<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by Big Data cloud methodologies via an Apache Spark system. Events were detected using a traffic related typology and the clustering K-Means model, where related event classification was achieved applying Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. We provide experimental results for 2-class and 3-class classification examples indicating that the ensemble performs with accuracy and F-score reaching 98.5%.</p></abstract>

Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by Big Data cloud methodologies via an Apache Spark system. Events were detected using a traffic related typology and the clustering K-Means model, where related event classification was achieved applying Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. We provide experimental results for 2-class and 3-class classification examples indicating that the ensemble performs with accuracy and F-score reaching 98.5%.

Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic...

Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification

Publié en ligne: 26 nov. 2020

Pages: 285 - 294

DOI: https://doi.org/10.2478/ttj-2020-0023

Mots clés
Textual, Traffic, Clustering, Classification, Ensemble, Deep-Learning

© 2020 Konstantinos Kokkinos et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification

Publié en ligne: 26 nov. 2020

Pages: 285 - 294

DOI: https://doi.org/10.2478/ttj-2020-0023

Mots clésTextual, Traffic, Clustering, Classification, Ensemble, Deep-Learning

© 2020 Konstantinos Kokkinos et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Textual, Traffic, Clustering, Classification, Ensemble, Deep-Learning