  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.
