Real time road traffic monitoring alert based on incremental learning from tweets

Di Wang; Ahmad Al-Rubaie; John Davies; Sandra Stincic Clarke

doi:10.1109/EALS.2014.7009503

Real time road traffic monitoring alert based on incremental learning from tweets

Di Wang, Ahmad Al-Rubaie, John Davies, Sandra Stincic Clarke

Emirates ICT Innovation Center

BT Innovate and Design

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Scopus citations

Abstract

Social media has become an important source of near-instantaneous information about events and is increasingly also being analysed to provide predictive models, sentiment analysis and so on. One domain where social media data has value is transport and this paper looks at the exploitation of Twitter data in traffic management. A key issue is the identification and analysis of traffic-relevant content. A smart system is needed to identify traffic related tweets for traffic incident alerting. This paper proposes an instant traffic alert and warning system based on a novel LDA-based approach ('tweet-LDA') for classification of traffic-related tweets. The system is evaluated and shown to perform better than related approaches.

Original language	British English
Title of host publication	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014
Subtitle of host publication	2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	50-57
Number of pages	8
ISBN (Electronic)	9781479944958
DOIs	https://doi.org/10.1109/EALS.2014.7009503
State	Published - 13 Jan 2014
Event	2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014 - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014

Publication series

Name	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings

Conference

Conference	2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014
Country/Territory	United States
City	Orlando
Period	9/12/14 → 12/12/14

Keywords

Incremental learning
Latent Dirichlet Allocation (LDA)
Text mining
Tweet mining

Access to Document

10.1109/EALS.2014.7009503

Cite this

Wang, D., Al-Rubaie, A., Davies, J., & Clarke, S. S. (2014). Real time road traffic monitoring alert based on incremental learning from tweets. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings (pp. 50-57). Article 7009503 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EALS.2014.7009503

Wang, Di ; Al-Rubaie, Ahmad ; Davies, John et al. / Real time road traffic monitoring alert based on incremental learning from tweets. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 50-57 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings).

@inproceedings{5efa02cd5f9249d490e1b28d43bf85dd,

title = "Real time road traffic monitoring alert based on incremental learning from tweets",

abstract = "Social media has become an important source of near-instantaneous information about events and is increasingly also being analysed to provide predictive models, sentiment analysis and so on. One domain where social media data has value is transport and this paper looks at the exploitation of Twitter data in traffic management. A key issue is the identification and analysis of traffic-relevant content. A smart system is needed to identify traffic related tweets for traffic incident alerting. This paper proposes an instant traffic alert and warning system based on a novel LDA-based approach ('tweet-LDA') for classification of traffic-related tweets. The system is evaluated and shown to perform better than related approaches.",

keywords = "Incremental learning, Latent Dirichlet Allocation (LDA), Text mining, Tweet mining",

author = "Di Wang and Ahmad Al-Rubaie and John Davies and Clarke, {Sandra Stincic}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014 ; Conference date: 09-12-2014 Through 12-12-2014",

year = "2014",

month = jan,

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doi = "10.1109/EALS.2014.7009503",

language = "British English",

series = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014",

address = "United States",

}

Wang, D, Al-Rubaie, A, Davies, J & Clarke, SS 2014, Real time road traffic monitoring alert based on incremental learning from tweets. in IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings., 7009503, IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 50-57, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014, Orlando, United States, 9/12/14. https://doi.org/10.1109/EALS.2014.7009503

Real time road traffic monitoring alert based on incremental learning from tweets. / Wang, Di; Al-Rubaie, Ahmad; Davies, John et al.
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 50-57 7009503 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wang, Di

AU - Al-Rubaie, Ahmad

AU - Davies, John

AU - Clarke, Sandra Stincic

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AB - Social media has become an important source of near-instantaneous information about events and is increasingly also being analysed to provide predictive models, sentiment analysis and so on. One domain where social media data has value is transport and this paper looks at the exploitation of Twitter data in traffic management. A key issue is the identification and analysis of traffic-relevant content. A smart system is needed to identify traffic related tweets for traffic incident alerting. This paper proposes an instant traffic alert and warning system based on a novel LDA-based approach ('tweet-LDA') for classification of traffic-related tweets. The system is evaluated and shown to perform better than related approaches.

KW - Incremental learning

KW - Latent Dirichlet Allocation (LDA)

KW - Text mining

KW - Tweet mining

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PB - Institute of Electrical and Electronics Engineers Inc.

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Wang D, Al-Rubaie A, Davies J, Clarke SS. Real time road traffic monitoring alert based on incremental learning from tweets. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 50-57. 7009503. (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings). doi: 10.1109/EALS.2014.7009503

Real time road traffic monitoring alert based on incremental learning from tweets

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