Ghasemi, Farnoud (Jagiellonian University), Tabatabaei, Seyed Hassan (School of Computer Engineering, Iran University of Science and T), Ghanadbashi, Saeedeh (University College Dublin), Kucharski, Rafal (Jagiellonian University), Golpayegani, Fatemeh (School of Computer Science, University College Dublin)

Reinforcement Learning Approach for Improving Platform Performance in Two-Sided Mobility Markets

Scheduled for presentation during the Regular Session "ITS Policy and markets" (FrAT12), Friday, September 27, 2024, 11:50−12:10, Salon 20

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on November 21, 2024

Abstract

Two-sided mobility markets, with platforms like Uber and Lyft, are complex systems by nature due to intricate, non-linear interactions between the platform and the involved parties including travelers and drivers. These interactions give rise to phenomena underlying market evolution, mainly cross-side network effects. Currently, such platforms rely on rule-based (RB) strategies with a constant commission rate to grow and achieve sustainability in terms of market share and profitability. However, the constant commission rate significantly constrains the platform's ability to leverage network effects, leading to inefficient growth.

In this study, a Reinforcement Learning-based (RLB) strategy is proposed to improve the platform performance through strategic levers. We employ a Deep Q-Network (DQN) within an agent-based framework, enabling the platform to adjust the commission rate on a day-to-day basis while learning the complex, non-linear interactions in the market.

The results show that the RL-based strategies successfully generate and control the essential cross-side network effects in the market enhancing the platform performance via dynamic commission rate. Our results indicate 12% improvement in the platform revenue with the RL-based strategy in comparison to the rule-based strategy without significantly compromising the platform market share which can essentially impact the platform's viability in the long term.