{"title":"A deep learning approach for robust traffic accident information extraction from online chinese news","authors":"Yancheng Ling, Zhenliang Ma, Xiaoxian Dong, Xiaoxiong Weng","doi":"10.1049/itr2.12493","DOIUrl":null,"url":null,"abstract":"<p>Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence-wise and context-wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon-BiLSTM-CRF method. The proposed approach is validated by comparing it with state-of-the-art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1-score. It improves the performance of the best benchmark model (BiLSTM-CRF) by 18.8% in precision and 12.08% in F1-score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.12493","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Intelligent Transport Systems","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/itr2.12493","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence-wise and context-wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon-BiLSTM-CRF method. The proposed approach is validated by comparing it with state-of-the-art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1-score. It improves the performance of the best benchmark model (BiLSTM-CRF) by 18.8% in precision and 12.08% in F1-score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice.
道路交通事故是造成人员伤亡的主要原因。了解交通事故的发生规律及其诱因是有效进行交通安全管理的前提。本文提出了一种从中文在线新闻中进行交通事故识别和信息提取的深度学习方法，以自动提取和整理交通事故。该方法由三个模块组成，包括新闻自动采集、新闻分类和交通事故信息提取。自动新闻采集模块从网络资源中抓取新闻，并将其清理和整理成一个包含不同类别新闻的通用新闻数据库。新闻分类模块通过融合句子和上下文的语义新闻信息，从各类新闻中稳健地识别出交通事故新闻。事故信息提取模块使用 SoftLexicon-BiLSTM-CRF 方法从新闻文本中提取交通事故的关键属性（如原因、时间、地点）。我们利用在线抓取的中文新闻数据，将所提出的方法与最先进的文本挖掘方法进行了比较，从而验证了所提出的方法。结果表明，该方法在精确度、召回率和 F1 分数方面都能达到较高的信息提取性能。它在精确度和 F1 分数上分别比最佳基准模型（BiLSTM-CRF）提高了 18.8% 和 12.08%。此外，从在线新闻中自动提取的事故数据也说明了其在补充传统权威事故数据方面的潜在价值，从而在实践中推动更有效的交通安全管理。
期刊介绍：
IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following:
Sustainable traffic solutions
Deployments with enabling technologies
Pervasive monitoring
Applications; demonstrations and evaluation
Economic and behavioural analyses of ITS services and scenario
Data Integration and analytics
Information collection and processing; image processing applications in ITS
ITS aspects of electric vehicles
Autonomous vehicles; connected vehicle systems;
In-vehicle ITS, safety and vulnerable road user aspects
Mobility as a service systems
Traffic management and control
Public transport systems technologies
Fleet and public transport logistics
Emergency and incident management
Demand management and electronic payment systems
Traffic related air pollution management
Policy and institutional issues
Interoperability, standards and architectures
Funding scenarios
Enforcement
Human machine interaction
Education, training and outreach
Current Special Issue Call for papers:
Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf
Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf
Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
