Traffic Technology Today Transportation Dataset (TTIT202201)

Published: 16 May 2022| Version 1 | DOI: 10.17632/k4bgjwktyp.1


The Traffic Technology Today Transportation Dataset (TTIT202201) was collected from a technology-focused magazine, Traffic Technology International (TTI), a popular magazine reporting the latest transport technologies and news. Using a web scraping technique, we collected all the articles (a total of 10,620 articles) from the magazine website without any filters or search queries (because this magazine only covers transportation-related news). The articles are dated between February 2015 and January 2022. Each document in the dataset has five attributes: News Article, Heading, Article Link, Category, and Publication Date. This dataset was built to discover parameters for industrial aspects of transportation as part of our deep journalism approach and DeepJournal tool. The deep journalism approach uses big data, deep learning, and digital methods to discover and analyse cross-sectional multi-perspective information to enable better decision making and develop better instruments for academic, corporate, national, and international governance. We discovered a total of 15 parameters from this dataset and grouped them into 5 macro-parameters, namely Industry, Innovation, & Leadership; Autonomous & Connected Vehicles; Sustainability; Mobility Services; and Infrastructure. The other two transportation datasets related to this dataset used in the deep journalism approach include the Guardian Transportation Dataset (GT202201: and the Web of Science Transportation Dataset (WST202201: Further details of the dataset, its collection, and usage for deep journalism including detection of the multi-perspective parameters for transportation can be found in our article here:



King Abdulaziz University


Computer Science, Transport, Natural Language Processing, Machine Learning, Deep Learning, Textual Analysis