Publication Date:
2024-04-11
Description:
Transportation is one of the most crucial aspects across the world, supporting the daily life of human beings and the sustainable development of the whole of society. Generally, meteorology causes various impacts on transportation operation, safety and efficiency. In the context of global warming, increasing numbers of extreme weather and climate events (such as fog, icy roads, and extreme winds) have been detected worldwide and are expected to occur more frequently in the future. Meanwhile, extreme events, such as dense fog, rainstorm, and blizzard, tend to damage transportation and traffic facilities (such as express ways, port, airport, and high-speed railway) and induce serious traffic blocks and accidents. In recent decades, concentrated and continuous efforts have been made to carry out meteorological analyses regardless of urban traffic or transportation conditions, including those of highways, shipping, aviation, etc. A number of methods and techniques have been intensively developed to promote the qualities of both observations and forecasts. More recently, state-of-the-art machine learning frameworks have also been widely introduced into studies regarding transportation meteorology and many other fields.
Keywords:
transportation meteorology
;
pavement temperature prediction
;
deep learning
;
BiLSTM
;
attention mechanisms
;
winter icing
;
air pollution
;
traffic vitality
;
built environment
;
spatial correlation
;
spatial lag model
;
phone signaling data
;
air quality
;
behavioral habits
;
activity density
;
population distribution
;
land use mix
;
wind forecast
;
error decomposition
;
bias
;
distribution
;
sequence
;
urban meteorology
;
observation
;
forecast
;
early warning
;
review
;
China
;
low-level wind shear
;
ensemble learning classifiers
;
Bayesian optimization
;
SHapley Additive exPlanations
;
wind shear
;
go-around
;
machine learning
;
dynamic ensemble selection
;
civil aviation safety
;
pilot reports
;
self-paced ensemble
;
Shapley additive explanations
;
climate change
;
climatology
;
sea ice
;
marginal sea
;
East Asia
;
time-series modeling
;
pavement temperature
;
nowcasting
;
variation characteristics
;
forecast validation
;
relative humidity
;
microwave radiometer data
;
total rainfall
;
precipitation duration
;
vertical distribution
;
Beijing–Tianjin–Hebei region
;
rail breakage
;
frequency
;
high-speed railway
;
Siberian high
;
teleconnection
;
temperature
;
Qinling mountains
;
rainfall
;
change characteristics
;
geographical factors
;
highways
;
road blockage
;
fuzzy analytic hierarchy process
;
CRITIC weight assignment method
;
road network vulnerability
;
spatiotemporal distribution
;
precipitation forecast
;
ConvLSTM
;
PredRNN
;
expressway
;
agglomerate fog
;
risk level prediction of fog-related accidents
;
meteorological conditions
;
road hidden dangers
;
traffic flow conditions
;
visibility
;
Yellow Sea and Bohai Sea
;
observation data
;
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
;
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
;
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades
Language:
English
Format:
application/octet-stream
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