Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data

Duan, Zhenhai; Ye, Yun; Zhang, Kai; Ni, Yuxiang; Bajgain, Saurab

doi:10.1109/access.2018.2845863

Cited by 132 publications

(58 citation statements)

References 39 publications

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“…As artificial intelligence technology has developed the adoption of DNN has significantly increased. LSTM is a DNN algorithm, which can be effective for any type of time series data [34]. LSTM is an artificial RNN architecture applied in the field of deep learning.…”

Section: Long Short-term Memory Algorithmmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

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Section: Long Short-term Memory Algorithmmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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“…The inferiority comes from the fact that, the SVR method does not have a sophisticated scheme to learn the underlying spatial dependency, or the long-term temporal dependency. In contrast, CNN has a convolution layer, and LSTM or BDLSTM has a memory and gate to handle the two situations [42,43], respectively. On the other hand, the BDLSTM-CNN based hybrid solution always achieves the lowest error rates of 20% to 22.5%, which implies a forecasting accuracy of 77.5% to 80%.…”

Section: Comparison With the Conventional Methodsmentioning

confidence: 99%

Using Deep Learning to Forecast Maritime Vessel Flows

Zhou

Liu

Wang

et al. 2020

Sensors

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Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

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“…They can learn temporal relationships and dependencies from time series data and have been applied to short-term traffic prediction [5,[39][40][41]. Deep learning models combining CNNs and LSTMs are widely used in the literature regarding traffic flow prediction and can capture both the spatial correlations and temporal dependencies of traffic flow variables on road networks [5,36,[42][43][44].…”

Section: Prediction Modelmentioning

confidence: 99%

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

Zhang

et al. 2020

Algorithms

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In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

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Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data

Cited by 132 publications

References 39 publications

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Using Deep Learning to Forecast Maritime Vessel Flows

Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

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