RISECURE: Metro Incidents And Threat Detection Using Social Media

Omer Zulfiqar, Yi-Chun Chang, Po-Han Chen, Kaiqun Fu, Chang-Tien Lu, David Solnick, Yanlin Li

Abstract: Open and accessible public utilities such as mass public transit systems are some of the vexing venues that are vulnerable to several criminal acts due to the large volumes of commuters. Existing forms of threat or event detection for the rail-based transit systems are either not working in real-time or do not provide complete coverage. In this paper, we present RISECURE1, an open-source system, that uses real-time social media mining to aid in the early detection of such possible events within a rail-based/metro system. The system leverages dynamic query expansion to keep track of any new emerging information about any particular incident. The Real Time Incident panel of the proposed system provides a comprehensible representation of the evolution of threatening transit events, which are further shown in the storyline modal for each respective station. The alert notification module of the system is capable of monitoring threats to the rail-based/metro systems in real-time. We demonstrate the system by including case studies involving incidents occurring within the Washington DC Metropolitan Area Transit Authority (WMATA) metro system to justify the effectiveness of our approach.