A Classification Model for Road Traffic Incidents on Twitter Data

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dc.contributor.authorRaksachat, Thawatchai
dc.contributor.authorChawuthai, Rathachai
dc.date.accessioned2026-08-06T10:34:59Z
dc.date.available2026-08-06T10:34:59Z
dc.date.issued2022-01-01
dc.description.abstractThis study aims to create a classification model for road traffic incidents in Thailand using Twitter data. The challenging issue of our work is to deal with highly imbalanced dataset of 5 classes. As we surveyed, some pieces of research solved this issue by the Markov Chains method. However, using the Markov Chains in our dataset provides low performance, so we study the Undersampling, Oversampling, Markov Chains, and Bi-directional Long Short-Term Memory (Bi-LSTM). As we use the Markov Chains as the baseline, the result of our experiment found that using Bi-LSTM provides the improvement of F1-score up to 15.44% against the baseline.
dc.identifier.citationItc Cscc 2022 37th International Technical Conference on Circuits Systems Computers and Communications, 442-445, 2022
dc.identifier.doi10.1109/ITC-CSCC55581.2022.9894853
dc.identifier.other2-s2.0-85140573874
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12663
dc.sourceItc Cscc 2022 37th International Technical Conference on Circuits Systems Computers and Communications
dc.subjectDeep Learning
dc.subjectImbalance Dataset
dc.subjectRoad Traffic Incident
dc.subjectText Classification
dc.subjectTwitter Data Analytics
dc.titleA Classification Model for Road Traffic Incidents on Twitter Data
dc.typeConference Paper

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