Research Article
Towards Data-Driven On-Demand Transport
@ARTICLE{10.4108/eai.27-6-2018.154835, author={Malcolm Egan and Jan Drchal and Jan Mrkos and Michal Jakob}, title={Towards Data-Driven On-Demand Transport}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={5}, number={14}, publisher={EAI}, journal_a={INIS}, year={2018}, month={6}, keywords={on-demand transport, market mechanisms, auctions, taxis}, doi={10.4108/eai.27-6-2018.154835} }- Malcolm Egan
Jan Drchal
Jan Mrkos
Michal Jakob
Year: 2018
Towards Data-Driven On-Demand Transport
INIS
EAI
DOI: 10.4108/eai.27-6-2018.154835
Abstract
On-demand transport has been disrupted by Uber and other providers, which are challenging the traditional approach adopted by taxi services. Instead of using fixed passenger pricing and driver payments, there is now the possibility of adaptation to changes in demand and supply. Properly designed, this new approach can lead to desirable tradeo s between passenger prices, individual driver profits and provider revenue. However, pricing and allocations—known as mechanisms—are challenging problems falling in the intersection of economics and computer science. In this paper, we develop a general framework to classify mechanisms in on-demand transport. Moreover, we show that data is key to optimizing each mechanism and analyze a dataset provided by a real-world on-demand transport provider. This analysis provides valuable new insights into eÿcient pricing and allocation in on-demand transport.
Copyright © 2018 Malcolm Egan et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
