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Incident detection using data from social media
Salas, A Georgakis, P Petalas, Y
Salas, A
Georgakis, P
Petalas, Y
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2018-03-14
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Abstract
© 2017 IEEE. Due to the rapid growth of population in the last 20 years, an increased number of instances of heavy recurrent traffic congestion has been observed in cities around the world. This rise in traffic has led to greater numbers of traffic incidents and subsequent growth of non-recurrent congestion. Existing incident detection techniques are limited to the use of sensors in the transportation network. In this paper, we analyze the potential of Twitter for supporting real-time incident detection in the United Kingdom (UK). We present a methodology for retrieving, processing, and classifying public tweets by combining Natural Language Processing (NLP) techniques with a Support Vector Machine algorithm (SVM) for text classification. Our approach can detect traffic related tweets with an accuracy of 88.27%.
Citation
Salas, A., Georgakis, P. and Petalas, Y. (2017) Incident detection using data from social media, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 16-19 October, 2017, Yokohama, Japan.
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en
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This is an accepted manuscript of an article published by IEEE in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) on 15/03/2018, available online: https://ieeexplore.ieee.org/document/8317967/citations#citations
The accepted version of the publication may differ from the final published version.
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2153-0017
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9781538615263