The Center for Advanced Transportation Technology Laboratory at the University of Maryland was originally established in 2002 as an academic applied research and development lab to support national, state, and local efforts to solve important transportation, safety, and security problems. The CATT Lab accomplishes this mission through innovative technology deployments and user-centered design of software and information visualization systems. Our work spans many disciplines including Intelligent Transportation Systems, law enforcement, network security, private business, defense and homeland security.
The CATT Lab develops visual analytics and information visualization tools that lead users to insights that would usually be difficult, if not impossible, to discover through traditional data analysis techniques. The old adage that “a picture is worth a thousand words” rings true. Properly designed visual analytics tools can increase a user’s ability to:
CATT Lab developers have the skills to properly design visual analytics tools so that they enhance the user experience, increase productivity, and help to market a particular product. Examples of some of our visualization work can be seen in our portfolio.
The CATT Lab develops real-time systems that fuse and integrate hundreds of Gigabytes of data per day in real-time from emergency operations centers, transportation management centers, thousands of sensors, CCTV cameras, and sub-systems throughout the country.
CATT Lab employees are experts in database management systems, database tuning, standards, geospatial data management, and data processing optimization. Their real-time data fusion systems are used by thousands of clients throughout the country on a daily basis. The CATT Lab’s data fusion, dissemination, and analytical systems and are considered the gold standard by planners, law enforcement, operations, defense, and private sector freight companies.
Understanding the “big picture” and working effectively in data rich environments can be extremely complex. The CATT has extensive expertise in developing user interfaces for large-scale, complex systems that enable users to quickly and easily comprehend the potential and immediate results of decisions and to help them to navigate through sophisticated software with minimal effort and maximum efficiency. Proper user interface design is both a learned skill and a trained art. The CATT Lab’s design team and human computer interaction experts design software that significantly enhances user experiences–leading to greater acceptance and increased workflow.
Effectively training traffic control personnel, emergency management personnel, and first responders is both a challenge and critical to effectively managing the flow of people and goods on the nation’s roadways. The CATT Lab has worked with private sector businesses, government agencies, and the Department of Defense to develop web-based training systems that are highly effective at teaching people a variety of skills including:
The CATT Laboratory’s curriculum developers have designed multi-media courses that can be web-based or installed directly on a computer. Other CATT Lab simulation and digital entertainment experts have designed, implemented, and deployed a massively multi-player online gaming system that enables real-world, complex simulation and communications-based exercises that can be rehearsed and analyzed.
We live in an age of data—massive amounts of data. It is everywhere and data analysis is a key agency asset. An agency’s effectiveness in collecting, analyzing, and sharing information (both internally and to customers) often determines their success amongst competitors. With advances in data collection comes a new problem: how to analyze and then display it—and more importantly, how to display it in a way that makes it understandable and accessible. The CATT Lab works closely with local, state, and federal agencies in the support of analyzing large datasets from sensors, probes, police accident records, or other databases in a manner that makes the information easily understandable by many user groups from engineers to managers, public officials, and the public directly—making sense of large datasets to “tell a story” about an important issue that conveys meaning and importance.