Dataset on fatal road traffic crash attributes extracted via natural language processing of online media articles in India

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-04-23 DOI:10.1016/j.dib.2025.111578

Ashutosh Ashutosh, Sai Chand

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引用次数: 0

Abstract

Road traffic crashes are among the leading causes of death globally, resulting in substantial social and economic impacts. Online media is a key source of public information on road safety. Understanding how crashes are reported is crucial for detecting potential reporting biases and enhancing safety awareness. Hence, to address the issue of the lack of high-quality, media-reported fatal crash data, fatal crash reports were extracted for 2022–2023 from The Times of India, a prominent Indian news outlet. The resulting dataset comprised 2898 fatal crashes, 6584 fatalities and 7812 injuries, including 16 detailed crash attributes. This dataset was developed using web scraping and natural language processing (NLP) techniques. Automated tools such as Selenium and BeautifulSoup were employed to extract raw data from the news source. NLP algorithms were then applied to identify key crash attributes, including crash date, location, vehicles involved and number of fatalities. This study provides a replicable framework for constructing robust datasets from media sources, enabling multidisciplinary research on transportation safety, media reporting and public perception of crashes. The dataset is expected to serve as a valuable resource for analysing how the media shapes road safety narratives and for investigations on identifying high-fatality crash locations.

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通过自然语言处理印度在线媒体文章提取的致命道路交通事故属性数据集

道路交通事故是全球死亡的主要原因之一，造成重大的社会和经济影响。网络媒体是道路安全公共信息的重要来源。了解事故的报告方式对于发现潜在的报告偏差和提高安全意识至关重要。因此，为了解决缺乏媒体报道的高质量致命事故数据的问题，从印度著名新闻媒体《印度时报》（the Times of India）提取了2022-2023年的致命事故报告。由此产生的数据集包括2898起致命事故，6584人死亡和7812人受伤，包括16个详细的事故属性。该数据集是使用网络抓取和自然语言处理（NLP）技术开发的。使用Selenium和BeautifulSoup等自动化工具从新闻来源中提取原始数据。然后应用NLP算法来识别关键的碰撞属性，包括碰撞日期、地点、涉及的车辆和死亡人数。本研究为构建来自媒体的可靠数据集提供了一个可复制的框架，从而可以对交通安全、媒体报道和公众对事故的看法进行多学科研究。该数据集有望成为分析媒体如何塑造道路安全叙事和调查确定高死亡率事故地点的宝贵资源。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.