The Texas A&M Transportation Institute launched the Transportation Policy Research Center in 2013 under the direction and support of the Texas State Legislature, which recognizes that Texas must move to a more robust transportation system for the state to ensure economic prosperity and provide a high quality of life for its citizens.
The TTI Policy Research website houses the research studies and products developed by the Center between 2013 and 2017.
Specific research studies of state interest
TTI’s policy research is structured to address specific research studies of state interest, bringing together engineering, finance, economic, technology, policy and public engagement experts from within TTI, the broader Texas A&M System, other institutions of higher education and the private sector. The work performed by TTI will also be closely coordinated with TxDOT, local agencies and other transportation stakeholders. The ever-changing landscape of transportation needs and new technologies, as well as funding issues and a rapidly growing population, has created a complex set of challenges, opportunities and decisions for state and local decision makers.
While infrastructure development will continue to be an important part of the 21st century transportation model, more efficient use of available infrastructure, effective use of improved technology, increased coordination among transportation modes and development of a “smarter” transportation system will be important components of the 21st century system.
Transportation — to attract businesses and jobs
The state’s transportation system is central to its ability to attract businesses and jobs to Texas, as the state has become increasingly dependent on the efficient movement of goods to maintain and enhance global economic competitiveness. An effective transportation system is also central to a good quality of life for Texas citizens.
Working with legislative leaders, TTI identified the following set of initial transportation issues which must be better understood in order to position the state in an optimal manner: