## 6 Discussion and Conclusions  
Owing to the time-consuming deployment, high-cost maintenance, and isolated-information problem in physical-sensor-based ITSs, a social-sensor-based ITS for adverse-weather-affected city traffic awareness and warning was proposed in this paper. The proposed ITS leverages the advantages of social media, such as crowdsourcing, real time, multiple sources, and easy acquisition. The social approach has been validated experimentally in cyberspace and physical space.  
Specifically, for data collection, the word2 vec for transportation was trained to construct the traffic search keyword list. In addition, China National Standard GB/T 27962-2011, which contains 14 types of adverse weather conditions, was used to construct the meteorology search keyword list. Combining these two word lists, we collected 128,815 tweets from 1 January to 1 August 2017. For data preprocessing, three types of features that influence the traffic incidents--temporal, spatial, and meteorological features--were extracted. Meanwhile, the traffic incidents' heat values were also calculated as the forecasting target label. For forecasting and alerting, we compared the effectiveness of six typical machine learning models of traffic incident forecasting. For verification, we considered news media as an objective measurement and took Beijing as a case study to prove the generality of our proposed social approach in the real world.  
Although our proposed model is a preliminary attempt at city-level traffic situation awareness and alerting assisted by adverse weather in a social approach, additional valuable work can be conducted in the future. First, the authority of each related tweet should be considered. The authors may be categorized as governments, news agencies, companies, spammers, online sellers, Internet water army and Internet celebrities, etc. and the posted tweets may be categorized as news abstracts, announcements, records, review, spams, advertisements, fake news, etc. Different types of authors and tweets should be given unequal weight in the forecasting model to achieve higher accuracy [59]. Second, the model needs to consider more impacts that may be related to traffic incidents, e.g., geological disasters [60; 61], disease disasters, social events, etc. Integration of different types of impacts into a unified model will provide much better support for decision-making. Third, instead of machine learning-based approaches, this problem could also benefit from knowledge-based decision support methods. The knowledge services of transport and meteorology are both important to relieve the pinch points of social management. Finally, when improving and validating the proposed methods in various domains related to meteorology, more potential values need to be explored by the government and the public in the near future.  
Conceptualization, H.L.; Data analysis, Y.Z. and K.S.; Methodology, H.L., Y.Z. and K.S.; Project administration, Z.N.; prototype design, all authors; Supervision and suggestion, Y.L. and Z.N.; Visualization, P.S.; Writing, H.L. and Y.Z.  
This work was supported by the National Natural Science Foundation of China under grants 61671485, 61533019, 61233001, and 61370137, Ministry of Education-China Mobile Research Foundation Project No.2016/2-7 and also supported by The Public Weather Service Center of China Meteorological Administration.  
The authors would like to thank the China Meteorological Administration for providing professional business requirement and advice on improving our model. The authors also would like to thank NSFC for supporting this research. Finally, the authors would like to thank the editor and anonymous reviewers for their suggestions that improved the work.  
The authors declare no conflicts of interest.