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Mining the Situation: Spatiotemporal Traffic Prediction With Big Data
2015
IEEE Journal on Selected Topics in Signal Processing
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One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations, which may differ from that of the historical ...
In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the ...
Instead, our scheme is applicable to all traffic situations and learns to match the current traffic situation to the best traffic prediction model, by exploiting spatiotemporal and other context similarity ...
doi:10.1109/jstsp.2015.2389196
fatcat:y3lo3vk4avdqlftva26spot4fu
Situation recognition
2012
Proceedings of the 20th ACM international conference on Multimedia - MM '12
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This paper motivates and computationally grounds the problem of situation recognition. It describes a systematic approach for combining multimodal real-time big data into actionable situations. ...
With the growth in social media, internet of things, and planetaryscale sensing there is an unprecedented need to assimilate spatiotemporally distributed multimedia streams into actionable information. ...
): Situations can be used for future prediction and/or action taking. ...
doi:10.1145/2393347.2396421
dblp:conf/mm/SinghGJ12
fatcat:5i427qq7yvdflahr6col6h2aoi
Research on Traffic Situation Analysis for Urban Road Network Through Spatiotemporal Data Mining: A Case Study of Xi'an, China
2021
IEEE Access
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This study aims to identify and predict the traffic operation status in the road network within the Third Ring Road in Xi'an and explore spatiotemporal patterns of traffic congestion. ...
The time series clustering method was used to divide the whole road network's locations into distinct clusters with similar spatiotemporal characteristics. ...
There is a lack of research on big data with multi-source traffic for the whole road network. The clustering method based on time series is widely used in the field of spatiotemporal data mining. ...
doi:10.1109/access.2021.3082188
fatcat:grewaldzfrfsxnjd7vuxhlgqp4
On how to incorporate public sources of situational context in descriptive and predictive models of traffic data
2021
European Transport Research Review
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Methodology We propose a methodology anchored in data science methods to integrate situational context in the descriptive and predictive models of traffic data, with a focus on the three following major ...
and predictive models of urban traffic data? ...
Acknowledgements The authors thank the support of CARRIS, METRO and Câmara Municipal de Lisboa) (particularly, Gabinete de Mobilidade and Centro de Operações Integrado) for the data provision and valuable ...
doi:10.1186/s12544-021-00519-w
pmid:38624925
pmcid:PMC8613527
fatcat:u3y4vk33orbsjeualot2kqawjm
The Current Research Status of AI-Based Network Security Situational Awareness
2023
Electronics
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Network security situational awareness is based on the extraction and analysis of big data, and by understanding these data to evaluate the current network security status and predict future development ...
Finally, the future development trends of network security situational awareness are summarized, and its prospects. ...
co-data mining of spatiotemporal measurement data. ...
doi:10.3390/electronics12102309
fatcat:uwmbbucbpbe4hhpbr3pieklfty
Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives
2022
Applied Sciences
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Combined with the technical elements of DTs, the coupling effect of DTs technology and urban construction and the internal logic of DTs technology embedded in urban construction are discussed. ...
First, the historical process and construction content of a Digital City (DC) under modern demand are analyzed, and the main ideas of a DC design and construction are discussed in combination with the ...
Acknowledgments: Thanks for the reviewers' suggestions.
Conflicts of Interest: The authors declare no conflict of interest. Appl. Sci. 2022, 12, 12820 ...
doi:10.3390/app122412820
fatcat:u4vea5md6nfijivzir5j5p67hi
MINING METHOD OF TRAFFIC IMPACT AREAS OF RAINSTORM EVENT BASED ON SOCIAL MEDIA IN ZHENGZHOU CITY
2022
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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At the same time, to verify the effectiveness of the social media-based method for mining the traffic impact areas of the Zhengzhou extreme rainstorm, this experiment compares Weibo data with official ...
Through statistical analysis and spatiotemporal analysis to filter, classify, analyse and manipulate the crawled Weibo's data, and then study the influence of the extreme rainstorm weather on the traffic ...
a wide range and complement the data released officially, reflecting the feasibility and accuracy of the Weibo-based method for mining the traffic impact areas in Zhengzhou. ...
doi:10.5194/isprs-archives-xlviii-3-w1-2022-79-2022
fatcat:lmki4eczdbb53h5kyi2ezcu6hu
Effective Following Patterns Mining Scheme for the Movements of Objects
2016
International Journal of Grid and Distributed Computing
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The study on patterns mining in movements not only has a direct bearing on human life but also play an important part in environment protection, traffic and transportation, privacy and security and so ...
and giving effective following patterns mining scheme for the movements of objects. ...
In [36] introduced a Big Data approach for mining the invaluable trajectory knowledge from such data. ...
doi:10.14257/ijgdc.2016.9.6.19
fatcat:tikphhhgw5hgzchjrsthnx2ugq
Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery
2015
ISPRS International Journal of Geo-Information
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A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. ...
Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. ...
Acknowledgement We would like to thank Caitlin McKown for the help and support during this research. Work performed under this project has been funded by the UW-Madison WARF grant # PRJ93XF. ...
doi:10.3390/ijgi4031549
fatcat:jthenqdbfrh4ril2ocynx5kxzm
The use of big data and data mining in the investigation of criminal offences
2022
Revista Amazonía investiga
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Improving the use of Big Data and Data Mining requires standardization of procedures with strict adherence to the fundamental ethical, organizational and procedural rules. ...
The aim of this study was to determine the features and prospects of using Big Data and Data Mining in criminal proceedings. ...
In this regard, one should agree with the opinion that forecasting crime requires significant improvement in the quality of Big Data, including the analysis of housing prices, population density, traffic ...
doi:10.34069/ai/2022.56.08.27
fatcat:5b6elrxecnag5mxcwfxl2g3x5i
GMove
2016
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16
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Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. ...
Furthermore, to reduce text sparsity of GeoSM data, GMove also features a text augmenter. The augmenter computes keyword correlations by examining their spatiotemporal distributions. ...
W911NF-09-2-0053 (NSCTA), National Science Foundation IIS-1017362, IIS-1320617, and IIS-1354329, HDTRA1-10-1-0120, and Grant 1U54GM114838 awarded by NIGMS through funds provided by the trans-NIH Big Data ...
doi:10.1145/2939672.2939793
pmid:28163978
pmcid:PMC5288006
dblp:conf/kdd/ZhangZYZHH16
fatcat:2ubbjfh6mjh5bgk6zbuorhxbwm
Unveiling the complexity of human mobility by querying and mining massive trajectory data
2011
The VLDB journal
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How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic in the near future? How to characterize traffic jams and congestions? ...
of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the ...
This work has been possible with the scientific contributions of all researchers involved in the GeoPKDD European project. We also acknowledge Octotelematics S.p.A for providing the data sets. ...
doi:10.1007/s00778-011-0244-8
fatcat:vgs4tmllafg4tl3wmrx74qysry
Smart Flood Resilience: Harnessing Community-Scale Big Data for Predictive Flood Risk Monitoring, Rapid Impact Assessment, and Situational Awareness
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2111.06461v1 fatcat:chmhag5flzailk2gqq62tmzn2e
The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk ...
The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping ...
The authors would also like to acknowledge INRIX, Inc. and SafeGraph for providing data. ...
arXiv:2111.06461v2
fatcat:2ugdb6geivdsjp5ypzkspvldey
Research Challenges in Developing Multimedia Systems for Managing Emergency Situations
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
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Given enormous progress in concept recognition using machine learning in the last few years, situation recognition may be the next major challenge for learning approaches in multimedia contextual big data ...
With an increasing amount of diverse heterogeneous data and information, the methodology of multimedia analysis has become increasingly relevant in solving challenging societal problems such as managing ...
The data is essentially multimedia big data, because of the volume, variety, and velocity [19] . ...
doi:10.1145/2964284.2976761
dblp:conf/mm/TangPJ16
fatcat:75w6uonma5fm7fqndoz7vdzoni
Data stream mining in ubiquitous environments: state-of-the-art and current directions
2014
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
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We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. ...
Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. ...
ACKNOWLEDGMENTS We acknowledge the contribution of the two visiting students to the University of Portsmouth, Oscar Campos and Victor Mandujano in porting the system from the desktop to the Android smartphones ...
doi:10.1002/widm.1115
fatcat:bkmbcpdot5cpvax4n3wvfqag2i
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