Közgazdaság- és Regionális Tudományi Kutatóközpont Magyar Tudományos Akadémia Kiváló Kutatóhely

Esemény kezdete: 2017.11.30. 14:00

Esemény vége: 2017.11.30. 16:00

Hely: MTA Humán Tudományok Kutatóháza, Budapest, Tóth Kálmán utca 4. földszinti előadóterem

Leírás:

Lecture:

Szell Michael
Exploring quantitatively sharing benefits and inequalities in urban transportation

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. 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. Applying the same framework to a diverse set of world cities, we find that a natural rescaling collapses all shareability curves 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!?” (whatthestreet.moovellab.com), an online platform for the interactive exploration of city-wide mobility spaces. The aim of What the Street!? is to facilitate the intuitive exploration of (wasted) mobility space in cities, exploring 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.