Traffic Technology Today Transportation Dataset (TTIT202201)

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: http://dx.doi.org/10.17632/yvxx6s5xhh.1) and the Web of Science Transportation Dataset (WST202201: http://dx.doi.org/10.17632/tnfw2dh5nj.1).

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: https://doi.org/10.3390/su14095711.

《今日交通技术交通数据集》（TTIT202201）系从专注于技术的杂志《交通技术国际》（TTI）收集而来，该杂志为广受欢迎的报道最新交通技术和新闻的出版物。通过网络爬虫技术，我们从杂志网站上收集了所有文章（总计10,620篇文章），未进行任何筛选或搜索查询（因为该杂志仅涵盖与交通相关的新闻）。这些文章的出版时间介于2015年2月至2022年1月之间。数据集中的每份文档包含五个属性：新闻文章、标题、文章链接、类别和出版日期。该数据集的构建旨在发现交通产业方面的参数，作为我们深度新闻方法与DeepJournal工具的一部分。深度新闻方法运用大数据、深度学习和数字方法，以发现和分析跨截面多视角信息，从而促进更好的决策制定，并为学术、企业、国家和国际治理开发更好的工具。我们从该数据集中发现了总共15个参数，并将它们分为五个宏观参数，即产业、创新与领导力；自动驾驶与联网汽车；可持续性；移动服务；以及基础设施。与该数据集相关的其他两个交通数据集，在深度新闻方法中使用，包括《卫报交通数据集》（GT202201：http://dx.doi.org/10.17632/yvxx6s5xhh.1）和《科学引文索引交通数据集》（WST202201：http://dx.doi.org/10.17632/tnfw2dh5nj.1）。关于该数据集的详细信息、收集方法以及在深度新闻中的应用，包括检测交通的多视角参数，可在我方文章中找到：https://doi.org/10.3390/su14095711。