TY  - GEN
T1  - Advances in Transportation Meteorology
A2  - Liu, Duanyang
LA  - eng
PB  - MDPI - Multidisciplinary Digital Publishing Institute
YR  - 2023
UL  - https://ebooks.jgu.edu.in/Record/doab-20.500.12854-113879
AB  - 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.
SN  - 9783036584614
SN  - 9783036584607
KW  - transportation meteorology
KW  - pavement temperature prediction
KW  - deep learning
KW  - BiLSTM
KW  - attention mechanisms
KW  - winter icing
KW  - air pollution
KW  - traffic vitality
KW  - built environment
KW  - spatial correlation
KW  - spatial lag model
KW  - phone signaling data
KW  - air quality
KW  - behavioral habits
KW  - activity density
KW  - population distribution
KW  - land use mix
KW  - wind forecast
KW  - error decomposition
KW  - bias
KW  - distribution
KW  - sequence
KW  - urban meteorology
KW  - observation
KW  - forecast
KW  - early warning
KW  - review
KW  - China
KW  - low-level wind shear
KW  - ensemble learning classifiers
KW  - Bayesian optimization
KW  - SHapley Additive exPlanations
KW  - wind shear
KW  - go-around
KW  - machine learning
KW  - dynamic ensemble selection
KW  - civil aviation safety
KW  - pilot reports
KW  - self-paced ensemble
KW  - Shapley additive explanations
KW  - climate change
KW  - climatology
KW  - sea ice
KW  - marginal sea
KW  - East Asia
KW  - time-series modeling
KW  - pavement temperature
KW  - nowcasting
KW  - variation characteristics
KW  - forecast validation
KW  - relative humidity
KW  - microwave radiometer data
KW  - total rainfall
KW  - precipitation duration
KW  - vertical distribution
KW  - Beijing–Tianjin–Hebei region
KW  - rail breakage
KW  - frequency
KW  - high-speed railway
KW  - Siberian high
KW  - teleconnection
KW  - temperature
KW  - Qinling mountains
KW  - rainfall
KW  - change characteristics
KW  - geographical factors
KW  - highways
KW  - road blockage
KW  - fuzzy analytic hierarchy process
KW  - CRITIC weight assignment method
KW  - road network vulnerability
KW  - spatiotemporal distribution
KW  - precipitation forecast
KW  - ConvLSTM
KW  - PredRNN
KW  - expressway
KW  - agglomerate fog
KW  - risk level prediction of fog-related accidents
KW  - meteorological conditions
KW  - road hidden dangers
KW  - traffic flow conditions
KW  - visibility
KW  - Yellow Sea and Bohai Sea
KW  - observation data
KW  - thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
KW  - thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
KW  - thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TR Transport technology and trades
ER  -