Loading...
Traffic event detection framework using social media
Salas, A Georgakis, P Nwagboso, C Ammari, A Petalas, I
Salas, A
Georgakis, P
Nwagboso, C
Ammari, A
Petalas, I
Editors
Other contributors
Affiliation
Epub Date
Issue Date
2017-09-18
Submitted date
Alternative
Abstract
© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.
Citation
Salas, A., Georgakis, P. Nwagboso, C., Ammari, A. and Petalas, I. (2017) Traffic event detection framework using social media, 2017 IEEE International Conference on Smart Grid and Smart Cities, ICSGSC 2017, 23-26 July 2017, Singapore, Singapore.
Publisher
Journal
Research Unit
PubMed ID
PubMed Central ID
Embedded videos
Additional Links
Type
Conference contribution
Language
en
Description
This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595
The accepted version of the publication may differ from the final published version.
Series/Report no.
ISSN
EISSN
ISBN
9781538605042