LEADER 05145nam 2201345z- 450 001 9910743274703321 005 20230911 035 $a(CKB)5690000000228561 035 $a(oapen)doab113879 035 $a(EXLCZ)995690000000228561 100 $a20230920c2023uuuu -u- - 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in Transportation Meteorology 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2023 215 $a1 online resource (304 p.) 311 08$a3-0365-8460-9 330 $aTransportation 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. 606 $aHistory of engineering and technology$2bicssc 606 $aTechnology: general issues$2bicssc 606 $aTransport technology and trades$2bicssc 610 $aactivity density 610 $aagglomerate fog 610 $aair pollution 610 $aair quality 610 $aattention mechanisms 610 $aBayesian optimization 610 $abehavioral habits 610 $aBeijing-Tianjin-Hebei region 610 $abias 610 $aBiLSTM 610 $abuilt environment 610 $achange characteristics 610 $aChina 610 $acivil aviation safety 610 $aclimate change 610 $aclimatology 610 $aConvLSTM 610 $aCRITIC weight assignment method 610 $adeep learning 610 $adistribution 610 $adynamic ensemble selection 610 $aearly warning 610 $aEast Asia 610 $aensemble learning classifiers 610 $aerror decomposition 610 $aexpressway 610 $aforecast 610 $aforecast validation 610 $afrequency 610 $afuzzy analytic hierarchy process 610 $ageographical factors 610 $ago-around 610 $ahigh-speed railway 610 $ahighways 610 $aland use mix 610 $alow-level wind shear 610 $amachine learning 610 $amarginal sea 610 $ameteorological conditions 610 $amicrowave radiometer data 610 $anowcasting 610 $aobservation 610 $aobservation data 610 $apavement temperature 610 $apavement temperature prediction 610 $aphone signaling data 610 $apilot reports 610 $apopulation distribution 610 $aprecipitation duration 610 $aprecipitation forecast 610 $aPredRNN 610 $aQinling mountains 610 $arail breakage 610 $arainfall 610 $arelative humidity 610 $areview 610 $arisk level prediction of fog-related accidents 610 $aroad blockage 610 $aroad hidden dangers 610 $aroad network vulnerability 610 $asea ice 610 $aself-paced ensemble 610 $asequence 610 $aShapley additive explanations 610 $aSHapley Additive exPlanations 610 $aSiberian high 610 $aspatial correlation 610 $aspatial lag model 610 $aspatiotemporal distribution 610 $ateleconnection 610 $atemperature 610 $atime-series modeling 610 $atotal rainfall 610 $atraffic flow conditions 610 $atraffic vitality 610 $atransportation meteorology 610 $aurban meteorology 610 $avariation characteristics 610 $avertical distribution 610 $avisibility 610 $awind forecast 610 $awind shear 610 $awinter icing 610 $aYellow Sea and Bohai Sea 615 7$aHistory of engineering and technology 615 7$aTechnology: general issues 615 7$aTransport technology and trades 906 $aBOOK 912 $a9910743274703321 996 $aAdvances in Transportation Meteorology$93560545 997 $aUNINA