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In the past decades, there has been rapid urbanization as more and more people migrate into cities. The World Health Organization (WHO) estimates that by 2017, a majority of people will be living in urban areas. By 2030, 5 billion people—60 percent of the world’s population—will live in cities, compared with 3.6 billion in 2013. Developing nations must cope with this rapid urbanization. Transportation and urban planners must estimate travel demand for transportation facilities and use this to plan transportation infrastructure. Presently, the technique used for transportation planning uses data inputs from local and national household travel surveys. However, these surveys are expensive to conduct, cover smaller areas of cities and the time between surveys range from 5 to 10 years. This calls for new and innovative ways for Transportation Planning using new data sources.
In recent years, we have witnessed the proliferation of ubiquitous mobile computing devices in developing countries. These mobile phones capture the movement of vehicles and people in near real time and generate massive amounts of new data. My PhD research investigates how we can utilize anonymized mobile phone data ( i.e. Call Detail Records) and probabilistic machine learning to infer travel/mobility patterns. One of the objectives of this research is to demonstrate that these new “big” data sources are cheaper alternatives for transport modeling and travel behavior studies.