Time prediction of human evacuation from passenger ships based on machine learning methods

Zhang, Z, Liu, Z, Zhou, Z, Wang, X, Majumdar, A, Cao, Y and Yang, Z ORCID: 0000-0003-1385-493X (2025) Time prediction of human evacuation from passenger ships based on machine learning methods. Transportation Research Part A: Policy and Practice, 200. ISSN 0965-8564

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Abstract

During an emergency evacuation scenario, accurately and timely predicting human evacuation time beforehand is crucial for developing an efficient evacuation plan. This study aims to develop an innovative simulation-based framework in which series state-of-the-art Machine Learning (ML) models are applied to predict human evacuation time from passenger ships. It also develops a multi-dimensional decision-making approach to evaluate their performance from the perspectives of high prediction accuracy and timeliness to support rapid response during emergencies. Firstly, an agent-based modelling technique incorporating two objectives and seven influential factors specific to human evacuation scenarios onboard ships is used to simulate the evacuation process. Then, the evacuation model is validated using three indicators to ensure its accuracy and relevance. Secondly, nine state-of-the-art ML models are applied to predict and analyse human evacuation time. To further investigate the role of feature interactions and enhance predictive accuracy, an additional model called the Attention-enhanced Light Gradient Boosting Machine (Attention-LightGBM) is proposed. Additionally, four statistical indicators are utilised to monitor the performance of each model. Finally, a new weighted selection method based on analytic hierarchy process and entropy weight method is created to conduct a comprehensive assessment from the perspectives of accuracy and timeliness. The findings reveal that the Attention-LightGBM demonstrates significant advantages in prediction accuracy, while the LightGBM excels in prediction timeliness. This study not only provides theoretical and technical support for emergency management onboard ships but also suggests methodological advancements for future research on complex human evacuation scenarios from passenger ships. The source code is publicly available at: https://github.com/AdvMarTech/Eva_Predict_ML.

Item Type:	Article
Uncontrolled Keywords:	Maritime safety; Emergency evacuation; Passenger ship; AnyLogic; Machine learning; Model selection; 3509 Transportation, Logistics and Supply Chains; 33 Built Environment and Design; 35 Commerce, Management, Tourism and Services; 3304 Urban and Regional Planning; Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD); Generic health relevance; 1205 Urban and Regional Planning; 1507 Transportation and Freight Services; Logistics & Transportation; 3304 Urban and regional planning; 3509 Transportation, logistics and supply chains
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Divisions:	Engineering
Publisher:	Elsevier
Date of acceptance:	28 August 2025
Date of first compliant Open Access:	28 July 2026
Date Deposited:	28 Jul 2026 14:47
Last Modified:	28 Jul 2026 14:47
DOI or ID number:	10.1016/j.tra.2025.104664
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29076

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