Online acceptance probability approximation in peer-to-peer transportation

Crowdsourced transportation by independent suppliers (or drivers) is central to urban delivery and mobility platforms. While utilizing crowdsourced resources has several advantages, it comes with the challenge that suppliers are not bound to assignments made by the platforms. In practice, suppliers often decline offered service requests, e.g., due to the required travel detour, the expected tip, or the area a request is located. This leads to inconveniences for the platform (ineffective assignments), the corresponding customer (delayed service), and also the suppliers themselves (non-fitting assignment, less revenue). Therefore, the objective of this work is to analyze the impact of a platform approximating and incorporating individual suppliers' acceptance behavior into the order dispatching process and to quantify its impact on all stakeholders (platform, customers, suppliers). To this end, we propose a dynamic matching problem where suppliers' acceptances or rejections of offers are uncertain. Suppliers who accept an offered request are assigned and reenter the system after service looking for another offer. Suppliers declining an offer stay idle to wait for another offer, but leave after a limited time if no acceptable offer is made. Every supplier decision reveals only their acceptance or rejection information to the platform, and in this paper, we present a corresponding mathematical model and an approximation method that translates supplier responses into updated approximations of the likelihood of a specific supplier to accept a specific future offer and use this information to optimize subsequent offering decisions. We show via a computational study based on crowdsourced food delivery that online approximation and incorporating individual supplier acceptance estimates into order dispatching leads to overall more successful assignments, more revenue for the platform and most of the suppliers, and less waiting for the customers to be served. We also show that considering individual supplier behavior can lead to unfair treatment of more agreeable suppliers.