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Facing autonomous ride-hailing: Will manual ride-hailing team up or go solo?
Journal article   Peer reviewed

Facing autonomous ride-hailing: Will manual ride-hailing team up or go solo?

Faqi Xie, Xiang Li, Zhong Du and Benjamin Lev
Transportation research. Part E, Logistics and transportation review, v 211, 104888
Jul 2026
DOI: https://doi.org/10.1016/j.tre.2026.104888
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Abstract

Aggregation cost sharing Autonomous ride-hailing platform Loyal passengers Team-up mode
•Two manual (MR) and one autonomous (AR) ride-hailing platforms are modeled.•Horizontal cooperation paradigm is extended by MR platforms’ team-up mode.•Effects of team-up mode on price and fleet establishment costs are investigated.•Team-up mode is rational given a relatively small increase in loyal passengers. While coping with the competition from autonomous ride-hailing (AR) platforms is a major challenge for manual ride-hailing (MR) platforms, existing literature rarely investigates their responses through horizontal cooperation. We build a game-theoretic model involving one AR platform and two MR platforms to analyze how MR platforms, with one MR platform as the initiator of the cooperation, choose the team-up mode under the influence of passenger structure and aggregation cost sharing. In equilibrium, when the increase in loyal passengers is small, the team-up mode enlarges the loyal passenger base without incurring excessive aggregation costs and strengthens the competitiveness of the MR platforms in the non-loyal passenger market, thereby increasing their profits. In this case, both MR platforms choose the team-up mode. When the increase is moderate, the aggregation cost-sharing structure becomes the decisive factor: if MR platform a bears a lower cost, it chooses cooperation, while MR platform b chooses the go-solo mode, and vice versa, leading to failed cooperation. When the increase is large, the profits under the team-up mode are lower than those under the go-solo mode, so both MR platforms choose the go-solo mode. The results provide a theoretical basis for MR platforms to respond to the impact of AR platforms.

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