CTS Discretionary Projects - Center for Transportation Safety

Cts discretionary

In addition to conducting work for our sponsors, CTS also uses a portion of its state allocation to fund a limited number of discretionary projects each year. These projects are intended to conduct research, education, or outreach within Texas to improve transportation safety.

Behavior Change to Support Transportation Safety

Project Summary

This project developed a comprehensive approach to integrating behavior change theories (BCTs) into transportation safety research, education, and outreach at the Center for Transportation Safety (CTS). Through interviews, training sessions, and the creation of a Behavior Change Theory Toolkit, the project equipped CTS staff with the knowledge and resources to apply BCTs in their work. The initiative aimed to enhance the effectiveness of traffic safety programs by addressing the human factors behind unsafe behaviors among drivers, pedestrians, and bicyclists. The project supports CTS’s mission to reduce roadway fatalities and injuries by promoting theory-based, multidisciplinary strategies for behavior change.

Resources and Reports

Project Report to be added shortly

Employer-Driven Safety Innovation

Project Summary

The Employee Driver Safety Innovation (EDSI) initiative enhances employee driver safety through evidence-based, theory-driven programs and resources. Supporting key emphasis areas of the Texas Strategic Highway Safety Plan, the project develops tailored safety curricula, outreach materials, and innovative training tools—including virtual reality simulations. It also explores predictive evaluation methods to assess the effectiveness of safety interventions. EDSI equips employers and drivers with practical strategies to reduce transportation-related injuries and fatalities, helping ensure employees return home safely.

Resources and Reports

EDSI Web Portal

Texas Traffic Safety Culture Survey

Project Summary

This statewide survey evaluated Texans’ perceptions and behaviors related to traffic safety. With input from over 1,300 residents, the study found growing concern over distracted and impaired driving, strong support for safety laws, and a persistent gap between what drivers consider unacceptable and what they actually do. The findings help guide traffic safety policy and public awareness efforts.

Resources and Reports

Project Report to be added shortly

Texas Pedestrian Safety Toolkit

Project Summary

This project applied advanced techniques like natural language processing, computer vision, and machine learning to extract detailed insights from pedestrian crash reports. By analyzing crash narratives, diagrams, and integrating weather and sun glare data, the team developed models to classify pedestrian maneuvers and intentionality—whether the person intended to be a pedestrian. The study also created a dashboard for spatial analysis and predictive models for crash severity. These tools work to improve pedestrian safety by enabling more accurate crash data interpretation and supporting targeted countermeasures.

Resources and Reports

Project Report to be added shortly

Crash Analytics to Improve Pedestrian Safety

Project Summary

This project focused on improving pedestrian safety near bus stops in Fort Worth by identifying high-risk locations and the factors contributing to crashes. Using crash data and bus stop characteristics, researchers developed a risk scoring system to prioritize 75 bus stops for targeted safety improvements. Outreach efforts included educational materials, social media campaigns, and collaboration with local agencies. The findings support data-informed infrastructure upgrades and align with Texas’s Vision Zero goals to eliminate traffic fatalities.

Resources and Reports

Project Report to be added shortly

Driver Stress and Transportation Safety

Project Summary

Resources and Reports

Project Report to be added shortly

VR and Driving Simulator Training for Safety

Project Summary

This project developed a Safety Visualization Virtual Platform using connected virtual reality (VR) devices to enhance road safety education for young pedestrians and bicyclists, particularly those aged 11 to 14. The platform simulates real-world traffic environments, allowing users to interact with and learn from virtual scenarios involving street and intersection crossings. By enabling immersive, instructor-led training sessions with up to four participants, the platform aims to improve safety awareness, decision-making, and confidence in navigating traffic situations—ultimately reducing crash risks and injuries among vulnerable road users.

Resources and Reports

Improving Pedestrian Safety Through Modeling and Simulation

Project Summary

This study uses finite element simulations to assess pedestrian head and chest injuries from vehicle impacts on neighborhood streets. Researchers modeled collisions between a pedestrian and two vehicle types—a passenger car and a pickup truck—at various speeds and masses. Results show that higher vehicle speed and mass increase injury severity, with pickup trucks posing greater risks. The findings support recommendations for safer neighborhood speed limits and highlight the importance of secondary impacts in injury outcomes.

Resources and Reports

Project Report to be added shortly

Texas Crash Narrative De-Identification

Project Summary

The Texas Crash Narratives De-identification Tool project developed an advanced NLP system to automatically remove personally identifiable information (PII) from free-text crash narratives in Texas crash reports. These narratives contain valuable details about crash circumstances but often include sensitive data that limits their use in research. By fine-tuning a BERT-based transformer model on manually annotated data, the tool accurately identifies and redacts PII such as names, addresses, and phone numbers. This enables secure data sharing while preserving the richness of the narrative content, supporting traffic safety research and policy development aimed at reducing crashes and fatalities across Texas.

Resources and Reports

Project Report to be added shortly

Infrastructure-Based Countermeasures to Improve Pedestrian Safety

Project Summary

This project examined the link between driver yielding behavior and crash rates at pedestrian crossings with three types of traffic control devices: Pedestrian Hybrid Beacons (PHBs), Rectangular Rapid Flashing Beacons (RRFBs), and LED-Embedded (LED-EM) signs. Using staged pedestrian crossings and crash data, researchers found a negative correlation—higher driver yielding rates were associated with lower crash rates—supporting the use of driver yielding as a surrogate safety measure. The study highlights the value of this approach for evaluating pedestrian safety, while also noting the need for further research to account for additional site-specific factors.

Resources and Reports

Project Report