Automated Vehicle Rideshare Pilot

UTA and the City of Arlington are conducting a pilot project that deploys autonomous vehicles (AVs) in and around UTAcampusand areas in downtown Arlington. Passengers are able to request a ride on the AVs using the Via Mobile On-Demand (MOD) rideshare app. UTA students have the opportunity to ride the AVs for free. This project is funded by the US DOT

Unmanned Aerial Vehicles and Ground Traffic Management Integration

The research involves innovative solutions at multiple spatiotemporal scales and developing higher-level decision-making techniques for traffic capacity and flow management robust to uncertain environmental conditions. Collaborators include the Hillwood Alliance Texas Mobility Innovation Zone, North Texas Central Government of Texas, and other industrial and government agencies.

Data Collection for Unserved Trips and Transportation Disadvantaged Populations

Through the Transportation Research Center for Livable Communities and NCTCOG, UTA developed an app for collecting mixed methods data related to transportation needs of disadvantaged populations. This innovative strategy identifies the transportation barriers and needs, and the consequences of unmet transportation demand.The app continues to be tested and improved through funding from the Ohio Department of Transportation, NITC and NCTCOG.

Emerging Vehicle Technology Scenario Planning

In a TxDOT project, UTA is developing a scenario planning framework that identifies trends and influencing factors, gathers expert feedback, and assesses probabilities of the impact of emerging transportation technologies. The framework will help public agencies prepare for future scenarios.

Big-Data Driven Traffic Monitoring and Management System

Using both traditional traffic data and emerging internet-based traffic big data, UTA researchers are integrating these data to provide better interpretation and prediction of highway traffic conditions , including high-resolution traffic signal events, crowd sourced vehicle positioning data, lane-level internet multi-modal travel time/speed, open (road) weather, air quality, etc. Several system variants are in place and will serve as a live platform for multiple research topics, such as congestion management, truck signal priority, arterial traffic performance evaluation.

Data Fusion Platforms

To evaluate, integrate, and process emerging sources of travel behavior and activity data to understand marginalized transportation users such as pedestrians, cyclists, and commercial trucks. The team uses new sources of activity data –GPS based smartphone apps, social media, and wearable device –to demonstrate potential uses in safety, health, equity, and environmental impact analysis.