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Sustainable Urban Transport Planning using Big Data from Mobile Phones

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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.

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Sustainable Urban Transport Planning using Big Data from Mobile Phones

  1. 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 1 / 12
  2. 2. Challenge Potential Solution Potential Benefits Appendix Challenge 2 / 12
  3. 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. 3 / 12
  4. 4. Challenge Potential Solution Potential Benefits Appendix Consequences Figure 1: Traffic Congestion 4 / 12
  5. 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. 5 / 12
  6. 6. Challenge Potential Solution Potential Benefits Appendix Potential Solution 6 / 12
  7. 7. Challenge Potential Solution Potential Benefits Appendix Big Data from Mobile Phones Figure 2: Annonymized CDR data in South Africa 7 / 12
  8. 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. 8 / 12
  9. 9. Challenge Potential Solution Potential Benefits Appendix Potential Benefits 9 / 12
  10. 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 10 / 12
  11. 11. Challenge Potential Solution Potential Benefits Appendix Appendix 11 / 12
  12. 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. 12 / 12

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