IDEAS home Printed from https://ideas.repec.org/a/eee/jaitra/v108y2023ics0969699723000170.html
   My bibliography  Save this article

Exploring topics and trends in Chinese ATC incident reports using a domain-knowledge driven topic model

Author

Listed:
  • Bao, Jie
  • Chen, Yixuan
  • Yin, Jianan
  • Chen, Xinyuan
  • Zhu, Dan

Abstract

The primary objective of this study is to discover hidden topics and trends from historical ATC incident reports. A novel domain-knowledge driven topic (DDT) model is proposed to explore the embedded patterns and hidden connections in ATC incident reports. Seventeen-year ATC incident records are collected from Local Air Traffic Management Branch of Civil Aviation Administration of China to illustrate the procedure. First, a total of twenty topics are identified from the collected reports, including aircraft-flight-operation related topics, crew-activities related topics, airspace-control-transfer related topics and others. Then, the topic evolution trend over years is explored, which identifies four hot topics and four cold topics over the study period. The results reveal that in general the contributing factors of ATC incidents are gradually shifting from external factors (e.g., radar equipment or aircraft components) to human related factors (e.g., instruction communication or handover of airspace control) due to the improved quality of communication equipment and some adjustments of ATC rules over the past two decades. Finally, the topic evolution analyses across different ATC areas and flight phases are further conducted. The findings indicate that the potential causes of ATC incidents are different across various ATC areas and flight phases due to the variation in geographical environment and local policies. The results of this research can help local ATC authorities conduct efficient safety performance assessment, implement proactive countermeasures for specific areas to enhance air traffic safety, and provide aviation authorities with insightful suggestions for ATC process optimization and design.

Suggested Citation

  • Bao, Jie & Chen, Yixuan & Yin, Jianan & Chen, Xinyuan & Zhu, Dan, 2023. "Exploring topics and trends in Chinese ATC incident reports using a domain-knowledge driven topic model," Journal of Air Transport Management, Elsevier, vol. 108(C).
    • Handle: RePEc:eee:jaitra:v:108:y:2023:i:c:s0969699723000170
    DOI: 10.1016/j.jairtraman.2023.102374
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969699723000170
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jairtraman.2023.102374?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hakyeon Lee & Pilsung Kang, 2018. "Identifying core topics in technology and innovation management studies: a topic model approach," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1291-1317, October.
    2. Jie Bao & Chengcheng Xu & Pan Liu & Wei Wang, 2017. "Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests," Networks and Spatial Economics, Springer, vol. 17(4), pages 1231-1253, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Dejian & Xiang, Bo, 2024. "An ESTs detection research based on paper entity mapping: Combining scientific text modeling and neural prophet," Journal of Informetrics, Elsevier, vol. 18(4).
    2. Bilal Barış Alkan & Leyla Karakuş & Bekir Direkci, 2023. "Knowledge discovery from the texts of Nobel Prize winners in literature: sentiment analysis and Latent Dirichlet Allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5311-5334, September.
    3. Bi, Hui & Ye, Zhirui & Hu, Liyang & Zhu, He, 2021. "Why they don't choose bus service? Understanding special online car-hailing behavior near bus stops," Transport Policy, Elsevier, vol. 114(C), pages 280-297.
    4. Kyoungok Kim, 2024. "Discovering spatiotemporal usage patterns of a bike-sharing system by type of pass: a case study from Seoul," Transportation, Springer, vol. 51(4), pages 1373-1407, August.
    5. Xue, Mengtian & Zhang, Bin & Chen, Siyuan & Zhao, Yuandong & Wang, Zhaohua, 2024. "How does extreme temperature affect shared travel? Evidence from bike-sharing order flow in China," Journal of Transport Geography, Elsevier, vol. 118(C).
    6. Yuanrong Zhang & Wei Guo & Jian Ma & Zhonglin Fu & Zhixing Chang & Lei Wang, 2023. "Evolution analysis of cross-domain collaborative research topic: a case study of cognitive-based product conceptual design," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(12), pages 6695-6718, December.
    7. Pan, Yingjiu & Chen, Shuyan & Niu, Shifeng & Ma, Yongfeng & Tang, Kun, 2020. "Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity," Journal of Transport Geography, Elsevier, vol. 83(C).
    8. Li, Mengya & Kwan, Mei-Po & Wang, Fahui & Wang, Jun, 2018. "Using points-of-interest data to estimate commuting patterns in central Shanghai, China," Journal of Transport Geography, Elsevier, vol. 72(C), pages 201-210.
    9. Hua, Mingzhuang & Chen, Xuewu & Chen, Jingxu & Huang, Di & Cheng, Long, 2022. "Large-scale dockless bike sharing repositioning considering future usage and workload balance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    10. Xiumei Tang & Yu Liu & Yuchun Pan, 2020. "An Evaluation and Region Division Method for Ecosystem Service Supply and Demand Based on Land Use and POI Data," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    11. Qiang Gao & Man Jiang, 2024. "Exploring technology fusion by combining latent Dirichlet allocation with Doc2vec: a case of digital medicine and machine learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4043-4070, July.
    12. Wieslaw Urban & Elzbieta Krawczyk-Dembicka, 2020. "An In-depth Investigation of Technology Management Process in the Metal Processing Industry," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 115-136.
    13. Gong, Mengjie & Xin, Rui & Yang, Jian & Wang, Jiaoe & Li, Tingting & Zhang, Yujuan, 2024. "Spatio-temporal dynamics and recovery of commuting activities via bike-sharing around COVID-19: A case study of New York," Journal of Transport Geography, Elsevier, vol. 121(C).
    14. Qing Yu & Weifeng Li & Dongyuan Yang & Yingkun Xie, 2020. "Policy Zoning for Efficient Land Utilization Based on Spatio-Temporal Integration between the Bicycle-Sharing Service and the Metro Transit," Sustainability, MDPI, vol. 13(1), pages 1-14, December.
    15. Kim, Kyoungok, 2023. "Investigation of modal integration of bike-sharing and public transit in Seoul for the holders of 365-day passes," Journal of Transport Geography, Elsevier, vol. 106(C).
    16. Xing, Yingying & Wang, Ke & Lu, Jian John, 2020. "Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 87(C).
    17. Dejian Yu & Bo Xiang & Zhuoya Pan, 2024. "Combining text analytics and network path extraction to trace CSR in the social sciences: Intellectual structures and diffusion trajectories," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(5), pages 4532-4554, September.
    18. Judith Y. T. Wang & Richard D. Connors, 2018. "Urban Growth, Transport Planning, Air Quality and Health: A Multi-Objective Spatial Analysis Framework for a Linear Monocentric City," Networks and Spatial Economics, Springer, vol. 18(4), pages 839-874, December.
    19. Ma, Changxi & Liu, Tao, 2024. "Demand forecasting of shared bicycles based on combined deep learning models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    20. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jaitra:v:108:y:2023:i:c:s0969699723000170. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-air-transport-management/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.