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Discovering urban activity patterns in cell phone data

Author

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  • Peter Widhalm

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  • Yingxiang Yang
  • Michael Ulm
  • Shounak Athavale
  • Marta González

Abstract

Massive and passive data such as cell phone traces provide samples of the whereabouts and movements of individuals. These are a potential source of information for models of daily activities in a city. The main challenge is that phone traces have low spatial precision and are sparsely sampled in time, which requires a precise set of techniques for mining hidden valuable information they contain. Here we propose a method to reveal activity patterns that emerge from cell phone data by analyzing relational signatures of activity time, duration, and land use. First, we present a method of how to detect stays and extract a robust set of geolocated time stamps that represent trip chains. Second, we show how to cluster activities by combining the detected trip chains with land use data. This is accomplished by modeling the dependencies between activity type, trip scheduling, and land use types via a Relational Markov Network. We apply the method to two different kinds of mobile phone datasets from the metropolitan areas of Vienna, Austria and Boston, USA. The former data includes information from mobility management signals, while the latter are usual Call Detail Records. The resulting trip sequence patterns and activity scheduling from both datasets agree well with their respective city surveys, and we show that the inferred activity clusters are stable across different days and both cities. This method to infer activity patterns from cell phone data allows us to use these as a novel and cheaper data source for activity-based modeling and travel behavior studies. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
  • Handle: RePEc:kap:transp:v:42:y:2015:i:4:p:597-623
    DOI: 10.1007/s11116-015-9598-x
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    References listed on IDEAS

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    1. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    2. Carlo Ratti & Riccardo Maria Pulselli & Sarah Williams & Dennis Frenchman, 2006. "Mobile Landscapes: using location data from cell phones for urban analysis," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 33(5), pages 727-748, September.
    3. Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
    4. Boarnet, Marlon & Crane, Randall, 2001. "The influence of land use on travel behavior: specification and estimation strategies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(9), pages 823-845, November.
    5. McNally, Michael G. & Ryan, Sherry, 1992. "A Comparative Assessment of Travel Characteristics for Neo-Traditional Developments," University of California Transportation Center, Working Papers qt03n6593v, University of California Transportation Center.
    6. Kees Maat & Bert van Wee & Dominic Stead, 2005. "Land use and travel behaviour: expected effects from the perspective of utility theory and activity-based theories," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 32(1), pages 33-46, January.
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    1. repec:eee:appene:v:195:y:2017:i:c:p:810-818 is not listed on IDEAS
    2. repec:eee:energy:v:140:y:2017:i:p1:p:716-728 is not listed on IDEAS
    3. repec:gam:jsusta:v:10:y:2018:i:1:p:214-:d:127295 is not listed on IDEAS
    4. Zhao, Shuangming & Zhao, Pengxiang & Cui, Yunfan, 2017. "A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 143-157.
    5. repec:kap:transp:v:45:y:2018:i:3:d:10.1007_s11116-016-9756-9 is not listed on IDEAS
    6. Pozdnukhov, Alexey, 2016. "Demand Forecasting and Activity-based Mobility Modeling from Cell Phone Data," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4hc9r218, Institute of Transportation Studies, UC Berkeley.

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