Data and Modeling

Data Project 1

Enhancing First Responder Efficiency via Waze-911 Integration

CTR evaluated the value of enhanced incident detection and response by integrating crowdsourced data from the Waze navigation app and 9-1-1 calls. The project developed a robust methodological framework to analyze a large volume of Waze data and formulated recommendations to help Traffic Management Center operators get the most value from integration.

D&M Picture 2

Improving Evacuation Decision-Making Through Real-Time Traffic Data

CTR researchers investigated how Texans use real-time traffic information during hurricane evacuations and post-storm returns. Drawing on survey data from evacuees across past Texas hurricane events, the study examined what information sources people rely on, how that information influences their routing and departure decisions, and what barriers prevent effective use of real-time data. The findings offer actionable guidance for improving how agencies communicate traffic conditions during large-scale evacuations.

D&M 3 Picture

Traffic Signal Operations Supporting All Users

CTR researchers developed a comprehensive catalogue of traffic signal strategies designed to improve safety for pedestrians, cyclists, and transit riders without sacrificing traffic flow. The research team evaluated over 30 countermeasures, from leading pedestrian intervals and exclusive bike phases to bus queue jumps and transit signal priority, documenting the safety benefits and real-world applicability of each for Texas roadways.

D&M 4 Picture

Post-COVID-19 Travel Patterns: UT Austin Survey

CTR researchers investigated post-pandemic travel behavior at UT Austin through survey-based analysis of how individuals’ travel habits, perceptions, and expectations evolved over time. The work identifies notable shifts in travel patterns and contributes practical insights for shaping more resilient and sustainable transportation planning.

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