RIOMS: An intelligent system for operation and maintenance of urban roads using spatio-temporal data in smart cities

2021 ◽  
Vol 115 ◽  
pp. 583-609
Author(s):  
Gang Yu ◽  
Yi Wang ◽  
Min Hu ◽  
Lihua Shi ◽  
Zeyu Mao ◽  
...  
Keyword(s):  
Intelligent System ◽  
Smart Cities ◽  
Temporal Data ◽  
Operation And Maintenance ◽  
Spatio Temporal

scholarly journals Prediction of Air Pollution in Smart Cities Using Machine Learning Techniques

2021 ◽  
Vol 9 (12) ◽  
pp. 273-277
Author(s):  
Mrs. Gowri G
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Predictive Model ◽  
Regression Models ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Temporal Data ◽  
Spatio Temporal

Abstract: Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five years. Smart cities are called to play a decisive role to increase such pollution in real-time. The increase in air pollution due to fossil fuel consumption as well as its ill effects on the climate has made air pollution forecasting an important research area in today’s times. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. prediction of spatio-temporal data has been one of the major challenges in creating a good predictive model. There are many different approaches which have been used to create an accurate predictive model. Primitive predictive machine learning algorithms like simple linear regression have failed to produce accurate results primarily due to lack of computing power but also due to lack of optimization techniques. A recent development in deep learning as well as improvements in computing resources has increased the accuracy of predicting time series data. However, with large spatio-temporal data sets spanning over years. Employing regression models on the entire data can cause per date predictions to be corrupted. In this work, we look at dealing with pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW). K-means is then used to classify the spatio-temporal pollution data over a period of 16 years from 2000 to 2016. Here Mean Absolute error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of regression models. Keywords: Spatio-temporal data, Primitive predictive machine learning algorithms, regression models

scholarly journals Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 199
Author(s):  
Ling Zhao ◽  
Hanhan Deng ◽  
Linyao Qiu ◽  
Sumin Li ◽  
Zhixiang Hou ◽  
...  
Keyword(s):  
Data Analysis ◽  
Network Structure ◽  
Smart Cities ◽  
Graph Embedding ◽  
Practical Significance ◽  
Knowledge Graph ◽  
Temporal Data ◽  
Semantic Relationships ◽  
Spatio Temporal ◽  
Temporal Data Analysis

Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.

scholarly journals RESEARCH ON CONSTRUCTION OF SPATIO-TEMPORAL DATA VISUALIZATION PLATFORM FOR GIS AND BIM FUSION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 555-563
Author(s):  
S. Q. Dou ◽  
H. H. Zhang ◽  
Y. Q. Zhao ◽  
A. M. Wang ◽  
Y. T. Xiong ◽  
...  
Keyword(s):  
Data Visualization ◽  
Feasible Solution ◽  
Smart Cities ◽  
Spatial Scales ◽  
Technical Support ◽  
Virtual Simulation ◽  
Twin Cities ◽  
Temporal Data ◽  
Data Platform ◽  
Spatio Temporal

Abstract. The visualization model of GIS and BIM fusion can provide data bearing platform and main technical support for future urban operation centers, digital twin cities, and smart cities. Based on the analysis of the features and advantages of GIS and BIM Fusion, this paper proposes a construction method of the spatio-temporal data visualization platform for GIS and BIM Fusion. It expounds and analyzes the overall architecture design of platform, multi-dimensional and multi-spatial scales visualization, space analysis for GIS and BIM fusion, and platform applications and so on. The urban virtual simulation spatio-temporal data platform project of Teda New District in Tianjin has verified and demonstrated that the effect of application is good. This provides a feasible solution for the construction of spatio-temporal Data Visualization Platform.

Spatial-temporal data collection process models using mobile sinks

2019 ◽  
Vol 942 (12) ◽  
pp. 22-28
Author(s):  
A.V. Materuhin ◽  
V.V. Shakhov ◽  
O.D. Sokolova
Keyword(s):  
Energy Consumption ◽  
Operation Time ◽  
Point Of View ◽  
Process Models ◽  
Temporal Data ◽  
Network Nodes ◽  
Mobile Sinks ◽  
Autonomous Operation ◽  
Data Collection Process ◽  
Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

scholarly journals From spatio-temporal data to chronological networks

2019 ◽  
Author(s):  
Didier A. Vega-Oliveros ◽  
Moshé Cotacallapa ◽  
Leonardo N. Ferreira ◽  
Marcos G. Quiles ◽  
Liang Zhao ◽  
...  
Keyword(s):  
Temporal Data ◽  
Spatio Temporal
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