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As ride-hailing services have experienced significant growth, the majority ofresearch has concentrated on the dispatching mode, where drivers must adhere tothe platform's assigned routes. However, the broadcasting mode, in whichdrivers can freely choose their preferred orders from those broadcast by theplatform, has received less attention. One important but challenging task insuch a system is the determination of the optimal matching radius, whichusually varies across space, time, and real-time supply/demand characteristics.This study develops a Transformer-Encoder-Based (TEB) model that predicts keysystem performance metrics for a range of matching radii, which enables theride-hailing platform to select an optimal matching radius that maximizesoverall system performance according to real-time supply and demandinformation. To simultaneously maximize multiple system performance metrics formatching radius determination, we devise a novel multi-task learning algorithmthat enhances convergence speed of each task (corresponding to the optimizationof one metric) and delivers more accurate overall predictions. We evaluate ourmethods in a simulation environment specifically designed forbroadcasting-mode-based ride-hailing service. Our findings reveal thatdynamically adjusting matching radii based on our proposedpredict-then-optimize approach significantly improves system performance, e.g.,increasing platform revenue by 7.55% and enhancing order fulfillment rate by13% compared to benchmark algorithms.

Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach