Advances in crowd analysis for urban applications through urban event detection

Kaiser, MS, Lwin, KT, Mahmud, M ORCID: https://orcid.org/0000-0002-2037-8348, Hajializadeh, D, Chaipimonplin, T, Sarhan, A and Hossain, MA, 2017. Advances in crowd analysis for urban applications through urban event detection. IEEE Transactions on Intelligent Transportation Systems. ISSN 1524-9050

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Abstract

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Intelligent Transportation Systems
Creators:	Kaiser, M.S., Lwin, K.T., Mahmud, M., Hajializadeh, D., Chaipimonplin, T., Sarhan, A. and Hossain, M.A.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	13 December 2017
ISSN:	1524-9050
Identifiers:	Number Type 10.1109/tits.2017.2771746 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	23 Jul 2018 10:31
Last Modified:	23 Jul 2018 10:31
URI:	https://irep.ntu.ac.uk/id/eprint/34128

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