Maximal Compatibility Matching for Preference-Aware Ride-Hailing Systems

TL;DR

This paper introduces the Maximal Compatibility Matching framework for ride-hailing that enhances passenger comfort by learning personalized zones and driver behavior, improving match quality without sacrificing efficiency.

Contribution

The paper proposes a novel matching strategy that explicitly incorporates passenger comfort and driver behavior into ride-hailing assignment algorithms.

Findings

MCM improves passenger satisfaction in simulated environments.

The framework maintains high operational efficiency.

Personalized comfort zones lead to more socially acceptable matches.

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.