Incident detection using data from social media

Abstract

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%.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1109/itsc.2017.8317967 under the license https://creativecommons.org/licenses/by-nc-nd

• https://wlv.openrepository.com/bitstream/2436/623169/4/Salas_et_al_Incident_detection_using_data_from_social_media_2017.pdf

• http://xplorestaging.ieee.org/ielx7/8307147/8317580/08317967.pdf?arnumber=8317967,

http://dx.doi.org/10.1109/itsc.2017.8317967 under the license cc-by-nc-nd

• https://dblp.uni-trier.de/db/conf/itsc/itsc2017.html#SalasGP17,

https://academic.microsoft.com/#/detail/2792918557