NSSC OpenIR
GeoTraPredict: A machine learning system of web spatio-temporal traffic flow
Alternative Title20203409064583
Li, Jingjing1,2; Li, Jun2; Jia, Nan3; Li, Xunchun4; Ma, Wenzhen5; Shi, Shanshan1,2
Source PublicationNeurocomputing
2020
DOI10.1016/j.neucom.2020.06.121
ISSN09252312
Language英语
KeywordAnomaly detection - Forecasting - Machine learning - Population distribution - Population statistics - Predictive analytics - Time series analysis Cloud computing environments - Computation function - Spatial data structure - Spatio-temporal prediction - Spatiotemporal distributions - Spatiotemporal patterns - Temporal dimensions - Traffic flow prediction
AbstractTraffic flow prediction is an important component for self-driving. Traffic flow is closely related to population distribution, and the traffic flow is not only related to the absolute number of human population but also to their concerns and interests. Accurate spatio-temporal web traffic flow prediction is critical in many applications, such as bandwidth allocation, anomaly detection, congestion control and admission control. Most existing traffic flow prediction methods use models based on time-series analysis and remain inadequate for many real-world applications. Web traffic flow is found to be strongly associated with the spatio-temporal distribution of the population. Increasingly, it is critical to understand and make decisions based on the relationship between population patterns and web traffic flow patterns. It has been proven that different people have different responses to web events. Due to the complexity of spatial data structures and the huge volume of web traffic flow log data, it is difficult to routinely find the relationship between web events and population distributions without an appropriate processing framework. In this paper, we propose an innovative framework named GeoTrafficPredict to support the accurate spatio-temporal prediction of web traffic flow. GeoTrafficPredict provides a machine learning platform to learn the spatio-temporal pattern of traffic flow and use the pattern to predict the trend in both spatial and temporal dimension. Also, GeoTrafficPredict provide data aggregation portal and cloud-based computation function. GeoTrafficPredict deploys a series of computational images in a cloud computing environment, and the implementation on China's CSTNET illustrates the performance of our platform. © 2020 Elsevier B.V.
Indexed ByEI
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/7478
Collection中国科学院国家空间科学中心
Affiliation1.Computer Network Information Center, Chinese Academy of Sciences, Beijing; 100190, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China;
3.People's Public, Security University of China, Beijing; 100038, China;
4.Academy of Broadcasting Science, National Radio and Television Administration, Beijing; 100866, China;
5.National Space Science Center, Chinese Academy of Sciences, Beijing; 100190, China
Recommended Citation
GB/T 7714
Li, Jingjing,Li, Jun,Jia, Nan,et al. GeoTraPredict: A machine learning system of web spatio-temporal traffic flow[J]. Neurocomputing,2020.
APA Li, Jingjing,Li, Jun,Jia, Nan,Li, Xunchun,Ma, Wenzhen,&Shi, Shanshan.(2020).GeoTraPredict: A machine learning system of web spatio-temporal traffic flow.Neurocomputing.
MLA Li, Jingjing,et al."GeoTraPredict: A machine learning system of web spatio-temporal traffic flow".Neurocomputing (2020).
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