This study addresses the challenges associated with urban transportation by providing a framework for exploiting data analytics, with application to transportation data, to achieve an effective and time-efficient metropolitan city transportation system. We aim to understand traffic in different areas of the city, as well as trying to categorize the various zones within numerous areas in the city, such as: business destination, residential destination, or touristic destination according to its popularity given both the time-range and the day of the week. In this project, a logistic regression classification model is built to classify locations into hotspots/non-hotspots.
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Shubair, Hadeel Raed, "Utilizing Data Analytics for Optimum Urban Transportation System" (2020). Thesis. Rochester Institute of Technology. Accessed from