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.

Short-term traffic flow prediction using time-varying Vasicek model

2017 ◽  
Vol 74 ◽  
pp. 168-181 ◽  
Author(s):  
Yalda Rajabzadeh ◽  
Amir Hossein Rezaie ◽  
Hamidreza Amindavar
Keyword(s):  
Traffic Flow ◽  
Time Varying ◽  
Short Term ◽  
Vasicek Model ◽  
Traffic Flow Prediction ◽  
Flow Prediction

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 A Hybrid Model for Short-Term Traffic Flow Prediction Based on Variational Mode Decomposition, Wavelet Threshold Denoising, and Long Short-Term Memory Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Yang Yu ◽  
Qiang Shang ◽  
Tian Xie
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Long Short Term Memory

Traffic flow prediction plays an important role in intelligent transportation system (ITS). However, due to the randomness and complex periodicity of traffic flow data, traditional prediction models often fail to achieve good results. On the other hand, external disturbances or abnormal detectors will cause the collected traffic flow data to contain noise components, resulting in a decrease in prediction accuracy. In order to improve the accuracy of traffic flow prediction, this study proposes a mixed traffic flow prediction model VMD-WD-LSTM using variational mode decomposition (VMD), wavelet threshold denoising (WD), and long short-term memory (LSTM) network. Firstly, we decompose the original traffic flow sequence into K components through VMD and determine the number of components K according to the sample entropy of different K values. Then, each component is denoised by wavelet threshold to obtain the denoised subsequence. Finally, LSTM is used to predict each subsequence, and the predicted values of each subsequence are combined into the final prediction results. In addition, the performance of the proposed model and the latest traffic flow prediction model is compared on the several well-known public datasets. The empirical analysis shows that the proposed model not only has good prediction accuracy but also has superior robustness.

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