scholarly journals Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network

2020 ◽  
Vol 12 (9) ◽  
pp. 3678 ◽  
Author(s):  
Xinqiang Chen ◽  
Jinquan Lu ◽  
Jiansen Zhao ◽  
Zhijian Qu ◽  
Yongsheng Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Ensemble Empirical Mode Decomposition ◽  
Flow Data ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition

Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.

scholarly journals Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

2020 ◽  
Vol 10 (6) ◽  
pp. 2038 ◽  
Author(s):  
Yanpeng Wang ◽  
Leina Zhao ◽  
Shuqing Li ◽  
Xinyu Wen ◽  
Yang Xiong
Keyword(s):  
Traffic Flow ◽  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Original Data ◽  
Time Varying ◽  
Mean Square ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition

Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

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.

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