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Big data for urban & transportation analysis /projectOrganization logo: Eindhoven University of Technology

Over deze cursus

To find good solutions one needs to have a good understanding of the problem. This holds true also for the problems and challenges urban planners are facing in areas such as mobility (congestion and accessibility), health (air pollution, passive lifestyles), energy (smart grids and transformation to renewable sources of energy), ageing (social exclusion, social satisfaction), and tourism (crowding). In this project you consider a planning problem of your choice, select a big data source and apply a suitable analysis approach to better understand the problem and evaluate scenarios.
The approach includes information from big data source(s): (i) data from devices: e.g., GPS data, WIFI data, sensor data; (ii) user generated data such as online textual data (Twitter), photo data (Flickr); or (iii) transaction data: web search data, online booking data, that provide rich information on micro-level of individuals and or the environment. In this approach the data, or combination of data sources are analyzed with advanced modeling approaches such as data mining (e.g., Bayesian network learning), choice modeling (e.g., mixed logit model), regression analysis (e.g., multilevel regression), or machine learning approaches to achieve a better understanding of behavior of individuals with regard to the planning problem considered. During the project, the following steps will be carried out: formulation of a research question; literature research; specification of a conceptual model; identification of relevant variables; finding and obtaining relevant big data; preparation of the data; conducting the analysis and interpreting the results. The data and analysis technique(s) used will be chosen depending on the specific research question.


After completion of the project the student is able to:

  • Formulate a research question for a problem in urban planning (e.g., transportation, tourism, energy, healthy living environment, housing)
  • Find relevant big data source(s)
  • Identify a suitable big data analysis technique for the research question concerned
  • Carry out all the steps involved in the chosen methodology
  • Assess various future planning scenarios and identify implications for planning
  • Judge the limitations of the carried out research and identify remaining problems for future research.


7ZW7M0 Urban research methods

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  • Startdatum

    5 februari 2024

    • Einddatum
      7 april 2024
    • Periode *
      Blok GS3
    • Locatie
    • Voertaal
    • Inschrijven tussen
      15 nov 00:00 - 7 jan 2024
    Inschrijvingsperiode gesloten
Gast inschrijvingen worden rechtstreeks behandeld door TU Eindhoven