Daniel Emaasit (Civil & Environmental Engineering) was invited by the to at the 2016 Data Science Africa Workshop. This unique conference brought together experts from academia, industry, government, and development partners to discuss how big data and data science can be used to monitor and track progress in achieving the United Nations' sustainable development goals in Africa. He presented work that he is doing as part of his Ph.D. research at 51³Ô¹ÏºÚÁÏ, which involves using big data from mobile phones for sustainable urban planning.
He presented his research under the session, “Data Science for Sustainable Cities." His research involves leveraging readily available ubiquitous data from mobile phones as an alternative and cheaper source of data for transport planning. He has developed a model-based machine learning framework to infer travel patterns from these data. The potential benefits of this research to developing countries are the ability to use low-cost effective techniques for transport planning and also getting an accurate measure of travel patterns in cities so that planners can optimally estimate the right amount of travel demand and reduce traffic congestion that has plagued most developing cities in Africa. There were several urban planners at the conference from the United Nations and government who found his research to be instrumental for city planning.
