Traffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of “isolated information” for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media “heat” for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance.