Using the data of five agglomerate-fog-related traffic accidents during 2014–2017 and fog-related expressway accidents during 2013–2018 assessed by the Anhui provincial traffic authorities and corresponding meteorological observation data, this article analyzes the spatio-temporal characteristics, geographical environment and weather background of agglomerate-fog-related accidents in detail and identifies the characteristics of changes in terms of visibility, humidity, temperature and wind when agglomerate fog occurs. Furthermore, an agglomerate fog forecast index has been established in Anhui Province to provide a reference basis for the agglomerate fog forecast, early warning and prevention.

Based on the pavement temperature observation data of the transportation meteorological stations along the Xianyang Airport Expressway, China, as well as the datasets of precipitation and sunshine hours obtained from the nearby weather stations, the variation characteristics of local pavement temperatures are investigated for winter in this study. Results indicate that during the daytime, the pavement temperatures are always higher on sunny and cloudy days than those on rainy and snowy days, while during the nighttime, the temperatures on sunny and cloudy days are higher than those on the days with freezing rain and snow, and with the temperatures on rainy and snowy days without icing being further higher. In general, the pavement temperatures in winter features significant periodic oscillations with cycles of roughly 24 h, 12 h, 8 h, 6 h, 5 h and 4 h, which differ slightly at different times for different stations. Moreover, the nowcasting experiments on the local pavement temperatures are also carried out using a regression model via extracting the corresponding periodic features. It shows the mean absolute errors of about 0.6 °C, 1.2 °C, and 1.5 °C for lead times of 1 h, 2 h, and 3 h, respectively. The nowcasting skills are higher on rainy and snowy days, while are inferior on sunny days. For nowcasting cases initialized at nighttime (daytime), the mean absolute errors are 0.4 °C (0.7 °C) and 0.9 °C (1.4 °C) for lead times of 1 h and 2 h. Examinations suggest that the nowcasting system could be well utilized in plain areas of China, whereas it shows relatively larger biases in plateau areas with complex terrain.

Based on meteorological observations, traffic flow data and information of traffic accidents caused by fog or agglomerate fog along the expressways in Jiangsu Province and Anhui Province in China from 2012 to 2021, key impact factors including meteorological conditions, road hidden dangers and traffic flow conditions are integrated to establish the prediction model for risk levels of expressway agglomerate fog-related accidents. This model takes the discrimination of the occurrence conditions of agglomerate fog as the starting term, and determines the hazard levels of agglomerate fog-related accidents by introducing the probability prediction value of meteorological conditions for fog-related accident as the disaster-causing factor. On this basis, the hourly road traffic flow and the location of road sections with a hidden danger of agglomerate fog are taken as traffic and road factors to construct the correction scheme for the hazard levels, and the final predicted risk level of agglomerate fog-related accident is obtained. The results show that for the criteria of disaster-causing factor classification threshold, 72.3% of fog-related accidents correspond to a hazard of a medium level or above, and 86.2% of the road traffic flow conditions are consistent with the levels of the traffic factor defined based on parametric indexes. For risk level prediction, six out of the seven agglomerate fog-related accidents correspond to the level of higher risk or above, which can help provide meteorological support for traffic safety under severe weather conditions. Moreover, the model takes into account the impacts of traffic flow and the road environment, which is conducive to further improving the reliability of the risk assessment results.