Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires-Reference-Cited by-同舟云学术

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Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires

Published:2024-12 Issue: Volume:190 Page:104242
ISSN:0965-8564
Container-title:Transportation Research Part A: Policy and Practice
language:en
Short-container-title:Transportation Research Part A: Policy and Practice

Author:

Zhang Xiaojian^ORCID,Zhao Xilei^ORCID,Xu Yiming^ORCID,Nilsson Daniel,Lovreglio Ruggiero

Funder

National Institute of Standards and Technology

U.S. Department of Commerce

Publisher

Elsevier BV

Reference111 articles.

1. The Promise and Limitations of Locational App Data for Origin-Destination Analysis: A Case Study;Adler,2017

2. Origin–destination trips by purpose and time of day inferred from mobile phone data;Alexander;Transp. Res. C,2015

3. Gravy analytics;Analytics,2023

4. Improving human behaviour in macroscale city evacuation agent-based simulation;Barnes;Int. J. Disaster Risk Reduct.,2021

5. Predicting wildfire burns from big geodata using deep learning;Bergado;Saf. Sci.,2021

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