On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis
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DOI: 10.1016/j.tranpol.2020.05.023
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- Johan Rose Santos & Nur Diana Safitri & Maya Safira & Varun Varghese & Makoto Chikaraishi, 2021. "Road network vulnerability and city-level characteristics: A nationwide comparative analysis of Japanese cities," Environment and Planning B, , vol. 48(5), pages 1091-1107, June.
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- E. Mary Jasmine & A. Milton, 2022. "The role of hyperparameters in predicting rainfall using n-hidden-layered networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 489-505, March.
- Xiaoqing Dai & Han Qiu & Lijun Sun, 2021. "A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System," Sustainability, MDPI, vol. 13(17), pages 1-15, August.
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Keywords
Short-term traffic prediction; Non-recurrent congestion; Machine learning; Disaster;All these keywords.
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