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Against the backdrop of intensified global warming, extreme weather events such as dense fog, low visibility, heavy precipitation, and extreme temperatures have been increased and enhanced to a great extent. They are likely to pose severe threats to the operation of urban transportation and associated services, which has drawn much attention in recent decades. However, there are still plenty of issues to be resolved in improving the emergency meteorological services and developing targeted urban transportation meteorological services in modern cities. The present review briefly illustrates the current cutting-edge developments and trends in the field of urban transportation meteorology in China, including the establishment of observation networks and experiments and the development of early warning and prediction technologies, as well as the related meteorological commercial services. Meanwhile, reflections and discussions are provided in terms of the state-of-the-art observation channels and methods and the application of numerical model forecasts and artificial intelligence. With the advantages of various advanced technologies from multiple aspects, researchers could further expand explorations on urban transportation meteorological observations, forecasts, early warnings, and services. Associated theoretical studies and practical investigations are also to be carried out to provide solid scientific foundations for urban transportation disaster prevention and mitigation, for implementing the action of meteorological guarantees, and for the construction of a high-quality smart society.

Pavement temperature is the main factor determining road icing, and accurate and timely pavement temperature prediction is of significant importance to regional traffic safety management and preventive maintenance. The prediction of pavement temperature at the micro-scale has been a challenge to be tackled. To solve this problem, a bidirectional extended short-term memory network model based on the attention mechanism (Att-BiLSTM) was proposed to improve the prediction performance by using the time series features of pavement temperature and meteorological factors. Pavement temperature data and climatic data were collected from a road weather station in Yunnan, China. The results show that the MAE, MSE, and MAPE of the proposed Att-BiLSTM model were 0.330, 0.339, and 10.1%, respectively, which were better than the other baseline models. It was shown that 93.4% of the predicted values had an error less than 1 °C, and 82.1% had an error less than 0.5 °C, indicating that the proposed Att-BiLSTM model enables significant performance improvement. In addition, this paper quantified and analyzed the effects of parameters such as the size of the sliding window, the number of hidden layer neurons, and the optimizer on the performance of the prediction model.