Inferring Urban Travel Patterns From Cellphone Data

Inferring Urban Travel Patterns From Cellphone Data

Published By: MIT News, 8/29/2016

Summary: Researchers are using data on the locations people make calls from to model the movement patterns of Boston commuters; the system may replace or supplement surveys of residents. The article discusses the benefits of gathering and processing more data more quickly and cheaply, though students may be able to identify some disadvantages of using call data.

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Extended Discussion Questions:

  • What potential impacts might this project have on transportation planning in Boston? The U.S.? Anywhere?
  • What are some advantages of the cell phone-based data collection? What are some advantages of using surveys?
  • Can you think of any ways that using cell phone data might lead to biased results?What kind of consequences could biased data have for transportation planning? How could transportation planners try to account for the bias?
  • The researchers used the data about call origins and times to infer where (anonymized) people lived and where they worked. How might they have done that? What could someone figure out by looking at your call records?

Note for Discussion: The appendix to the original article indicates that the data came from bulk anonymized records from mobile providers, not self-selected volunteer participants.

CSP Global Impact Learning Objectives/EKs:

LO 7.3.1 Analyze the beneficial and harmful effects of computing.
LO 7.4.1 Explain the connections between computing and real-world contexts, including economic, social, and cultural contexts.

EK 7.1.1I Global Positioning System (GPS) and related technologies have changed how humans travel, navigate, and find information related to geolocation.
EK 7.1.2G The move from desktop computers to a proliferation of always-on mobile computers is leading to new applications.
EK 7.2.1A Machine learning and data mining have enabled innovation in medicine, business, and science.
EK 7.3.1J Technology enables the collection, use, and exploitation of information about, by, and for individuals, groups, and institutions.

Other CSP Big Ideas:

2 Abstraction
3 Data and Information
4 Algorithms
6 The Internet

Banner Image: “Network Visualization – Violet – Crop 10”, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0

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