Integrated reward scheme and surge pricing in a ride sourcing market
Surge pricing is commonly used in on-demand ride-sourcing platforms (e.g., Uber, Lyft and Didi) to dynamically balance demand and supply. However, since the price for ride service cannot be unlimited, there is usually a reasonable or legitimate range of prices in practice. Such a constrained surge p...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2018
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/4336 https://ink.library.smu.edu.sg/context/sis_research/article/5339/viewcontent/SSRN_id3198081.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Surge pricing is commonly used in on-demand ride-sourcing platforms (e.g., Uber, Lyft and Didi) to dynamically balance demand and supply. However, since the price for ride service cannot be unlimited, there is usually a reasonable or legitimate range of prices in practice. Such a constrained surge pricing strategy fails to balance demand and supply in certain cases, e.g., even adopting the maximum allowed price cannot reduce the demand to an affordable level during peak hours. In addition, the practice of surge pricing is controversial and has stimulated long debate regarding its pros and cons. In this paper, to address the limitation of current surge pricing practice, we propose a novel reward scheme integrated with surge pricing: users can pay an additional amount on top of the regular surge price to a reward account during peak hours, and then use the balance in the reward account to compensate for their trips during off-peak hours. The integrated mechanism is valuable for both transportation and operations management research community. It also proposes another important practical tool to balance demand and supply in ride-sourcing platforms. Specifically, we build up an optimization model to determine the number of travel requests in the platforms on demand side of the market, an equilibrium model to characterize the number of active drivers on supply side of the market, and an optimization model on platform’s decision to maximize platform profit. We compare scenarios with and without reward scheme and explore them from three perspectives: user utility, driver income, and platform revenue and profit. We find that, in some situations, all the three stakeholders, i.e., users, drivers, and the platform, will be better off under the reward scheme integrated with surge pricing. It shows that the integrated reward scheme is a potentially powerful tool for the on-demand ride-sharing market. |
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