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2019
Journal Article
Titel
Estimating Advertisement Revenue for Robocabs
Abstract
Connected, autonomous driving and social sustainability issues in urban environments demand to rethink the way, in which public and private transport is financed. The idea of in-vehicle advertisement is often discussed in press and literature as the prevailing solution for a cheap or even free-to-use mobility (see e.g. Gergshorn, 2017; Bertoncello et al., 2016). However, a transparent calculation of the revenue potentials and distributions is still missing. Within this paper, a data-driven approach is conducted. Based on real-world datasets about mobility demand and supply, a fleet of autonomous taxis - so-called Robocabs - is simulated and the advertisement revenue for multiple trips in New York City (NYC) is predicted. Within the analysis, we formulate three revenue model scenarios: One conservative and two, which focus especially on new advertisement opportunities for Robocabs. The conducted calculations show that advertisement approaches are not sufficient to guarantee a free-to-use Taxicab fleet: The average proportion of advertising revenue relative to the taxi fare varies from 1.99% in the first scenario to 5.56% in the third. Additionally, the dispersion of per-trip-revenue increases too, posing additional challenges with respect to the attribution of the value.