Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs

TL;DR

This paper introduces a multi-view neural network model with knowledge distillation to accurately predict ride-matching success rates in real-time, addressing data imbalance and deployment efficiency in ride-hailing platforms.

Contribution

It proposes a novel multi-view model for passenger-driver matching prediction and a knowledge distillation framework to handle data scarcity in smaller cities.

Findings

The model outperforms existing methods in prediction accuracy.

Knowledge distillation improves performance in data-scarce environments.

The approach enables efficient real-time deployment.

Abstract

In recent years, online ride-hailing platforms have become an indispensable part of urban transportation. After a passenger is matched up with a driver by the platform, both the passenger and the driver have the freedom to simply accept or cancel a ride with one click. Hence, accurately predicting whether a passenger-driver pair is a good match turns out to be crucial for ride-hailing platforms to devise instant order assignments. However, since the users of ride-hailing platforms consist of two parties, decision-making needs to simultaneously account for the dynamics from both the driver and the passenger sides. This makes it more challenging than traditional online advertising tasks. Moreover, the amount of available data is severely imbalanced across different cities, creating difficulties for training an accurate model for smaller cities with scarce data. Though a sophisticated neural network architecture can help improve the prediction accuracy under data scarcity, the overly complex design will impede the model's capacity of delivering timely predictions in a production environment. In the paper, to accurately predict the MSR of passenger-driver, we propose the Multi-View model (MV) which comprehensively learns the interactions among the dynamic features of the passenger, driver, trip order, as well as context. Regarding the data imbalance problem, we further design the Knowledge Distillation framework (KD) to supplement the model's predictive power for smaller cities using the knowledge from cities with denser data and also generate a simple model to support efficient deployment. Finally, we conduct extensive experiments on real-world datasets from several different cities, which demonstrates the superiority of our solution.