SPEAKER: 李瑞杰，西南交通大学

TIME/DATE: 2024年9月12日下午 14:00

TENCENT: 340 406 261

PW: 615640

ABSTRACT:

We consider a ride-hail system in which a third-party integrator receives ride requests and allocates them to ride service platforms. The ride allocation problem (RAP) is modeled as a Stackelberg game. The integrator, as the leader, chooses the allocation that maximizes its profit, by pricing the rides such that no platform (i.e., follower) can find a more profitable allocation. In pursuit of self-interest, the integrator may refuse to match as many rides as the platforms are willing to serve, thereby injecting an artificial scarcity into the system. To protect the platforms from over exploitation, an exogenous reserve price is introduced to bound their per capita profit from below. We formulate RAP as a bilevel pricing problem, and convert it to a single-level problem by dualizing the lower level. When artificial scarcity is eliminated and all reserve prices are set to zero, we prove the single-level problem can be turned into a mixed integer-linear program that equals its linear relaxation, thus becoming polynomially solvable. Moreover, this version of RAP is shown to be related to cooperative assignment games. Numerical experiments confirm that artificial scarcity negatively affects matching productivity and social welfare. The integrator is favored to take most profits, and leveraging artificial scarcity strengthens its dominance. Moreover, the tighter the supply, the more the integrator benefit from artificial scarcity. The reserve price helps redistribute benefits from the integrator to the platforms. However, demanding an excessively large reserve price may depress the platforms’ profits, while undermining system efficiency.

GUEST BIO:

李瑞杰，现任西南交通大学交通运输与物流学院讲师。2015至2019年于西南交通大学取得博士学位，期间于2019年前往美国西北大学进行联合培养。2020至2021年，在新加坡国立大学从事博士后研究。长期专注于城市交通系统管理优化的研究，尤其在共享出行服务运营优化方面。至今已发表10余篇SCI论文，其中5篇发表于交通领域高水平期刊《Transportation Science》和《Transportation Research Part B》。主持了1项国家自然科学基金青年项目和1项四川省自然科学基金青年项目。