow prediction model of wavelet neural network based on mind evolutionary algorithm (MEA-WNN). The optimal connection weight and wavelet parameters of wavelet neural network (WNN) are searched globally by MEA, and the convergence capacity of wavelet neural network is improved. The experimental data show that, compared with the prediction model of the traditional WNN and the WNN based on genetic algorithm (GA-WNN), the prediction model of MEA-WNN has higher global prediction accuracy.

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.

scholarly journals An Incremental Learning Based Convolutional Neural Network Model for Large-Scale and Short-Term Traffic Flow

2021 ◽  
Vol 11 (2) ◽  
pp. 143-151
Author(s):  
Feng Yu ◽  
◽  
Jinglong Fang ◽  
Bin Chen ◽  
Yanli Shao
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Neural Network Model ◽  
Incremental Learning ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Flow Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Traffic flow prediction is very important for smooth road conditions in cities and convenient travel for residents. With the explosive growth of traffic flow data size, traditional machine learning algorithms cannot fit large-scale training data effectively and the deep learning algorithms do not work well because of the huge training and update costs, and the prediction accuracy may need to be further improved when an emergency affecting traffic occurs. In this study, an incremental learning based convolutional neural network model, TF-net, is proposed to achieve the efficient and accurate prediction of large-scale and short-term traffic flow. The key idea is to introduce the uncertainty features into the model without increasing the training cost to improve the prediction accuracy. Meanwhile, based on the idea of combining incremental learning with active learning, a certain percentage of typical samples in historical traffic flow data are sampled to fine-tune the prediction model, so as to further improve the prediction accuracy for special situations and ensure the real-time requirement. The experimental results show that the proposed traffic flow prediction model has better performance than the existing methods.

Sign in / Sign up

Export Citation Format

Share Document