Proceedings of journal and conference papers are good sources of big textual data to examine research trends in various branches of science. The contents, usually unstructured in nature, require fast machine-learning algorithms to be deciphered. Exploratory analysis through text mining usually provides the descriptive nature of the contents but lacks quantification of the topics and their correlations. Topic models are algorithms designed to discover the main theme or trend in massive collections of unstructured documents. Through the use of a structural topic model, an extension of latent Dirichlet allocation, this study introduced distinct topic models on the basis of the relative frequencies of the words used in the abstracts of 15,357 TRB compendium papers. With data from 7 years (2008 through 2014) of TRB annual meeting compendium papers, the 20 most dominant topics emerged from a bag of 4 million words. The findings of this study contributed to the understanding of topical trends in the complex and evolving field of transportation engineering research.
Trends in Transportation Research
Exploring Content Analysis in Topics
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2614 , 1 ; 27-38
2017-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Trends Over Two Decades of Transportation Research
Transportation Research Record | 2017
|Trends Over Two Decades of Transportation Research
Online Contents | 2017
|Pipeline Transportation Trends
British Library Conference Proceedings | 1996
|Engineering Index Backfile | 1968