sustainable urban transport planning using big data from mobile phones

Challenge Potential Solution Potential Benefits Appendix

Sustainable Urban Transport Planning usingBig Data from Mobile Phones

Daniel Emaasit1

1Department of Civil and Environmental EngineeringUniversity of Nevada Las Vegas

Las Vegas, NV USAemaasit@unlv.nevada.edu

June 30 2016

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Challenge

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Rapid Urbanization in Developing Countries

I The World Health Organization (WHO) estimates that by2017, a majority of people will be living in urban areas.1

I By 2030, 5 billion people—60 percent of the world’spopulation—will live in cities.

I The United Nations Population Fund (UNPF) reported thatthis rapid urbanization is particularly extraordinary inAfrica and Asia.2

1World Health Organization, (2015). Global Health Observatory data.2United Nations Population Fund, (2007). State of World Population

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Consequences

Figure 1: Traffic Congestion 4 / 12

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Challenges

I Transportation and urban planners must estimate traveldemand for transportation facilities.

I Presently, the technique used for transportation planninguses data inputs from local and national household travelsurveys:

I these surveys are expensive to conduct,I cover smaller areas of cities, andI the time between surveys range from 5 to 10 years.

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Potential Solution

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Big Data from Mobile Phones

Figure 2: Annonymized CDR data in South Africa 7 / 12

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Modeling the Solution

I Emaasit et al. (2016) 3 proposed a model-based machinelearning approach to infer travel patterns from mobile phonedata (Call Detail Records).

3D. Emaasit, A. Paz, and J. Salzwedel (2016). “A Model-Based MachineLearning Approach for Capturing Activity-Based Mobility Patterns usingCellular Data”. IEEE ITSC.

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Potential Benefits

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Benefits for Developing Countries

I Planners can levarage low cost solutionsI CDR data captured over short periods of time are sufficient

enough to capture actual mobility patterns in citiesI Wide area coverage, hence inclusive of all demograhpics.I Planners can develop detailed responses to congestion events

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Appendix

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Methodology: Model-Based Machine Learning

I A different viewpoint for machine learning proposed byBishop (2013)4, Winn et al. (2015)5

I Goal:I Provide a single development framework which supports the

creation of a wide range of bespoke models

I The core idea:I all assumptions about the problem domain are made

explicit in the form of a model

4Bishop, C. M. (2013). Model-Based Machine Learning. PhilosophicalTransactions of the Royal Society A, 371, pp 1–17

5Winn, J., Bishop, C. M., Diethe, T. (2015). Model-Based MachineLearning. Microsoft Research Cambridge. http://www.mbmlbook.com.

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