Abstract :

In recent years, the pervasive adoption of online ride-hailing platforms, exemplified by industry leaders like Uber and Didi, has seamlessly integrated into urban transportation, significantly enhancing the convenience of our lives. The pivotal moment in this service occurs when a passenger and driver are matched through the platform, providing both parties with the autonomy to accept or cancel a ride with a simple click. The accuracy in predicting the success of this passenger-driver pairing, termed the matching success rate (MSR), emerges as a critical factor for ride-hailing platforms. This accuracy informs instant strategies such as order assignment, making the decision-making process inherently complex due to the dynamic nature of both driver and passenger interactions. This challenge is distinct from traditional online advertising tasks, which typically involve predicting a user's response to an object, such as click-through rate predictions for advertisements. The uniqueness of