Predicting metro incident duration using structured data and unstructured text logs

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science Pub Date : 2024-08-29 DOI:10.1080/23249935.2024.2396951

Yangyang Zhao, Zhenliang Ma, Hui Peng, Zhanhong Cheng

引用次数: 0

Abstract

Predicting metro incident duration is crucial for passengers and transit operators to choose appropriate response strategies. Most existing research focuses on structured data, the rich information...

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利用结构化数据和非结构化文本日志预测地铁事故持续时间

预测地铁事故持续时间对于乘客和公交运营商选择适当的应对策略至关重要。现有的研究大多集中在结构化数据、丰富的信息...

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

Transportmetrica A-Transport Science TRANSPORTATION SCIENCE & TECHNOLOGY-

CiteScore

8.10

自引率

12.10%

发文量

期刊介绍： Transportmetrica A provides a forum for original discourse in transport science. The international journal''s focus is on the scientific approach to transport research methodology and empirical analysis of moving people and goods. Papers related to all aspects of transportation are welcome. A rigorous peer review that involves editor screening and anonymous refereeing for submitted articles facilitates quality output.