scholarly journals Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction

2021 ◽  
Vol 13 (16) ◽  
pp. 3338
Author(s):  
Xiao Xiao ◽  
Zhiling Jin ◽  
Yilong Hui ◽  
Yueshen Xu ◽  
Wei Shao
Keyword(s):  
Smart Cities ◽  
Convolutional Networks ◽  
Temporal Correlations ◽  
Spatial Features ◽  
Temporal Features ◽  
Prediction Scheme ◽  
Temporal Graph ◽  
Real World Datasets ◽  
The Internet Of Things

With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called distAtt to measure the similarity of parking duration distributions. Through the distAtt mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate.

scholarly journals Surveillance System in Smart Cities: A Dependability Evaluation Based on Stochastic Models

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 876
Author(s):  
Igor Gonçalves ◽  
Laécio Rodrigues ◽  
Francisco Airton Silva ◽  
Tuan Anh Nguyen ◽  
Dugki Min ◽  
...  
Keyword(s):  
Surveillance System ◽  
Smart Cities ◽  
Heterogeneous Data ◽  
Time To Failure ◽  
Video Streams ◽  
Dependability Evaluation ◽  
The Mean ◽  
Mean Time ◽  
The Internet Of Things

Surveillance monitoring systems are highly necessary, aiming to prevent many social problems in smart cities. The internet of things (IoT) nowadays offers a variety of technologies to capture and process massive and heterogeneous data. Due to the fact that (i) advanced analyses of video streams are performed on powerful recording devices; while (ii) surveillance monitoring services require high availability levels in the way that the service must remain connected, for example, to a connection network that offers higher speed than conventional connections; and that (iii) the trust-worthy dependability of a surveillance system depends on various factors, it is not easy to identify which components/devices in a system architecture have the most impact on the dependability for a specific surveillance system in smart cities. In this paper, we developed stochastic Petri net models for a surveillance monitoring system with regard to varying several parameters to obtain the highest dependability. Two main metrics of interest in the dependability of a surveillance system including reliability and availability were analyzed in a comprehensive manner. The analysis results show that the variation in the number of long-term evolution (LTE)-based stations contributes to a number of nines (#9s) increase in availability. The obtained results show that the variation of the mean time to failure (MTTF) of surveillance cameras exposes a high impact on the reliability of the system. The findings of this work have the potential of assisting system architects in planning more optimized systems in this field based on the proposed models.

scholarly journals Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

2018 ◽  
Author(s):  
Bing Yu ◽  
Haoteng Yin ◽  
Zhanxing Zhu
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Time Series Prediction ◽  
Urban Traffic ◽  
Traffic Forecasting ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Temporal Correlations ◽  
Temporal Graph ◽  
Spatio Temporal

Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

scholarly journals GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification

2020 ◽  
Author(s):  
Ziyu Jia ◽  
Youfang Lin ◽  
Jing Wang ◽  
Ronghao Zhou ◽  
Xiaojun Ning ◽  
...  
Keyword(s):  
Sleep Stage ◽  
Disease Diagnosis ◽  
Sleep Stages ◽  
Convolutional Networks ◽  
Spatial Features ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Temporal Graph ◽  
Brain Connection ◽  
Open Question

Sleep stage classification is essential for sleep assessment and disease diagnosis. However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging. In particular, owing to the limited knowledge of the human brain, predefining a suitable spatial brain connection structure for sleep stage classification remains an open question. In this paper, we propose a novel deep graph neural network, named GraphSleepNet, for automatic sleep stage classification. The main advantage of the GraphSleepNet is to adaptively learn the intrinsic connection among different electroencephalogram (EEG) channels, represented by an adjacency matrix, thereby best serving the spatial-temporal graph convolution network (ST-GCN) for sleep stage classification. Meanwhile, the ST-GCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. Experiments on the Montreal Archive of Sleep Studies (MASS) dataset demonstrate that the GraphSleepNet outperforms the state-of-the-art baselines.

scholarly journals TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jinmao Zhang ◽  
Huanchang Chen ◽  
Yiming Fang
Keyword(s):  
Intelligent Transportation System ◽  
Urban Traffic ◽  
Trajectory Data ◽  
Convolutional Network ◽  
Network Information ◽  
The Road ◽  
Convolutional Networks ◽  
Spatial Features ◽  
Temporal Features ◽  
The City

Taxi flow is an important part of the urban intelligent transportation system. The accurate prediction of taxi flow provides an attractive way to find the potential traffic hotspots in the city, which helps to avoid serious traffic congestions by taking effective measures in advance. The current prediction of taxi flow and its impact on urban transportation are closely related to the passenger origin-destination (OD) information. However, high-quality OD information is not always available. To address this problem, a prediction model, named as TaxiInt, is proposed in this study. Different from other density-clustering-based approaches, neural network, or OD information based models, TaxiInt predicted the taxi flow using the trajectory data of taxis. The spatial features and temporal features of each road were extracted using a graph convolutional network, which was trained with the road network information and the trajectory data. The experiments carried on a real taxi dataset showed the validity of our model. It can predict the taxi flow at a given urban intersection with high accuracy.

scholarly journals AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN

2019 ◽  
Author(s):  
Li Zheng ◽  
Zhenpeng Li ◽  
Jian Li ◽  
Zhao Li ◽  
Jun Gao
Keyword(s):  
Anomaly Detection ◽  
Dynamic Graphs ◽  
Dynamic Graph ◽  
Short Term ◽  
Convolutional Network ◽  
Attention Model ◽  
Temporal Features ◽  
Structural Content ◽  
Real World Datasets

Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e.g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data. It is better to learn the anomaly patterns by considering all possible features including the structural, content and temporal features, rather than utilizing heuristic rules over the partial features. In this paper, we propose AddGraph, a general end-to-end anomalous edge detection framework using an extended temporal GCN (Graph Convolutional Network) with an attention model, which can capture both long-term patterns and the short-term patterns in dynamic graphs. In order to cope with insufficient explicit labelled data, we employ the negative sampling and margin loss in training of AddGraph in a semi-supervised fashion. We conduct extensive experiments on real-world datasets, and illustrate that AddGraph can outperform the state-of-the-art competitors in anomaly detection significantly.

scholarly journals STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

2019 ◽  
Author(s):  
Lei Bai ◽  
Lina Yao ◽  
Salil S. Kanhere ◽  
Xianzhi Wang ◽  
Quan Z. Sheng
Keyword(s):  
State Of The Art ◽  
Demand Forecasting ◽  
Short Term ◽  
Demand Prediction ◽  
On Demand ◽  
Passenger Demand ◽  
Temporal Correlations ◽  
Output Module ◽  
Real World Datasets

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.

Commercial Potentials of NBIoT and Its Impact on the Economy

2021 ◽  
pp. 86-104
Author(s):  
Sasmita Mohanty
Keyword(s):  
Large Scale ◽  
Emergency Services ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Potential Game ◽  
Healthcare Applications ◽  
The Social ◽  
Social Landscapes ◽  
The Internet Of Things

The internet of things (IoT) has emerged as a potential game changer in the modern ICT landscape. Like several other technologies, it is going to disrupt the global markets to a large extent. The main potentials of IoT are its pervasive presence and multiple applications. For the large-scale deployment of the IoTs, a huge amount of resources are needed. In that case, a lighter or energy efficient version of the IoT is preferred for the large scale projects such as the smart cities and healthcare applications. In this regard, narrowband IoT (NBIoT) is preferred which is a resource efficient version of the modern IoTs. The pervasive uses of the IoTs are going to change the economy and the social landscapes to a large extent. IoT will be pervasive, and it will touch every aspect of the human lives. Due to its pervasiveness, the economy associated with it is going to be equally pervasive. In this chapter, the aim is to provide the commercial potentials of NBIoT and to analyze the changes it can bring to the economy. In the commercial potentials the author include the common applications where NBIoT is the main choice and can play a crucial role in the long term. Normally, these sectors are widely accessible to the general public such as the healthcare, traffic monitoring systems, smart cities, utilities management, retail management, industrial automation, and the emergency services.

scholarly journals Multi Scale Temporal Graph Networks for Skeleton-Based Action Recognition

2020 ◽  
Author(s):  
Tingwei Li ◽  
Ruiwen Zhang ◽  
Qing Li
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Convolutional Networks ◽  
Multi Scale ◽  
Spatial Features ◽  
Skeleton Graph ◽  
Temporal Graph ◽  
Temporal And Spatial ◽  
Generic Representation ◽  
Novel Model

Graph convolutional networks (GCNs) can effectively capture the features of related nodes and improve the performance of model. More attention is paid to employing GCN in Skeleton-Based action recognition. But existing methods based on GCNs have two problems. First, the consistency of temporal and spatial features is ignored for extracting features node by node and frame by frame. To obtain spatiotemporal features simultaneously, we design a generic representation of skeleton sequences for action recognition and propose a novel model called Temporal Graph Networks (TGN). Secondly, the adjacency matrix of graph describing the relation of joints are mostly depended on the physical connection between joints. To appropriate describe the relations between joints in skeleton graph, we propose a multi-scale graph strategy, adopting a full-scale graph, part-scale graph and core-scale graph to capture the local features of each joint and the contour features of important joints. Experiments were carried out on two large datasets and results show that TGN with our graph strategy outperforms state-of-the-art methods.

scholarly journals Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

2019 ◽  
Vol 33 ◽  
pp. 922-929 ◽  
Author(s):  
Shengnan Guo ◽  
Youfang Lin ◽  
Ning Feng ◽  
Chao Song ◽  
Huaiyu Wan
Keyword(s):  
Traffic Flow ◽  
Critical Issue ◽  
Performance Measurement System ◽  
Traffic Data ◽  
Traffic Flows ◽  
Convolutional Network ◽  
Traffic Flow Forecasting ◽  
Temporal Correlations ◽  
Temporal Graph ◽  
Real World Datasets

Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns. Most existing traffic flow prediction methods, lacking abilities of modeling the dynamic spatial-temporal correlations of traffic data, thus cannot yield satisfactory prediction results. In this paper, we propose a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to solve traffic flow forecasting problem. ASTGCN mainly consists of three independent components to respectively model three temporal properties of traffic flows, i.e., recent, daily-periodic and weekly-periodic dependencies. More specifically, each component contains two major parts: 1) the spatial-temporal attention mechanism to effectively capture the dynamic spatialtemporal correlations in traffic data; 2) the spatial-temporal convolution which simultaneously employs graph convolutions to capture the spatial patterns and common standard convolutions to describe the temporal features. The output of the three components are weighted fused to generate the final prediction results. Experiments on two real-world datasets from the Caltrans Performance Measurement System (PeMS) demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines.

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