A Multitask Learning Neural Network for Short-Term Traffic Speed Prediction and Confidence Estimation

2019 ◽  
pp. 434-449 ◽  
Author(s):  
Yanyun Tao ◽  
Xiang Wang ◽  
Yuzhen Zhang
Keyword(s):  
Neural Network ◽  
Multitask Learning ◽  
Short Term ◽  
Confidence Estimation ◽  
Speed Prediction ◽  
Traffic Speed

scholarly journals A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Pan Wu ◽  
Zilin Huang ◽  
Yuzhuang Pian ◽  
Lunhui Xu ◽  
Jinlong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Preprocessing ◽  
Prediction Performance ◽  
Attention Mechanism ◽  
Learning Method ◽  
Short Term ◽  
Prediction Module ◽  
Speed Prediction ◽  
Traffic Speed

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance.

An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic

2017 ◽  
Vol 18 (9) ◽  
pp. 2340-2350 ◽  
Author(s):  
Jinjun Tang ◽  
Fang Liu ◽  
Yajie Zou ◽  
Weibin Zhang ◽  
Yinhai Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
Periodic Characteristic ◽  
Speed Prediction ◽  
Traffic Speed
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