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 work proposes BoaT, a transportation-specific programming language, and its 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 is easier to realize as a BoaT computer program, an order of magnitude faster when this program is run, and exhibits 12–14× decrease in storage requirements.
Available at: http://works.bepress.com/anuj_sharma1/67/
This is a pre-print of an article published as Islam, Md Johirul, Anuj Sharma, and Hridesh Rajan. "A Cyberinfrastructure for Big Data Transportation Engineering." Journal of Big Data Analytics in Transportation 1, no. 1 (2019): 83-94. The final authenticated version is available online at DOI: 10.1007/s42421-019-00006-8. Posted with permission.