A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

Zhang, Yang; Cheng, Tao; Ren, Yibin; Xie, Kun

doi:10.1080/13658816.2019.1697879

Cited by 72 publications

(37 citation statements)

References 51 publications

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“…In addition, in recent research, hybrid integrated deep learning models for traffic forecasting have been proposed. For example, the graph neural network or the spatiotemporal residual network have been integrated into the LSTM in order to improve its prediction accuracy, and computational cost [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

et al. 2021

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Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.

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Section: Introductionmentioning

confidence: 99%

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

et al. 2021

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“…Stacked vector and sequentiality were used for the input data representation. In [34], the spatial dependency is represented as a graph based on the temporal correlation coefficient using historical traffic observations to capture heterogeneous spatial correlations. To calculate the correlation coefficient, min-max normalization is first used to calculate the coefficient so that spatial heterogeneity may be eliminated from capacity or speed limits.…”

Section: Traffic Flow Predictionmentioning

confidence: 99%

Short-Term Traffic Prediction With Deep Neural Networks: A Survey

Lee

Jung

et al. 2021

IEEE Access

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In modern transportation systems, an enormous amount of traffic data is generated every day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep learning methods have recently been applied. In traffic networks with complex spatiotemporal relationships, deep neural networks (DNNs) often perform well because they are capable of automatically extracting the most important features and patterns. In this study, we survey recent STTP studies applying deep networks from four perspectives. 1) We summarize input data representation methods according to the number and type of spatial and temporal dependencies involved. 2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and meta-learning networks. 3) We summarize previous STTP studies in terms of the type of DNN techniques, application area, dataset and code availability, and the type of the represented spatiotemporal dependencies. 4) We compile public traffic datasets that are popular and can be used as the standard benchmarks. Finally, we suggest challenging issues and possible future research directions in STTP.INDEX TERMS Artificial intelligence, deep neural network (DNN), intelligent transportation systems (ITS), neural networks, prediction algorithms, short-term traffic prediction (STTP), traffic forecasting

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“…In the deep Q-Learning, the state dimension of the agent is high, and the table obviously cannot meet the demand. is problem is solved by using f (s, a) to approximate Q (s, a) [46,47]. erefore, based on the corresponding value function neural network model, approximate values can be obtained, thereby reducing the storage pressure of the Qtable and providing ideas and methods for Q-Learning to be applied to traffic state prediction.…”

Section: Q-learning Principle and Application Stepsmentioning

confidence: 99%

Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

Hao

Rong

et al. 2020

Journal of Advanced Transportation

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With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.

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A novel residual graph convolution deep learning model for short-term network-based traffic forecasting

Cited by 72 publications

References 51 publications

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

Short-Term Traffic Prediction With Deep Neural Networks: A Survey

Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

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