Abstract
The abnormal change in the global climate has increased the chance of urban rainstorm disasters, which greatly threatens people’s daily lives, especially public travel. Timely and effective disaster data sources and analysis methods are essential for disaster reduction. With the popularity of mobile devices and the development of network facilities, social media has attracted widespread attention as a new source of disaster data. The characteristics of rich disaster information, near real-time transmission channels, and low-cost data production have been favored by many researchers. These researchers have used different methods to study disaster reduction based on the different dimensions of information contained in social media, including time, location and content. However, current research is not sufficient and rarely combines specific road condition information with public emotional information to detect traffic impact areas and assess the spatiotemporal influence of these areas. Thus, in this paper, we used various methods, including natural language processing and deep learning, to extract the fine-grained road condition information and public emotional information contained in social media text to comprehensively detect and analyze traffic impact areas during a rainstorm disaster. Furthermore, we proposed a model to evaluate the spatiotemporal influence of these detected traffic impact areas. The heavy rainstorm event in Beijing, China, in 2018 was selected as a case study to verify the validity of the disaster reduction method proposed in this paper.
Highlights
In recent years, the abnormal change in the global climate has induced frequent meteorological disasters, especially torrential rainstorms in urban areas
Cameras distributed in urban areas can provide detailed disaster information of local areas in near real time, but the cost of using these devices is high, and it is difficult to popularize them in some underdeveloped areas
We propose a framework that can automatically acquire, parse, and process social media data and intelligently mine disaster-related information contained in it, including time, location, fine-grained road condition information, and public emotional information
Summary
The abnormal change in the global climate has induced frequent meteorological disasters, especially torrential rainstorms in urban areas. This causes many problems for normal urban management and public travel. Many modern monitoring methods are very helpful for the timely acquisition of disaster information, there are many limitations. Cameras distributed in urban areas can provide detailed disaster information of local areas in near real time, but the cost of using these devices is high, and it is difficult to popularize them in some underdeveloped areas. The undulating buildings in the urban environment have a great impact on its imaging, and the longer revisit period is not conducive to the continuous observations of disasters
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