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.

1. Challenge Potential Solution Potential Benefits Appendix
Sustainable Urban Transport Planning using
Big Data from Mobile Phones
Daniel Emaasit1
1Department of Civil and Environmental Engineering
University of Nevada Las Vegas
Las Vegas, NV USA
emaasit@unlv.nevada.edu
June 30 2016
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2. Challenge Potential Solution Potential Benefits Appendix
Challenge
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3. Challenge Potential Solution Potential Benefits Appendix
Rapid Urbanization in Developing Countries
The World Health Organization (WHO) estimates that by
2017, a majority of people will be living in urban areas.1
By 2030, 5 billion people—60 percent of the world’s
population—will live in cities.
The United Nations Population Fund (UNPF) reported that
this rapid urbanization is particularly extraordinary in
Africa and Asia.2
1
World Health Organization, (2015). Global Health Observatory data.
2
United Nations Population Fund, (2007). State of World Population
2007.
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4. Challenge Potential Solution Potential Benefits Appendix
Consequences
Figure 1: Traffic Congestion 4 / 12

5. Challenge Potential Solution Potential Benefits Appendix
Challenges
Transportation and urban planners must estimate travel
demand for transportation facilities.
Presently, the technique used for transportation planning
uses data inputs from local and national household travel
surveys:
these surveys are expensive to conduct,
cover smaller areas of cities, and
the time between surveys range from 5 to 10 years.
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6. Challenge Potential Solution Potential Benefits Appendix
Potential Solution
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7. Challenge Potential Solution Potential Benefits Appendix
Big Data from Mobile Phones
Figure 2: Annonymized CDR data in South Africa 7 / 12

8. Challenge Potential Solution Potential Benefits Appendix
Modeling the Solution
Emaasit et al. (2016) 3 proposed a model-based machine
learning approach to infer travel patterns from mobile phone
data (Call Detail Records).
3
D. Emaasit, A. Paz, and J. Salzwedel (2016). “A Model-Based Machine
Learning Approach for Capturing Activity-Based Mobility Patterns using
Cellular Data”. IEEE ITSC.
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9. Challenge Potential Solution Potential Benefits Appendix
Potential Benefits
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10. Challenge Potential Solution Potential Benefits Appendix
Benefits for Developing Countries
Planners can levarage low cost solutions
CDR data captured over short periods of time are sufficient
enough to capture actual mobility patterns in cities
Wide area coverage, hence inclusive of all demograhpics.
Planners can develop detailed responses to congestion events
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11. Challenge Potential Solution Potential Benefits Appendix
Appendix
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12. Challenge Potential Solution Potential Benefits Appendix
Methodology: Model-Based Machine Learning
A different viewpoint for machine learning proposed by
Bishop (2013)4, Winn et al. (2015)5
Goal:
Provide a single development framework which supports the
creation of a wide range of bespoke models
The core idea:
all assumptions about the problem domain are made
explicit in the form of a model
4
Bishop, C. M. (2013). Model-Based Machine Learning. Philosophical
Transactions of the Royal Society A, 371, pp 1–17
5
Winn, J., Bishop, C. M., Diethe, T. (2015). Model-Based Machine
Learning. Microsoft Research Cambridge. http://www.mbmlbook.com.
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