TY  - JOUR
PY  - 2022//
TI  - Modeling and analyzing the traffic flow during evacuation in Hurricane Irma (2017)
JO  - Transportation research part D: transport and environment
A1  - Feng, Kairui
A1  - Lin, Ning
SP  - e103412
EP  - e103412
VL  - 110
IS  - 
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.<p />  <p>Language: en</p>
LA  - en
SN  - 1361-9209
UR  - http://dx.doi.org/10.1016/j.trd.2022.103412
ID  - ref1
ER  -