Big data-driven transportation engineering has the potential to improve utilization of road infrastructure, decrease traffic fatalities, improve fuel consumption, and decrease construction worker injuries, among others. Despite these benefits, research on big data-driven transportation engineering is difficult today due to the computational expertise required to get started. This thesis proposes Boa_T, a transportation-specific programming language, and it's big data infrastructure that is aimed at decreasing this barrier to entry. Our

evaluation, that uses over two dozen research questions from six categories, shows that research questions

are easier to realize as Boa_T computer programs, an order of magnitude faster when these programs are run,

and exhibit 12-14x decrease in storage requirements.