Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values

Cui, Zhiyong; Ke, Ruimin; Pu, Ziyuan; Wang, Yinhai

doi:10.1016/j.trc.2020.102674

Cited by 434 publications

(312 citation statements)

References 38 publications

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“…By employing the feature engineering, delicate descriptions of the raw features can be obtained. With the captured long-term pattern of the data and inspired by the studies on traffic speed prediction [41][42][43], we extended the LSTM based approach to predict train speed for multi-step ahead. With the help of the train speed prediction, the train running status can be obtained proactively.…”

Section: Results Of N Steps Speed Predictionmentioning

confidence: 99%

“…Among the machine learning algorithms, the recurrent neural network (such as the LSTM network) is designed to seize the features of the temporal and spatial evolution process. Several studies [41][42][43] focused on the use of LSTM neural networks for traffic forecasting and demonstrated the advantages of this kind of algorithm. Encouraged by the successful applications of LSTM based algorithms in the domain of transportation, we propose to employ LSTM networks for train dynamic model construction and train speed prediction, since the train operation process can be regarded as a time sequence problem.…”

Section: Related Workmentioning

confidence: 99%

“…However, this treatment causes a great disturbance when handling time series problems. In this paper, similar to Cui [43], a masking mechanism is proposed to solve the problem of missing values.…”

Section: Data Preprocessingmentioning

confidence: 99%

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Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

Tang

Gao

2019

Algorithms

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The automatic train operation system is a significant component of the intelligent railway transportation. As a fundamental problem, the construction of the train dynamic model has been extensively researched using parametric approaches. The parametric based models may have poor performances due to unrealistic assumptions and changeable environments. In this paper, a long short-term memory network is carefully developed to build the train dynamic model in a nonparametric way. By optimizing the hyperparameters of the proposed model, more accurate outputs can be obtained with the same inputs of the parametric approaches. The proposed model was compared with two parametric methods using actual data. Experimental results suggest that the model performance is better than those of traditional models due to the strong learning ability. By exploring a detailed feature engineering process, the proposed long short-term memory network based algorithm was extended to predict train speed for multiple steps ahead.

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Section: Results Of N Steps Speed Predictionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

Tang

Gao

2019

Algorithms

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“…The relationship between y i (1) and original sequences x i (0) , can be calculated by (8) and (9). ; and X ¼ ðx ð0Þ i ð1Þ; x ð0Þ i ð2Þ; � � � ; x ð0Þ i ðn À 1ÞÞ T .…”

Section: Sdgm Modelmentioning

confidence: 99%

“…PLOS Although each of the three traffic parameters can be used to describe traffic congestion, both traffic flow and speed have correlated with occupancy [6]. Compared to the traffic flow, one speed is mapped to one occupancy, whereas one traffic flow can be mapped to two occupancies [7][8][9][10]. In addition, speed is more directly related to the traffic operation statues.…”

Section: Introductionmentioning

confidence: 99%

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Song

et al. 2019

PLoS ONE

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Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SAR-IMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy.

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Deep learning in MRI‐guided radiation therapy: A systematic review

Eidex,

Ding,

Wang

et al. 2023

J Applied Clin Med Phys

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Recent advances in MRI‐guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real‐time MRI monitoring, and the MRI‐only treatment planning workflow. Given the rapid growth and emergence of new state‐of‐the‐art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x‐ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi‐modal, visual transformer, and diffusion models.

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Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values

Cited by 434 publications

References 38 publications

Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model

Deep learning in MRI‐guided radiation therapy: A systematic review

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