In January, we presented our research at the Transportation Research Board (TRB) Annual Meeting during the poster session for the 2025 INRIX x MetroLab Challenge. For the University of Alabama, one research project examined travel and traffic pattern changes during tornado watch and warning periods compared to normal conditions. The project, titled “Detecting Behavioral and Network Responses to Tornado Threats: OD-Based Travel and Speed Pattern Analysis in Tuscaloosa, Alabama,” investigated how mobility patterns shift when tornado threats emerge. Using Tuscaloosa as a case study, with planned expansion to Birmingham, AL, we analyzed origin-destination (OD) flows and roadway speeds to identify behavioral and network-level responses during severe weather alerts. The goal was to translate observed travel and traffic changes into actionable insights that could support emergency management, transportation planning, and risk communication strategy development. Why Tornado Mobility? Tornado hazards present a unique research opportunity. Unlike hurricanes or wildfires, tornadoes develop rapidly, have short warning windows, and require different protective behaviors, often shelter-in-place rather than long-distance evacuation. Despite their frequency in regions like Alabama, tornado-related travel behavior remains understudied. Given that Tuscaloosa regularly experiences tornado watches and warnings, it provides a natural laboratory to investigate how individuals and transportation networks respond to short-notice extreme weather threats. The Advantages of INRIX Mobility Data I have experience in using survey data for hazards-related mobility behavior analysis. However, while survey-based research is valuable, it relies heavily on self-reported information, which may introduce recall bias and social desirability bias. Additionally, collecting post-disaster survey data can be challenging due to the emotional sensitivity of such events. This motivated me to shift toward real-world mobility datasets, such as OD and speed data, to directly observe behavioral responses without relying solely on self-reports. The INRIX speed data was particularly valuable. It provided comprehensive coverage and included richer detail than