STGNN-TTE: Travel time estimation via spatial–temporal graph neural network

Jin, Guangyin; Wang, Min; Zhang, Jinlei; Sha, Hao; Huang, Jincai

doi:10.1016/j.future.2021.07.012

Cited by 61 publications

(24 citation statements)

References 37 publications

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“…e input and output quantities of the BP neural network hidden layer are, respectively, shown in the following formulas: net (2) i (k) � 􏽘 m j�0 w (2) ij O (1)…”

Section: Bp Neural Network Pid Controllermentioning

confidence: 99%

“…At the same time, it is also faced with serious geological environment problems such as karst collapse, tunnel water in ow, drought and waterlogging, water and soil pollution, and so on. As one of the most important geological environment problems in the karst area, karst collapse is widely distributed in the world [1]. e formation of karst collapse requires three basic conditions as follows: karst space, a certain thickness of caprock, and trigger factors.…”

Section: Introductionmentioning

confidence: 99%

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Study on Early Warning of Karst Collapse Based on the BP Neural Network

Chen

2022

Journal of Chemistry

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In order to comprehensively grasp the dynamics of karst collapse, promote the comprehensive prevention and control level of karst collapse, and prevent secondary disasters caused by lava collapse, this study presents a method of karst collapse early warning based on the BP neural network. This method does not need to set the sliding surface in the finite element calculation model. The stress of the sliding surface is fitted according to the spatial stress relationship of the deep karst layer through the improved BP neural network PID control algorithm and BP neural network algorithm, which avoids the modeling and mesh generation of the complex sliding block and has good accuracy and ease of use. According to the basic theory of the BP neural network, the calculation formulas of multilayer feedforward and error back propagation processes are derived, and the two-dimensional and three-dimensional finite element models of gravity dams without and with sliding blocks are established, respectively. Finally, according to the common formulas of viscoelastic artificial boundary and equivalent load, the two-dimensional and three-dimensional input programs of the karst fluid state are compiled, and a neural network early warning model is obtained. The experimental results show that the process karst state simulated by the algorithm is very close to the actual situation, and the minimum value of antisliding coefficient and its occurrence time can be accurately predicted, with an error range of less than 3%. Conclusion. BP neural network prediction can effectively predict karst collapse, with higher prediction accuracy, and can effectively simulate the actual collapse risk.

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“…e input and output quantities of the BP neural network hidden layer are, respectively, shown in the following formulas: net (2) i (k) � 􏽘 m j�0 w (2) ij O (1)…”

Section: Bp Neural Network Pid Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on Early Warning of Karst Collapse Based on the BP Neural Network

Chen

2022

Journal of Chemistry

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“…[39,111,112,200,301,308]. Due to the network nature of traffic flows, GNN fits the mold to model the interaction between placed sensors via GPS location or roads [114,174,288]. For more details of using GNN to forecast traffic, we refer to [112].…”

Section: 24mentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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“…With great achievements of the attention mechanism [34] in nature processing language, recent studies have concentrated on applying the relevant techniques in traic ield. To achieve accurate TTP, a multi-layer GCN module paralleled with a transformer layer [34] was devised to capture both spatial and temporal features [16] and then integrated with another transformer layer to obtain TTP. Khaled et al [17] exploited the gated attention mechanism to fetch the spatial-temporal features and a feature selection module to obtain precise TTP.…”

Section: Deep Learning For Travel Time Predictionmentioning

confidence: 99%

“…Since the traic network is naturally a graph, the graph neural networks (GNNs) have been the most popular way used to learn the spatial features of traic network. One typical example is the graph convolutional neural networks (GCNs) which have been extensively applied in TTP [16] and other traic prediction [13,49]. Though the GNN and CNN based methods are capable of learning the topological structure of traic networks and making accurate predictions, large DL models would consume huge computational resources.…”

Section: Introductionmentioning

confidence: 99%

Travel Time Prediction Method Based on Spatial-Feature-based Hierarchical Clustering and Deep Multi-input Gated Recurrent Unit

Fang

Liu

Chen

et al. 2022

ACM Trans. Sen. Netw.

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Accurate travel time prediction (TTP) is a significant aspect in the intelligent transportation system (ITS). Travel times of certain road segments explicitly reflect the traffic conditions of those sections. Effective TTP of road segments is instrumental in route planning, traffic control, and traffic management. However, the accuracy of TTP is greatly affected by the intricate topological structure of traffic network and the dynamics of traffic flow over time. This paper develops a TTP method based on the spatial-feature-based hierarchical clustering (SFHC) and deep multi-input gated recurrent unit (DMGRU). The proposed two-stage method is capable of capturing the spatial-temporal features of traffic network. Specifically, the SFHC divides the road segments into several clusters having similar traffic features, and then the clustered data is fed into the DMGRU for TTP. Our experiments conducted on the practical dataset demonstrate that the designed prediction method can achieve the mean absolute percentage error (MAPE) of 3.3109% and mean absolute error (MAE) of 2.5658, which outperform various combinations of baseline clustering algorithms and prediction models.

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STGNN-TTE: Travel time estimation via spatial–temporal graph neural network

Cited by 61 publications

References 37 publications

Study on Early Warning of Karst Collapse Based on the BP Neural Network

Study on Early Warning of Karst Collapse Based on the BP Neural Network

Graph Neural Networks in IoT: A Survey

Travel Time Prediction Method Based on Spatial-Feature-based Hierarchical Clustering and Deep Multi-input Gated Recurrent Unit

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