Enhancing decision-making in transportation management: A comparative study of text classification models

Eduardo Carneiro, Tânia Fontes, Rosaldo J. F. Rossetti, Zafeiris Kokkinogenis

Abstract: Machine learning algorithms offer the capability to analyze large volumes of real-time data, providing transport authorities with valuable insights into traffic conditions, congestion hotspots, and incident detection from diverse data sources. However, these algorithms face challenges related to data quality and reliability. We conducted a comparative analysis of machine-learning models that can be used to identify and filter transportation content from social media or other sources that can provide small and concise text. The filtrated result can then feed models and/or tools used to improve and automate traffic control, operational management, and tactical management decision-making. We consider factors such as run time, generalization capacity, and performance metrics as criteria to assess their suitability for different decision levels. The analysis is supported by a dataset consisting of Twitter content. The predictions from three groups of algorithms are evaluated: traditional machine learning algorithms (Support Vector Machines, Logistic Regression, and Random Forest), a fine-tuned Google BERT model, and Google BERT models without training (BERT-base and BERT-large). The tests are performed using New York, London, and Melbourne data. The findings of this research aim to assist decision-makers in making informed choices when selecting the most appropriate method to filtrate information subsequently used for models that contribute to different traffic management tasks.