Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach
This work addresses a specific challenge in ride-hailing services for platforms using broadcasting modes, offering incremental improvements over existing methods.
The study tackled the problem of optimizing matching radii in ride-hailing broadcasting modes, where drivers choose orders freely, by developing a Transformer-Encoder-Based model and multi-task learning algorithm to predict and select optimal radii, resulting in a 7.55% increase in platform revenue and 13% improvement in order fulfillment rate compared to benchmarks.
As ride-hailing services have experienced significant growth, the majority of research has concentrated on the dispatching mode, where drivers must adhere to the platform's assigned routes. However, the broadcasting mode, in which drivers can freely choose their preferred orders from those broadcast by the platform, has received less attention. One important but challenging task in such a system is the determination of the optimal matching radius, which usually varies across space, time, and real-time supply/demand characteristics. This study develops a Transformer-Encoder-Based (TEB) model that predicts key system performance metrics for a range of matching radii, which enables the ride-hailing platform to select an optimal matching radius that maximizes overall system performance according to real-time supply and demand information. To simultaneously maximize multiple system performance metrics for matching radius determination, we devise a novel multi-task learning algorithm that enhances convergence speed of each task (corresponding to the optimization of one metric) and delivers more accurate overall predictions. We evaluate our methods in a simulation environment specifically designed for broadcasting-mode-based ride-hailing service. Our findings reveal that dynamically adjusting matching radii based on our proposed predict-then-optimize approach significantly improves system performance, e.g., increasing platform revenue by 7.55% and enhancing order fulfillment rate by 13% compared to benchmark algorithms.