inis 18(14): e4

Research Article

Towards Data-Driven On-Demand Transport

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  • @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
Malcolm Egan1,*, Jan Drchal2, Jan Mrkos2, Michal Jakob2
  • 1: CITI, INSA Lyon, INRIA, Université de Lyon, France
  • 2: Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
*Contact email: malcom.egan@insa-lyon.fr

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.