Journal
CITIES
Volume 96, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.cities.2019.102410
Keywords
Social media; New York; Traffic operation; Short-term planning; Machine learning; Traffic management policy
Categories
Funding
- USDOT Connected Multimodal Mobility University Transportation Center (C2M2) (Tier1 University Transpiration Center) headquartered at Clemson University, Clemson, South Carolina, USA
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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 Labeled 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 > 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.
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