TY - JOUR
T1 - Modeling and analyzing the traffic flow during evacuation in Hurricane Irma (2017)
AU - Feng, Kairui
AU - Lin, Ning
N1 - Funding Information:
The authors would like to acknowledge the Post Graduate Education and Research Development Project in Postharvest Technology, Kasetsart University, Kamphaengsaen Campus, Nakhon Pathom, Thailand, for sponsoring this research. Heartfelt appreciation goes to Dr. P. Chen, Professor Emeritus, Department of Agricultural and Biological Engineering, University of California, Davis, who kindly reviewed the manuscript.
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/9
Y1 - 2022/9
N2 - Hurricane evacuation modeling is challenging due to a scarcity of evacuation data and the complexity of human decision-making and travel behavior. We build a system for rapidly predicting the hurricane evacuation traffic flow based on hurricane forecasting, evacuation orders, the road network, and population information. The system integrates an evacuation demand model, an origin–destination model, and a route choice model into a link flow-based mean-field traffic model. We evaluate and calibrate the model with traffic observations from Hurricane Irma (2017), which induced a massive evacuation and traffic congestions throughout Florida State. The model skillfully captures the spatial and temporal evacuation features, including peak traffic flows and daily traffic fluctuations. The model can be applied to support evacuation management. Our analysis shows that a minor adjustment to the evacuation order could considerably alleviate the traffic congestion during Hurricane Irma.
AB - Hurricane evacuation modeling is challenging due to a scarcity of evacuation data and the complexity of human decision-making and travel behavior. We build a system for rapidly predicting the hurricane evacuation traffic flow based on hurricane forecasting, evacuation orders, the road network, and population information. The system integrates an evacuation demand model, an origin–destination model, and a route choice model into a link flow-based mean-field traffic model. We evaluate and calibrate the model with traffic observations from Hurricane Irma (2017), which induced a massive evacuation and traffic congestions throughout Florida State. The model skillfully captures the spatial and temporal evacuation features, including peak traffic flows and daily traffic fluctuations. The model can be applied to support evacuation management. Our analysis shows that a minor adjustment to the evacuation order could considerably alleviate the traffic congestion during Hurricane Irma.
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U2 - 10.1016/j.trd.2022.103412
DO - 10.1016/j.trd.2022.103412
M3 - Article
AN - SCOPUS:85136046540
SN - 1361-9209
VL - 110
JO - Transportation Research Part D: Transport and Environment
JF - Transportation Research Part D: Transport and Environment
M1 - 103412
ER -