Michael Szell Visit

We are very lucky to have Michael Szell visiting the week of April 3rd. Micheal is a research fellow at the Hungarian Academy of Sciences, Centre for Social Sciences and visiting at Northeastern University, Center for Complex Network Research. He’s previously worked at the MIT Media Lab’s Senseable City Lab.

Michael’s research focuses on a quantitative understanding of collective behavior. How the the underlying patterns of our interlinked actions and decisions can be modeled in computational social science, and his past research involves mining and modeling large-scale data sets of human activity following a complex networks/systems approach.

His exciting work has been featured in PNAS, Nature Physics, Science, and many other fine journals. During his visit, Michael will give a talk at DTU Compute.

  • Date. Tuesday April 4th, 2017
  • Time: 14:00
  • Location. Technical University of Denmark, Building 321, 1st floor lab space.
Title: Using network science and data visualization to assess the potential of urban sharing  economies
Abstract: We introduce the notion of shareability network, which allows us to model the collective benefits of sharing rides as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We first apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting toward a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low. Indeed, applying the same framework to a diverse set of world cities, using data on millions of taxi trips beyond New York City, in San Francisco, Singapore, and Vienna, we compute the shareability curves for each city, and find that a natural rescaling collapses them onto a single, universal curve. We explain this scaling law theoretically with a simple model that predicts the potential for ride sharing in any city, using a few basic urban quantities and no adjustable parameters. Accurate extrapolations of this type will help planners, transportation companies, and society at large to shape a sustainable path for urban growth. Finally, we present “What the Street!?”, an online platform for the interactive exploration of city-wide mobility spaces, published in April 2017. The aim of What the Street!? is to facilitate the intuitive exploration of (wasted) mobility space in cities, exploring why and to which extent space is distributed unevenly between different modes of transportation. We demonstrate how this data visualization of re-ordered city spaces can effectively inform relevant stakeholders and the public about large-scale reductions of parking spaces in future scenarios of wide-spread car-sharing.

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