Predicting metro incident duration using structured data and unstructured text logs-Reference-Cited by-同舟云学术

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Predicting metro incident duration using structured data and unstructured text logs

Published:2024-08-29 Issue: Volume: Page:1-29
ISSN:2324-9935
Container-title:Transportmetrica A: Transport Science
language:en
Short-container-title:Transportmetrica A: Transport Science

Author:

Zhao Yangyang¹^ORCID,Ma Zhenliang²^ORCID,Peng Hui¹,Cheng Zhanhong³^ORCID

Affiliation:

1. College of Transportation Engineering, Chang’an University, Xi’an, People’s Republic of China

2. Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

3. Department of Civil Engineering, McGill University, Montreal, Canada

Funder

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi Province

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23249935.2024.2396951

Reference68 articles.

1. Empirical Assessment of the Likelihood and Duration of Highway Project Time Delays

2. A comparative study of k-NN and hazard-based models for incident duration prediction

3. Asuncion, Arthur, Max Welling, Padhraic Smyth, and Yee Whye Teh. 2009. “On Smoothing and Inference for Topic Models.” Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 27–34. Arlington, Virginia, USA.

4. Research topics and trends in the maritime transport: A structural topic model

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

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