Engaging Big Data

Jan 2016 - Dec 2020

Big Data-driven agent-based transport simulations

Data pipeline for Large Scale Transport Simulations
Data pipeline for Large Scale Transport Simulations

Traditionally, disaggregate demand modelling has been useful in explaining travel behaviour and forecasting travel demand. However, the biggest challenge in disaggregate demand modelling has perhaps been the reliance on a few observations of (usually) reported human behaviour. Recorded in travel diary surveys, these observations are later generalised to the population at large. The reliability of the model predictions are compromised when uncertainties or errors occur in this process of generalisation, and when these inaccuracies further interact and compound. Moreover, surveys are very expensive to conduct, take a long time to encode and release, and are only performed periodically, further compounding problems of generalisation with the added dimension of time.

With the advent of big data, from sources like transit smart cards and cellular phones, comes the potential to revolutionise disaggregate travel demand modelling by overcoming many of these problems. These data sources are congruent with the granularity and mode of analysis of the disaggregate modelling approach, providing direct observation of individual travel behaviour between activity locations on a daily basis.

In the Engaging Big Data research  project, we build on the research that started in the first phase of FCL, where we developed a simulation of transit entirely driven by smart card data. In this phase, our aim is to extend our scope to include a wide array of big data sources, including mobile phone data. We fuse these with travel survey data to derive a complete and constantly updated travel demand description based on the actual behaviour of commuters.

We develop novel machine learning methods that can associate activity purpose and sociodemographic information with commuter trajectories. In order to ensure privacy, we investigate generative techniques that can produce endless streams of commuter trajectories that are, while not linked to specific individuals, but are realistic and representative of the population, and which will allow the system to be deployed in a ‘machine-eyes-only’ mode. The project also aims to address implementation challenges, including distributed simulation, and automatic calibration of simulation parameters.

Publications

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  • Digital twin travellers: Disaggregated travel demand from aggregated mobile phone data – A privacy by design approach
    Cuauhtemoc Anda
    Public PhD Presentation. Zurich, Switzerland. 2022. Presentation.
  • Synthesising digital twin travellers: Individual travel demand from aggregated mobile phone data
    Cuauhtémoc Anda, Sergio Arturo Ordonez Medina and Kay W. Axhausen
    Transportation Research Part C: Emerging Technologies, vol. 128, pp. 103118, Amsterdam: Elsevier, 2021.
  • Synthesising digital twin travellers: Individual travel demand from aggregated mobile phone data
    Cuauhtémoc Anda, Sergio Arturo Ordonez Medina and Kay W. Axhausen
    Arbeitsberichte Verkehrs- und Raumplanung, vol. 1559, Singapore: Future Cities Laboratory (FCL), Singapore ETH Centre, 2020.
  • Privacy-by-design generative models of urban mobility
    Cuauhtémoc Anda and Sergio Arturo Ordonez Medina
    Arbeitsberichte Verkehrs- und Raumplanung, vol. 1454, Singapore: FCL, Singapore ETH Centre, 2019.
  • A single trajectory is a tragedy, 1.2 million is Big Data
    Pieter Jacobus Fourie, Cuauhtémoc Anda and Sergio A. Ordoñez Medina
    ETH Blog - Engaging Mobility, Singapore: Engaging Mobility Group, Future Cities Laboratory (FCL), 2018.
  • Big data, AI, and data privacy for transport planning
    Cuauhtémoc Anda
    Department of Urban Planning, Tongji University. Shanghai, China. 2018. Presentation.
  • A time-space model of disaggregated urban mobility from aggregated mobile phone data
    Cuauhtémoc Anda
    15th International Conference on Travel Behavior Research (IATBR 2018), Santa Barbara, CA, USA Singapore; Zurich: Future Cities Laboratory (FCL); IVT, ETH Zurich, July 15-20, 2018.
  • A time-space model of disaggregated urban mobility from aggregated mobile phone data
    Cuauhtémoc Anda and Sergio A. Ordoñez Medina
    15th International Conference on Travel Behavior Research (IATBR 2018), Santa Barbara, CA, USA Zurich: IVT, ETH Zurich, July 15-20, 2018.
  • Archetypes of urban travelers: Clustering of mobile phone users in Singapore
    Cuauhtémoc Anda
    Mobile Tartu 2018, Tartu, Estonia Singapore: FCL, Singapore ETH Centre, June 27-29, 2018.
  • Archetypes of urban travellers: Clustering of mobile phone users in Singapore
    Cuauhtémoc Anda and Sergio A. Ordoñez Medina
    Mobile Tartu 2018, Tartu, Estonia Zurich: ETH Zurich, June 27-29, 2018.