## 1 Introduction  
From the view of decision-making and disaster prevention, adverse weather (or inclement weather and extreme weather) is a significant factor affecting traffic [1]. For instance, an experiment showed that from 2005 to 2014, the number of traffic accidents caused by wind, snow, fog, dust, and hail equaled 287,783 in mainland China and resulted in 82,064 deaths [2]. Many studies have indicated that adverse weather negatively affects drivers' decision-making ability [3], the accident rate in traffic systems [4], and traffic flow [5]. Therefore, weather effects on transport systems are common research concerns among meteorological departments, transport agencies, and government administrations [6]. This work demands multichannel data collection, data extraction, knowledge fusion, environmental feature introduction, and machine-leaning models to establish an intelligent transport system (ITS) for traffic situation estimation and alerting [7]. However, the physical sensor-based ITSs are time-consuming and expensive in deployment and maintenance, requiring large amounts of human power and resources [8]. The burden of data acquisition is also heavy for many government departments, especially in developing countries [9]. Furthermore, for historical and procedural reasons, transportation and meteorological departments in various regions and at various levels use different channels and systems to manage their business data, which results in significant obstacles to data aggregation and mining. Today, the social sensor-based ITSs, or so-called social transportation, provide a significant opportunity for improving ITSs and thus have received increasing attention [10; 11]. Social transportation collects, retrieves, and mines data from social media, GPS, and mobile phones by taking advantage of crowdsourcing, easy acquisition, and real-time data from the virtual world of the Internet and mobile communication [12]. The deployment of social transportation systems has demonstrated great power in traffic incident detection, traffic situation awareness, traffic flow forecasting, traffic opinion mining, routing plan, etc. [13]. However, how to build a social transportation system assisted by meteorological features is still uncertain. More specifically, although previous studies found that people's travel behavior can be affected by reading meteorologically related tweets [10], the relationship between meteorologically related opinions and the corresponding traffic reaction in social media still needs to be explored, which leaves an essential question unanswered: Can we build a city-level traffic situation awareness and alerting model assisted by adverse weather data that benefits from social media? To answer this question, in this paper, by collecting temporal, spatial, traffic, and meteorological data, we mined the correlation between adverse weather topic heat and traffic incidents in social media, and further propose a traffic situation awareness and alerting model assisted by adverse weather data to provide information on city-level traffic situations reflected in social media. Finally, we verify these warning results from cyberspace with real-world urban traffic situations. To the best of our knowledge, this is the first time that traffic situations have been estimated and forecasted using social media assisted by adverse weather data. The road map of our research idea and approach is shown in Figure 1. In particular, we leverage the social path described in the figure below to achieve city-level traffic situation awareness and alerting. Moreover, by combining these social media data with physical sensed data, the ITSs can be shifted from cyber-physical systems (CPS) to cyber-physical-social systems (CPSS), which give more comprehensive and robust results for decision-making [11; 14]. The rest of this paper is organized as follows.