Towards Data-Driven On-Demand Transport

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, th...

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Bibliographic Details
Published in:EAI endorsed transactions on industrial networks and intelligent systems 2018-06, Vol.5 (14), p.154835-10
Main Authors: Egan, Malcolm, Drchal, Jan, Mrkos, Jan, Jakob, Michal
Format: Article
Language:English
Subjects:
Allocations
auctions
Civil Engineering
Computer Science
Demand analysis
Engineering Sciences
Infrastructures de transport
Machine Learning
market mechanisms
Multiagent Systems
on-demand transport
Passengers
Pricing
Taxicabs
taxis
Transport
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Summary: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.
ISSN:2410-0218
2410-0218
DOI:10.4108/eai.27-6-2018.154835