A Short-term Traffic Speed Prediction Model Based on LSTM Networks

2021 ◽  
Author(s):  
Yu-Ling Hsueh ◽  
Yu-Ren Yang
Keyword(s):  
Prediction Model ◽  
Short Term ◽  
Model Based ◽  
Speed Prediction ◽  
Traffic Speed

D-LSTM: Short-Term Road Traffic Speed Prediction Model Based on GPS Positioning Data

2020 ◽  
pp. 1-10
Author(s):  
Xianwei Meng ◽  
Hao Fu ◽  
Liqun Peng ◽  
Guiquan Liu ◽  
Yang Yu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Road Traffic ◽  
Short Term ◽  
Gps Positioning ◽  
Model Based ◽  
Speed Prediction ◽  
Traffic Speed

Short-term wind speed prediction model based on GA-ANN improved by VMD

2020 ◽  
Vol 156 ◽  
pp. 1373-1388 ◽  
Author(s):  
Yagang Zhang ◽  
Guifang Pan ◽  
Bing Chen ◽  
Jingyi Han ◽  
Yuan Zhao ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction

A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework

2015 ◽  
Vol 4 (3) ◽  
pp. 159-186 ◽  
Author(s):  
Haizhong Wang ◽  
Lu Liu ◽  
Shangjia Dong ◽  
Zhen Qian ◽  
Heng Wei
Keyword(s):  
Prediction Model ◽  
Work Zone ◽  
Short Term ◽  
Vehicle Type ◽  
Speed Prediction ◽  
Traffic Speed

A multi-location short-term wind speed prediction model based on spatiotemporal joint learning

2021 ◽  
Author(s):  
Yuanyuan Xu ◽  
Genke Yang ◽  
Jiliang Luo ◽  
Jianan He ◽  
Haixin Sun
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Short Term ◽  
Joint Learning ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

scholarly journals A novel short-term blood pressure prediction model based on LSTM

2019 ◽  
Author(s):  
Qingxiang Zhao ◽  
Xiaobing Hu ◽  
Jing Lin ◽  
Xi Deng ◽  
Hang Li
Keyword(s):  
Blood Pressure ◽  
Prediction Model ◽  
Short Term ◽  
Model Based
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