Citation

Chowdhury S, Alzarrad A. Information (Basel) 2023; 14(4): e201.

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/info14040201

PMID

unavailable

Abstract

Transportation infrastructure is vital to the well-functioning of economic activities in a region. Due to the digitalization of data storage, ease of access to large databases, and advancement of social media, large volumes of text data that relate to different aspects of transportation infrastructure are generated. Text mining techniques can explore any large amount of textual data within a limited time and with limited resource allocation for generating easy-to-understand knowledge. This study aims to provide a comprehensive review of the various applications of text mining techniques in transportation infrastructure research. The scope of this research ranges across all forms of transportation infrastructure-related problems or issues that were investigated by different text mining techniques. These transportation infrastructure-related problems or issues may involve issues such as crashes or accidents investigation, driving behavior analysis, and construction activities. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)-based structured methodology was used to identify relevant studies that implemented different text mining techniques across different transportation infrastructure-related problems or issues. A total of 59 studies from both the U.S. and other parts of the world (e.g., China, and Bangladesh) were ultimately selected for review after a rigorous quality check. The results show that apart from simple text mining techniques for data pre-processing, the majority of the studies used topic modeling techniques for a detailed evaluation of the text data. Other techniques such as classification algorithms were also later used to predict and/or project future scenarios/states based on the identified topics. The findings from this study will hopefully provide researchers and practitioners with a better understanding of the potential of text mining techniques under different circumstances to solve different types of transportation infrastructure-related problems. They will also provide a blueprint to better understand the ever-evolving area of transportation engineering and infrastructure-focused studies.

Language: en

Keywords

infrastructure; natural language processing (NLP); review; text mining; transportation