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作者

Sakib Mahmud Khan;Mashrur Chowdhury;Linh B. Ngo;Amy Apon

来源

CITIES,Vol.96,Issue1,Article 102410

语言

英文

关键字

Social media;New York;Traffic operation;Short-term planning;Machine learning;Traffic management policy

作者单位

Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;Computer Science Department, West Chester University, West Chester, PA 19383, USA;School of Computing, Clemson University, SC 29634, USA;Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA;Computer Science Department, West Chester University, West Chester, PA 19383, USA;School of Computing, Clemson University, SC 29634, USA

摘要

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.