Inferring Activities and Optimal Trips: Lessons From Singapore’s National Science Experiment

  • Barnabé Monnot
  • Erik Wilhelm
  • Georgios Piliouras
  • Yuren Zhou
  • Daniel Dahlmeier
  • Hai Yun Lu
  • Wang Jin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 426)


The following paper presents three novel and efficient algorithms to tackle pressing questions asked by city planners as well as policy makers: Where are people starting and ending their trips? Which activities are people traveling to/from? Are they taking the most efficient route? In order to capture large-scale travel data, a novel sensor was developed by the Singapore University of Technology and Design together with industrial partners. Using computationally simple and scalable algorithms, we are able to understand the large amounts of data collected by the sensors and shed light on the three questions above.


Urban data Large-scale experiment Sensor data Optimal routing Data visualization 



This work was supported by the Singaporean National Research Foundation (NRF) and the SUTD International Design Center (IDC). Production of the sensors was possible due to strong support from Delta Electronics DRC, IABG, and Taoyuan Factory 2.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Barnabé Monnot
    • 1
  • Erik Wilhelm
    • 2
  • Georgios Piliouras
    • 1
  • Yuren Zhou
    • 2
  • Daniel Dahlmeier
    • 3
  • Hai Yun Lu
    • 3
  • Wang Jin
    • 3
  1. 1.Engineering Systems and DesignSingapore University of Technology and DesignSingaporeSingapore
  2. 2.Engineering Product DevelopmentSingapore University of Technology and DesignSingaporeSingapore
  3. 3.SAP SingaporeSingaporeSingapore

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