Maximal Compatibility Matching for Preference-Aware Ride-Hailing Systems
Journal:
arXiv
Published Date:
May 5, 2025
Abstract
This paper presents the Maximal Compatibility Matching (MCM) framework, a
novel assignment strategy for ride-hailing systems that explicitly incorporates
passenger comfort into the matching process. Traditional assignment methods
prioritize spatial efficiency, but often overlook behavioral alignment between
passengers and drivers, which can significantly impact user satisfaction. MCM
addresses this gap by learning personalized passenger comfort zones using
gradient-boosted decision tree classifiers trained on labeled ride data, and by
modeling driver behavior through empirical operating profiles constructed from
time-series driving features. Compatibility between a passenger and a driver is
computed as the closed-form volume of intersection between their respective
feature-space regions. These compatibility scores are integrated into a
utility-based matching algorithm that balances comfort and proximity through a
tunable trade-off parameter. We validate the framework using a Unity-based
driving simulator with real-time passenger feedback, demonstrating that MCM
enables more personalized and socially acceptable matchings while maintaining
high levels of operational performance.
