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 initially expected, allowing us to examine roadway performance changes at a granular temporal scale. 

The origin-destination (OD) dataset was also instrumental in detecting changes in trip volumes and flow concentration patterns. Even with relatively minimal preprocessing, the OD data revealed clear behavioral signals during tornado watch and warning periods. 

In addition, we obtained speed profile and trajectory data, which we have not yet fully explored. These datasets offer promising opportunities for deeper network-level and corridor-level analyses in future phases of the research.

Key Findings: How Mobility Responds to Tornado Threats  

The analysis uncovered consistent and measurable mobility shifts during tornado watch and warning periods. 

• Tornado watches and warnings are linked to noticeable reductions in trip volumes, with the most pronounced declines occurring during official warning periods. 
• The most frequent origin–destination flows display temporal patterns that closely track overall hourly trip volume trends, suggesting consistent and systematic flow-level responses to tornado threats. 
• Origin–destination patterns become increasingly dispersed during warning periods, characterized by fewer high-density blocks, lower frequencies within those concentrated areas, and distributions that appear closer to random compared to control days. 
• During tornado watch and warning periods, roadway speeds generally slow down or remain stable, compared to the historical average speed, which contrasts with the positive speed change rates observed on normal days. After the warning period ends, speed patterns gradually return to typical conditions. 

Turning Findings into Action  

The research translates directly into operational and policy applications. Clear reductions in trip volumes during tornado watches and, especially, official warnings provide emergency management agencies with measurable indicators of short-term mobility contraction. Understanding when and how quickly trip volumes declines improves real-time situational awareness and supports more effective resource deployment during severe weather events.  

The increasing dispersion of origin–destination patterns during warning periods suggests that travel demand becomes less concentrated and more spatially scattered. This insight can assist transportation agencies in anticipating redistributed or unpredictable traffic flows and implement adaptive traffic management strategies during high-risk periods.  

The observed slowing or stabilization of roadway speeds during watch and warning windows, followed by a gradual return to normal conditions, provides measurable indicators of temporary network disruption. These speed pattern shifts can inform incident management, signal timing adjustments, or post-warning recovery monitoring. 

Additionally, the close alignment between frequent OD flows and overall trip volume trends suggests consistent behavioral responsiveness to tornado threats. This provides valuable feedback for risk communication strategies. If mobility reduction occurs rapidly following warnings, it may indicate effective public messaging. If not, communication strategies may need refinement. 

By translating mobility data into actionable indicators of behavioral and network response, the research supports data-informed emergency planning and operational decision-making.

Future Growth Opportunities 

The next phase includes: 

• Expanding the framework to Birmingham, Alabama for cross-city comparison 
• Integrating meteorological intensity metrics to connect threat severity with behavioral magnitude 
• Using trajectory-level data to distinguish between trip cancellation and shelter-seeking behaviors 
• Developing predictive models to estimate expected travel reduction under varying warning intensities 
• Exploring real-time decision-support applications 

Mobility as a Real-Time Hazard Signal 

This research highlights a fundamental shift in transportation analytics: mobility data is more than a record of movement; it is a real-time behavioral sensor. By analyzing OD flows and roadway speeds, we can observe how communities react to emerging risks, how networks absorb sudden disruptions, and where operational adjustments are most critical. In tornado-prone regions, where warning windows last minutes, these insights enable faster, evidence-based decisions. The University of Alabama’s work demonstrates how translating mobility patterns into actionable indicators can strengthen emergency preparedness and support more resilient, data-informed responses to severe weather events. 

Learn more about the 2026 INRIX x MetroLab Challenge.