Advances in Transportation Meteorology

Edited by
August 2023
304 pages
  • ISBN978-3-0365-8461-4 (Hardback)
  • ISBN978-3-0365-8460-7 (PDF)

This book is a reprint of the Special Issue Advances in Transportation Meteorology that was published in

Chemistry & Materials Science
Environmental & Earth Sciences

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.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
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; transportation 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; SHapley Additive exPlanations; civil aviation safety; low-level wind shear; pilot reports; machine learning; self-paced ensemble; Shapley additive explanations; climate change; climatology; sea ice; marginal sea; East Asia; observation; wind shear; time-series modeling; machine learning; Bayesian optimization; 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; temperature; rainfall; change characteristics; geographical factors; highways; road blockage; fuzzy analytic hierarchy process; CRITIC weight assignment method; road network vulnerability; spatiotemporal distribution; precipitation forecast; nowcasting; deep learning; ConvLSTM; PredRNN; expressway; agglomerate fog; risk level prediction of fog-related accidents; meteorological conditions; road hidden dangers; traffic flow conditions; climatology; visibility; Yellow Sea and Bohai Sea; observation data