  This study details the progress in transportation data analysis with a novel
computing framework in keeping with the continuous evolution of the computing
technology. The computing framework combines the Labelled Latent Dirichlet
Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with
the supporting computing strategy on publicly available Twitter data in
determining transportation-related events to provide reliable information to
travelers. The analytical approach includes analyzing tweets using text
classification and geocoding locations based on string similarity. A case study
conducted for the New York City and its surrounding areas demonstrates the
feasibility of the analytical approach. Approximately 700,010 tweets are
analyzed to extract relevant transportation-related information for one week.
The SVM classifier achieves more than 85% accuracy in identifying
transportation-related tweets from structured data. To further categorize the
transportation-related tweets into sub-classes: incident, congestion,
construction, special events, and other events, three supervised classifiers
are used: L-LDA, SVM, and L-LDA incorporated SVM. Findings from this study
demonstrate that the analytical framework, which uses the L-LDA incorporated
SVM, can classify roadway transportation-related data from Twitter with over
98.3% accuracy, which is significantly higher than the accuracies achieved by
standalone L-LDA and SVM.
