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 tradeoffs 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 efficient pricing and allocation in on-demand transport.
Highlights
Catching a cab has changed over the last few years
We develop a general framework to design allocation and pricing algorithms within on-demand transport, with a focus on profit-driven services; that is, we focus on taxi-like services rather than on dial-a-ride services [11]
Our framework provides a unifying perspective for existing allocation and pricing algorithms, which can be viewed as implementations of market mechanisms from economics in the context of on-demand transport
Summary
Catching a cab has changed over the last few years. In New York City at the turn of the millennium, hailing a cab from the side of the road was the norm. Our framework provides a unifying perspective for existing allocation and pricing algorithms, which can be viewed as implementations of market mechanisms from economics in the context of on-demand transport. The algorithms in our framework all rely on information either obtained directly from real-time passenger requests and driver reports, or from historical data collected from previous requests. In this sense, effective mechanisms for pricing and allocation in on-demand transport are data-driven. Our framework provides a way to systematically identify the requirements of each pricing and allocation algorithm, which forms a basis for mechanism selection by on-demand transport providers.
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